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Unraveling the Role of Maize Cell Wall Hydroxycinnamates in Digestibility, Biofuel Production and Pest Resistance Using A Multi- Parent Advanced Generation Intercross (MAGIC) Approach
Background: Mechanical resistance due to higher hydroxycinnamate content makes maize tissues more recalcitrant to damage by insects, less digestible by ruminants, and less suitable for biofuel production. The integrated study of the maize functional genetic variability for each hydroxycinnamate component could be crucial to identify relevant genetic variants that may be incorporated into selection programs to breed maize varieties for multiple uses. A Genome Wide Association study was carried out a in a maize Multiparent-Advanced Intercross (MAGIC) Population to indentify Single Nucleotide Polymorphisms (SNPs) associated with cell wall bound hydroxycinnamates;and we checked thereafter their relationship with SNPs significantly associated with saccharification efficiency, digestibility of organic matter and corn borer damage. Results: We found 24 SNPs, corresponding to15 QTL, significantly associated with cell wall bound hydroxycinnamates. Each SNP explained between 6 and 8% of the total variability. We define new genomic regions and genes involved in polysaccharide synthesis and modifications, and the oxidative coupling associatted to cell wall hydroxycinnamates content. Conclusions: SNPs explained a small proportion of the variability for hydroxycinnamates, saccharification efficiency, digestibility or insect damage, therefore we recommend a genomic selection approach for future breeding programs of these traits. In addition, no colocalizations were found between hydroxycinnamates and final-use-related traits so breeding strategies can be focus on each particular trait with no side effects on the others.
Background
The structural and functional properties of the plant cell wall are controlled by the composition and organization of each of its individual components [1]. In this sense, cell walls are mainly composed by micro brils of celluloses embebed in a matrix of hemicellulose, lignin and other phenolic compounds (mostly hydroxicinnamic acids). Particularly, the role of stiffening, streghtening and forti cation that hydroxycinnamic acid play within the cell wall has been either positively or negatively correlated with economically important uses and characteristics of cereal grasses such as resistance to insects, ethanolic production and feedstock digestibility [2][3][4].
The most common hydroxycinnamates in those grasses are p-coumaric (PCA) and ferulic (FA) acid. PCA is involved in the radical coupling of S units of the lignin polymer and is known to be ester bond to the γ position of the side chains of the lignin S units [5]. On the other hand, FA is ester bond to arabinose residue of arabinoxylans chains, later crosslinked through ether bond and C-C bonds to the G unit of lignin, working as nucleation site of this polymer. Furthermore, FA is able to form links between proteins and polysaccharides trough cysteine and tyrosine residues, and can form dimers (DFAs) and even oligomers through enzymatic or non-enzymatic oxidative coupling reactions, cross-linking this way hemicelullose polysaccharides. This net confers greater mechanical resistance to the cell wall [6][7][8].
The relationship between cell wall cross-linking and insect resistance was rst suggested by Fry [1]. Since then, many studies have pointed out the role of hydroxycinnamate content in plant resistance to pests and diseases [9][10][11][12]. Greater amount of hydroxycinamates monomers and diverse diferulic isomers, have been found in the pith tissues of borer resistant genotypes compared to susceptible ones. In addition, Barros-Rios et al. [11] found negative correlations between stem tunneling by borers and total DFAs, DFA-8-5'-and PCA esters, or negative correlations between larval weight in feeding bioassays and total DFAs content. At rst sight, modi cations of their concentration would results in the improvement of borer resistance, however, a successful divergent selection program for changing diferulate ester concentration resulted in changes in the cell wall features [13]. When DFAs increased, glucose concentration and total cell walls concentration decreased, affecting properties related to animal digestibility [14]. Likewise, both, xylan-to-xylan ferulate linking [15] and ferulate-to-lignin cross-links [16,17] limit the enzymatic depolymerization of cell wall polysaccharides increasing cell wall recalcitrance to deconstruction. This cell wall recalcitrance impacts negatively in digestiblity, decreasing cell wall deconstruction by microorganisms and enzymes, thence limiting the energy intake by the catle [18][19][20].
Finnally, strategies that could reduce the incidence of ferulate cross-links in the cell wall have the potential to improve cell wall degradability properties relevant to cellulosic ethanol production [16]. Cell wall polysaccharides are the main substrate for ethanol fermentation, and cell wall recalcitrance increases the energy requirements, the cost and complexity of bio re nery operations and/or reduces the recovery of biomass carbon into desired products [21]. Some maize genomic regions mediating hydroxycinnamate accumulation have been detected throughout the genome [22][23][24]. Fontaine et al. [25], in a subset of 135 maize RILs, found seven QTL associated with FA and PCA. In addition, several QTL for hydroxycinnamate content, including PCA and FA, were identi ed by Barrière et al. [22] in a bi-parental population obtained from the cross F286 x F838, one of which colocalized with a QTL previously described by Fontaine et al. [25]. In the IBM population, Lorenzana et al. [26] found eight QTL associated with PCA and eight QTL associated with FA, whereas analyzing pith samples of second internode below the main ear in a biparental population Santiago et al. [27] found two QTL associated with FA, seven with PCA and seven with total diferulates. Moreover, in one of the rst high resolution association mapping analyses made to detect QTL associated to hydroxycinnamate content in the maize pith using a diversity panel, López-Malvar [24] found 22 QTL associated with cell wall bound hydroxycinnamates. However, in the mentioned studies, mainly biparental populations have been used. In these populations you have limited variability, and although they are useful to identify QTL, the region where the QTL is located is usually quite wide. The use of inbred panels resolves both problems: they are diverses and enable high-resolution mapping of QTL to narrow genomic regions, where the searching for genes contributing to trait variability is more feasible. In this sense, as previously mentioned, a diversity panel of 272 inbreds was used to study cell wall bound hydroxycinnamates by our group [24]. Association studies using diversity panels still showing some disadvantages, as limited power to detect QTLs due to the small effect and/or low frequency (rare alleles) of some genetic variants, therefore we could have many undetected rare alleles that could be on the other hand valuable for breeding purposes when they show major effects [28][29][30]. Results from QTL mapping in Multi-Parent Advanced Generation Inter-Cross (MAGIC) populations could be complementary to those obtained by linkage mapping in biparental populations or association mapping using inbred panels. In this case several alleles can be simultaneously studied and none of them would be in low frequency. Besides, MAGIC populations present a known underlying structure that prevents from false positive associations [28]. Finally, in contrast to maize inbred panels constructed by inbreds of diverge origins, MAGIC populations, in general, are better adapted to a particular environment, so adaptation differences would not impair the phenotyping.
Therefore, a complete study of the maize functional genetic variability for cell wall hydroxycinnamates and nal-use-related traits could be practical in order to identify relevant genetic combinations that may be incorporated into selection programs. The nal objective will be to breed maize varieties for multiple purposes, without compromising each other.
To reach this goal we use cell wal bound hydroxycinnmates data and reports from three previous studies involving the same MAGIC population (López-Malvar et al. 2020 a,b submitted; [31]). We described genes in high resolution genomic regions..
Plant Materials
The MAGIC population was developed by the Maize Genetics and Breeding group at Mision Biologica de Galicia-CSIC. The eight parents of the MAGIC population were chosen because they show partial resistance to Mediterranean Corn Borer attack and high speci c combining ability with the Reid germplasm group. Six of them come from European germplasm (EP17, EP43, EP53, EP86, PB130 and F473) and two from American germplasm (A509, EP125). Procedures used to release the 672 recombinant inbred lines of the population have been previously reported [31,32]. The pedigree of each founder line is shown in Table 1.
Experimental Design
A subset of 408 RILs of the MAGIC population together with the eight founders were tested in a single augmented design with 10 blocks in Pontevedra, Spain (42° 24'N, 8° 38'W and 20 m above sea level) in 2016 and 2017. Forty-two non replicated RILs plus the eight parents of the MAGIC (PB130 was replaced by EC212 in 2017 and EP43 by EP80 in both years due to lack of seed availability) were randomly assigned to each block. Only 30 RILs were evaluated in block 10. Each experimental plot consisted of a single row with 13 single-kernel hills planted manually, spacing between consecutive hills in a row being 0.18 m and 0.8 m between rows, obtaining a nal density of ~ 70,000 plants ha -1 .
Local agronomical practices were ful lled.
Phenotypic Data
Biochemical analyses were performed in stover samples that were harvested approximately 55 days after silking (days from planting until half of the plants in the plot showed visible silks). Each sample was composed of tissue from at least two plants per plot. Samples were chopped, pre-dried at 35 °C in a forced air camera and dried at 60 °C in a stove. Last of all, dry stover samples from each plot were grounded in a Wiley mill with a 0.75 mm screen to be used in subsequent biochemical analysis.
A recently optimized protocol was used for hydroxycinnamate quanti cation [33]. Brie y, subsamples of 500 mg were extracted in 30 ml of 80% methanol and mixed using a Polytron mixer. Samples were extracted for 1 h and then centrifuged for 10 min at 1000 g. The remaining pellet containing the cell wall-bound material was shaken in 20 ml of 2 N NaOH under a nitrogen ow for 4 h. Digested samples were neutralized with 6 N HCl, and the pH was adjusted to 2.0. After centrifugation, the supernatant was collected and the pellet washed twice with distilled water (10 ml each). Supernatants were pooled and then extracted twice with ethyl acetate (40 ml each). Collected organic fractions were combined and dried using a SpeedVac for 6 h at a medium setting without a radiant cover. The nal extract was dissolved in 1.5 ml of HPLC grade methanol and stored at −20 °C prior to HPLC analysis. Standards and samples were ltered through a 22 μm tetra uoroethylene lter before being analysed.
Chromatographic quanti cation was completed using a 2690 Waters Separations Module equipped with a model 996 photodiode array detector and a Waters YMC ODS-AM narrow bore column (100 × 2 mm internal diameter; 3 μm particle size). The solvent system consisted of acetonitrile (solvent A) and tri uoroacetic acid (0.05 %) in water (solvent B) as follows: initial conditions of 10:90 A:B, changing to 30:70 over 3.5 min, 32:68 over 6.5 min, 100:0 over 4 min, isocratic elution at 100:0 for 4.5 min, and nally returning to the initial conditions (10:90) over 3 min. The mobile phase ow rate was 0.3 ml/min, and the total analysis time was 17.5 min. The sample injection volume was 4 μl. Phenolic standards ferulic acid (FA) and p-coumaric acid (PCA) were purchased from Sigma-Aldrich Quimica SL, Madrid, Spain. The identities of FA dimers were con rmed by a comparison with the authentic 5−5 standard or published retention times and UV spectra. The total diferulate content (DFAT) was calculated as the sum of the following three identi ed and quanti ed DFA isomers: DFA 8-O-4, DFA 5-5, and DFA 8-5.
Statistical Analysis
Inbred lines were previously genotyped with 955.690 SNPs using a genotyping-by-sequencing (GBS) method. Genotype matrix was ltered , i.e. SNPs with more than 50% missing data and a minor allele frequency less than 5% were omitted. Heterozygous genotypes were considered missing data. After ltering, 215.131 SNPs distributed across the maize genome were retained.
For each phenotypic trait, data from indivual as well as from combined trials were analyzed according to the mixed model procedure (PROC MIXED) of the SAS program (version 9.4) [34] and the best linear unbiased estimator (BLUE) of each RIL was calculated based on the combined data for the 2-year analysis.. The BLUES constituted the phenotype matrix The comparison of means was carried out using the Fisher's protected least signi cant difference (LSD). Heritabilities (ĥ 2 ) were estimated for each trait on a family mean basis as previously described by Holland et al. [35]. The genetic (r g ) and phenotypic (r p ) correlation coe cients between each pair of traits were calculated using REML estimates according to a published SAS mixed model procedure [36].
We used the current eld trials and population in previous studies in order to quanti ed the average sacchari cation e ciency, the digestibility of the organic matter (DOM) (López-Malvar et al. 2020 a,b submitted) and the tunnel length borers resistance [31]. In order to understand the interrelation among hydroxycinnamates and those nal-use-related traits, multiple linear regression models using the stepwise method of the SAS PROC REG procedure . Sacchari cation e ciency, DOM and tunnel length were considered as dependent variables.
Variables with a p value less than 0.15 were retained or excluded in the regression model.
The genome-wide association analysis was completed with Tassel 5 [37] based on a mixed linear model using a genotype-phenotype matrix and a kinship matrix using the centered IBS method [38]. Among the mixed linear model options, we used the optimum compression level and P3D to estimate the variance components.
SNPs, QTL and Candidate Gene Selection
Two approaches were used to calculate the comparison-wise threshold for declaring signi cant an association between a trait and a SNP: (i) A modi cation of the classic Bonferroni approach where, rst the number of independent tests was estimated by the Haploview program using the option four gamete rules, resulting in 12397 independent comparisons [39][40][41]. Then, the comparison-wise threshold was the coe cient between the experiment-wise threshold established (0.3) and the number of independent tests (12397); (ii) it was determined as the point where the observed and expected F test statistics deviated in the Q-Q plot of the model, performed with Tassel 5 [37] (Supplementary Figure 1).
We considered a +/-700 kbp con dent interval region around each signi cant SNP following previous association studies using the same mapping population [42]. In case con dence intervals of two SNPs overlapped they were assigned to a single QTL. The two described genes that delimit the +/-700 kbp region around the SNP in the reference genome assembly version 2 were positioned in version 4 of the reference genome, and all genes contained in the region delimited by those genes were then identi ed and characterized based on the maize B73 reference genome assembly (version 4) available on the MaizeGDB browser [43]. Those genes are listed in Supplementary Table 1.
Means, Analysis of Variance and Heritabilities
Means and ranks are shown in Table 2. Signi cant differences were found among the founders means and among RILs means for all the quanti ed hydroxycinnamates. p-coumaric and ferulic acid were the most abundant cell wall bound hydroxycinnamates. We found signi cant variation for all traits; and also noted that the founder lines of the MAGIC population showed high variation for hydroxicinnamic acids as in previous evaluations [10]. Monomers presented moderate heritability values (0.59 for PCA and 0.60 for FA), whereas, dimers and total diferulate amount presented lower values (ranging from 0.32-0.42).
Genotypic and Phenotypic Correlations
Correlation coe cients are detailed in Table 3. We found high positive genotypic and phenotypic correlation (r g,p <0.66) between FA, total diferulates and individual dimers. On the other hand, very low correlation values were found between PCA and any other trait.
Multiple Linear Regression Analysis
We looked for the multiple linear regression models that better t data on sacchari cation e ciency, digestibility of the organic matter, and tunnel length damage using cell wall bound hydroxycinnamates as independent variables. For each model, the percentage of the partial variance for the dependent trait explained by each independent variable, the percentage of accumulated variance explained after incorporating that particular independent variable in the model, as well as estimates for regression coe cients are shown in Table 4.
The best model for sacchari cation e ciency only explained less than 1 % of the variance, mainly by PCA. Variation among RILs for DOM was partially determined by PCA (11 %) and DFA 8-5-l (2 %). By last, only 0.8 % of the variation for tunnel length was explained by cell wall bound hydroxycinnamates.
Association Analysis
Following the modi cation of Bonferroni approach a marker was considered signi cantly associated with a trait at p values less than 2.42×10 -5 ( p-value = 4.6). For the Q-Q plots we stabilised that a marker was signi cantly associated with a trait with values less than 1.00x10 -4 ( p-value = 4.0). We followed the results of the Bonferroni modi cation approach for every trait with the exception of DFAT and DFA 5-5. We considered a +/-700kbp region around each signi cant SNP as the SNP con dence interval and two SNPs were clustered in the same QTL when their con dence intervals overlapped (Table 5).
A total of 24 SNPs, corresponding with 15 QTL, were signi cantly associated with cell wall bound hydroxycinnamates ( levels, while minor frequency alleles generally increased cell wall-bound DFA 8-5 and total diferulates concentrations; nally, major frequency alleles increased p-coumaric and ferulic acid concentrations.
Candidate Gene Selection
The genes containing or physically close to SNPs signi cantly associated with traits were identi ed and characterized according to the maize B73 reference genome assembly (version 4). Analyses of +/-700kbp regions surrounding signi cant SNPs resulted in the identi cation of the genes listed in Supplementary Table 1.
Discussion
First of all, we have observed genetic variation for cell wall bound hydroxycinnamates in the maize MAGIC population evaluated, for both the RILs and the founders agreeing with former evaluations [10]. The correlations between cell wall components traits followed the trends previously reported in the literature [2,24,44]. FA, particular dimers and DFAT showed co-variation. This means that if the target for improvement is FA, individual and total dimers would be also modi ed. On the other hand, this would not happen for PCA, as this trait was not correlated with any other trait.
Furthemore, moderate to high heritability for hydroxycinnamate contents agreed with the results obtained in previous studies [2,24]. From those heritabiliy values, we would expect a good response to selection, since additive effects are more important than additive × environment interaction effects. Although phenotypic selection could be effective based on those heritability estimates, a genomic selection approach could be implemented to speed up selection, in contrast to a time consuming phenotyping method.
QTL Co-localization
With respect to other studies, novel genomic regions involved in hydroxycinnamate content were found, such as those in bins 10.04 (PCA), 5.06 and 7.01 (for FA), 2.08 and 7.02 (for DFA 5-5), 7.04 (for DFA 8-O-4), 5.06 (for DFA 8-5) and 5.01 and 7.02 (for DFAT). Nine of the ten SNPs associated with PCA, that correspond to three QTL, are located in bin 10.04. These QTL appear to be good candidates for selection targeting PCA. We found overlapping QTLs for FA and DFA 8-5 in the bin 5.06 and for DFA 5-5 and DFAT in bin 7.02. Those co-localizations have sense because genotypic correlations between those traits are high.
In addition, SNPs signi cantly associated with a trait in the current study co-localized with QTL for the same trait found by other authors. This is the case of the marker associated with PCA in bin 1.02, that co-localized in the same bin with one QTL associated with the same trait portrayed by García-Lara et al. [45], studying the cell wall phenolic composition of maize pericarp tissues in 163 F2:3 families; or the case of
Candidate Genes
Among genes proposed as candidates for cell-wall bound hydroxycinnamateswe highlight peroxidases involved in oxidative coupling of FA to form dimers, genes that are responsible for transcriptional control of the phenylpropanoid pathway, implicated in xyloglucan and arabinoxylan synthesis, and involved in polysaccharides synthesis and modi cation. Finally, we also spot genes involved in gibberellin and suberin biosynthesis.
Cellulose and glucurono-arabinoxylans are the main constituents of ligni ed secondary walls; among the genes involved in the upstream parts of cell wall carbohydrate biosynthesis, we found several UDP-glycosyltranferases within the supporting intervals of QTL for PCA (qPCA_10_3), DFA 5-5 (qDFA5-5_7_1) and 8-O-4 (qDFA-8-O-4_1_1), and total diferulates (qDFAT_7_1). UDP-glycotransferases are enzymes involved in the elongation of carbohydrate chains using nucleotide sugar as substrates and thereby causing variation in the cell wall polysaccharides structure [46]. For example, in rice, the gene underlying the brittle-culm-14 mutants, was a nucleotide sugar transporter that causes reduced mechanical strength by decreasing cellulose content and altering wall structure, including higher xylan extractability. In addition, we spotlight a reduced residual arabinose 3 gene, which encodes an arabinosyltransferase that adds the second arabinose residue in a β-1,2 linkage in arabinoxylan chains, as candidate for the QTL qDFA8-O-4_7_1 [47]. Besides, FA is ester bond to arabinose residues of arabinoxylans chains, and FA dimers crosslink hemicellulose chains binding speci cally to arabinose [48,49]. Modifying arabinosyl transferase activities could be a promising strategy for modulating ferulate cross-linkages in the walls [50,51], so we consider Zm00001d021974 a good candidate for DFA 8-O-4 content. Similarly, in the con dence interval of DFA 5-5, we found a glycosyl hydrolase (Zm00001d006940). Glycosyl hydrolases are mainly cellulases and xylanases; that modify the phenolic composition of cell walls as they cleave phenolic ester linkages. Thus, we considered Zm00001d006940, which encodes a glycosyl hydrolase, a good candidate for qDFA5-5_2_1. Besides, FA could be also bound to α-xylosil of xyloglucan (XyG) [18] and diferulate crosslinking could also anchor the xyloglucan chains. XyG participates in the formation of cellulose-XyG network, allowing the attachment of cellulose to other wall polymers and playing an important role in cell wall extension during plant growth [52]. In this sense , we spot a xyloglucan endotransglucosylase/hydrolase gene that codi es for an enzyme involved in modi cations of the xyloglucan for cell wall elongation as candidate for the overlapping QTL for FA and DFA 8-5.
Similarly, it has been hypothesized that GA could in uence phenolic cross-linking via an effect on peroxidases [53]. As we mentioned throughout this work, phenolic groups can be oxidatively coupled by peroxidase + H 2 O 2 [54] and/or by oxidases (laccases) + O 2 [55] to form dimers. The conditions that increase the number of diferuoyl crosslinking included, among others, low gibberellin supply [56]. In this context, within the supporting intervals of qDFA8-O-4_1_1, we highlight a gibberllin 20-oxidase 2, as candidate for DFA 8-O-4 content. In the same way, we also spotlight several peroxidase genes as candidates for FA and individual dimers in QTL qFA_5_1, qDFA8-5_5_1 and qDFA5-5_2_1.
Among the genes found in the interval of qPCA_3_1, we spot MYB tf 41 which has been classi ed by Du et al. [57] as involved in the "Phenylpropanoid Pathway". Barrière et al. [58] proposed ZmMYB041 as the probable gene underlying a QTL for lignin content,and this could be also involved in PCA biosynthesis because increased ligni cation has been commonly associatedto higher PCA concentration [59].
Finally, we found, in the QTL interval for PCA in chromosome 10, a gene encoding a ω-hydroxypalmitate O-feruloyl transferase (Zm00001d024864) which takes part in the esteri ed suberin biosynthesis pathway. Suberin is a polymeric constituent of plant cell wall, which consists of two domains that are cross-linked. Concerning the aromatic fraction of suberin, hydroxycinnamates esters, such as PCA, fortify the crosslinking between arabinoxilans and suberin fatty acids; besides PCA deposition has been associated with highly suberized tissues. Even though there is some of the esteri ed PCA esteri ed to polysaccharides, as previously mentioned, most of the PCA in grasses is ester bond to S units of lignin [60,61].
Conclusions
To sum up, we pointed out new genomic regions associated to cell wall bound hydroxycinnamates in maize stover that could have an impact on their content across different genetic backgrounds. Based on their annotated functions the putative candidate genes for the traits under study are involved in polysaccharides synthesis and oxidative coupling, however, the genes that are not yet characterized, or the ones encoding a "hypothetical protein" that fell within QTLs intervals, could have an in uence on the nal hydroxycinnamates content. However, the effects of individual SNPs signi cantly associated with hydroxycinnamate content were low, and each SNP explained a low percentage of total genetic variability. Based on these results, and on the moderate heritability estimates observed, we suggest that the best breeding strategy to improve hydroxycinnamates content would be a genomic selection. On the other hand, we determine that the variation for cell wall bound hydroxycinnamates will not vary tunnel length damage, sacchari cation e ciency or forage digestibility, so we recommend carried out a direct selection program independently for each nal use related trait.
Declarations
Ethics approval and consent to participate Not applicable in this study.
Consent for publication
Not applicable in this study.
Availability of data and materials
The data sets used and/or analysed during the current study will be availableupon reasonable request to the corresponding author.
Competing interests
The authors declare that they have no competing interests.
Funding
This research has been developed in the frame of the "Agri-Food Research and Transfer Centre of the Water Campus (CITACA)" at the University of Vigo (Spain), which is economically supported by the Galician Government, and in the Misión Biológica de Galicia (CSIC). It was funded by the "Plan Estatal de Ciencia y Tecnología de España" (projects RTI2018-096776-B-C21, and RTI2018-096776-B-C22 co-nanced with European Union funds under the FEDER program). A. López-Malvar's scholarship for the PhD ful lment has been granted by University of Vigo and by a contract charged to the project RTI2018-096776-B-C22. The funding body played no role in study design, data analysis and manuscript preparation. Table 4 Multiple linear regression models (using stepwise selection) with sacchari cation e ciency, digestibility of organic matter and tunnel lenght on cell wall composition as dependent variables, and hydroxycinnamate-related traits in a maize MAGIC population.
Step Wise Selection Sacchari cation E ciency (nmol mg − 1 material − 1 hour − 1 ) Step Variable introduced in the Model Table 5 SNPs and QTL signi cantly associated with cell wall component traits: including SNP's chromosome, bin and position within chromosome, allelic variants and additive effect for the SNP, proportion of total variance explained by the SNPs and P-value for the association between the SNP and the phenotype
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Domain: Agricultural And Food Sciences Biology
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Prevalence of endoparasitic helminths of donkeys in and around Haramaya district , Eastern Ethiopia
A cross sectional study was conducted to determine the prevalence of endoparasites of donkeys in and around Haramaya district, East Hararghe zone of Oromia regional state. Coprological examination was carried out on fecal samples collected from 384 donkeys. Simple flotation, sedimentation and fecal culture techniques were used for the detection of eggs and larvae of helminth parasites. The overall prevalence of endoparasitic infection was 93.75% (n=360). Four species of helminths: Parascaris equorum (20.5%), Fasciola species (15.36%), Oxyuris equi (15.36%) and Dictyocaulus arnfieldi (21.88%) and two genera (Strongylus and Trichonema), were encountered. Identification of L3 of nematodes from coprocultured faeces of donkeys showed the predominance of strongyles species (65%), Dictyocaulus Arnfieldi (25%), and Oxyuris equi (10%). The high prevalence of helminth parasites noted in this study calls for regular monitoring and intervention measures such as strategic deworming of donkeys.
INTRODUCTION
Ethiopia claimed to have the largest livestock population in Africa, with an estimated population of 47.5 million cattle, 26.1 million sheep, 21.7 million goat, 7.8 million equines out of which 5.42 million are donkeys, 1 million camel and 39.6 million chickens (CSA, 2009). These include rampant animal diseases, poor nutrition, poor husbandry, poor infrastructure, and shortage of trained man power and lack of government policies (PACE-Ethiopia, 2003). Among the livestocks, donkeys are tolerant to hot, arid environments where the agriculture is subsistence and are popular among pastoralists. It has been suggested that donkeys can comfortably pull more weight than they can carry provided that the harness is suitable (Saul et al., 1997), but their health aspect have been ignored (Taylor et al., 2007).
Parasitic helminthes are one of the most common factors that constrain the health and working performance of donkeys worldwide (Zerihun et al., 2011). Some of them are active bloodsuckers and cause various degrees of damage depending on the species and numbers present, nutritional and the immune status of equids. Though, the available information suggests that gastrointestinal helminthes are the main reason for early demises of donkey (Zerihun et al., 2011). There are more than 150 species of helminth parasites that can infect donkeys. The most common and troublesome are: large *Corresponding author. E-mail: [email protected]. Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License strongyles, small strongyles, roundworms, tapeworms, lungworm, pinworms, threadworms and bots. Probably the most important in terms of health risk are the large and small strongyles, roundworms and tapeworms (Radostits et al., 2007).
Some works have been done in different parts of the country such as: Endoparasites of donkeys in Sululta and Gefersa Districts by Zerihun et al. (2011), strongyles and parascaris parasites population in working donkeys of Central Shoa by Ayele and Dinka (2010), occurrence of lungworm infection in equine, and their risk factors in and around Jimma town by Tihitna et al. (2012) and prevalence of gastro-intestinal parasites of donkeys in Dugda Bora District by Ayele et al. (2006). However, there has not been any study conducted in the study area. Therefore, the objectives of the study were: to determine the prevalence of helminth parasites of donkeys in and around Haramaya District, and to identify species of gastro-intestinal helminthes recovered from selected samples.
Description of study area
The study was conducted in and around Haramaya district, eastern Hararghe zone, Oromia regional state, Ethiopia. It is located about 507 kms away from Addis Ababa to east and 14 kms west of Harar town. The altitude of the district ranges from 1400 to 2340 m.a.s.l. Geographically, the area is located at 41°59 ' 58 " N latitude and 09°24 ' 10 " E longitudes. It receives an annual rain fall approximately 900 mm and climatically the district has two ecological zones of which 66% mid land and 33.3% low land (CSA, 2009).
Study animals
The study was conducted on donkeys that came to the different markets in and around the Haramaya district. Donkeys of different age, sex and body condition score were tried to be included in the study. The body condition score was done according to Henneke (1983).
Study design
A Cross-sectional study was conducted to determine the prevalence of gastrointestinal helminth parasites of donkeys in the study areas.
Sampling method and sample size determination
By using simple random sampling methods and 95% confidence interval, the sample size was calculated using the formula of Thursfield (2005). Where; n = required sample size, Pexp = expected prevalence, d = required precision (usually 0.05).
By using an expected prevalence of 50%, a total 384 donkeys were included in the study.
Coprological examination
The fecal samples were collected directly from the rectum of the donkeys by using rectal gloves or from freshly passed droppings.
Each sample was labeled with animal identification, owner's name, date and area of collection with indelible pen. After collecting, the sample was transported to Haramaya University parasitology laboratory for immediate processing and examination of the sample. The observation of helminth parasites eggs in the faeces of the donkeys was evaluated by using the coprological flotation and sedimentation techniques (MAFF, 1979).
Fecal culture
Fecal culture was done for 65 positive fecal samples according to Bowman (2003) to appreciate the gastrointestinal helminths parasites larvae profile. Identification of larvae (L3) was based on the shape and gut cells, relative size of sheath tail and shape of tail of larvae (Zerihun et al., 2011).
Data management and analysis
The datas were entered into Microsoft excel 2007 spread sheets and were analyzed using STATA (version 11) statistical software package. The association of infection with the different variables was analyzed using χ 2 test. A statistically significant association between variables is considered to exist if the calculated p-value was less than 0.05 with 95% confidence level.
DISCUSSION
The result indicated that donkeys are a host to different species and genera of helminth parasites. The prevalence of strongyles disagrees with the result of 99.5% by Zerihun et al. (2009), a 100% report by Alemayehu (1995), 96.77% by Sinasi (2009), 92% by Ayele and Dinka (2010), and 65.1% by Hussen (2011). This may be due to the differences in the agro-ecological and climatic conditions between the study areas. The prevalence of Trichonema species (23.44%) in the study was higher than the 15.85% reported by Shrikhande et al. 2009). This may be due to the management system and nature of the grazing area. The current prevalence of D. arnfieldi (21.88%) in the study area was greater than the reports of 9.67% by Sinasi (2009), 3.65% of Shrikhande et al. (2009) and 13.8% by Tihtina et al. (2012). This difference could be due to the difference in environmental conditions and management practice favoring the survival of the larvae of the parasite. The prevalence of P. equorum (20.5%) recorded in this study, was higher than the previous report of 17.3% by Fikru et al. (2005) but less than the results of 29.26% by Shrikhande et al. (2009), 43% reported by Ayele et al. (2006) and 22.58% reported by Sinasi (2009). This may be due to the agro-ecological and climatic difference of the study areas and lack of awareness about the health animals. The prevalence of O. equi was higher than the 8.53% report by Shrikhande et al. (2009) and 6.4% reported by Sinasi (2009). This may be due to the climatic difference between the study areas and the management systems. The prevalence for F. hepatica was higher than the previous report of 1.5% by Ayele et al. (2006) in Dugda Bora district. This higher prevalence suggests that F. hepatica is common in highlands where donkeys share the same grazing area with ruminants that are considered as primary host of liver fluke and favorable ecological conditions which allow multiplication and spread of intermediate snail host in the district.
The higher prevalence in adult donkeys in the study disagrees with the result of Zerihun et al. (2011). This may be due to high risk of getting infection from the market and other work areas. In Haramaya district the seasonal differences between months have no a great effect on the prevalence of equine helminth parasites. This may be due to the presence of almost similar climatic condition between the months and because of the permanent marshy grazing field in the study area. This result disagreed with the reports of Hussen (2011). This may be due to agro-ecological and climatic difference between the study areas. There was a significant difference in helminth prevalence on the basis of body condition score which was in agreement with previous reports by Matthee et al. (2002), Getachew et al. (2009) and Brady and Nichols (2009).
Conclusion
The study revealed a high prevalence of a wide range of species of gastro-intestinal helminths parasites that play a great role in confronting the health and welfare of donkeys in and around Haramaya district. The result also suggests the presence of favorable environmental condition for the survival, infection and perpetuation of helminthes of donkey in the district.
Table 1 .
Prevalence of helminth parasites by month, sex, age and body condition of the animals in the study sites along with their statistical significance.
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Domain: Agricultural And Food Sciences Biology
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Detection of Escherichia coli O157:H7 prevalence in foods of animal origin by cultural methods and PCR technique
A total of 250 samples (50 each of beef, mutton and chicken and 50 samples each of beef swabs and mutton swabs) collected from various sources were subjected to PCR and cultural methods for the presence of Escherichia coli O157:H7. Primers for hlyA, stx1 & 2 genes were used for the detection of Escherichia coli O157:H7 and shiga toxins respectively. Out of 250 samples, 27 showed presence of Escherichia coli O157:H7 (5 beef, 6 beef swabs, 2 mutton, 12 mutton swabs and 2 chicken out of 50 samples each) by PCR where as only 11 samples (one beef, 2 beef swabs, 1 mutton, 6 mutton swabs and one chicken sample) were positive by cultural method. Of the 27 Escherichia coli O157:H7 positive samples by PCR, 12 showed stx1, 7 showed stx2 and 5 showed both stx1 and stx2. The sensitivity of PCR method for Escherichia coli O157:H7 was 1.7cfu. Enrichment with mTSB broth containing novobiocin gave good results compared to mEC broth with novobiocin by both PCR and cultural methods.
Introduction
Escherichia coli O157:H7 is an important emerging human pathogen causing Haemmorhagic Colitis, Haemolytic Uraemic Syndrome and Thrombotic Thrombocytopaenic Purpura (Nataro and Kaper, 1998;Zhao et. al., 1998). Escherichia coli O157:H7 serotypes are identified as enterohaemorrhagic E. coli (Oksuz et. al., 2004). In 1982, an investigation by the Centres for Disease Control and Prevention (CDC, United States) of two severe bloody diarrhea outbreaks associated with a fast food restaurant led to identification of unique E.coli strain, O157:H7 (Wells et. al., 1983). The infections by Escherichia coli O157:H7 have been reported of increasing frequency from all parts of the world in the form of food poisoning outbreaks (Jo et. al., 2004). Because of the severity of these illnesses and the apparent low infective dose (< 10 cells, Bach et.al., lysin (hlyA) and intimin (eae) genes are potentially dangerous to human health (Manna et. al., 2006).
Cattle are the biggest reservoir of E. coli O157:H7 and beef and milk contaminated with cattle faeces are the most common sources of human infection (Armstrong et. al., 1996). E. coli O157:H7 is a foodborne pathogen that has been associated with meat products (Sivapalasingam et. al., 2004) particularly associated with the consumption of undercooked ground beef (Doyle, 1991).52% of E. coli O157:H7 outbreaks have been associated with bovine products (WHO, 1997).
This work has been undertaken to detect the presence of Escherichia coli O157:H7 in some of the foods of animal origin using both PCR and cultural methods and to investigate the presence of shiga toxins 1 and 2 (stx1 and stx2).
Materials and methods
A total of 150 different meat samples (50 samples each of beef, mutton and chicken) and 100 meat surface swabs (50 each of beef swabs and mutton swabs) were collected from freshly dressed and washed animal carcasses at slaughter houses and markets in Hyderabad. Meat samples (10gm each) and swabs were enriched in 90 ml of modified Escherichia Coli (mEC) broth and modified Tryptic Soy broth (mTSB) both supplemented with novobiocin at 370C for 18 hours. The broth cultures were spread plated onto MacConkey Sorbitol agar containing Cefixime-Tellurite supplement at 420C for 24 hours for isolation of Escherichia coli O157:H7. The sorbitol negative colourless colonies were taken for further confirmation by biochemical tests like IMViC (Indole, Methyl Red, Voges Proskeur and Citrate utilization) tests, nitrate, lysine decarboxylase, ONPG (Orthonitrophenyl galactosidase) and sugar fermentation tests (ferments sucrose, maltose, lactose, mannitol and mannose but does not ferment sorbitol and cellobiose).
All the enriched samples were subjected to PCR analysis for the presence of Escherichia coli O157:H7 using primers specific to haemolysin gene (hlyA). The samples positive for Escherichia coli O157:H7 by PCR method were further examined for the presence of shiga toxins (stx1 and stx2) using specific primers (Table .1).
An Escherichia coli O157:H7 strain, obtained from National Institute of Enteric diseases, Kolkata was used as known positive strain in PCR analysis.1.5 ml of enriched broths were taken into eppendorf tubes and bacteria were pelleted by centrifugation at 6000rpm for 5 min. To the pellet 50µl of molecular grade water was added and incubated at 650C for 15 min.and snap chilled to release DNA. Then centrifuged at 13000rpm for 5 min.and the supernatants were used as DNA templates for PCR analysis.
Bacterial DNA amplification was done in 20µl reaction mixture containing 2µl of 10X Taq DNA polymerase buffer (containing 100mM Tris with PH 9.0, 500mM KCl, 15mM MgCl2 and 1% Triton X-100), 2µl of 10mM of dNTP mix, 0.9U of Taq DNA polymerase (Genei), 2µl each of 4 pmoles/µl of forward and reverse primers and 5µl of crude bacterial cell lysate. Make this mixture to 20µl using molecular grade water. Amplification was done in thermal cycler following standardized conditions.(Table .2). The amplified DNA fragments were resolved by agarose gel electrophoresis, stained with ethidium bromide (0.5µg/ml) and visualized with an UV transilluminator (Fig. 1). Spiking studies: To know the sensitivity of PCR method for Escherichia coli O157:H7, homogenized beef was inoculated at the rate of 170cfu, 17cfu, 1.7cfu and 0.17cfu/10gm of beef and transferred to two different enrichment media i.e. modified EC broth and modified Tryptic Soy broth. The PCR and cultural testing were carried after 10hr and 18hr of incubation.
Results and Discussion
The detection level of Escherichia coli O157:H7 was 17cfu and 1.7cfu for mEC broth after 10hr and 18hr incubation respectively, where as it was 1.7cfu for mTSB after 10hr as well as 18hr incubation using PCR. But it was 170cfu and 17cfu for mEC and 17cfu and 1.7cfu for mTSB after 10hr and 18hr incubation respectively by cultural method. Almost similar results were reported by Arthur et. al. (2005) using mTSB with minimum detection level of 1.7cfu by both PCR and cultural method after 18hr incubation.
The growth of Escherichia coli O157:H7 in samples as well as spiking studies was good in mTSB compared to mEC. Restaino et. al. (1996) reported that mTSB took less time for doubling of Escherichia coli O157:H7 (28 min) than mEC (58 min).
The results for the presence of Escherichia coli
Detection of Escherichia coli O157:H7 prevalence in foods of animal origin by cultural methods and PCR technique
Escherichia coli O157:H7. Similar opinion was given by Weagent et.al, (1995).
Of the 27 samples positive for Escherichia coli O157:H7 by PCR method, 12 (44.4%)showed presence of stx1, 7 (26%) showed stx2 and 5 (18.5%) showed both stx1 and stx2. Incidence of stx1 was high compared to stx2 and it is in co-ordination with the reports of Cerqueira O157:H7 in different meat samples are presented in Table .3. Escherichia coli O157:H7 was isolated from 10% (5 out of 50) of beef samples by PCR and 2% (1 out of 50) by cultural method. Almost similar results of 14.7% and 17% were reported in beef by Mora et. al. (2007) and Wilshaw et.al. (1993) respectively. Higher incidence of 31% in beef was reported by Doyle and Schoeni (1987) and 35.8% by Elder et. al. (2000). Low levels of 0-3.7% was reported in ground beef by Desmarchelier and Grau (1997). The swab samples taken from the surface of beef carcasses yielded 12% (6 out of 50) and 4% (2 out of 50) by PCR and cultural methods respectively. Low levels of 3.7% was reported in beef swab samples by Manna et. al. (2006).
Escherichia coli O157:H7 was isolated from 4% (2 out of 50) of mutton samples by PCR and 2% (1 out of 50) by cultural method where as in mutton swabs it was 24% (12 out of 50) and 12% (6 out of 50) by PCR and cultural methods respectively. Doyle and Schoeni (1987) reported 2% incidence in mutton. Chapman (2000) reported higher incidence of Escherichia coli O157:H7 in lamb products than beef products. Escherichia coli O157:H7 were isolated form 4% (2 out of 50) of chicken samples by PCR and from 2% (1 out of 50) of samples by cultural methods. Almost similar results were reported by Doyle and Schoeni (1987) and Desmarchelier and Grau (1997). Compared to meat samples, meat swabs yielded more positive results by both PCR and cultural methods. This may be due to the probable faecal contamination during slaughter.
For a total of 250 samples (meat and meat swabs), cultural method yielded 4.4% positive results where as for PCR it was 10.8%. So, PCR method is more accurate than cultural method for the isolation of et. al. (1997) and Sidjabat-Tambunan et.al. (1998) which showed higher production of stx1 during summer.
Table - 3. Occurence of Escherichia coli O157:H7 in different meat samples
Detection of Escherichia coli O157:H7 prevalence in foods of animal origin by cultural methods and PCR technique
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Domain: Agricultural And Food Sciences Biology
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Genetic parameters and gain from selection in sweet potato genotypes with high beta-carotene content
Estimating genetic parameters is an essential procedure to define strategies for breeding and selection of higher yielding genotypes. The use of a selection index can assist in decision making by combining the high yield trait with other desirable traits. The objective of this study was to verify the possibility of gains from selection in a sweet potato population to select potentially promising genotypes. The experiment was conducted in a randomized block design, with three plants per plot and two replications, consisting of 255 sweet potato genotypes and a commercial cultivar (256 treatments). The data were analyzed through REML/BLUP. Genetic gains were evaluated using selection indexes based on the rank sun. The plant population tested showed high genetic variability; 81.25% of the traits had genotypic coefficients of variation above 20%, which indicates conditions favorable to selection with considerable genetic advances. CERAT31-01, CERAT21-02, and CERAT51-30 can be recommended as the most promising genotypes.
INTRODUCTION
Sweet potato (Ipomoea batata) is a tuberous vegetable grown throughout Brazil due to its hardiness, easy management, low production cost, and wide range of use. Its root tubers are consumed and are the main commercial part of this plant. Sweet potato has high yield potential and wide phenotypic and genotypic variability, but the average yield in Brazil is low: approximately 12 Mg ha -1 (Oliveira et al. 2017). This is because most sweet potato crops are grown from local non-selected varieties and use a low level of technology, which results in yields below crop potential. With new market opportunities for this crop, research aims to obtain new genetic materials to add value to the product and encourage consumption based on the versatility of this food (EMBRAPA 2017). Current expectations are that Brazilian sweet potato breeding programs will achieve development of new cultivars that meet the demands of farmers for increased yield and that result in sustainable development of sweet potato production in Brazil (EMBRAPA 2016).
Sweet potato has considerable socioeconomic importance as a result of the high nutritional value of its roots. It is a significant source of carbohydrates, vitamins, and minerals, which makes it a food that can ensure the food security MEF Otoboni et al.
of a population (Vargas et al. 2017). In addition, the orange-fleshed cultivars are rich in carotenoids, mainly beta-carotene. Beta-carotene has provitamin A and antioxidant activity. Vitamin A deficiency is a public health problem in Brazil, mainly in the North, Northeast, and Southeast regions (Milagres et al. 2007), and the consumption of orange-fleshed sweet potato is a good alternative for reducing deficiency of this vitamin. Dietary requirements of vitamin A depend on age, sex, and lifestyle. The recommended daily allowance of vitamin A for healthy men from 10 years of age on is 600 µg RAE (Retinol Activity Equivalent) day -1 . For pregnant women this value is 800 µg RAE day -1 , and for lactating women the values are 850 µg RAE day -1 (Burri 2011). The consumption of 100 grams of orange-fleshed sweet potato daily is estimated as sufficient to satisfy the necessary daily requirement of vitamin A (Roca and Manrique 2005).
Thus, selecting sweet potato cultivars with high levels of beta-carotene, adapted to local environmental conditions, and with good yields can assist in developing cultivars to supply human nutritional requirements, especially for low-income populations. Estimates of genetic parameters and the use of a selection index are promising strategies for selection of genotypes, since they provide guidelines for the selection process. Estimates of genetic parameters are essential for choosing methods for selection; they allow inferences of predicted gains from selection (Azevedo et al. 2015) and guide different selection strategies that allow breeders to make most efficient use of these parameters.
Success in plant breeding depends on the existence of genetic variability for selection of superior genotypes; the selected individuals must have favorable attributes (Leite et al. 2016) that result in better performance and meet market demands. Correct classification of genotypes must consider several traits simultaneously (Krause et al. 2012). The use of selection indexes is an alternative for selection through estimation of percentage gains in plant breeding programs. Thus, the objective of this study was to verify the presence of genetic variability and the possibility of gains from selection in a sweet potato population with high beta-carotene content to select potentially promising genotypes.
MATERIAL AND METHODS
The experiment was conducted under field conditions in the experimental area of the Teaching, Research, and Extension Farm of the FEIS/UNESP, Ilha Solteira campus, in Selvíria, MS, Brazil (lat 20º 20' 39.40" S, long 51º 23" 51.00" W, alt 335 m asl). The soil of the experimental area was classified as a Latossolo Vermelho Distroférrico (Typic Hapludox) of clayey texture (EMBRAPA 2013). Conventional tillage was used in the area, and 40-cm height seed beds were prepared, spaced at 1.20 m. The soil was fertilized together with tillage by incorporating 500 kg ha -1 of a 04-14-08 NPK fertilizer, supplemented with 133 kg ha -1 of potassium chloride and 166 kg ha -1 of single superphosphate.
The sweet potato genotypes used were from an elite population developed by the breeding program of the International Potato Center (CIP) and the Agricultural Research Institute of Mozambique (IIAM). Fifteen families of half-sib progenies obtained through open pollination (polycrosses) were evaluated. Each family was represented by 17 progenies, which were cloned, resulting in a clonal treatment, for a total of 255 new treatments. A control treatment was also used, consisting of an orange-fleshed commercial cultivar (Beauregard) that exhibited good yield (Cecílio Filho et al. 2016), thus resulting in a clonal test of 256 treatments.
Before implementation of the experiment, the sweet potato seeds were chemically scarified with sulfuric acid and placed in 162-cell polystyrene trays filled with commercial substrate for germination. The seedlings were transplanted to 5-L pots filled with a composting material for propagation.
On January 17, 2019, stems with a length of approximately 30 cm and 8 to 10 nodes were chosen to set up the experiment in a randomized block design with two replications. Plots consisted of 1-m rows with spacing of 1 m between rows and 0.33 m between plants, constituting a total area of 520 m 2 . Fertilization was side-dressed at 30 days after planting (DAP), using 30 kg ha -1 of N. Weeds were controlled manually in the plant rows, and chemical control with a Linuron-based product (0.6 L ha -1 ) and a Clethodim + Alkylbenzene-based product (0.20 L ha -1 ) was used between the plant rows. A central pivot irrigation system was used throughout the crop cycle, applying a 12-mm water depth every three days.
The plants were harvested at 127 DAP to evaluate the following quantitative traits: total root yield (TRY) -total root weight of plants harvested in the plot, converted to Mg ha -1 ; commercial root yield (CRY) -roots with weights greater than 80 g in the plot, converted to Mg ha -1 ; non-commercial root yield (NCRY) -roots with weights of less than 80 g in the plot, converted to Mg ha -1 ; percentage of commercial root yield (%CRY) -obtained by [(CRY / TRY) × 100]; total number of roots (TNR) -number of roots per plant harvested in the plot, converted to roots ha -1 ; number of commercial roots (NCR) -number of roots per plant harvested in the plot with weights above 80 g, converted to roots ha -1 ; number of non-commercial roots (NNCR) -number of roots per plant harvested in the plot, with weights below 80 g, converted to roots ha -1 ; mean root weight (MRW) -obtained by TRY / TNR; mean commercial root weight (MCRW) -obtained by CRY / NCR; total root dry weight (TRDW) -root samples were oven dried at 65 °C for 72 hours until constant weight to determine their dry weight (%), which was converted to Mg ha -1 ; and root dry matter content (RDMC) -obtained by [(TRDW × 100) / fresh weight].
Root quality traits were visually evaluated using a scoring scale from 1 to 5, where 1 corresponds to the least attractive roots and 5 to the most attractive ones. The scores were attributed to the traits considering sweet potato peel color (PC), flesh color (FC), root damage (RD), root shape (RSh), and root size (RSi). The final scores were the mean of the scores attributed by two evaluators.
Analyses were performed with the Selegen REML/BLUP software (Resende 2016) using model 20 (randomized blocks, unrelated clone test, one observation per plot), given by y = Xr + Zg + e, where y is the vector of the known data observed; r is the vector of repeating effects (fixed effects); g is the vector of genotype effects (random effects); e is the vector of errors or residuals (random); and X and Z are the incidence matrices for these effects. The unrelated clone test (model 20) was chosen because preliminary analysis identified that the variation between families was lower than the variation within a family. This greater variation within a family than between families is because the sweet potato is a self-incompatible hexaploid species with multiple allelism for several loci, which provides considerable genetic variability. Genetic parameters and variance components were estimated. The genetic parameters and variance components included genetic variance σ 2 g ; experimental error variance σ 2 e ; the residual coefficient of variation CV g ; the genotypic coefficient of variation (CV g ); broad sense heritability, based on the mean of clones h 2 mc ; the relative coefficient of variation CV r ; the overall mean; gain from selection (GS); and gain from selection -percentage (GS%), given by GS% = GS/MeanGain * 100.
Deviance analysis was performed to check for significance in the evaluated traits. A selective pressure of 11.72% was adopted, for a total of 30 genotypes; the direct gains for the studied traits were calculated and, subsequently, the selection index was obtained from the sum of ranks (ranks) of Mulamba and Mock (1978), based on genotypic values. This index has proven efficiency in diverse crops, such as potato (Terres et al. 2015), alfalfa (Vasconcelos et al. 2010), popcorn (Vieira et al. 2017), macaúba palm (Costa et al. 2018), yellow passion fruit (Krause et al. 2012), and coffee (Carias et al. 2016). It has proven to be reliable and has provided balanced cultivars.
This index orders genotypes according to the desired trait, and then the sum of ranks is performed, based on the multiple traits evaluated. Economic weights of 3, 1.5, 2, and 2 were adopted for CRY, %CRY, RDMC, and FC, respectively, which are the traits of greatest agronomic and economic importance. Weights were adopted after several attempts to favor gains in desirable traits (CRY, %CRY, RDMC, and FC). These higher weights were used to favor gain in these traits, selecting the genotypes that show superiority in these traits for the next evaluation and selection cycle. A weight of 1 was adopted for the other traits.
The CRY trait is the most important trait, considering that increasing commercial yield is essential. This also explains the choice of trait %CRY. The RDMC was chosen because of the consumer preference factor; selection in the present study was based on fresh consumption, and Brazilian consumers prefer larger sweet potatoes; thus, higher dry weights are desirable for selection. FC is the most important trait related to root quality because it indicates the presence of beta-carotene, which is the main trait evaluated in the present study. Weight 1 was adopted for all the other traits. Several weights were tested to choose those to be used, trying to promote gains in all the traits to maximize them and make the index as balanced as possible.
Regarding gain from selection for traits related to quality of the roots, the values were rounded off, without decimal values, to follow the evaluation pattern. Values of 0.51 or more were rounded up, and values less than or equal to 0.50 were rounded down.
RESULTS AND DISCUSSION
MEF Otoboni et al.
The genotypic coefficients of variation (CV g ) showed genetic variability among the 255 genotypes for most of the 16 traits evaluated (Table 1): 81.25% of the traits had CV g above 20%; 12.5% had CV g from 10% to 20%; and 6.25% had CV g below 10%. All traits showed significance at 1% probability by the chi-square test at 1 degree of freedom. The estimates of CV g for yield traits were higher than those of the residual coefficient of variation, except for percentage of commercial root yield (%CRY) and root dry matter content (RDMC), showing a favorable situation for selection. Regarding the traits related to root quality (PC, FC, RD, RSh, and RSi), the CV g was higher than the CV e only for flesh color (FC), which is the most important quality trait because it indicates the presence or absence of beta-carotene, the main trait evaluated in the present study.
The residual coefficient of variation (CV e ) is the parameter that indicates the magnitude of experimental precision (Pimentel-Gomes and Garcia 2002). Although CV e values were high, estimates of heritability (h²) and relative coefficient of variation (CV r ) were promising (Table 1). These parameters measure the degree of genetic determination of a trait, predicting that the genotypes evaluated have high genetic variability, even with significant environmental effect on the traits analyzed. Genetic variability is essential for the establishment of a breeding program, but efficient selection of superior genotypes depends on the genetic and environmental parameters related to the traits of interest (Blind et al. 2018).
The genetic variation found was positive for the estimates of heritability. The estimated values showed high variation, from 0.42 to 0.88 (Table 1), indicating the possibility of successful selection. The highest heritabilities found were for total root yield (TRY; 0.88), commercial root yield (CRY; 0.88), mean commercial root weight (MCRW; 0.86), and FC (0.86). Among the traits evaluated, 56.25% had heritability above 0.80, and 31.25% had heritability from 0.50 to 0.79. High heritability is essential for successful selection, allowing the breeder to use the most appropriate selection strategies. The results found for the most important traits (CRY, %CRY, RDMC, and FC) show an excellent perspective for the genotype selection process based on them. Heritability in sweet potato is important because dominance and epistasis effects are maintained by vegetative propagation (Gonçalves Neto et al. 2012). Vegetative propagation is the most efficient propagation system for sweet potato; thus, these effects are focused on the varieties selected for commercial growing (Azevedo et al. 2015).
The success of selection was confirmed by attaining CV r values close to or greater than 1 for all traits under evaluation, ranging from 0.61 (RD) to 1.94 (TRY) ( Table 1), which indicates the possibility of more genetic gains. According to Vencovsky and Barriga (1992), when the CV r is greater than or equal to 1, genetic variation is the factor most responsible for the estimated variation in the experimental data, and this parameter can be used as an index, indicating the degree of ease in selection of genotypes for each trait. Carmona et al. (2015) evaluated the genetic divergence among 23 accessions of sweet potato in Brasília, DF, Brazil, and found lower CV r for TRY (0.93) and CRY (1.05) compared to the values found TRY: total root yield (Mg ha -1 ); CRY: commercial root yield (Mg ha -1 ) NCRY: non-commercial root yield (Mg ha -1 ); %CRY: percentage of commercial root yield; MRW: mean root weight (kg); MCRW: mean commercial root weight (kg); TNR: total number of roots (ha -1 ); NCR: number of commercial roots (ha -1 ); NNCR: number of non-commercial roots (ha -1 ); RDMC: root dry matter content (%); TRDW: total root dry weight (Mg ha -1 ); PC: peel color; FC: flesh color; RD: root damage; RSh: root shape, RSi: root size; σ 2 g : genotypic variance; σ 2 e : residual variance; σ 2 f : phenotypic variance; h²mc: mean heritability of the clones; CV g (%): genotypic coefficient of variation; CV e (%): residual coefficient of variation; CV r (%) = CV g CV e : ratio between genotypic coefficient of variation and environmental coefficient of variation; GS: gain from selection; GS%: gain from selection gain (percentage) by index based on direct selection. ** Significant at 1% in the chi-square test.
in the present study (Table 1). The same occurred for the heritability estimates and genotypic coefficients of variation. Azevedo et al. (2015) evaluated the agronomic performance and genetic parameters of 65 sweet potato genotypes in Diamantina, MG, Brazil, and found CV g of 31.14% (TRY) and 45.53% (CRY), and heritability of 0.71 (TRY) and 0.78 (CRY), estimates which were lower than those found in the present study.
Little improved species, such as sweet potato, tend to express high genetic variability. This provides a significant number of genotypes to be evaluated and increases the potential for breeding programs to obtain new commercial cultivars through genetic improvement.
Estimates of direct selection had significant predicted gains, ranging from 10.27% to 126.08%, indicating a favorable condition for selection (Table 1). However, to develop a cultivar, a genotype must have several favorable attributes simultaneously because selection based on only one trait can result in unsatisfactory performance in other traits. Therefore, selection based on selection indexes was a reliable strategy for obtaining balanced gains for all traits evaluated, maximizing simultaneous gains for the genetic parameters.
The 30 best genotypes were selected through the index based on the sum of ranks. The index provided considerable gains from selection, except for root dry matter content (RDMC) ( Table 2). The low gain (7.67%) found for non-commercial root yield (NCRY) is favorable for selection because inverse selection was performed for this trait, searching for the genotypes with the lowest values. The same procedure was adopted for number of non-commercial roots, which resulted in negative gain (-3.71%), i.e., a reduction in the number of non-commercial roots. The negative gain in RDMC (-2.95%), though it constitutes a main trait, indicates that the genotypes selected tend to show a reduction in this trait for the next selection cycle. However, as this negative gain was low, this reduction will not be substantial. Furthermore, increases in gains for CRY, maximizing the weight attributed to this trait, negatively affected the RDMC gains -the index weighs the gains by considering all traits evaluated. In addition, the environmental effect was greater than the genetic effect on this trait (Table 1). Rodrigues and Pereira (2003) evaluated inter-and intragenerational correlations and heritability of color, dry matter, and yield of 250 potato (Solanum tuberosum) clones in Pelotas, RS, Brazil. They reported that the selection of superior genotypes based on dry matter content should be made only after the third generation because first-generation selection has little effect on plant yields and often exhibits little genetic control of this trait. In the present study, RDMC was the trait with the second lowest heritability and the lowest expected gain.
The gains with the selection index ranged from -3.71% to 75.27% (Table 2). The results presented in Table 2 show the expected mean (M1) of the selected population, including the genetic gains obtained. The index gain for TRY was 16.56 Mg ha -1 , representing 69.81% efficiency of the index compared to direct gain, with a selection differential of 35.32 Mg ha -1 , i.e., the mean of the improved population (M1) will be 35 Mg ha -1 higher than the mean of the original population. CERAT31-01 was the highest yielding genotype among those selected, with TRY of 92.73 Mg ha -1 , which is 434.5% higher than that of the control (21.34 Mg ha -1 ); CERAT16-20 was the second highest yielding genotype (88.56 Mg ha -1 ). Santos Neto et al. (2017) evaluated the yield of three sweet potato clones in São Cristóvão, SE, Brazil, and found a mean total root yield of 41.78 Mg ha -1 , which was 15.39 Mg ha -1 less than that found in the present study (a mean of 57.17 Mg ha -1 for the 30 genotypes selected).
CRY showed the highest gain (75.27%). CERAT31-01 and CERAT16-20 were once more superior to the others, at 88.20 and 87.17 Mg ha -1 , respectively. The selected genotypes were much superior to the control, which had a CRY of 15.31 Mg ha -1 . Andrade Júnior et al. (2018) evaluated the quantitative and qualitative potential of 40 sweet potato genotypes in Diamantina, MG, Brazil; the highest TRY values they found were 47.1 Mg ha -1 , and the highest CRY values were 36.7 Mg ha -1 , significantly lower than the values found in the present study. Silva et al. (2015) Although the selection of sweet potato genotypes is mainly based on commercial root production, total root yield is also important, since roots with weight insufficient for commercial sale can be used for other purposes, such as in the food industry, for animal feed, and for ethanol production. However, in the search for genotypes with the lowest values for NCRY, CERAT52-09 was the most promising, with 100% commercial roots. CERAT51-30 had the highest NCRY MEF Otoboni et al.
(13.83 Mg ha -1 ). The gain for NCRY was 7.67%, which is favorable for selection, because the lower the non-commercial root production, the higher the percentage of commercial root production.
The gain for percentage of commercial root yield (%CRY) was 68.50%; 21 out of the 30 genotypes selected had 100% commercial roots, with a mean of 97.58%. Amaro et al. (2019) found a mean %CRY of 81.52%, with results ranging from 72.44% to 88.97%.
The mean root weight (MRW) accounts for both commercial (above 80 g) and non-commercial roots. The gain for this trait through the index was 37.21%, representing a gain of 0.093 kg. The highest MRW found by the index was for the genotype CERAT52-25, with a MRW of 1.676 kg per root. This high MRW found for CERAT52-25 is not common; TRY: total root yield (Mg ha -1 ); CRY: commercial root yield (Mg ha -1 ) NCRY: non-commercial root yield (Mg ha -1 ); %CRY: percentage of commercial root yield; MRW: mean root weight (kg); MCRW: mean commercial root weight (kg); TNR: total number of roots (ha -1 ); NCR: number of commercial roots (ha -1 ); NNCR: number of non-commercial roots (ha -1 ); RDMC: root dry matter content (%); TRDW: total root dry weight (Mg ha -1 ); PC: peel color; FC: flesh color; RD: root damage; RSh: root shape, RSi: root size; SD: selection differential; GS: gain from selection gain; GS%: gain from selection (percentage) by index based on direct selection. however, this was due to its low number of roots (16,246 roots; total root weight of 42.22 Mg ha -1 ) compared to the other genotypes, which indicates that most of its roots are commercial. This can be shown by the NCRY, which was 0.35 Mg ha -1 , the third lowest among the selected populations. The second highest MRW (0.764 kg) was found for the genotype CERAT21-02, followed by CERAT16-20 (mean of 0.587 kg per root). These results were higher than those found by Azevedo et al. (2015), for whom the highest MRW was 0.205 kg.
The CERAT52-25 genotype had the highest mean commercial root weight (MCRW), 1.692 kg, for the same reasons indicated for MRW. When selecting genotypes for fresh consumption, the MCRW should not exceed 0.500 kg. Thus, CERAT31-01 (0.486 kg) and CERAT31-22 (0.485 kg) were the most promising genotypes. The lowest MCRW was found for CERAT52-23 (0.287 kg). The mean MCRW was 0.525 kg. The MCRW of all the genotypes was superior to that of the control (0.200 kg). Amaro et al. (2019) found lower results of MCRW (mean of 0.180 kg); the commercial cultivar Beauregard had MCRW of 0.182 kg. Amaro et al. (2017) found mean MCRW of 0.299 kg and 0.273 kg for Beauregard. The highest MCRW found by Azevedo et al. (2015) was 0.253 kg.
The gain for total number of roots (TNR) was 26.89%, and the gain for number of commercial roots (NCR) was 53%. The number of non-commercial roots (NNCR) had a negative gain of -3.71%. CERAT25-01 showed the highest TNR (397,295) but the lowest MRW (0.162 kg) and the second lowest MCRW (0.300 kg). CERAT31-01 had the highest NCR (185.208), which was 239.6% greater than that of the commercial cultivar (Beauregard) evaluated. CERAT52-09 was promising with the smallest NNCR, only 2 non-commercial roots, while the control had 109.780 non-commercial roots.
The gain from selection for root dry matter content (RDMC) was -2.95%, ranging from 34.40% (CERAT16-03) to 26.41% (CERAT21-13), with a mean of 29.43%. According to EMBRAPA (2008), sweet potato roots have a RDMC of 30%. The RDMC of the commercial cultivar (Beauregard) was 24.68% lower than that of the genotype CERAT21-13, which had the lowest RDMC among the genotypes selected. These results were superior to those found by Andrade Júnior et al. (2012), who evaluated 12 sweet potato clones in Diamantina, MG, Brazil, and found a mean RDMC of 27.30%. However, in general, they were lower than the results of Vieira et al. (2015), who evaluated 60 clones with a focus on ethanol production in Palmas, TO, Brazil; they found RDMC of 33.05%. RDMC is directly related to the specific density of the sweet potato roots; from the industry perspective, high dry matter content of roots is desirable because this trait leads to higher processing yields.
Genotypes with the highest scores for traits related to root quality were selected (Table 2); scores 4 and 5 indicate superior genotypes for the trait evaluated. The gain from selection for peel color (PC) was 26.49%, with a mean score of 3. Only two genotypes among the 30 selected attained score 5: CERAT21-13 and CERAT35-09. Genotypes with scores 4 and 5 for flesh color (FC) indicate that they have intense orange flesh; the selection of orange-fleshed sweet potato genotypes was the focus of the present study. The selection differential for this trait was 1.21, which resulted in a gain of 31.31%. Based on the visual evaluation pattern we adopted, this indicates that the genotypes selected in the present study tend to show improvements in the next selection cycle. Among the 30 genotypes selected, 14 attained score 5 for FC, and 7 genotypes had score 4, i.e., 70% of the selected genotypes presented intense orange flesh and, consequently, high beta-carotene content. The Beauregard cultivar used as a standard had score 4 for FC.
The gain for root damage (RD) was 46.89%; the genotypes CERAT21-13 and CERAT25-01 attained score 5. Six genotypes had score 5 for root shape (RSh). The gain for RSh (57.64%) was higher than that of the other traits related to visual quality. Root size (RSi) exhibited gain from selection of 53.94%, and 4 genotypes attained score 5 for that trait. The traits involving root quality are highlighted to obtain genotypes with visually appealing roots containing high beta-carotene content. CERAT51-30, CERAT21-13, and CERAT25-01 are highly promising genotypes; however, 83% of the genotypes also showed high potential for traits of visual interest.
Considering the results from analysis of estimates of genetic parameters, the population tested in the present study showed high genetic variability, indicating favorable conditions for selection to achieve considerable genetic advances. Combined evaluation of all the traits tested shows that CERAT51-30, CERAT31-01, and CERAT21-02 can be recommended MEF Otoboni et al.
as the most promising sweet potato genotypes. The genotypes evaluated were significantly superior to the commercial Beauregard cultivar, and they can be placed in Value for Cultivation and Use trials. The use of these clones as cultivars will lead to considerable gains in yield. However, after recombination, even more promising and better quality genotypes are expected through the cycle 1 population and throughout the program to make use for the promising genetic base.
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Domain: Agricultural And Food Sciences Biology
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Duodenal morphology and immune responses of broiler chickens fed low doses of deoxynivalenol
Morphometry and flow cytometry for intraepithelial lymphocyte phenotyping were used to determine the changes in duodenal mucosae after administration of low doses of deoxynivalenol in chickens. Moreover, functions of phagocytes and immunocompetent cells in peripheral blood were evaluated by flow cytometry. In total, sixty chickens of Ross hybrid broilers 308 were used in this experiment. Two experimental groups of 20 birds were continually fed for 14 days a diet containing deoxynivalenol at a dose of 1 and 3 mg·kg-1; 20 birds of the control group were fed uncontaminated diet. Morphometry showed only tendency to decrease the height of villi and surface area of duodenal mucosae in chickens fed the diet supplemented with 3 mg·kg-1 deoxynivalenol. Phenotyping of intraepithelial lymphocytes showed a decrease of CD45+ (P < 0.034) in duodenum of birds fed diets supplemented with deoxynivalenol. Examination of white blood cells showed a decrease of monocytes (P < 0.020) in chickens fed 3 mg·kg-1 of deoxynivalenol. Both experimental groups revealed higher metabolic burst of peripheral blood heterophils (P < 0.001). Phenotyping of immunocompetent cells showed an increase (P < 0.003) of CD3+ and a decrease (P < 0.001) of MHC II+ cells in peripheral blood of chickens fed with 3 mg·kg-1 dose of deoxynivalenol. The experimental feeding of chickens with deoxynivalenol resulted in immunomodulation of immunocompetent cells in duodenum and blood with mild atrophy of intestinal villi, mainly after the feeding of the dose of 3 mg·kg-1. We proved that even low doses of deoxynivalenol can cause changes in haemathological, immunological and morphological profiles already during two weeks, and lead to the activation of compensatoryadaptive mechanisms with unfavourable impact on health and performance of birds. Intestine, immunity, vomitoxin, morphometry, toxicity, poultry Deoxynivalenol (DON, vomitoxin) is mycotoxin produced by Fusarium graminearum and is included into B type trichothecenes which can cause serious problems of animals and poultry when consumed via contaminated cereal grains. Consumption of lower amounts of fungal toxins may result in impaired immunity and decreased resistance to infection diseases (Oswald et al. 2005). Mycotoxins may act on all types of immune cells and on different levels of the immune response to produce their adverse effects. Numerous studies conducted on host resistance, antibody responses, and cell mediated immunity have revealed that trichothecenes stimulate or suppress the immune function depending on the dose, exposure frequency, and timing of functional immune assay (Pestka et al. 2004). It is very likely that mycotoxins have their greatest effect on the mucosal lymphoid tissue (particularly gut and bronchial) before they are absorbed and subsequently metabolized (Oswald et al. 2005). The avian gut associated lymphoid tissue (GALT) comprises a diverse set of cell subsets, distinct from that of systemic tissues but includes representatives of each of the major cell populations found on other sites. Overall the gut is populated with heterophils, macrophages, dendtritic cells, natural killer cell, and B and T lymphocytes, although the proportions of each cell type differ widely according to site and age (Brown et al. 2008). Intraepithelial lymphocytes (IEL) and lamina propria lymphocytes (LPL) have ACTA VET. BRNO 2013, 82: 337–342; doi:10.2754/avb201382030337 Address for correspondence: Doc. MVDr. Viera Revajová, PhD. Department of Pathological Anatomy and Pathological Physiology University of Veterinary Medicine and Pharmacy in Košice Komenského 73, Košice 041 81, Slovak Republic Phone: +421 915 984 708 Fax: +421 55 67 11 674 E-mail [URL]/ fundamental importance in host prevention after antigenic stimulation in the intestinal tract (Davison et al. 2008). Enterocytes as part of the integral mucosal immune system are multi-functional epithelial cells that play an important role in the organization and function of the enteric immune system. Studies using the mouse model have shown the importance of enterocyte-expressed chemokines in the recruitment and retention of IEL cell populations (Onai et al. 2002). Diets contaminated with low DON that induce a negative impact on health and performance could affect small intestinal morphology in broilers by diffusion of mycotoxins. Awad et al. (2006) observed that feeding DON for 21 days to broiler chickens at a concentration of up to 5 mg·kg-1 of diet influenced the weight of the small intestine as well as intestinal histology, especially the duodenum, as evidenced by shorter and thinner villi. Changes in the villi may influence also the immunocompetent cells of the intestine. For that reason, the aim of the present study was to examine the effects of low doses of DON in naturally contaminated maize on duodenal morphology and immune responses of broilers. Materials and Methods Animals, housing and diets One-day-old sixty chickens of Ross hybrid broilers 308 were randomly divided into 3 groups of 20 birds. The broilers were reared in large pens with wood shavings and had free access to water and feed. The experiment was carried out in accordance with established standards for use of animals. Local ethics and scientific authorities approved the Ro-2518/05-211/c protocol. Chickens of all groups were fed the commercial diet HYD-02 for 2 weeks, and during the following 2 weeks broilers of DON groups were fed diets contaminated with different doses of DON mycotoxin. Commercial diet of the control group was naturally contaminated with 0.2 mg·kg-1 DON. Diets of the second and third groups were experimentally contaminated with 1 mg·kg-1 and 3 mg·kg-1 DON, respectively. The final mycotoxin contents in the diets for each group of birds are shown in Table 1. Contaminated batches of maize were obtained by their cultivation with Fusarium graminearum for four weeks at the Slovak Agriculture University in Nitra (Labuda et al. 2003). To provide stable dietary contents of mycotoxins throughout the whole experimental period, the chickens were fed only one type of diet, HYD-02. The composition of this diet is given in Table 2. At the age of 4 weeks, 6 randomly chosen chickens from each group were anaesthetized with intraperitoneal injection of xylazine and ketamine (Rometar 2% and Narkamon 5%, Spofa, Czech Republic) at doses of 0.6 and 0.7 ml·kg-1 body weight, respectively. After laparotomy, blood was collected into heparinized tubes by intracardial punction and immediately used for counting of leukocytes, and flow cytometry analysis of granulocytes and lymphocytes. Duodenal samples were taken into Hanks solution for flow cytometry, as well as into 10% formaline for histology and morphometry. Mycotoxin analysis Mycotoxins in maize were detected using gas chromatography-mass spectrophotometry (GC-MS) method (Raymond et al. 2003). Mycotoxin contents in the basal diet (the part of HYD-02 diet before addition of 40% portion of control or contaminated maize) were analyzed using NOACK kits for enzyme-linked immunosorbent assay with spectrophotometric evaluation. 338 Table 1. Content of mycotoxins in complete diets for control and experimental groups of chickens. DON deoxynivalenol, ZEA zearalenone, 15-ADON 15-acetyldeoxynivalenol Groups of birds Mycotoxins (mg·kg-1 of complete feed) DON ZEA 15-ADON Total aflatoxins Control group 0.2 0.03 0 0.002 Group fed 1 mg DON 1.0 0.06 0.07 0.002 Group fed 3 mg DON 3.0 0.15 0.24 0.002 Table 2. Composition of diet HYD-02 fed to broilers during the experiment. Metabolizable energy (12.75 MJ·kg-1 of complete feed), CP crude protein analysed by Kjelahl method (210.6 g·kg-1 of complete feed) Components g·kg-1 Wheat ground, 10.5% of CP 260
Deoxynivalenol (DON, vomitoxin) is mycotoxin produced by Fusarium graminearum and is included into B type trichothecenes which can cause serious problems of animals and poultry when consumed via contaminated cereal grains. Consumption of lower amounts of fungal toxins may result in impaired immunity and decreased resistance to infection diseases (Oswald et al. 2005).
Mycotoxins may act on all types of immune cells and on different levels of the immune response to produce their adverse effects. Numerous studies conducted on host resistance, antibody responses, and cell mediated immunity have revealed that trichothecenes stimulate or suppress the immune function depending on the dose, exposure frequency, and timing of functional immune assay (Pestka et al. 2004). It is very likely that mycotoxins have their greatest effect on the mucosal lymphoid tissue (particularly gut and bronchial) before they are absorbed and subsequently metabolized (Oswald et al. 2005).
The avian gut associated lymphoid tissue (GALT) comprises a diverse set of cell subsets, distinct from that of systemic tissues but includes representatives of each of the major cell populations found on other sites. Overall the gut is populated with heterophils, macrophages, dendtritic cells, natural killer cell, and B and T lymphocytes, although the proportions of each cell type differ widely according to site and age (Brown et al. 2008). Intraepithelial lymphocytes (IEL) and lamina propria lymphocytes (LPL) have fundamental importance in host prevention after antigenic stimulation in the intestinal tract (Davison et al. 2008).
Enterocytes as part of the integral mucosal immune system are multi-functional epithelial cells that play an important role in the organization and function of the enteric immune system. Studies using the mouse model have shown the importance of enterocyte-expressed chemokines in the recruitment and retention of IEL cell populations (Onai et al. 2002).
Diets contaminated with low DON that induce a negative impact on health and performance could affect small intestinal morphology in broilers by diffusion of mycotoxins. Awad et al. (2006) observed that feeding DON for 21 days to broiler chickens at a concentration of up to 5 mg•kg -1 of diet influenced the weight of the small intestine as well as intestinal histology, especially the duodenum, as evidenced by shorter and thinner villi. Changes in the villi may influence also the immunocompetent cells of the intestine.
For that reason, the aim of the present study was to examine the effects of low doses of DON in naturally contaminated maize on duodenal morphology and immune responses of broilers.
Animals, housing and diets
One-day-old sixty chickens of Ross hybrid broilers 308 were randomly divided into 3 groups of 20 birds. The broilers were reared in large pens with wood shavings and had free access to water and feed. The experiment was carried out in accordance with established standards for use of animals. Local ethics and scientific authorities approved the Ro-2518/05-211/c protocol.
Chickens of all groups were fed the commercial diet HYD-02 for 2 weeks, and during the following 2 weeks broilers of DON groups were fed diets contaminated with different doses of DON mycotoxin. Commercial diet of the control group was naturally contaminated with 0.2 mg•kg -1 DON. Diets of the second and third groups were experimentally contaminated with 1 mg•kg -1 and 3 mg•kg -1 DON, respectively. The final mycotoxin contents in the diets for each group of birds are shown in Table 1. Contaminated batches of maize were obtained by their cultivation with Fusarium graminearum for four weeks at the Slovak Agriculture University in Nitra (Labuda et al. 2003). To provide stable dietary contents of mycotoxins throughout the whole experimental period, the chickens were fed only one type of diet, HYD-02. The composition of this diet is given in Table 2. At the age of 4 weeks, 6 randomly chosen chickens from each group were anaesthetized with intraperitoneal injection of xylazine and ketamine (Rometar 2% and Narkamon 5%, Spofa, Czech Republic) at doses of 0.6 and 0.7 ml•kg -1 body weight, respectively. After laparotomy, blood was collected into heparinized tubes by intracardial punction and immediately used for counting of leukocytes, and flow cytometry analysis of granulocytes and lymphocytes. Duodenal samples were taken into Hanks solution for flow cytometry, as well as into 10% formaline for histology and morphometry.
Mycotoxin analysis
Mycotoxins in maize were detected using gas chromatography-mass spectrophotometry (GC-MS) method (Raymond et al. 2003). Mycotoxin contents in the basal diet (the part of HYD-02 diet before addition of 40% portion of control or contaminated maize) were analyzed using NOACK kits for enzyme-linked immunosorbent assay with spectrophotometric evaluation.12.5 Premix HYD-02 (vitamins and minerals)
White blood cell counting
Routine laboratory method using haemocytometer and Hemacolor staining (Merck, Germany) were used for evaluation of total count of leukocytes and their differentiation on blood smears. Absolute numbers (total numbers) of different white blood cell counting (WBC) were counted as follows: total leukocyte count/100 counted cells × relative % of differential cell count.
Phagocytosis and oxidative burst assay
The functions of polymorphonuclear cells were measured by flow cytometry in whole heparinized blood (heparin 10-20 U•ml -1 in PBS, Zentiva, Czech Republic). A commercial Phagotest and Bursttest kits (ORPEGEN ® Pharma, Germany) were used for examination of phagocytosis and metabolic activity by the manual instructions.
Flow cytometry
Duodenal intraepithelial lymphocytes were isolated by modification some methods and published in detail by Levkut et al. (2009). Mononuclear cells from blood and intestine were separated over Histopaque-1077 gradient sedimentation (Sigma, Germany). Indirect and direct immunofluorescence methods of single staining cells were used. Labelled and unlabelled primary mouse anti-chicken monoclonal antibodies (Serotec, GB, and Southern Biotechnology Associates, Inc., Birmingham, USA) were used (Table 3). Polyclonal goat antimouse FITC-conjugated immunoglobulins F(ab') 2 fragment (Dako, Denmark) at a working dilution 1:50 with phosphate-buffered saline and 0.1% natrium azide (PBS+NaN 3 ) was used for staining lymphocytes in indirect immunofluorescence.
For each cell suspension (1.10 6 lymphocytes in PBS), cell population acquisition and analysis was carried out based on 10,000 cells using FACScan flow cytometer and Cell Quest programme (Becton Dickinson, Germany). For each marker, the relative percentage of fluorescent positive cells was recorded and absolute subpopulation's lymphocyte counts in peripheral blood were computed as follows: absolute lymphocyte counts/100 × relative % subpopulation's lymphocytes.
Histology and morphometry of duodenum
Routine histological method with haematoxylin-eosin staining was used. Photos of histological sections were taken by Nikon LABOPHOT-2 with camera adapter (DS Camera Control Unit DS -U2) with × 4 magnification. The height and surface area of villi were measured by NIS-Elements programme. The height of villi was measured from the base to the apex.
Statistical analysis
Statistical analysis was done using one-way analysis of variance (ANOVA) with post hoc Tukey multiple comparison test. Differences between the mean values for the groups of chickens were considered significant when P < 0.05.
Results
Determination of peripheral blood heterophils demonstrated their increased (P < 0.002) frequency in birds fed with the 3 mg•kg -1 DON dose than in chickens fed with the 1 mg•kg -1 DON dose. On the contrary, values of monocytes were lower (P < 0.02) in chickens fed with the 3 mg•kg -1 DON dose than in control. Metabolic burst of heterophils was higher (P < 0.001) in birds fed with 1 mg•kg -1 and 3 mg•kg -1 DON doses compared to control (Table 4).
Density of lymphocyte subpopulations in peripheral blood revealed higher frequency of CD3+ cells in birds fed with 3 mg•kg -1 DON dose than in 1 mg•kg -1 DON dose (P < 0.003) and in control (P < 0.003). Numbers of CD4+ and CD8+ cells were higher (P < 0.003, P < 0.016, respectively) in birds fed with 3 mg•kg -1 DON dose than in chickens fed with 1 mg•kg -1 DON dose. However, density of MHC II+ cells was lower (P < 0.001) in birds fed with 3 mg•kg -1 DON dose than in control (Table 5).
Evaluation of intraepithelial lymphocytes showed lower (P < 0.034) density of CD45+ cells in both experimental groups fed with DON than in control chickens, but with tendency in increase of CD8+ cells (Table 6).
During the experiment, chickens did not reveal clinical signs. Similarly, no gross and histological lesions were found in the intestine of birds fed the diet naturally contaminated with deoxynivalenol. Duodenal morphology demonstrated only tendency to decrease the height of villi and surface area of villi in birds fed with 3 mg•kg -1 DON dose compared to 1 mg•kg -1 and control (Table 7).
Table 1 .
Content of mycotoxins in complete diets for control and experimental groups of chickens.
Table 2 .
Composition of diet HYD-02 fed to broilers during the experiment.
Table 3 .
Primary mouse anti-chicken monoclonal antibodies used in the experiment.
Table 4 .
Number of peripheral white blood cells (G•l -1 = 10 9 •l -1 ) and functions of phagocytes in broilers fed diets contaminated with deoxynivalenol. DON -deoxynivalenol. Data are presented as means ± SD (n = 6), different letters within the same row mark significant differences (P < 0.05).
Table 6 .
Relative percentage of duodenal intraepithelial lymphocytes of broilers fed diets contaminated with deoxynivalenol. DON -deoxynivalenol. Data are presented as means ± SD (n = 6), different letters within the same row mark significant differences (P < 0.05).
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Domain: Agricultural And Food Sciences Biology Medicine
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Marginal Water Productivity of Irrigated Durum Wheat in Semi-Arid Tunisia
Recent studies on agricultural water management in Tunisia report low water productivity for some of the currently widely cultivated crops such as durum wheat. The objective of this study is to estimate water productivity and marginal value of irrigation of durum wheat in central Tunisia. We develop a production function, in which the durum wheat irrigation revenue of farmers per hectare is expressed in terms of the used water volume in addition to other production factors. The function was estimated for a sample of durum wheat farms from Central Tunisia. Results show that 31.7% of the farmers were applying water volumes above the economic optimal volume (more than 2700 m/ha). Moreover, 50% of the farmers were found to be applying less irrigation water than this optimal volume. Applying water above the optimal volume means that the benefit farmers obtain from each supplementary unit of irrigation water is lower than the market price of irrigation water currently applied in the region (0.110 TND/m). Then, water is wasted. However, using less water than the optimal volume means that farmers can make further supplementary irrigations and obtain more benefit from it (extra-yield). The study also shows that most of the farmers in the study area do not apply good practices with respect to irrigation scheduling and irrigation doses. Improving irrigation performance will largely preserve water resources and enhance food security in Tunisia.
Introduction
The portion of fresh water currently available for agriculture is globally decreasing (Cai & Rosegrant, 2003) while at the same time the agricultural production from irrigated areas must be increased in order to satisfy the growing food demand, especially in developing countries. The search for sustainable methods to increase crop water productivity becomes more and more urgent especially in arid and semi-arid regions (Debaeke & Aboudrare, 2004). This productivity issue is particularly important in irrigated areas where deficit irrigation is used as alternative production strategy.
Optimal techniques and management practices of irrigation water at farm and local level are determinant factors for its productivity (Oweis & Hachum, 2005). Wichelns (2002) states that economic efficiency of irrigation water, which is defined as maximizing social net benefits from water resources, often requires improved water management even when basin-wide measures of irrigation efficiency are relatively high. Optimal irrigation management includes the choice of crops, varieties, techniques, and institutions. This may increase the productivity of each unit of water applied for irrigating the cultivated crops (Pereira et al., 2002).
In Tunisia, during the last 30 years, irrigated area has increased from 250,000 ha in 1990 to 450,470 ha in 2010 (MA, 2011). Although the irrigated areas represent only 8% of the total agricultural surface, irrigation contributes with 35% to the total agricultural production and with 20% to agricultural exports. The growth of the agricultural production in the recent years is mainly due to the expansion of irrigated areas (Al-Atiri, 2009). However, the increase of irrigated area has clear impact on the country's water resources (Frija et al., 2011). The issues of improving agricultural water management and increasing water savings are highly debated and undertaken by policy makers and researchers. Recent studies about irrigation economics in Tunisia focus on the assessment of the current efficiency of the resource use (Dhehibi et al., 2007;Albouchi et al., 2007;Frija et al., 2009;Chemak et al., 2010;Chebil et al., 2012), on the impact of some agricultural policies (such as water pricing), on water allocation and use (Bachta & Talbi, 2005;Zekri, 2005;Frija et al., 2011), and on the effectiveness of local collective irrigation water management (Frija et al., 2010(Frija et al., , 2008;;Ben Salem et al., 2005). While most of these researches call to encourage farmers to adopt higher valued crops as a strategy to face water scarcity in the country, little research has been done to evaluate irrigation water productivity and to analyze the marginal benefit of water use for different crops and seasons.
In this study, we are interested to investigate the productivity of water applied for the irrigation of durum wheat in Central Tunisia. Durum wheat holds the most important place among cereals in Tunisia in terms of production and cultivated area. It occupies about 50% of all cereals area (estimated to be around 1 370 000 Ha in 2012, of which 25 % is located in the semi-arid central part of the country) and represents, in average, almost 55% of the total cereals production (MA, 2010). Total cereal production, including durum wheat, tender wheat, barley and triticale, is estimated around 1.6 Million tons in 2012. Durum wheat production in the same year was about 0.67 Million tons. Around 17.7% of the total irrigated area is cultivated with cereal crops. The potential increase of water value used in the irrigation of this crop is crucial. It may have important implications on food security and resources preservation.
Wheat is produced in most areas in Tunisia, but the most important areas are the North (sub-humid) and the Centre (semi-arid). Rain is more abundant in the North than the Center, which means that supplemental irrigation is needed especially in the Center. Moreover, irrigation water reservoirs are more important in the North. The most important governorates producing cereals in Tunisia can be classified based on the cultivated area as follows, El Kef (North), Seliana (North), Beja (North), and Kairouan (Center), with an area of 214,000 ha, 158,000 ha, 145,000 ha, and 144,000 ha respectively (MA, 2010). Kairouan is considered as the most important governorate for cereal production in the Central part of Tunisia, and will be considered for this case study.
The objective of this paper is then twofold; first, to calculate the marginal value of water used for the irrigation of durum wheat in the region of Chebika (Governorate of Kairouan). This will be done through the estimation of a "Cob Douglas" production function using field data from 170 farmers located in the region of Chebika. Marginal value of irrigation water can be then derived from the estimates of the production function. According to the neoclassical economic theory, the comparison of this marginal value to the market price of irrigation water may provide insights on the effectiveness of water use at the farm level. The second objective is to evaluate the implication of this water use effectiveness both on private farmers' income and food security in Tunisia.
The paper is divided into 6 major sections. The next section describes the cereal production sector in Tunisia and stresses the importance of the efficient use of irrigation water in Tunisia. The third section presents the "Cobb Douglas" production function used for the marginal water value estimation; as well as the characteristics of the study area. The fourth section presents results, and the fifth one discusses them. The last section concludes.
Methodology
Marginal water value for the durum wheat crop will be estimated in this study in order to see if farmers are producing at the economic optimum when the marginal value of irrigation water is equal to market price of this resource (Frija et al., 2011). Moreover, marginal value of irrigation water is an indicator of the extra income generated by any additional unit of irrigation water applied to the crop. In this methodological section, water productivity, and marginal water productivity concepts will be defined and the estimation method will be explained.
Economic Water Productivity
The concept of water productivity may carry different meanings when it is looked from different perspectives (agronomic, economic, and domestic). Moreover, it may differ between as well as within groups of water users (Wesseling & Feddes, 2006;Dugan et al., 2006;Playán & Mateos, 2006). Water productivity can be defined with respect to different water-using production sectors (e.g.crop production, fishery, forestry, domestic and industrial uses) (Igbadun et al., 2007) as the amount of output produced per unit of water involved in the production, or the value added by water in a given circumstances (Ali & Talukder, 2008).
In crop production systems, water productivity is generally used to define the relationship between crop produced and the amount of water involved in crop production, expressed as crop production per volume of water. Molden et al. (2010) distinguish between physical water productivity defined as the ratio of agricultural output to the amount of water consumed, and economic water productivity defined as the value derived per unit of water used for producing a given agricultural output. The crop production used to calculate water productivity may be expressed in terms of total yield (kg) of seed (grain) or, when dealing with different crops (e.g.water productivity at the farm level, all crops included), yield may be transformed into a monetary value (Ali & Talukder, 2008;Hellegers et al., 2009). The economic formulation of water productivity used in this study can be written as follows (based on Hellegers et al., 2009;Ali & Talukder, 2008): Where WP is water productivity (expressed in TND/m 3 ) and W is the volume of applied water per hectare of durum wheat (expressed in m 3 ). PV is the production value defined as in Equation ( 2) (expressed in TND/ha): Where Y: Gross output (Kg per ha); P y : Unit price of durum wheat (in TND/Ton).
WP calculated using Equation (1) gives useful information about average income generated by one cubic meter (m 3 ) of water used to produce durum wheat. However, for a policy maker who may need to act to change the water use pattern, this information is not sufficient. In fact, policy makers need to have information about the marginal value of water or productivity of a one-unit-increase or decrease of water use on the different crops income, this variable is called the marginal productivity of irrigation water. Ali et al. (2007) define the marginal productivity of irrigation water as the addition to the gross output caused by the use of one extra unit of water while other inputs remain constant. According to the economic theory, as long as the marginal value of water applied for the irrigation of a given crop, is higher than the market price of water (unitary water cost for surface water or pumping cost per cubic meter for groundwater), it will be still profitable to apply supplement doses of irrigation to the crop.
Marginal productivity (MP) of irrigation water can be calculated as: Where ∆Y is the variation of the gross output due to the variation of irrigation water (∆W) applied.
Based on this latter definition, "marginal profitability of irrigation water" (MWP) in this paper will be calculated as (Hellegers et al., 2009): ∆PV is the variation (change) of the production value per hectare after addition of one unit of irrigation water. Marginal water productivity will be derived from the estimation of the "Cobb-Douglas" production function explained in the next section.
Production Function
In agricultural water management, production functions are mostly used to predict the yield of crops given some input parameters (Igbadun et al., 2007). For agronomists the crop-water production function expresses the relationship between yield (Y) and the applied water (W). In this paper, considering the economic and policy-advising perspective, the water production function is used to model revenue response to various levels of irrigation (Oweis & Hachum, 2009). Our production function is expected to relate the income generated by durum wheat in the region of Chebika to the water volumes used by this crop in addition to other production factors. The general production function used can be implicitly presented in the following form: Where (PV) is the output value per hectare (TND/ha); (W) is the volume of applied water (m 3 ) per hectare and (X j ) are the quantities of other (j) productions factors (expressed in m 3 for water and TND for other inputs: labor and fertilizers).
The most widely used functional forms for production functions in the analysis of agricultural production are the "Cobb-Douglas" and Translog function (Sahibzada, 2002). The second functional form can be approximated by a second order Taylor series and requires estimating a large number of parameters. For this reason, large datasets are usually needed when estimating a Translog production function, otherwise multi-collinearity can be often a major problem. We therefore rely on the Cobb-Douglas production function. Advantages of the "Cobb-Douglas" function are the parsimony in parameters, the ease of interpretation, and the computational simplicity (Sahibzada, 2002).
The general logarithmic form of the production function presented in (5), thus becomes: Where Ln is the Natural logarithm, u is the error term, (a) is a constant and (b), (c j ) are the estimates of the production function.(b) can also be considered as the output elasticity of the water variable. Output elasticity measures the responsiveness of output to a change in the volume of water applied to the crop. The marginal value of irrigation water applied to durum wheat is calculated from the coefficient (b) in the Equation ( 6) above. In fact, since (b) is expressing the elasticity of water use, it can be written as follows: For a given water volume, if we multiply b by W PV , we may obtain which can also be written as : This latter term is interpreted as the variation of the output value (PV) due to a given change of the water input (W). The result will be a value expressed in TND/m 3 , which is corresponding to the marginal value of irrigation water at a given level of water use.
Figure 1 illustrates the decreasing marginal productivity derived from the production function. The economic optimum volume of water applied should be, according to the neoclassical economic theory, equal to the market price of water. In Figure 1, the economic optimum corresponds to the volume V * . A farmer applying a volume V 2 of water may increase his production from PV 2 to PV * if he makes supplementary irrigation of (V * -V 2 ). This means that the farmer will have extra income from supplementary irrigation as far as the value of this extra income per unit of water (MWP 2 ) is higher than the price of acquisition of this production factor (MWP * ). Using the same logic, farmers who are applying V 1 volume of water (higher than V * ) are generating less benefit (MWP 2 ) from their supplementary irrigations than the price they are paying for the acquisition of water. Consider MWP = f(Water) the Equation of the marginal production curve (Figure 1), which is obtained from deriving the production function on the water variable. The farmers' loss, due to application of V 1 cubic meter of irrigation (higher than the economic optimum V * ), can be calculated by the following formula: Where S is the hatched area comprised between V 1 and V * ; and P w is the market price of water in the region. The same concept can also be used to calculate farmers' loss due to application of any water volume lower than the economic optimum.
Data and Study Area
The study area is the Chebika region, located in Central Tunisia in the governorate of Kairouan. Chebika has an annual average rainfall of about 290 mm. This average is varying between 250 and 400 mm. The main crops cultivated in the area are: wheat, vegetables (especially Tomato and Chilli pepper), fodder and olives. The number of farmers in the irrigated area of Chebika is around 1000. Total overall cereals area is 17500 ha (in 2009). The irrigated cereal area is 4500 ha and the average regional yield of the irrigated durum wheat is around 3.9 tons/ha (CRDA, 2010).
The data employed in this study consists of the information about the production structure of 150 random wheat farms located in the irrigated area of Chebika. The used data corresponds to a farm level data where each of our 150 observations corresponds to a different farm. Inputs and yields were aggregated in some cases from plots to farm level. In order to ensure homogeneity in land and weather conditions, farms in the sample have been chosen from the same region of Chebika, and are located in a 20 Km diameter. Chebika is facing growing problems of water scarcity. Some of the data used in the study was collected in 2011. This data was completed and updated in 2013 with the collaboration of the extension service in the region, by face-to-face interviews with cereal-growing farmers.
Farmers in the study area are irrigating from both groundwater and surface water source. One among the largest dams of Tunisia is located nearby Chebika. In addition, a wide and accessible water table, covering the whole surface of the governorate of Kairouan (in addition to extra surfaces from other neighboring regions) is also available for farmers' exploitation. The irrigation systems which can be found in the study area are diverse, ranging from gravity to drip irrigation. Drip irrigation is however widely spread and some farmers are even irrigating cereals using this technology.
Descriptive Results: Cropping System, Water Use Patterns, and Water Productivity
Average land distribution in the sample shows that the average farm size is 16.19 ha with 88% of this area cultivated under irrigation and the rest under rainfed conditions. The farming structure is characterised by the predominance of small-size farms and land fragmentation. Farms with a cropped area lower than 20 ha represent 80% of the total number of farms in Chebika. The size of 38% of the surveyed farms is lower than 10 hectares.
Regarding land use, most cultivated crops in the target area are vegetables (especially tomatoes and Chilli pepper) followed by durum wheat. They occupy, respectively, 30.24% and 27.46%, in average, of the total area of the sample farms.
The first irrigation of durum wheat in the public irrigated areas in Tunisia is free of charge. Moreover, farmers of our sample have different educational background, while some of them are highly educated and trained on irrigation scheduling, others are much less educated and believe that in semi-arid condition, as mush water you give will be beneficial. Another reason of this strong variability is the different level of water constraint in the investigated farms. In fact, while some farmers are specialized in cereal production, and then allow all available water resources to these crops, others have more diversified cropping systems and are cultivating different vegetable crops in addition to cereals. Thus, the latter ones will be faced to choices on water allocation among many cultivated crops; which can be implicitly interpreted as higher water shortage for the cereal crop.
Table 1 presents some descriptive statistics of main inputs and outputs used in durum wheat production in the study area. As discussed in the previous paragraph, the volume of irrigation water applied per hectare varies between farmers. It ranges from 480 m 3 /ha to 6172 m 3 /ha (Table 1). The sample average is 2720 m 3 /ha (standard deviations, variation coefficients, minimum, and maximum values are indicated in Table 1), which is a bit lower than the average estimated annual water requirement of durum wheat in the region (around 3000 m 3 ) (MA, 2000;Rezgui, 2005). Volumes of water applied by each farmer were recorded by the Water Use Association (WUA) to which the farmer belongs. In fact, volumetric tariffs are applied in the study area, and each volumetric water meters are installed to each farmer in order to record its water consumption. Other inputs considered are fertilizers and labor, both expressed in monetary terms. The labor variable includes both family as well as hired labor. The number of working days, as well as the daily wage were surveyed from farmers and used to elaborate this variable. The average production value per ha in our sample is equal to 2226.26TND, corresponding to an average yield of 3.9 tons/ha. Expenditures for other inputs like mechanization and seeds are very homogenous among farmers. In average, farmers in the study region apply around 200 kg of seeds per ha.
Marginal Water Values
The parameters of the "Cob Douglas" production function were estimated using Eviews (econometric views) software, version 4.1. Results of the coefficients and related tests are shown in Table 2. * Significant at 5% level.
Using the estimated parameters and the Equations ( 7) and ( 8), we calculated the marginal value of water applied to the wheat production in Chebika region. The marginal value of irrigation water varies according to the quantity of water applied, which is shown in Figure 2. The curve of marginal water value in Figure 2 corresponds to the theoretical expectations, where the marginal value of water is negatively correlated to the volume of water applied.
According to the economic theory, farmers will use water until the marginal value of water will be equal to the market price of this factor. Since water price in the study area is 0.110 TND/m 3 , this value corresponds to 2700 m 3 /ha of water. If we consider the range of water use between 2500 m 3 and 3500 m 3 as relatively economically rational, then we can observe from Figure 3 that most of farmers in the sample are either applying less than the economically optimal volume (44.6%) or more than this optimum (31.1%). Results derived from this descriptive analysis of the water use pattern in the study region show that most of the farmers are not using irrigation water effectively. Some wrong cropping practices related to the irrigation of durum wheat in the study region have then to be stressed.
In fact, the average supplemental irrigation of durum wheat reaches 2700 m 3 /ha in average; which is a bit lower than needed (after considering the average annual rainfall). Also, 44.6% of the surveyed farmers apply less than 2500 m 3 /ha (Figure 3). Moreover, we find out that 25% of the farmers irrigate durum wheat less than three times applying on average 780 m 3 per irrigation. Irrigation scheduling in the study area was also random and only few farmers are aware about the importance of scheduling irrigation and fertilization supplements (Figure 1).
According to the economic theory expectations, each unit of water applied beyond 2700 m 3 /ha, which is the economic optimum of producers, will generate less return than its price indicating that farmers incur a net loss from this last unit of irrigation. This is also true in our case study where we can see that the average per ha production value of the farmers applying more than 3500 m 3 /ha is lower than the average per ha production value of farmers irrigating with less than 3500 m 3 /ha (Table 2 and Figure 3).
Analysis of Private and Social Losses Due to Inefficient Irrigation Practices
Farmers' private losses, for different volumes of irrigation lower and higher than the economic optimum, were calculated based on Equation 9. It is clear from the results (Figure 4 and Table 3) that private losses due to underutilization of irrigation water are more important than the private losses due to the overuse of water. For example, farmers who are applying only 500 m 3 of water may win around 220 TND/ha of net benefit, which can be generated if they shift their irrigation to the economic optimum. This extra benefit is due to an increase of durum wheat production. Total yield increase for these farmers can be calculated by adding the cost of extra water applied per ha to the extra-net-income (220 TND/ha) and dividing by the price of durum wheat (570 TND/ton). For example, in the case of 500 m 3 irrigation, total yield may improve with [(220 + (2700 -500) * 0.11)] / 570, which corresponds to 0.81 tons/ha. Such yield improvement, generated only from performing the irrigation of durum wheat, will surely have important implications in terms of food security and trade balance in Tunisia. Table 3 shows the potential to enhance food security and natural resources preservation if irrigation of durum wheat will be better performed. It shows that farmers applying 1000 m 3 , 1500 m 3 and 2000 m 3 may increase their yield per hectare with successively 0.5 tons/ha, 0.31 tons/ha, and 0.16 tons/ha. Moreover, overuse of water also causes a social loss which calculated in the Table 3 as the difference between the total cost of non optimal irrigations minus the value of the obtained extra-production. This cost is then expressed in cubic meters of water.
Discussion and Perspectives
Variability of water use among farmers for different cropping systems in Tunisia was also identified in different other studies (Dhehibi et al., 2007;Albouchi et al., 2007;Frija et al., 2009;Naceur et al., 2010;Chemak, 2010;Chebil et al., 2012). All of the previous studies point out large differences in applied water volumes among farmers of same regional and agro climatic areas. These differences can be due to technical and/or socioeconomic factors.saving technologies, are explaining much of the variability in water use among farmers growing horticultural crops under green houses in Tunisia. The same variables were found to be relevant by Naceur et al. (2010), andDhehibi et al. (2007). In addition, the latter authors found that farmers' education level and the farm size are also explaining the variability of water doses among farmers of same regions and cultivating the same crops. Access to credits was also found to be explicative of this variability (Dhehibi et al., 2007;Albouchi et al., 2007;Chebil et al., 2012).
Efficient water management in irrigated cereal production systems in Tunisia was found as having important implication in terms of farmers income, food security (yield enhancement), and natural resources preservation (Table 3 and Figure 4). In Tunisia, as it is in most semi-arid regions of the world, the water preservation and food security nexus is becoming more and more important. Urgent water policy actions need to be taken in order to deal with the climate change, water and energy scarcity, in addition to the micro-credits crisis. According to Hanjraa and Qureshi (2010) investments are needed today for enhancing future food security; this requires early actions on several fronts, including tackling climate change, preserving land and conserving water, modernizing irrigation infrastructure, shoring up domestic food supplies, etc. More specifically, the yield gap of the cereal production systems of central semi-arid Tunisia needs to be seriously studied, and the effect of wrong irrigation practices and scheduling in reducing the potential cereal yield will have to be identified.
Irrigation practices and scheduling were also identified by Chebil et al. (2012) as significant variables affecting the performance and the productivity of water use in semi-arid Tunisia. In fact, the latter authors found that the source of irrigation, the adherence to WUA, and the size of irrigated areas are highly affecting water use efficiency (and implicitly the waste of water) in the cereal production systems of Kairouan. In the region of Nadhoun (neighboring region to Kairouan) Chebil et al. (2010) also found that the contact with extension services is significantly and positively affecting farmers' water use efficiency at the farm level. Thus, clear targeted and efficient water-related policies should be taken in order to further enhance cereal production and water productivity in these cropping systems. Particularly, the role of extension services and farmers' technical training about water scheduling issues should be deeply settled in any future policies.
Conclusions
The objective of this paper is to evaluate the valorization of irrigation water applied for the irrigation of durum wheat in Central Tunisia. Water use patterns as well as the water productivity and marginal water profitability were calculated and estimated for this purpose.
Results show that cereal farmers in central Tunisia are not applying the appropriate water doses nor the irrigation scheduling adapted to their local conditions. In fact, we found that most of farmers are applying either lower or higher volumes than the economic optimum dose. Calculations also show that this may have great implications in terms of food security and water resources preservation. Improvements of irrigation performances in cereal production in Tunisia are absolutely necessary because of the significant challenges in terms of water scarcity and economic vulnerability. Further human, financial, and technical resources have to be mobilized in order to enhance the performances of the specialized regional and local extension services. These services in Tunisia are exclusively provided by public administrations. The creation and encouragement of private extension sector could be then proposed as an opportunity of agricultural productivity enhancement.
Further enhancement can be considered to improve the current version of the paper and to deepen the analysis. It will be in fact highly interesting to add further farming inputs to the production function. Even though this is not really a limitation of the current analysis (since the water coefficient used for our interpretation was highly significant), it will be indeed interesting to see whether the water coefficient change when we add some other variables or not. Moreover, testing further functional forms of the production function (such as translog function) will consolidate the obtained estimates of our production function. Furthermore, relevant surveys can be done in order to further understand the origin of the very remarkable variability of irrigation doses among farmers.
Figure 1 .
Figure 1. Derivation of marginal water value from the water production function (PV: production value (in TND/ha); MWP: marginal water productivity (TND/m 3 ); V: water volume applied)
Figure 2 .
Figure 2. Marginal value of water applied to the durum wheat crop in the study area (Hachured curve indicates the marginal water value for different applied volumes of water; continued line refers to the water market price, which is around 0.110 TND/m 3 )
Figure 3 .
Figure 3. Farmers distribution according to their total supplement irrigation water applied during durum wheat cycle
Figure 4 .
Figure 4. Private loss per hectare due to the irrigation beyond and below the economic optimum volume (in TND/ha) Frija et al. (2009) find out that farmers training about irrigation issues, in addition to their investments in water
Table 1 .
Descriptive statistics of production factors and production value per ha of durum wheat in the study area
Table 2 .
Coefficients of the production function and t-test
Table 3 .
Potential improvements in yields and water resources preservation
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Domain: Agricultural And Food Sciences Economics Biology
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Shelf life of antibiotic treated rohu fish , Labeo rohita ( Hamilton ) under ice storage condition
The study was conducted to evaluate the effect of antibiotic on shelf life in rohu fish, Labeo rohita (Hamilton) under ice storage condition. Oxytetracycline (OTC), the most widely used antibiotic, was fed to rohu (average body weight 16.0 g) at the rate of 2 g/kg through fish diet for 5 days and their shelf life was determined in iced condition. Organoleptically, fish were found to be acceptable up to 16 days before becoming inedible compared to 15 days for control fish which received pelleted diet with no antibiotic under the same condition. Initial moisture, ash, protein, lipid, NPN and TVB-N values were 70.42±1.91%, 2.80±0.10%, 17.90±0.50%, 3.12±0.04%, 0.0086±0.01% and 17.43±0.60 mg/100g respectively in the control which reached at values of 78.45±1.50%, 3.84±0.10%, 13.47±1.00%, 2.80±0.08%, 0.0053±0.001%, 26.17±0.76 mg/100g, respectively after 16 days of ice storage. There was no significant difference for these values compared to control group. In case of total bacterial load, values of aerobic plate count (APC) was 2.0±0.1×10 during the start of ice storage condition which increased significantly to 5.6±0.38×10, exceeding the acceptable limit for ice stored fish. The APC values also did not show any significant variation compared to control fish, suggesting that the use of antibiotic in fish diet had little or no effect on shelf life of rohu fish during ice storage condition.
Introduction
With the expansion and intensification of aquaculture in south Asian countries and other parts of the world, antibiotics have become an integral part to treat bacterial infections of fish, shrimp and other aquatic organisms to ensure health and productivity. These drugs are applied as a means to treat diseased fishes or for prevention of diseases (Christensen et al., 2006) as the production may get hampered by unpredictable mortalities that may be due to negative interactions between fish and pathogenic bacteria. Although they are used to improve aquaculture production, residues of these antibiotics and chemicals could influence quality of the final product. Such influences, in general, may include shelf life of the fish and fishery product under different storage conditions, nutritional aspects and food safety.
Among the antibiotics used to treat different infectious diseases in human, poultry, cattle, aquaculture and other animal husbandry, only a small number of them were approved by EU, FDA and other organizations for use in aquaculture. The FDA has approved five different drugs for use in aquaculture as long as the seafood contains less than a mandated maximum residue limit such as florfenicol, sulfamerazine, chorionic gonadotropin, oxytetracycline dihydrate, oxytetracycline hydrochloride, as well as a drug combination of sulfadimethoxine and ormetoprim (United States Government Accountability Office, GAO, 2011). Therefore, oxytetracycline (OTC) has been used as the preferred antibiotic in aquaculture. It is also the most widely used antibacterial agents in aquaculture worldwide (Smith et al., 1994). The vast majority of OTC supplied in mediated feed can be released to the culture system via fish excreta and even the portion of uneaten feed (Hektoen et al. 1995). These chemicals have effect on the bacterial population and also on the storage condition of fishes. Sometimes these antibiotics are indiscriminately used in the aquaculture system which may result in long term effect on fish and also to its consumers.
Rohu fish, Labeo rohita (Hamilton) is a member of the family Cyprinidae within the order Cypriniformes. It is native to the river systems of Bangladesh, India, Pakistan, Myanmar (Talwar and Jhingran, 1991). With the expanding aquaculture practice in the country, rohu culture is practicing in semi-intensive and extensive systems in ponds, low-lying areas like haor and baor. The features that made this species a potential candidate for aquaculture include faster growth rate, higher market price, greater feed utilization and ability to feed in all three columns in a water body. In 2008-09, rohu production from culture fishery was 226,585 mt, covering 24.84% of total aquaculture production in Bangladesh (BBS, 2009). It is expected that its production will continue to increase.
Although there were no systematic studies conducted in the past, the available reports suggest that considerable post-harvest changes take place during storage condition in ice or frozen for antibiotic treated fishes. Considering the importance of rohu fish in Bangladesh aquaculture, the study was carried out to determine the changes in its post-harvest quality fed with antibiotic treated feed and control and bacterial population during ice storage condition.
Experimental design
Six glass aquaria (size 37cm×30cm×60cm) were set at the Laboratory of Fish Harvesting, Department of Fisheries Technology, Bangladesh Agricultural University, Mymensingh where 3 aquaria were used for antibiotic treatment and 3 as control. These aquaria were filled to a depth of 15 cm with tap water. Rohu fry (average body weight 16g) was collected from Field Laboratory Complex, Faculty of Fisheries, BAU and released into the aquarium. Aerators were set in each aquarium and water was changed everyday. Antibiotic medicated feed was prepared at the rate of 2g OTC / kg feed and fed to the fish at 3-5% of their body weight for consecutive 5 days. Fish were killed by insertion of sharp pointer into the head and then transferred into an insulated box. Fresh block ice at ratio of ice and fish at 1:1(w ⁄ w) was used for icing the samples and was replenished on every alternate day until the termination of experiment. The icebox had a number of holes at the bottom to drain out the melted water. The quality change in ice stored samples was evaluated after every 4 days interval by determining the organoleptic, chemical and bacteriological tests.
Sampling procedure and sample preparation
At described intervals (starting from 0 day), 4 to 5 fish were randomly sampled and their sensory attributes were evaluated while keeping the fish in iced condition. The whole fishes were subjected to sensory analysis; 2 to 3 fish for chemical and rest for microbiological analysis. The muscle was skinned, deboned and homogenized by mincer, and was taken for the determinations of bacteriological aspects.
Organoleptic quality assessment
Sensory methods were used to assess the degree of freshness based on organoleptic characteristics such as odor, color, general appearance, eyes, slime and consistency of flesh. Starting from 0 day, 4 to 5 fish were randomly sampled and their raw sensory attributes were evaluated. The organoleptic characteristics were judged by a trained panel of expert members during the storage period. The grading of fish using score on the characteristics has been followed by ECC freshness grade for fishery products with slight modification (Howgate et al., 1992) to judge the quality of the fish.
Proximate composition and chemical analyses
Moisture content was determined by air drying of a given sample in a thermostat oven (Gallenkamp, HOT-BOX, Manchester, UK) at 105°C for 24 h until constant weight. Ash content was determined by igniting the sample in a muffle furnace at a temperature of 550 °C for 6 hrs. Crude protein was determined by the Macro Kjeldahl method through determination of total nitrogen and applying the protein conversion factor of 6.25 to the results to convert total nitrogen into total protein, assuming that fish protein contained 16% nitrogen, and lipid content was determined by extracting required quantity of samples with petroleum ether for 16-18 hrs in a ground joint Soxhlet apparatus at 70 0 C. The oil obtained by evaporation of the solvent on a steam bath was weighed in a sensitive balance and percent lipid was calculated. The TVB-N was determined according to the standard method described by (EC, 1995) with some modifications. Estimation of TVB-N was done at every 4 days interval up to 16 days of ice storage fish.
Bacteriological analyses
About 15 g of whole fish sample was blended with appropriate volume of 0.2% peptone water in a sterilized blender for a few min until homogenous slurry was obtained. Bacteriological analyses of ice stored fishes were done at every 4 days interval until termination of experiment. Total APC expressed as colony forming units per gram of muscle (CFU/g) of the representative samples was determined by standard plate count methods using plate count agar (Hi-media, Mumbai, India) according to Collins and Lyne (1976).
Statistical analysis
Data obtained in the experiment were recorded and preserved in computer and paired t-test were done using SPSS 11.0 (Statistical Package for the Social Science, Chigago, USA).
Changes in organoleptic qualities during ice storage condition
The changes in quality of chilled rohu fish during storage were assessed by organoleptic assessment. On the basis of the scores, all the fish samples were found in acceptable condition up to 16 days in ice storage before they became inedible (Table 1). The organoleptic characteristics of quality changes that occurred during storage period can be roughly divided into four phases corresponding to periods of 0-3, 4-7, 8-11 and 12-16 days in ice. In the phase 1, the fishes were very fresh with species taste and natural flavor and odor. At this stage, the fish samples had the characteristics of freshly caught fish. In phase 2, there was some slight change but similar to the first phage. In phase 3, there was little deterioration apart from some slight loss of natural flavour and characteristic odor. In phase 4, there was slight dullness with some off-flavour, but in acceptable condition. Although the estimated shelf life differed slightly from those reported by Dhanapal et al. (2013) where he reported shelf life of rohu was 17 days. It may be related to seasonal variation, size and other environmental factors. As the fish were supplied with antibiotic mediated feed in aquarium condition so the bacterial flora in the different parts of fish body was very low. Thus the quality deterioration process in the ice storage condition of fish was very slow.
Changes in chemical characteristics during ice storage condition
Proximate composition of rohu fish was determined during ice stored condition at an interval of 4 days interval is given in Table 2. It was found that moisture content of fish stored in ice condition was increased with increase in storage period. The initial moisture content was 70.4±1.91%,which increased to 78.45±1.50% at the end of the experiment. These values were not significantly different for those that obtained for control rohu fish (Table 1). The higher moisture content during ice storage was probably because of uptake of water during storage period (Reza et al., 2009). Similar condition was found for protein and lipid contents where the values were highest with 17.9±0.50%and 3.1±0.04%on 0 day respectively which decreased to values of 13.4±1.00%and 2.8±0.08%, on 16 th day respectively in carcass of fish feed an antibiotic treated feed. Changes in moisture content were followed by a reverse change in lipid content indicating an inverse relationship between water and fat content (Stansby, 1962).
The loss of crude protein in fish during ice storage was due to leaching of water soluble protein fraction from fish muscle. The initial ash and TVB-N contents of fish fed antibiotic treated feed were 2.80±0.10,7.43±0.60 on 0 day and reached to 3.84±0.10,26.1±0.76respectively on 16 th day. The level of increasing in the next was slow. The initial level of crude protein, lipid, and NPN content of fish were 17.90±0.50,3.12±0.04,0.0086±0.01,and reached 13.47±1.00,2.80±0.08,and 0.0053±0.01respectively in the last day of storage condition. Interestingly for ash content, statistically significant change was observed for rohu fish during 16 days of ice storage. It is well known that the variations in chemical composition of fish was closely related to feed intake and reproduction cycle. The most dramatic changes that occured in chemical components were water and fat content in the fish species. There were also significant differences in composition of muscles lipid and water content. An inverse relationship existed between lipid and moisture content where approximates to 80%, which was also more or less in agreement with the general rule formulated by (Stansby, 1962). The TVB-N was used for the determination of the spoilage level during the storage period (Cobb & Venderzont, 1975). It was suggested that TVB-N value may change depending on the spoilage flora and analysis method (Antonacopoulos and Vyncke, 1989). The concentration of TVB-N in freshly caught fish was typically between 5 and 20 mg TVB-N 100/g flesh (Connell, 1995). However, latest reports showed an initial TVB-N value of <20 mg100/g flesh (Kyrana et al. 1997;Rodriguez et al. 2004). The available reports suggest that the upper limit of 30 mg 100/g TVB-N with the lapse of storage may be attributed to bacterial spoilage, and pH and TVB-N levels were found closely related well with the changes in quality (Kietzmann et al. 1969;Cobb and Venderzont, 1975).
Changes in bacterial load during ice storage
The changes of bacterial flora during ice storage condition at an interval of 4 days interval were determined and data are shown in Table 3. Initial APC at 0 day sample before ice storage was very low. These values increased gradually until the end of the experiment. But it was interesting to see that the rate of increase in APC was very slow. The initial bacterial load was 2.02±0.01×10 4 , which reached to a value of 5.60±0.38×10 7cfu/g after 16 days of ice stored fish. It was found that the growth of bacteria was one log higher in control fish i.e., 2.60±1.50×10 8cfu/g, which indicates that OTC in experimental fish had some effect on viability of total bacterial load. Similar results were also found by Yeasmin et al. (2010) where they reported that bacterial growth was significantly lower in formalin treated rohu fish under ice storage condition compared to control group. This increase in bacterial load was also positively correlated to the increase in TVB-N values found in both groups of ice stored fishes.
Conclusion
Shelf life of antibiotic treated rohu fish was determined by organoleptic, biochemical and bacteriological method and it was found that fish can be kept in iced condition for 16 days which was slightly higher as compared control fish which were acceptable up to 15 days. The loss of quality in these fishes was due to bacterial and enzymatic activities during long time storage in ice although antibiotics added in experimental fish diet exerted little effect for reducing bacterial population on fish stored in ice. However, more studies are needed whether such antibiotic remain in fish body as residues or not, as they are great concerns of food safety.
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Domain: Agricultural And Food Sciences Environmental Science Biology
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Effect of Aflatoxin Contaminated Feed on Growth and Survival of Fish Labeo Rohita ( Hamilton )
Effect of aflatoxin contaminated feed on growth, survival and behaviour of the fish Labeo rohita was evaluated. There was a significant decrease in the growth rate and survival percentage of the fish with the increase in the amount of aflatoxin contaminated feed in the food of the fish.
INTRODUCTION
Aflatoxin is the metabolic by product of mols Aspergillus flavus and Aspergillus parasiticus. It is a toxic compound and the cause of high mortality in livestock, poultry and in some cases of human beings (Reed andKasali.,1987, Montessano et al.1995). Toxicogenic A. Flavus .produces Aflatoxin B1 and B2 whereas A. Parasiticus produces Aflatoxin G1 and G2. Aflatoxin B1 is classified as group I carcinogen by international agency for research on cancer. Effect of aflatoxin on fishes and other animals have been reported by many workers. Nunez et al. (1991) reported hepatocellular adenoma and hepatocellular carcinoma in Rainbow trout when exposed to aflaroxin B1. Caguan et al. (2004) reported loss of appetite, low survival percent and decreased mean total biomass in tilapia when fed with aflatoxin contaminated feed. Faisal et al . (2008) reported spermatotoxic effect of aflatoxin in male wister rat.
Labeo rohita a common Indian carp is widely distributed in Indian rivers and ponds. It is very important as a human food for its high quality flesh. In the present investigation effect of aflatoxin on growth rate , survival percentage and behavioural changes of Labeo rohita has been evaluated in order to explore the effect of toxin in the fish.
MATERIALS AND METHODS
The fish Labeo rohita was collected from river sone near Ara.72 fishes measuring about10 -20 cm and weighing about 30 -50 gm were selected and kept in twelve aquaria measuring 3 1 x 2 1 x 1 1 . Six fishes were kept in each aquarium. Three aquaria containing six fishes each were kept as control and nine aquaria containing six fishes each were kept as experimental set.
Four feeds were employed as follows: 1.
Feed I or good feed contained 0% moldy feed or unmixed feed. Feed I were given to control.
2.
Feed II contained 10 % moldy feed and 90% good feed. Feed II were given to first set of experimental fishes comprising aquaria 2A, 2B and 2C.
3.
Feed III contained 50% good feed and 50% moldy feed. Feed III were given to second set of experimental fishes comprising three aquaria 3A, 3B and 3C.
4.
Feed IV contained 100% moldy feed. Feed IV was given to fourth set of fishes comprising three aquaria 4A, 4B and 4C.
Moldy feed were prepared in laboratory. The commercial fish feed was first sprinkled with small amount of tap water to make the feed moist and then infected with cultured Aspergillus flavus by mixing 10 ml of cultured Aspergillus flavus. The inoculation was made in a transfer chamber to avoid contamination. The mixed feed was then covered with a plastic sack. The infected feed was kept in a condition which is favourable for the growth of mold. Required amount of moldy feed and good feed were weighed carefully for each treatment and then mixed thoroughly. The fish were fed a day after and and daily there after two times a day at 8.00 am and at 6.00 pm at a feeding rate of 4% of the body weight .
Data gathered were initial and final individual length and weight, average body length gain and average body weight gain, specific growth rate, survival percentage and behavioural changes.
Body Weight and Body Length
Body weight gain in aflatoxin treated fishes showed significant decrease(p>0.05)as compared to control or fishes given feed I or mold free feed. The average body weight gain in the fishes treated with feed IV was 60.3 gm as compared to 79.5 gm in fishes fed with feed I. The growth rate,Specific growth rate and percent body weight gain was also high in fishes fed with feed I and decreased gradually with increase in percentage of moldy feed in the food reaching its minimum in those fishes which were given feed IV or 100 percent moldy feed(Table II). The average body length gain and percent body length gain was also significantly lower(p>0.05) in fishes fed with feed II, II and III as compared to fishes given feed I or mold free feed(Table I). These results agree with the findings of Jantrarotai and lovel (1990) Glutathione enzymes are partly consist of methionine and cystein and hence this process of detoxification decreases availability of methionine resulting in poor growth in the fish.
Swimming, Feeding and Opercular Movement
The fishes fed with aflatoxin containing feed depicted less swimming, mostly off feed and greater opercular movement as compared to those fish group which were given aflatoxin free diet. Thus the present finding are in agreement with those of Boshy et al. (2008) and Caguan et al. (2004)in Nile tilapia. Aflatoxin causes loss of appetite thus creates weakness resulting in less agility and off food behavior in fishes as a result of aflatoxin. Aflatoxin induces stress and thereby increases oxygen demand resulting in greater opercular movement in fishes received aflatoxin containing food.
Survival Percentage
Survival percentage decreased with increase in aflatoxin containing feed. The fishes which were given aflatoxin free diet or feed I showed hundred percent survival whereas minimum survival ie forty four percent was found in those fishes which were fed with feed IV(Table III). Thus the present findigs are in agreement with those of Caguan et al. (2004). The decreased survival percentage was probably due to impaired liver function, loss of appetite and decreased immunity as a result of aflatoxin.
Table 3 :
Survival Percentage and coefficient of variation percentage of Survival in different groups of fishes showing effect of aflatoxin contaminated feed.
Table 2 :
Mean along with their standard errors (S. E.) and coefficient of variation of Body weight in different groups of fishes showing the effect of aflatoxin contaminated feed.
Table 1 :
Mean along with their standard errors (S. E.) and coefficient of variation of body length in different groups of fishes showing the effect of aflatoxin contaminated feed.
Cheeke and shull (1985) Roges et al. (2002) in Oreochromis nilotius, Nguyen et al. (2002) in Juvenile NileTilapia and Zaki et al. (2012) in Clarius lazera. Joner et al.(2000)reported that aflatoxin reacts negatively with different cell protein which leads to inhibition of carbohydrate and lipid metabolism and protein synthesis. So the decrease in growth rate in experimental fish may be due to disturbance in metabolic process of carbohydrates, lipids and proteins by aflatoxin . Cheeke and shull (1985)reported that aflatoxin causes loss of appetite. Thus the decrease in average weight gain and body length increase may also be due to loss of appetite. Also it might be due to utilization of glutathione enzymes for detoxification process under the condition of Stress Devegowda et al.(1998).\===
Domain: Agricultural And Food Sciences Environmental Science Biology. The above document has 2 sentences that start with 'The average body',
2 sentences that start with 'Thus the present',
2 sentences that start with 'E.) and coefficient of variation',
2 sentences that end with 'with their standard errors (S',
2 sentences that end with 'loss of appetite',
2 paragraphs that start with 'Mean along with their standard',
2 paragraphs that end with 'in the fish',
3 paragraphs that end with 'effect of aflatoxin contaminated feed'. It has approximately 1154 words, 71 sentences, and 30 paragraph(s).
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POLLEN PRODUCTION IN SELECTED SPECIES OF ANEMOPHILOUS PLANTS
In the study, structural features of fl owers of the following allergenic plant species were analysed: Betula verrucosa, Secale cereale, Rumex acetosella, Plantago major and Artemisia vulgaris. Pollen production was established by calculating the number of pollen grains produced by the stamen, fl ower and infl orescence. The dates of occurrence and pollen grains concentration in the air of Lublin were determined. A positive correlation was found between the length of anthers and the number of pollen grains produced. The largest number of pollen grains per anther is produced by Secale cereale (22 360), whereas the smallest one by Plantago major (5 870). The other species produced intermediate numbers of pollen grains in the anther: Betula verrucosa – 11 160, Rumex acetosella – 10 850, Artemisia vulgaris – 9 580. The birch pollen season in Lublin lasts about a month, and pollen of this taxon reaches the highest airborne concentrations among the studied taxa. Low values of pollen concentrations are characteristic for rye and plantain, whereas slightly higher values are recorded for sorrel pollen. Mugwort pollen reaches high concentrations which are noted at the beginning of August.
INTRODUCTION
Every year anemophilous plants release to the atmosphere huge amounts of pollen. The abundance of pollen is related to the number of pollen grains produced by the stamen and the fl ower. Levels of pollen production also depend on the number of fl owers and infl orescences on a plant, as well as the conditions in which it grows. For example, it is important whether a plant occurs solitary or forms clusters with other individuals (F a e g r i and I v e r s e n , 1978; M o l i n a et al. 1996). For pollen to perform its basic function, anemophilous plants must produce it in very large amounts. The volume of this production depends primarily on the length
MATERIALS AND METHODS
In the study, structural features of fl owers of the following allergenic plant species were analysed: Betula verrucosa Ehrh.(syn. Betula pendula Roth.), Secale cereale L., Rumex acetosella L., Plantago major L. and Artemisia vulgaris L. Measurements were made of particular fl ower elements, the number of stamens in the androecium as well as the average number of fl owers per infl orescence and per plant were determined in twenty herbaceous plants of each species. In the case of birch, the number of fl owers per infl orescence was established based on the examination of 30 infl orescences sampled from different branches of three trees. The number of pollen grains produced per anther was determined and the obtained results were then recalculated per fl ower and per infl orescence, and in the case of the herbaceous plants, also per plant. Mature stamens were sampled for the investigation before anther dehiscence. Pollen grains were washed out of the thecae with 70% alcohol onto a microscope slide using a stereoscopic microscope. Six replications were made for each species.
The size of pollen grains of the studied plants was determined based on the dimensions of their polar (P) and equatorial (E) axes.200 pollen grains were measured for each species.
The period of occurrence of airborne pollen -the atmospheric pollen season -and pollen concentrations were established using a Lanzoni VPPS 2000 pollen trap.
The study was conducted in Lublin in the years 2001-2007. The pollen trap was located on the roof of the building of the University of Life Sciences (51 0 14'37'' N and 22 0 32'25'' E) at a height of 18 m above ground.
RESULTS
Silver birch (Betula verrucosa Ehrh.) is one of tree species most frequently used for landscape plantings due to its decorative values and low habitat requirements. The common occurrence of this species is however adverse to many people allergic to birch pollen which contains one of the strongest allergens. Male infl orescences of silver birch are set in the summer of the previous year. In August they are already clearly visible. The measurements show that at that time they grow up to a height of 1.5 -2 cm. Immediately before fl owering, these infl orescences reach a length of about 4 cm. During fl owering, the infl orescence axis elongates up to about 7 cm (10 cm at a maximum). On the average, 150 three-fl owered cymes are clustered in male infl orescences; they grow spirally on the infl orescence axis (Fig. 1) and form secondary catkin infl orescences. Each cyme is subtended by a scaly, sparsely haired outer bract with the dimensions of 2.0 mm x 1.6 mm (Fig. 2). Particular fl owers grow in the axils of the inner bracts reaching the dimensions of 1.0 mm x 1.2 mm (Fig. 3). The male infl orescences investigated in the present study had an average of 450 fl owers.
The male birch fl ower is subtended by two perianth segments (1.8 mm x 0.8 mm) enclosing 2 stamens (Fig. 3), with the anthers composed of two separate thecae (Fig. 4). That is why it seems that there are 4 stamens in the fl ower. The anthers reach the dimensions of 1.44 mm x 0.73 mm. Birch pollen grains are triporate and they are classifi ed as small. Their dimensions were 21.34 μm x 18.17 μm (Fig. 5). The calculations show that there are, on the average, 900 stamens in one infl orescence, and each of them produces 11 160 pollen grains, thus the number of pollen grains per infl orescence is 10 044 000 (Tab. 1).
Female birch catkins grow in quite large density on short lateral shoots, below their apex. They are erect or slightly hanging. They are characterised by much smaller dimensions than male catkins, since they reach 1.52 x 0.17 cm. Similarly to male infl orescences, they contain three-fl owered cymes (Fig. 6). A single female fl ower is devoid of the perianth; it is composed of one pistil growing in the axil of the inner bract. The bracts are fused forming 3-lobed scale protecting the winged nutlet.
Common rye (Secale cereale L.) belongs to cereal plants often grown in Poland. Pollen grains of this plant exhibit a high degree of allergenic activity.
The spikes of Secale cereale cv. Dańkowskie Złote were 9.4 long, on the average. Each of them had an average of 60 fl owers. The anthers reach large dimensions of 9.50 mm x 0.97 mm (Fig. 7) and they are borne on a fi lament which initially is relatively short, as it reaches 1.6 mm, but before pollen shedding, during lemmae and paleae opening, it rapidly increases its length up to about 9.2 mm.
Pollen grains are shed in batches through elongated slits, starting from the apical part of the theca. Pollination starts before the stamens come out completely from the lemmae and paleae. Particular stamens of the fl ower mature successively. During the gradual elongation of the successive stamens, each of them touches the adjacent stamen, provoking pollen shedding. The process of elongation and coming out of three stamens from between lemma and palea takes about 2 minutes. Within about 5 minutes, the thecae empty half of their content, and after 1.5 up to 2 hours they are empty. The apical portion of the thecae forms characteristic bowl-shaped fragments in which pollen is caught in windless weather (Fig. 8).
Spikelets in the middle part of the spike fl ower fi rst. The further opening of the anthers takes place in both directions. In sunny weather, pollen shedding from one rye spike lasted 4 days, whereas the rye canopy bloomed for 14 days.
Rye pollen grains are oval, heteropolar, monoporate (Fig. 9). In the examined material, they reached the dimensions of 48.55 μm x 34.92 μm. It was calculated that one stamen of Secale cereale produced an average of 22 360 pollen grains, whereas the spike produced 4 024 800 of them (Tab.1).
The pistil of Secale has two stigmas with a length of 3.5 mm. The feathery stigmas developed an increased catching surface for sporomorphs borne by the wind. Each fi liform element of the stigma has outgrowths facilitating pollen capture (Figs 11,11). Two membranous lodicules, haired at the apex, adhere to the ovary (1.2 x 0.9 mm) (Fig. 10).
In Poland several sorrel species are found whose pollen causes allergies. One of them is Rumex acetosella L., fi eld sorrel, which is a unisexual, dioecious plant (Fig. 12) growing up to a height of 25 -55 cm. Severalfl owered clusters grow on the stem alternately in three sections. The stalk base is subtended by a small bract and membranous, fi mbriate-laciniate ochrea (Fig. 12).
Male fl owers usually grow in clusters of 5 -6, at an average distance of 4.6 mm from each other. The un-differentiated green-red perianth forms tripartite whorls: the outer whorl with smaller segments (1.3 mm x 0.6 mm), fused at their bases, and a more impressive inner whorl (1.6 mm x 1.2 mm). The fl ower pedicle grows up to 2.7 mm. In the fl ower, six stamens grow whose yellow-coloured anthers reach the dimensions of 1.13 mm x 0.64 mm. The thecae are not joined from the top down to almost half of their length (Fig. 14). During anthesis, the fl owers are directed downwards and the perianth segments straighten up, being arranged in a horizontal plane and forming a protective "roof" over the freely hanging stamens on the fi laments about 1 mm long. After pollen release, the anthers drop off (Fig. 13).
Tricolporate pollen grains of fi eld sorrel can be classifi ed as small, since their dimensions reached 22.1 x 20.0 μm. Inside the cells of pollen grains, large reserve starch grains were visible (Fig. 15).
The investigated male plants formed few branches (4 -7) on which 70 fl owers develop, on the average, irrespective of the plant height. One fi eld sorrel plant, depending on the number of branches, produced 280 -490 fl owers and 18 233 040 -31 907 820 pollen grains (Tab.1).
In the female plants, the infl orescences usually bore 7 fl owers at an average distance of 3 mm between the nodes. The female fl owers are triangular shaped (Fig. 16), with this shape being formed by the three-edged ovary of the pistil. Similarly to the male fl owers, the double, three-segmented perianth is composed of smaller greenish-coloured outer segments and larger green-or red-coloured inner segments. The dimensions of fl owers are 1.1 mm x 0.9 mm. Three stigmas of the pistil grow on very short styles fused to the ovary edges (Fig. 17). Single stigmas resemble a small star with over a dozen arms and a diameter of 1 mm.
Greater plantain, i.e. common plantain (Plantago major L.), is one of the most commonly found ruderal plants in Poland. Its pollen causes allergies in sensitive people.
Leafl ess plantain shoots ended with spikelike infl orescences reach different heights. They are short in trodden places and grow up to 60 cm under favourable conditions. Generally, the infl orescence stem is slightly longer than the spike.
Bisexual plantain fl owers grow in the axils of the bracts similar to calyx sepals (Fig. 18). The membranous-edged sepals (1.6 x 1.1 mm) with a green band of assimilation tissue in the middle are free and they grow in two whorls in twos (Fig. 18). The fl ower corolla, with fused petals, is greenish and transparent, reaching 22 mm in length. It produces four small bent lobes. The fi laments of 4 stamens are fused to the corolla tube in interopositus way. Their anthers are claret-coloured (major variety) (Fig. 19), sometimes yellow-coloured (sulphurea variety) (Fig. 20). Before the stamens come out, the anthers tightly hold the style and the long fi la- ment (3.5 mm) is folded in two (Fig. 20). At this stage, the anthers are longer and narrower -1.25 mm x 0.55 mm, but right before pollen shedding their dimensions change to 0.91 mm x 0.74 mm (Figs 21,22). The pistil has a long fi liform style ended with an undivided, elongated stigma (Figs 18,19).
Plantain pollen grains are polyporate, spherical (Fig. 23). Their diameter was 21.15 μm, on the average. In the case of my study, the spikes of the plants reached a length of from 2 up to 35 cm. It was determined that an average of 18 fl owers developed per 1 cm of infl orescence length. It was calculated that 5 870 pollen grains were produced in one anther, thus a spike with an average length of 15 cm can produce 6 339 600 pollen grains (Tab.1).
Plantain fl owers are protogynous. In accordance with the sequence of development of racemose infl orescences, the blooming process fi rst takes place in the lowest located fl owers. One spike fl owers 6 days, on the average. Particular infl orescences grow and bloom successively. One plant can produce a maximum of about 20 spikes.
Mugwort (Artemisia vulgaris L.) is a permanent component of vegetation accompanying man. It occurs in ruderal sites, on roadsides, in wasteland. It is found in all the housing estates of Lublin, and on the outskirts of the city this weed infests meadows and non-agricultural land, often forming dense canopies. Mugwort pollen is one of the most frequent causes of pollinosis in Poland Under favourable conditions, mugwort produces strongly branched plants, growing up to 2 m. Depending on the degree of branching and plant height, it produces a different number of capitula. By way of example, a mugwort plant 140 cm high and with 23 primary branches produced 4 987 infl orescences. It was calculated that the investigated plants produced an average of 8 fl ower heads per 1 cm of a branch with fl owers. A plant with 4 987 fl ower heads can produce 3 192 926 750 pollen grains (Tab.1).
Closed capitula are egg shaped; shortly before fl owering, their size is 2.6 mm x 1.8 mm, whereas during fl owering they reach the dimensions of 4.0 mm x 2.5 mm. The infl orescences are covered with membranousedged bracts of the involucre arranged in three 5-leaved whorls. The outer side of the bracts is strongly haired (Fig. 24), the inner side is smooth. In the present study, it was calculated that one fl ower head was composed, on the average, of 9 female ray fl owers situated on the circumference (Fig. 24, 26) and inside 13 bisexual disc fl owers (Figs 25,27) borne on the convex infl orescence receptacle.
The disc fl owers develop asynchronously in the fl ower head and their blooming also starts not simultaneously. The fl owers measured during fl owering were 2.5 -3 mm long. The corolla was initially transparent, greenish-yellow and it tightly shrouded the stamens and pistil. During fl owering, it took on the claret colour.
In their apical portion, the linear thecae, with the dimensions of 1.27 mm x 0.28 mm, have membranous, pointed outgrowths which probably perform a protective role for the pistil and the fl ower inside, since, being bent inwardly, they tightly close the entry to the corolla tube (Figs 28,29). The anthers become fused with one another right before anthesis. It points out to the fact that particular stamens can be isolated without any problem from the fl owers at the bud stage. Following the petal fall stage, the anthers remain joined. The bisexual fl owers have a pistil with a sigma 0.4 mm long, ended with two pollen presenters with glistening hairs. They function like a piston, pushing out pollen from the anthers and the stamen tube and raising it above the corolla level (Fig. 27).
The ligulate corolla of the female fl owers is transparent, with an elongated tube tightly adhering to the style (Fig. 21). The ligule reaches a length of 0.2 mm; two lobes are sometimes found in the top part of the corolla. The young perianth is greenish-yellowish-coloured; at a later stage of development, its upper part as well as the stigmas and apical fragments of the style become claret-coloured. The stigmas of the pistil reach large dimensions. Their length can be as much as 0.7 mm, whereas the whole fl ower reaches a length of 3 mm.
The studied species produced a different number of pollen grains per stamen. A clear correlation was found between anther length and number of pollen grains per stamen. The results relating to pollen production in the studied plant species are presented in Table 1.
Based on the results of pollen monitoring conducted under the seven-year-long study in Lublin, the average start and end dates of pollen seasons of the studied plant genera were established; they also included species other than those mentioned in the table due to the fact that it was impossible to distinguish their pollen. In Figure 31, the average duration of the pollen season is marked in dark colour, and in bright colour -the extreme start and end dates of the season over the seven-yearlong period of study. Birch pollen grains are present in the air of Lublin, on the average, from the middle of April until the middle of May. The birch pollen season was recorded earliest from 3 April in 2002. Birch pollen reaches very high concentrations in the air. The highest average concentration from the seven-year-long study is 2 364 pollen grains per day (Fig. 32). The rye pollen season lasts relatively short, on the average, from 23 May to 17 June. It started earliest on 13 May, also in 2002 (Fig. 31). The maximum pollen concentration of this taxon appears at the end of May or at the beginning of June. These are relatively low values (Fig. 33).no.days from January 1st.
The dates of Rumex and Plantago pollen seasons coincide to a large extent, but sorrel pollen grains are mostly recorded a week earlier than plantain pollen. The average duration of the sorrel and plantain pollen seasons is similar -118 and 119 days, respectively. The average sorrel pollen season lasted from 14 May to 8 September, whereas for plantain from 21 May to 16 September (Fig. 31). Plantago pollen reaches low airborne concentrations (Fig. 33). In July (on the average, 18 July), the mugwort season starts and it lasts until 26 September. The maximum airborne pollen concentration for this taxon in the air of Lublin occurs at the beginning of August (Fig. 33).
DISCUSSION
The number of airborne sporomorphs is of special signifi cance in aerobiology. The plant species causing allergic reactions, and the same time frequently occurring in the conditions of Lublin, were selected for our observations. Adaptations to anemophily are associated with a particular structure of fl owers. Among the studied plants, the Longistaminae type infl orescence (Secale, Rumex, Plantago) was predominant.d i , 1986; M o l i n a et al. 1996). Also We r y s z k o -C h m i e l e w s k a and B a r t y ś (2000) found that the number of pollen grains was positively correlated with anther length in entomophilous meadow plants. The size of pollen grains is also of essential importance. Molina et al. (1996) demonstrated that Acer negundo produces, in an anther which is twice longer than the anthers of common olive and with medium-sized pollen grains (27,2 x 30,5 μm), a similar number of pollen grains as Olea europaea producing small pollen grains (22 μm). But Juglans regia produces 18 times less pollen grains than common olive in the anther of small length, as it is slightly shorter than the anther of common olive, and with medium-sized sporomorphs (45 μm).
In the present study, consistent results were obtained with data given by other authors with respect to the number of pollen grains contained in the rye infl orescence and the birch fl ower. According to literature data, a single fl ower of Betula verrucosa produces 20 145 pollen grains, whereas Secale cereale -57 310. The number of pollen grains produced by the infl orescence is estimated at 5 450 000 for Betula verrucosa, and for Secale cereale -4 250 000 (P o h l , 1937 following E r dt m a n , 1954; D y a k o w s k a , 1959; M a u r i z i o and G r a f l , 1969; S z a f e r and Wo j t u s i a k o w a , 1969). A similar number of pollen grains per rye infl orescence, amounting to 4 200 000, was obtained by D e V r i e s (1971). No literature data has been found relating to pollen production by Rumex acetosella, Plantago major and Artemisia vulgaris. D e Vr i e s (1971) as well as A g n i h o t r i and S i n g h (1975) stress the variation in numbers of pollen grains produced between species of the same genus, and even between the varieties. It has been shown that different wheat varieties may produce in one anther from 856 up to 3867 pollen grains (D e Vr i e s , 1971). Signifi cant differences in the number of pollen grains produced by different species of the same genus were also found by S u b b a R e d d i and R e d d i (1986).
The period of birch pollen shedding is relatively short; there is generally only one high peak on the curve presenting the pollen season pattern. It is attributable to the fact that 70-80% of pollen is released from the anthers of birch within 2-3 days (S u s z k a , 1979). The intensity of the birch pollen season differs signifi cantly between particular years and depends, inter alia, on the number of infl orescences produced in the previous year (L a u k k a n e n et al. 2003). The occurrence of airborne rye pollen is characterised by great regularity. Maximum concentrations in Lublin in the years 1994-1997, likewise in the years 2001-2007, were found at the end of May or at the beginning of June (P i o t r o w s k a , 1999). In Lublin plantain pollen was noted from 21 May, on the average. A comparison of the pollen seasons in Lublin, Poznań and Rzeszów shows that Plantago pollen appeared earliest in Rzeszów (at the beginning of May) and latest in Poznań (at the end of May), whereas the dates of occurrence of Rumex pollen in the three cities were similar (P i o t r o w s k a , 2007). In Lublin the mugwort pollen season starts about the middle of July. When comparing the dates of occurrence of pollen of the abovementioned taxon in the air of 8 cities, it was found that it appears in the western part of Poland earlier than in the eastern part (We r y s z k o -C h m i e l e w s k a et al. 2006). In the recent years, an ever increasing number of people allergic to mugwort pollen has been recorded. R a p i e j k o and We r y s z k o -C h m i e l e w s k a (1999) note that it can be associated with an increased share of pollen of these plants in aeroplankton.
Figs
Figs 12-17. Rumex acetosella Fig. 12. Fragments of male and female plants, bract (arrow) and ochrea (arrow head) visible on the stem, x 10. Fig. 13. Male fl owers at different development stages. On the left, the fl ower after anthesis without anthers, perianth segments and fi laments are visible, x 12. Fig. 14. Schematic diagram of the stamen structure, x 16. Fig. 15. Pollen grain -LM x 1100. Fig. 16. Female fl owers with green and red inner parianth segments, x 30. Fig. 17. Pistil with star-shaped stigmas (two out of three are visible), x 25.
Figs
Figs 18-23. Plantago major Fig. 18. Flowers in the pistil stage, x 23. Fig. 19. Flowers in the stamen stage, x 20. Fig. 20. Flowers at different development stages. In the upper part, a fl ower with stamens coming out, the fi lament folded in two (arrow), x 18. Fig. 21. Schematic diagram of the stamen structure before pollination, x 16. Fig. 22. Schematic diagram of the stamen structure during pollination, x 16. Fig. 23. Pollen grain -LM x 1020.
Fig. 31 .
Fig. 31. The mean and extreme terms of the pollen seasons beginning and end of the selected taxa plants (averages from 2001-2007).
Many authors note the abundance of pollen produced by anemophilous plants (S u b b a R e d i and R e d d i , 1986; M o l i n a et al. 1996). The number of pollen grains in one anther of the studied plants was within the range of 5 870 -22 360. The largest amount of grains per anther was found in Secale cereale, whereas the least amount was in Plantago major, which is closely related to anther length. This correlation was confi rmed by many authors (D y a k o w s k a , 1959; D e V r i e s , 1971; A g n i h ot r i and S i n g h , 1975; S u b b a R e d d i and R e d -
==
Domain: Agricultural And Food Sciences Environmental Science Biology
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Skin Firming, Skin Smoothing, Skin Blemishes Elimination and Anti-aging Effects of Increased Protein Intake in the Form of Voandzeia hypogeal Seed Meal Utoh-Nedosa
Studies on the effect of excess calorie intake on the human body showed that it resulted in accumulation of excess body fat; the darkening of t he skin colour and the development of a bumpy skin. Problem statement: The present study investigated the effects of incr eased protein intake on the human skin and on body fat. The skin firming, skin smoothing and skin colour lightening effects of Vernonia amygdalina leaf extract has already been established. The ant i-obesity, body calming and anti-aging effects of VA leaf extract have also bee n stablished. Approach: The effects of increased protein intake (in the form of eating of 100 gm Voa ndzeia hypogeal seed paste meal two times daily), was tested on the skin, anti-obesity and anti-aging effects of VA leaf extract. The study was done for 10 days. Results: The results of the study showed that Voandzeia sub terranean (hypogea) cooked potentiated the skin tightening, skin smoothing, sk in colour lightening and the anti-obesity/antiaging effects of VA. Leaf extract. These results sh ow that increased Voandezia hypogeal seed paste meal [increased protein intake has skin tightening/ clearing/lightening/Smoothing and antiobesity/anti-aging effects]. They also show that co oked Voandzeia hypogeal seed paste meal has a potentiating effect on the skin and body effects of VA leaf extract. Conclusion: From the findings of this study the author to concludes that increase d protein intake potentiated the skin firming/skin Smoothing/skin colour lightening; body calming; ant i-obesity and anti-aging effects of Vernonia amygdalina leaf extract. The study also concludes that Voandz eia hypogeal seed meal has skin firming/skin Smoothing/skin colour lightening (clea ring); body calming; anti-obesity and anti-aging effects.
INTRODUCTION
Many people in the world know that proteins are body building foods. However, a large number of people do not know the far reaching effects of adequate protein intake on the health and looks of an individual. The aim of this research study was identify some physically visible effects of protein intake on the human body.
The nutritional potential of a number of raw tropical seeds were assessed in a series of feeding trials with rats in which seed lectin reactivity was monitored. Abelmoschus esculantus, Chenopodium quinoa, Delonix regia, Phaseolus calcaratus, Phaseolus lathyroides, Parkia biglandulosa, Papeaver somniferum, Sesbania Arabica, Terminalia catappa, Vigna sinensis and Voandzeia subterranea seeds were found to have supported moderate rat growth (Grant et al., 1991). Also five media formulated fromdried cow blood, mineral salts and salts from four species of legumes, were assessed for growth, sporulation and insecticidal properties of Bacillus sphaericus strain 1593. Bacterial powders prepared from broth were assayed against Bacillus Culex quinquefasciatus, Anoppheles gambiae and Aedes aegypti. Good growth and sporulation were obtained with all the media and the highest number of viable cells and spores per milliliter [8.6×10(8) and 8.1×10(8)] were obtained in media containing ground seeds of Vigna unguiculata, Voandzeia subterranean and Arachis hypogea ()beta abd Okafor, !983). These two cited examples show that Voandzeia hypogea or subterranean is a good source of protein that can support animal or human growth.
MATERIALS AND METHODS
Mature red stalked Vernonia amygdalina (bitter leaf) leaves were selected and dried in the shade, indoors or in low sunshine. V. amygdalina leaf effusion was made by extracting fragments of the dried V. amygdalina leaves with two times their own volume of boiling drinking water in a cooking pot.
The VA leaves were extracted fully by stirring them in the boiling water. The VA leaf effusion was then sieved out with a fine meshed sieve and stored in a clean plastic jar with a tight fitting cover.
Treatment:
The subject took 33×2 milliliter cupfuls of the V. amygdalina leaf infusion orally, 4 times daily or 33ml cupfuls of the V. amygdalina leaf infusion orally, 8 times daily. The VA leaf extract treatment was taken for one month before the Voandzeia hypogeal meal test. The VA leaf extract treatment was continued during the Voandezia hypogea seed past meal.
Dietary restrictions:
The subjects were disallowed consumption of the following foods; lard; vegetable oil; butter/margarine; mayonnaise; grated coconut; bacon; ground nut/peanut butter; nuts like palm nut, coconut, date palm; oil fried foods; oily seeds like mellon seeds, sun flower seeds, peanut seeds; oily fruits like avocado pea, Nigerian pea; oily soups, stews or gravies; oily cooked or prepared meals like salads, bean cakes. and pastries or flour foods like dough nuts, burns, cakes, spaghetti/macaroni, bread; acidic beverages like coffee, tea, cocoa; acidic fruit juices like lime juice, lemon juice, pineapple juice; soft drinks (including malt drinks and carbonated drinks); tobacco or tobacco products; garlic or acidic food spices and condiments; artificial food seasoning (artificial salt or salt substitutes including potash) and alcohol.
Permitted diet and eating behavior:
The subject was only permitted to eat meals containing their normal daily requirements of proteins, non-acidic fruits; mineral salts and water. The carbohydrate ration permitted for the subject was only one third of the previously consumed carbohydrates and one tenth of the previously consumed dietary fats and oils. Leafy vegetables were made to replace two thirds of the carbohydrates which the subject previously consumed.
The subject was required to eat only two meals in a day, one in the morning to mid-morning and the second one in the evening/night hours.
The Voandzeia seed paste meal: The Voandzeia seed paste meal consisted of the eating of 100 gm of Voandzeia sutarranea cooked seed paste. Powdered Voandzeia hypogea seed which had been made into a paste with warm water to which palm oil and common salt were added was cooked in white polythene bag wraps.
The subject ate 100gms of the Voandzeia hypogeal meal two times daily for 10 days in conjunction with the VA leaf extract treatment.
RESULTS
The results of the study on the physical looks of the subject are shown in the pictures in Fig. 1-3. The results of the study suggest that each of Vernonia amygdalina leaf extract and Voandezia subterranean seed paste meal could on its own produce body defattening; skin colour lightening (skin clearing effects); skin tissue tightening; skin Smoothing and anti-aging effects in the order of V. amygdalina leaf extract effects > Voandezia hypogeal seed meal.
The skin clearing, skin Smoothing; skin tightening; anti-obesity and anti-aging effects of Voandzeia subterranean or hypogeal obtained in this study is supported by the following submissions of the owner of the European patent EP1174144 on the properties of the extracts of Voandzeia subterranean particularly the protein extracts: • They have strong nutritive and cellular power • They have softening and bio-filmogenic effects • They have cutaneous conditioning and repair effects • They have anti-wrinkle effects • They have skin tightening effects • They have dermal protective and elastic tissue protective effects • They have anti-irritant, anti-free radical, antipollution, hydrating, anti -UVB photo-protection effects They have pacifying, anti-proteases, anti-elastases, anti-collagenases, anti-catalase, anti-aging and cutaneous firming effects (Silvano, 2002).
This same author of the sited patent found that Voandzeia subterranean seed extracts have the following advantageous effects on the hair: • Bio-conditioning effects • reparative effects • softening effects • Vitalizing post application effects on hair and nails (Anastasia, 2011a) The skin and hair effects noted here by the author of the European patent cited above have been noted in the studies of the present author and some other studies (Obeta and Okafor, 1983;Grant et al., 1991) for Vernonia amygdalina leaf extract (Anastasia, 2011a;Anastasia, 2011b) and with Voandzeia hypogeal seed meal (unpublished findings). The antiobesity and anti-aging effects of Vernonia amygdalina have also been demonstrated (Anastasia et al., 2011;Anastasia, 2010).
CONCLUSION
The findings of this study enable the author to conclude that increased protein intake potentiated the skin firming/skin Smoothing/skin colour lightening; body calming; anti-obesity and anti-aging effects of Vernonia amygdalina leaf extract. The study also concludes that Voandzeia hypogeal seed meal has skin firming/skin Smoothing/skin colour lightening/skinclearing; body calming; anti-obesity and anti-aging effects.\===
Domain: Agricultural And Food Sciences Biology Medicine. The above document has 2 sentences that start with 'The study also concludes that',
2 sentences that start with 'amygdalina leaf infusion orally',
2 sentences that start with 'The VA leaf extract treatment',
2 sentences that end with 'effects of VA leaf extract',
2 sentences that end with 'of Vernonia amygdalina leaf extract',
2 sentences that end with 'cupfuls of the V',
2 paragraphs that start with 'The subject was',
2 paragraphs that end with 'calming; anti-obesity and anti-aging effects'. It has approximately 1327 words, 50 sentences, and 23 paragraph(s).
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Effects of gamma ray and electron beam irradiation levels in spray-dried blood meal on nursery pig performance
Three hundred weanling pigs (initially 23.7 lbs and 17 ± 6 d of age) were used in a 19-d growth assay to determine the effects of increasing levels (2.5, 5.0, and 10.0, and 20.0 kGy) of gamma ray and electron beam irradiation of spray-dried blood meal on growth performance. Irradiation of blood meal resulted in decreased concentrations of aerobic bacteria, E. coli, mold, and yeast in spray-dried blood meal. The inclusion of irradiated spray-dried blood meal tended to improve F/G from d 0 to 7 and for the overall trial (d 0 to 14), but had no effects on ADG or ADFI. Comparison of the two types of irradiation and dosage level showed no differences in growth performance. In this experiment, the inclusion of spray-dried blood meal did not improve growth performance over that obtained with the control diet.; Swine Day, Manhattan, KS, November 16, 2000
Introduction
Recent research at Kansas State University showed improvements in growth performance of nursery pigs consuming blood products that have undergone irradiation treatment. However, different methods and dosage levels of irradiation are available. Irradiation involves exposing a given substance to ionizing energy to create ions and free radicals. The result of this energy is the destruction of living microorganisms. Also, antinutritional factors can be broken down with an increase in dosage level. Therefore, our objective was to determine the effects of increasing levels (2.5, 5.0, 10.0, and 20.0 kGy) of gamma ray (cobalt-60 source) and electron beam irradiation of spray-dried blood meal on growth performance of weanling pigs.
Procedures
A total of 300 pigs (BW of 23.6 and 17 ± 6 d of age) was used in a 19-d growth assay. Pigs were blocked by weight and allotted to one of 10 dietary treatments. There were five pigs/pen and 10 pens/treatment. Pigs were housed in the Kansas State University Segregated Early W eaning Facility. Each pen was 4 × 4 ft and contained one selffeeder and one nipple waterers to provide ad libitum access to feed and water.
All pigs were fed the same pelleted SEW and transition diets (Table 1) to 4 d postweaning. All pigs were fed 1 lb of SEW diet, then they were fed the transition diet for the remainder of the 4 d pretreatment period. At d 4, the pigs were switched to experimental diets, which included a control diet with no added spray-dried blood meal, and diets with 5% regular spray-dried blood meal or irradiated 5% spray-dried blood meal. Irradiated treatments included either gamma ray (cobalt-60 source) or electron beam irradiation at increasing dosage levels (2.5, 5.0 10.0, and 20.0 kGy). All blood meal used in this experiment was from the same lot. Treatment diets were fed in meal form and formulated to contain 1.40% lysine, .90Ca, and .54available P (Table 1). In addition, all diets were balanced for Na (.26%) and Cl (.43%). Synthetic amino acids were added as well to exceed the pig requirement and ensure that no amino acid would be limiting in the diets. The ADG, ADFI, and F/G were determined by weighing pigs and measuring feed disappearance on d 4, 11, and 18. Blood meal samples were taken for analysis to determine bacterial concentrations prior to manufacturing of the complete diet.
Data were analyzed as a randomized complete block design with pen as the experimental unit. Pigs were blocked based on postweaning weight, and analysis of variance was performed using the GLM procedure of SAS. Linear, quadratic, and cubic polynomial contrasts were used to determine the effects of increasing dosage levels of irradiation. In addition, contrasts were utilized to test differences between irradiated and nonirradiated treatment diets. Initial pig weight at the start of the experimental period was used as a covariate for statistical analysis.
Results and Discussion
Irradiation of blood meal proved effective in the reduction of aerobic bacteria, E. coli, mold, and yeast concentrations (Table 2). Blood meal subjected to gamma ray irradiation had lower concentrations of aerobic bacteria than that irradiated by electron beam at each level of irradiation. In fact, at 5.0, 10.0, and 20.0 kGy, no bacteria were detected with gamma ray treatments, but low levels of bacteria were cultured with electron beam treatment.
From d 0 to 7 of the treatment period (Table 3), as well as overall (d 0 to 14), the inclusion of irradiated spray-dried blood meal tended (P<.09) to improve F/G with no effects on ADG (P=.26) or ADFI (P=.86). However, for the overall experiment, ADG and F/G were increased by approximately 9 and 6%, respectively. In addition, the inclusion of spray-dried blood meal did not improve growth performance over the control diet without spray-dried blood meal.
These results indicate that irradiation is an effective technology to reduce or eliminate bacteria, molds, and yeast in spray-dried blood meal. However, increasing the dosage above 2.5 kGy, regardless of source, did not further enhance growth performance of nursery pigs. Also, both electron beam and gamma ray irradiation resulted in similar performance. Previous research at Kansas State University has consistently shown that ADG and ADFI increase when pigs are fed spray-dried blood meal or animal plasma that has been irradiated. However, in this trial, we found a response in feed efficiency, but not in ADG and ADFI. We believe the numerical responses were similar to the significant responses observed in other trials, but the larger variation (SEM, .038vs .022)observed in this trial prevented the detection of significant responses. This leads us to believe that pigs can more efficiently utilize irradiated spray-dried blood meal, which indicates that this processing technique either reduces antinutritional factors or alters the protein structure to make it more available for the weanling pig. a A total of 300 pigs (five pigs per pen and 6 pens per treatment) with an average initial BW of 23.7 lbs.b No effect of control diet vs added blood meal diets (P>.10).c No effect of gamma ray verses electron beam irradiation (P>.10).d Nonirradiated vs irradiated blood meal (P<.10).
a
Samples obtained prior to whole diet preparation for analysis.b Samples obtained at initiation of experiment for analysis.
Table 1 . Compositions of Diets (As-Fed Basis ) a
b Provided 50 g per ton carbadox.\===
Domain: Agricultural And Food Sciences Biology Medicine. The above document has 2 sentences that start with 'coli, mold, and yeast',
2 sentences that start with 'Pigs were blocked',
2 sentences that end with 'of aerobic bacteria, E',
2 sentences that end with 'yeast in spray-dried blood meal'. It has approximately 1042 words, 49 sentences, and 16 paragraph(s).
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Phenotypic traits detect genetic variability in Okra ( Abelmoschus esculentus . L . Moench )
There is low production of okra in Ghana due to lack of improved varieties and biotic constraints. This study was conducted to characterize okra genotypes to predict genetic variation in the crop. Field trial was conducted to determine genetic variability in 21 okra genotypes. The experiment was based on the randomized complete block design (RCBD) involving planting distance of 0.6 × 0.6 m. Thirty-one quantitative and qualitative data were used to generate a dendrogram. Variations in leaf shape, leaf rib colour, petiole colour, petal colour, colour of the darkest ridges and stem colour were distinctive among the okra genotypes. The mean plant height, canopy diameter, leaf length and breadth, petiole length, internode length, number of branches, days to 50% flowering and fruit yield differed significantly (p ≤ 0.05) among the 21 okra genotypes. These were discriminated into three clusters in a dendrogram with GH3731 as the most diverse. UCCC1, UCCC2, UCCC3, UCCC4 and UCCC5 appeared genetically similar with low fruit yield but early maturity. However, GH5332 had a significantly (p ≤ 0.05) the highest fruit yield of 11.88 t ha -1 but late maturing. UCCC5 or similar genotypes with early maturity trait can be hybridized with GH5332 to improve the yield and earliness.
INTRODUCTION
Okra (Abelmoschus esculentus L. Moench) of the family Malvaceae, is an important and widely cultivated annual crop in both the tropical and sub-tropical regions of the world (Eshiet and Brisibe, 2015;Ali et al., 2014). It is a vegetable rich in organic and inorganic nutrients that sustain human health and as feed for animals (Chattopadhyay et al., 2011;Ofoefule, 2001;Rahman et al., 2012;Wamanda, 2007;Siesmonsma and Kouame, 2004;Saifullah and Rabbani, 2009). In Ghana, okra is often consumed in the diet by both children and adults in *Corresponding author. E-mail+233 244954045.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License both rural and urban communities. However, the yield of this crop is low due to lack of improved varieties, biotic and abiotic stresses. Yield potential of 2000 to 3000 kg ha -1 has been reported for Okra (MoFA, 2007), depending on the cultivar, harvesting frequency and period for harvesting (Cudjoe et al., 2005). However, actual yields of okra are usually low and also decreased over the years in Ghana, in spite of its economic importance and health benefits (Asare-Bediako et al., 2014).
The development of new varieties with better adaptation and yield potential are crucial for sustainable production of okra. Genetic variation in okra is a necessary requirement to improve the crop. Omonhinmin and Osawaru (2005) reported that high degree of wide morphological variation was found among accessions of okra, especially in West African type. There are numerous cultivars of okra with varied plant height, degree of branching and pigmentation of the various parts, period of maturity, and pod shape and size (AdeOluwa and Kehinde, 2011). In addition, Bisht et al. (1995) observed that pigmentation and pubescence of stem, leaf, pods and seeds were important components of variability in okra germplasm.
Various types of okra in Ghana are cultivated in the savannah and forest agro-ecological zones that require assessment of their genotypes. Ahiakpa et al. (2013) has done some morphological characterization of okra in Ghana but lacked collections from the central region and inclusion of exotic genotypes. The current work considered collections from the central region and other regions of Ghana as well as Togo to enhance assessment of fully harness variability in the germplasm for breeding and conservation. Indeed, genetic variation may serve as recipe for controlled hybridization to improve the crop. Assessment of variable phenotypic traits of okra would be useful in predicting genetic diversity towards molecular characterization to establish the genetic structure of okra in Ghana. This will facilitate routine breeding and germplasm conservation of the crop. The objective of the current work was to explore phenotypic characteristics to predict genetic variability among 21 okra genotypes in Ghana and to establish basis for molecular characterization.
Experimental site
The experiment was conducted at the Teaching and Research Farm of the School of Agriculture, at University of Cape Coast during the 2015 major crop season (June to October). This site is located within the coastal savannah vegetation zone, with Acrisol soil type and is a highly endemic site for viral diseases and flea beetle infestation. The area has a bi-modal rainy season from May to June and August to October with an annual rainfall ranging between 750 and 1000 mm and temperatures ranging between 23.2 and 33.2°C with an annual mean of 27.6°C (Owusu-Sekyere et al., 2011).
Plant
Twenty-one genotypes of okra (both landraces and improved) were used for the study (Table 1). These comprised of fifteen accessions from Plant Genetic Resource Research Institute (PGRR1) at Bunso (wide collections from regions of Ghana and Togo), four farmer varieties, a land race and an improved variety (Asontem) from the Central Region of Ghana. The local names and sources of 21 okra genotypes are shown in Table 1.
Field experiment
The randomized complete block design (RCBD) with twenty-one genotypes of okra was sown in four replications. A total land area of 1344 m 2 (84 × 16 m) was ploughed and harrowed to render the soil loose. It was then divided into four blocks and each block was further divided into 21 plots, with each plot measuring 3 × 3 m. A distance of 1.0 m was left as walkway between the blocks and 1 m between the plots. Planting was done in June, 2015. The 21 okra genotypes were sown directly at three seeds per hole at a planting distance of 0.6 × 0.6 m and a planting depth of not more than 0.5 cm. The seedlings were later thinned out leaving two seedlings per hill. Weed control was done as necessary using herbicides and a hoe (manual weeding). NPK fertilizer (15:15:15) was applied at a rate of 250 kg ha -1 . Watering was done when necessary using sprinklers.
Data collection and analysis
The quantitative and qualitative data were collected based on the International Plant Genetic Resource Institute (IPGRI, 1991) okra descriptor list and adopted the procedure by Ahiakpa et al. (2013) and Nwangburuka et al. (2011) with some modifications (Table 2). The quantitative data including plant height, canopy diameter, leaf length, breadth, and stem base diameter at first flowering stage and petiole length, fruit length and fruit girth were obtained using meter rule or tape measure. The number of branches and fruits per plant were counted and fresh weight of matured fruits were determined by electronic balance (Radwag, WPT 12C1, Poland). The qualitative parameters were determined by visual estimation and rated (Table 1).
Data on plant growth and yield parameters were subjected to one-way analysis of variance (ANOVA) to determine significant differences among the 21 okra genotypes. The means were separated by the least significant difference method, using GenStat Discovery version 4 (VSN International). The GenStat or Minitab 15 statistical software was used for all computations. The Pearson's correlation coefficients were estimated for the growth and yield parameters. The Eigen-vectors and factor scores were used respectively to measure the relative discriminative power of the PCaxes and their associated characters. The data involving 31 parameters (Table 2) were analyzed with the PowerMarker version 3.5 and the dendrogram generated in Molecular Evolutionary Genetics Analysis version 4 (MEGA4) to determine genetic relatedness among the okra genotypes (Tamura et al. 2007).
RESULTS AND DISCUSSION
Qualitative and quantitative characteristic variations exist among the 21 okra genotypes. Variations in matured leaf colour, leaf shape, leaf rib colour, petiole colour, petal colour, colour of the darkest ridges and stem colour were
Accession number Accession name Country of origin (Location
distinctive differentiation characters. In addition, differences in flowering span, fruit colour, fruit shape and number of ridges per fruit, pubescence, position of fruits on main stem and branching position at main stem were evident among the okra genotypes (Table 3).
The mean plant height, canopy diameter, leaf length and breadth, petiole length, internode length and number of branches as well as days to first flowering, 50% flowering and fruit yield differed significantly (p < 0.05) among the 21 okra genotypes. However, the stem diameter did not show significant (p > 0.05) variation among the okra genotypes. The highest average plant height of 68.95 cm was noted for UCCC3 and the least plant height of 28.72 cm was for GH4374 (Table 4). Plant height at flowering and fruiting are of particular interest for breeding programmes, because the presence of plants with tall and thin stems will increase the rate of lodging near harvesting and this could lead to loss of dry matter and subsequent decrease in fruit yield (Esthiet and Brisibe, 2015). In fact, Verma (1993), Ariyo et al. (1987) and Perdosa (1983) intimated that plant height is controlled by genetic factors and is closely associated with number of flowering node, average fruits per plant and number of internodes.
The leaves of okra serve as the main sites for photosynthesis, an increase or a decrease in their size could affect production of assimilates in the crop. Larger size leaves in any okra genotype may have higher ability to intercept solar radiation to assume higher photosynthetic capacity, which may enhance growth and crop yield. GH3734 had the widest canopy diameter of 100.86 cm compared to the least of UCCC5 (59.14 cm). The highest leaf length of 21.82 cm was produced by GH3734 and the lowest of 14.84 cm was associated with UCCC5. The mean leaf breadth of 29.55 cm was the highest observed for GH3760 compared to the least average leaf breadth of 19.89 cm for UCCC5 (Table 4). According to Ahiakpa (2013), an increased leaf area index and a resultant higher fraction of intercepted radiation and its utilization efficiency may increase crop yield. Significant (p < 0.05) correlations were observed between leaf length and canopy diameter (r = 0.72), breadth and canopy diameter (r = 0.53), leaf length and breadth (r = 0.65), petiole length and canopy diameter (r = 0.49) and stem diameter and canopy diameter (r = 0.70) in the okra germplasm could be determinants for plant vigour and yield indicators. GH2026 had the longest internodes of 21.53 cm per plant compared with the shortest for UCCC4 (14.69 cm per plant).
The mean petiole length of 20.64 cm was the highest for GH2052 compared with that of the lowest average petiole length of 14.29 cm for UCCC5. GH2026 had highest mean internodes (21.53 cm) and number of branches (5), respectively. However, GH2057, GH2063, GH4372 and GH5793 had the least number of 2 branches per plant. Variations in petiole length, leaf size, According to Ariyo and Odulaja (1991), variability in okra germplasm is more prominent in days to flowering, plant height and various fruit characteristics and these traits could be important in differentiating varieties of A. esculentus. Similarly, in the current study, fruit length, girth and weight as well as the days to first flowering and days to 50% flowering differ significantly (p < 0.05) among the 21 genotypes of okra (Table 5). However, UCCC1, UCCC2, UCCC3, UCCC4, and UCCC5 were very similar in early flowering and days to 50% maturity as well as average fruit number, weight and size. Generally, all okra genotypes with high vegetative growth delayed flowering and maturity. UCCC5 was first to flower at 47 days and attained 50% flowering at 53 days, respectively after sowing seeds, which were significantly early compared to others. On the contrary UCCC6 was very late to first flower at 139 days and 50% flowering at 141 days, which were significantly (p < 0.05) different from all the other okra genotypes.
In this study, the significantly (p < 0.05) high yielding okra genotype, GH5332, produced 20 fruits per plant, with the highest fruit weight of 11.88 t ha -1 , which compared well with the size of the fruits (mean fruit length of 14.2 cm, girth 20 mm, and weight of fruit per plant of 21.6 g) among the okra genotypes. However, GH5332 is late maturing with 50% flowering at 101 days. Indeed, Esthiet and Brisibe (2015) reported that fruit length, pod number and pod weight are the most important determinants of production or yield in okra. It has been suggested that the number of days and plant height at flowering are controlled by the same genetic variables (Choudhary et al., 2006;Hussain et al., 2006).
It is critical to consider early maturity in the phase of erratic rainfall as essential trait to complement yield for hybridization to produce climate-smart okra genotypes. Therefore, UCCC5 with very early flowering and fruiting traits can be hybridized with the high yielding but late maturing genotypes of GH5332 to improve the crop. A successful cross between unrelated varieties may result into an array of elite genotypes from which advantageous agronomic line may be selected (Ali et al., 2014).
The variation in the quantitative characteristics which accounted for the total variance includes number of fruits per plant, mean plant height, canopy diameter, leaf length, breadth, stem diameter, petiole length, internode length, and number of branches. The proportion contributed by each quantitative variable to determine the total variation within each Principal Component (PC) axis is shown in Table 6. The variations in the quantitative characters contributed significantly (Eigen vector ≥ 0.2) to the variation within each of the four PC-axes as 38.00, 14.90, 12.40 and 11.70% for PC1, PC2, PC3 and PC4, respectively. The cumulative proportion of variation explained by the first four PC-axes, 77.00% (Table 6) compared well with observations made by Campos et al. (2005) and Ogunbayo et al. (2005) that the PC-axes contributed 76.62 and 64.5% variations, respectively. Similarly, Ahiakpa et al. (2013) reported that the first four PC-axis contributed 82.97% of the variations in okra. The remaining six axes in the current study accounted for only 23.00% of the total variation. Indeed, canopy diameter, leaf length, breadth, stem diameter and petiole length contributed to the variation in PC1. Plant height, length of internodes and number of branches accounted for the variations observed in PC2 and fruit per plant as well as plant height contributed to the variations in PC4. These variations may suggest the existence of genetic diversity in okra that can be harnessed to improve the crop. Similar observation was made by Yonas et al. (2014).
The 21 okra genotypes were distinguished into 3 main clusters (I, II and III) in the dendrogram at 43% genetic dissimilarity based on 31 quantitative and qualitative morphological characters (Figure 1). All the clusters were made up of varied sub-clusters with the exception of Cluster II, which had a single okra genotype (4.8%) involving GH3731, but the most diverse of all. However, cluster III made of 33.3% of the 21 okra genotypes appeared more closely related, including GH3734, GH6211, UCCC1, UCCC2, UCCC3, UCCC4 and UCCC5, which may suggest genetic similarity. The remaining 61.9% of the okra genotypes were distinguished in cluster I, which is the largest and made up of all the okra genotypes collected from the national gene bank, the Plant Genetic Resources Research Institute at Bunso with the exception of UCCC6. At 25% genetic dissimilarity, all the 21 okra genotypes were fully distinguished in the dendrogram. The dendrogram generated from genetic distance matrices gave an overall pattern of variations and relatedness among the okra genotypes, which agreed with the observation made by Nwangburuka et al. (2011).
Indeed, the dendrogram offered distinctive synopsis of the genetic relatedness in the okra germplasm, which is in agreement with the observation made by Aliyu and Fawole (2001) as well as Aremu et al. (2007). According to Ahiakpa et al. (2013), there is a direct relation between the eco-geographical origins of okra collections and their
Conclusions
The 31 quantitative and qualitative characters distinguished all the 21 okra genotypes without identifying clones. The discriminatory ability of the 31 characters was evident in clustering of the 21 okra genotypes in the dendrogram. UCCC1, UCCC2, UCCC3, UCCC4, UCCC5, GH6211 and GH3734 appeared more closely related. On the whole, the most diverse okra genotype was GH3731. UCCC5 had 50% flowering at 53 days which suggests very early maturity, followed by UCCC3 (68 days), UCCC2 (70 days). Though, GH5332 is late maturing with 50% flowering at 101 days, it produced a significantly (p < 0.05) highest fruit yield of 11.88 t/ha at a rate of 20 fruit per plant which compared well with the size of the fruits among the okra genotypes. UCCC5, UCCC3 and UCCC2 with early maturing but low yield can be hybridized with the high fruit producing, but late maturing okra genotypes of GH5332 and GH6105 to improve earliness in fruiting and adapt the crop to escape terminal drought. The most diverse genotype GH3731 could also be incorporated into breeding to broaden the genetic base of the crop. The almost 50% of the okra genotypes that produced fruits and also had large leaves suitable for use as leafy vegetables and to feed cattle could serve a dual purpose.
RECOMMENDATION
Though the phenotypic characters were useful to detect genetic variations in okra germplasm collections, they are not absolutely reliable since the traits can be influenced by the environment. Hence, there is a need to employ molecular markers to characterize the okra germplasm including exotic genotypes to establish the genetic structure of the crop and establish baseline information for breeding and conservation of the crop.
Figure 1 .
Figure 1. The UPGMA Dendrogram of genetic relationship among 21 okra genotypes based on 32 phenotypic characteristics.
Table 1 .
Sources of okra genotypes used for the study.
Table 2 .
Rating of morphological characters of the okra genotypes used for the study.
Table 3 .
Qualitative parameters of 21 Okra genotypes
Table 4 .
Variation in growth characteristics of 21 okra genotypes.
Table 5 .
Variation in the average fruit yield and phenology among 21 okra genotypes.
Table 6 .
Principal component analysis of the 21 okra genotype showing the factor scores, Eigen values and percentage total variance accounted for by the first four principal component axes.
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Domain: Biology Agricultural and Food Sciences
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Physiological Effects of Ingesting Eucalyptus Essential Oil with Milk Casein Peptide
This study was conducted to clarify the effect of eucalyptus essential oil mixed with milk casein peptide food for human physiological relaxation. Fifteen male university students (21.2 ± 0.9 yr) participated in study as subjects. The subjects were given one of two types of experimental drink (peptide + eucalyptus flavor (Pep + EF), and peptide + grapefruit·orange flavor (Pep + G·O), each flavor contains natural essential oil). We measured the change in salivary cortisol concentration and POMS scores before and two hours after taking experimental drink. The results of a Type A behavior pattern test were used to classify subjects. The concentration of salivary cortisol decreased significantly two hours after taking Pep + EF. And Type B showed bigger change than Type A. In conclusion, the results show that eucalyptus essential oil has the effect of relaxation, and that the effects on Type A and Type B are different.
Introduction
Casein, which represents 80% of the protein in milk, is nutritionally excellent with amino acids, and is rich in branched-chain amino acids (BCAA), which have a variety of physiological functions in the human body (Nakamura et al. 1995, Fernstrom 2005). The form of casein that is most frequently employed as a food ingredient is milk casein peptide, which is effectively absorbed in the human body. Taking milk casein peptide is reported to be effective for increasing calcium absorption (Sato et al. 1986). Therefore, it is widely used as an ingredient in health-assisting foods. But because it has a bitter taste, milk casein peptide is used as a mixture with various flavors. Grapefruit and orange flavor are wellknown additives, and they are usually added to milk casein peptide.
Recently, there have been a number of studies on the relaxation effect of essential oil extracted from wood (Miyazaki and Motohashi 1996), and essential oils and their components are widely used in the food industry as flavoring additives (Cawan 1999). Eucalyptus essential oil has been shown to have antimicrobial activity (Cawan 1999, Schelz et al. 2006). Moreover, it is used as a typical essential oil in aromatherapy.
This study was carried out to clarify the physiological effect of eucalyptus essential oil added to milk casein peptide food, with grapefruit•orange flavor added to milk casein peptide food employed as control.
Assessment of cortisol in the saliva is a widely accepted and frequently employed method in various fields of study (Park et al. 2007, Tsunetsugu et al. 2007, Park et al. 2008). The ease of sampling is one of the most obvious advantages of cortisol assessment of saliva. In general, saliva samples can be obtained in a stress-free manner at almost any desired frequency in most subjects (Kirschbaum and Hellhammer 1994).
The POMS (Profile of Mood States) is a wellestablished, factor-analytically derived measure of psychological distress for which high levels of reliability and validity have been documented. There is growing evidence that psychological mood state change assessed by employing POMS (McNair andLorr 1964, Yokoyama et al. 1990). Miyazaki and Tsunetugu (2005) have categorized hemoglobin concentration changes in the prefrontal area into certain types when gustatory stimulus is applied, and explained them by using personality characteristics (Type A behavior pattern). The result shows that physiological responses can vary according to personality characteristics.
The aim of this study was to clarify the effect of eucalyptus essential oil mixed with milk casein peptide food on the physiological relaxation of humans and to confirm the manner in which physiological responses vary according to personality characteristics.
Subjects
Fifteen normal male university students (21.4 ± 0.9 years old; mean value ± standard deviation) participated in the study as subjects. None of the subjects reported any physiological or psychiatric disorders in their personal histories. The subjects' written consent to participate in the study was obtained after explaining the details of the study in advance. This study was performed under the regulations of the Institutional Review Committee of the Forestry and Forest Products Research Institute in Japan.
Measurement Items
Salivary cortisol concentration was measured for physiological response. Saliva was collected in a salivette (No. 51.1534, Sarstedt, Numbrecht, Germany) for 2 minutes (Park et al. 2007, Tsunetsugu et al. 2007, Park et al. 2008). The collected saliva was frozen, and the samples were transported to SRL, Inc. for analysis of salivary cortisol concentrations.
The POMS (McNair and Lorr 1964) has been utilized in many previous investigations in an attempt to assess transit, distinct mood states. The POMS brief form consists of 30 adjectives rated on a 0-4 scale that can be consolidated into six affective dimensions: T-A (tension-anxiety), D (depression-dejection), A-H (anger-hostility), V (vigor), F (fatigue), and C (confusion-bewilderment). It has been widely employed in the assessment of mood changes resulting from a variety of interventions due to its responsiveness (Bullington 1990, DiLorenzo et al. 1999). In the case of Japanese subjects, the Japanese edition of the POMS (Yokoyama et al. 1990) was employed for assessing the psychological response.
A Type A behavior pattern (brief questionnaire for the detection of Type A tendencies (Maeda 1985)) was measured for the classification of personality characteristics. Type A behavior pattern is reported to be associated with increased coronary heart disease, and independent of traditional risk factors such as smoking history, high cholesterol and triglyceride levels, and hypertension (Shekelle et al. 1976). Further, the Type A behavior pattern has been defined as a chronic struggle to obtain an unlimited number of goals in the shortest possible time (Friedman and Rosenman 1974). Overt manifestations of Type A behavior pattern comprise competitive achievementstriving, impatience, a sense of time urgency, and free-floating hostility. Individuals exhibiting Type A behavior pattern are twice as likely to incur heart disease compared to individuals who are low on this dimension, or exhibiting Type B behavior pattern (Friedman andRosenman 1974, Jenkins et al. 1974). With a brief questionnaire for the detection of Type A, we categorized the subjects whose scores were over 43.93 as Type A (9 subjects) and the subjects whose scores were under 43.93 as Type B (6 subjects).
Study Design
Two types of experimental drink were used in this study. One type per day was taken in random order. Before taking the experimental drink, we asked the subjects to fill out a POMS questionnaire, and we collected saliva from the subjects.
After taking the experimental drink, the subjects were asked to stay in a waiting room for two hours. While waiting, the subjects answered questions for the Type A behavior pattern. They were allowed to read a newspaper or book. After two hours, we asked the subjects to fill out the POMS questionnaire again, and saliva of the subjects was collected for cortisol measurement.
Experimental Drinks
The The ingested amount of experimental drink varied depending on the body weight (bw) of the subject. The ingested amount of peptide was 0.2 g/kg bw, and the ingested solution was 4 g/ kg bw. The concentration of the eucalyptus leaf essential oil was 0.01% (0.4 mg/kg bw) of the ingested fluid volume; and that of grapefruit and orange essential oil were 0.01% (0.4 mg/kg bw) and 0.001% (0.04 mg/kg bw) of the volume, respectively.
Statistical Analysis
As the significance test, a paired t-test was used for comparison of salivary cortisol concentration measurement before and after ingestion of the experimental drink. For the comparison of POMS scales before and after experimental drink ingestion, a Wilcoxon rank sum test was used. Each measured value is shown as the mean value ± standard deviation.
Results
Fig. 1 shows the change in the salivary cortisol concentration before and two hours after taking the experimental drink in all subjects. The concentration of salivary cortisol before taking Pe + EF was 0.65 ± 0.42 µg/dl; the concentration of salivary cortisol two hours after taking Pep + EF was 0.35 ± 0.26 µg/dl. The concentration of salivary cortisol before taking Pep + EF was significantly decreased (46%, P < 0.01) within two hours after taking Pep + EF. However, there is no statistically significant difference between the observed concentrations before and after taking Pep + G•O.
Fig. 2 shows the POMS scores of the subjects before and two hours after taking the experimental drink in all subjects. There are no statistically significant differences in all POMS scores between before and after taking Pep + EF and Pep + G•O.
We were able to identify a significant difference in the salivary cortisol concentrations that were present before and after the taking Pep + EF by the subjects. We divided the salivary cortisol concentration results into Type A and Type B only for the subjects that took Pep + EF.
Fig. 3 shows the change in the salivary cortisol concentration before and two hours after taking Pep + EF in the Type A subject group and in the Type B subject group. The results for the Type A subject group revealed no statistically significant differences in the salivary cortisol concentrations that were present before and after taking Pep + EF by the subjects. The results for the Type B subject group, however, revealed that the concentration of salivary cortisol decreased significantly (52.7%, P < 0.01) within two hours of the taking Pep + EF by subjects (salivary cortisol concentration before taking Pep + EF: 0.74 ± 0.33 µg/dl; salivary cortisol concentration two hours after taking Pep + EF: 0.39 ± 0.17 µg/dl).
Discussion
The concentration of cortisol, which is a typical stress hormone, decreased significantly within two hours of the taking Pep + EF. However, there were no statistically significant differences in the concentrations of cortisol that were present before and after taking Pep + G•O by the subjects. These results clarify the fact that taking Pep + EF relaxes humans with a greater efficiency than taking Pep + G•O. The relaxation effect of essential oil extracted from wood was reported by Miyazaki and Motohashi (1996) who used Taiwan hinoki essential oil as an odoriferous stimulus to decrease systolic blood pressure. The result of the POMS scores showed no significant change. The salivary cortisol concentration indicated that the subjects were in a relaxed state while the psychological index did not indicate not any difference. Park et al. (2007)'s field test showed the same conclusion previously. In their field test, the subject group that was scheduled to go to the forest showed significantly lower total hemoglobin concentrations in the prefrontal areas than the subject group that was scheduled to go to the city. The lower total hemoglobin concentration means that activity in the cerebral area is calmed down (Hoshi and Tamura 1993). Conversely, their psychological indexes showed no differences.
With the subjects divided into Type A and Type B, the results revealed a significant difference in the cortisol concentrations that were present before and after taking Pep + EF by Type B subjects; however, no significant difference in these concentrations were observed before and after taking Pep + EF by Type A subjects. Type A individuals who were extreme in time urgency, achievement striving, and aggressiveness were roughly twice as likely to develop heart disease as the Type B individuals (Jenkins et al. 1974). Several studies have demonstrated an association between Type A behavior pattern and autonomic nervous activity responsiveness to a variety of mental stressors such as arithmetic testing (Friedman et al. 1975, Dembroski et al. 1978, Manuck et al. 1978, Dembroski et al. 1979, Williams et al. 1982). Generally, the heart rate and blood pressure of Type A individuals, which are indicators of autonomic nervous activity, can easily be enhanced under stress conditions. However, Vermunt et al. (2007) reported that under low mental pressure, cortisol concentration of Type B subjects was higher than that in Type A, and under high mental pressure conditions, they found reverse results. In the present study, we observed a greater decrease in cortisol concentrations in Type B subjects than in Type A subjects when they take Pep + EF. This result is consistent with the report published by Miyazaki and Tsunetugu ( 2005). Their study also revealed that Type B subjects exhibited a more profound change than the Type A subjects, which is consistent with our findings. The underlying mechanism for greater decrease in cortisol concentration in Type B subjects following Pep + EF intake is unknown; however, in the present study, authors can only hypothesize that the cortisol concentration of Type B individuals decreased easily, and that the corresponding concentration in Type A hardly decreased following Pep + EF intake.
The conclusions are as follows: 1) Eucalyptus essential oil has the effect of stress control.2) The relaxation effect of eucalyptus essential oil shows a difference between Type A and Type B; Type B showed a bigger change than Type A.
Fig. 1 .
Fig. 1. Change in the salivary cortisol concentrations of the subjects before and two hours after taking the experimental drink.**: P < 0.01; mean ± SD; n = 15; Pep + EF: Peptide + Eucalyptus flavor; Pep + G•O: Peptide + Grapefruit•orange flavor; Before: Before taking the experimental drink; After: Two hours after taking the experimental drink.
Fig. 2 .
Fig. 2. POMS score of the subjects before and two hours after taking the experimental drink. T-A (tension and anxiety), D (depression and dejection), A-H (anger and hostility), V (vigor), F (fatigue), and C (confusion); mean ± SD; n = 14-15; Pep + EF: Peptide + Eucalyptus flavor; Pep + G•O: Peptide + Grapefruit•orange flavor; Before: Before taking the experimental drink; After: Two hours after taking the experimental drink.
Fig. 3 .
Fig. 3. Change in the salivary cortisol concentrations of the Type A subjects and Type B subjects before and two hours after taking the experimental drink (Pep + EF).**: P < 0.01; mean ± SD; Pep + EF: Peptide + Eucalyptus flavor; n = 9 (Type A group); n = 6 (Type B group); Before: Before taking the experimental drink; After: Two hours after taking the experimental drink.
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Domain: Biology Agricultural and Food Sciences
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Modelling the rheological properties of gruels produced from selected food products from Cameroon
This study investigated the flow behaviour of some food gruels obtained from pre-treated banana, sorghum or sesame flours. The Herschel-Bulkley and the power law models were used to evaluate the consistency and flow indices while an Arrhenius-type equation was used to analyse the effect of temperature on viscosity. The Ostwald de Waele model gave a good fit with experimental data, with pvalues less than 0.01 and R 2 values greater than 0.96 for most of the experiments. The results revealed a pseudoplastic, dilatant and time independent character for the sorghum gruels while the sesame and banana gruels revealed a pseudoplastic and time-dependent character. The effect of temperature on viscosity led to an activation energy and second Arrhenius parameter varying from 964 to 21,070 J.mol -1
INTRODUCTION
In Africa and particularly in Cameroon, several foods are traditionally used in the preparation of infant gruels, especially during weaning. Amongst these foods, sorghum (Matalanis et al., 2009;Onyango et al., 2010;Onyango et al., 2011;Sanoussi et al., 2013;Okoye and Ojobor, 2016;Wanjala et al., 2016), sesame (Arslan et al., 2005;Razavi et al., 2007;Elleuch et al., 2007;Çiftçi et al., 2008;Onabanjo et al., 2009;Ikujenlola, 2014) and banana (Guerrero and Alzamora, 1997;Forster et al., 2003;Abbas et al., 2009;Honfo et al., 2011) are mostly preferred. The three foods are of potential sources of nutrients; carbohydrates for sorghum, proteins and lipids for sesame, minerals and vitamins for bananas. Sorghum and sesame, in addition to being available, are commonly used in the preparation of food supplements for infants. Banana is well appreciated by children and is used by mothers as baby desserts. Moreover, Banana in the form of flour and incorporated into the porridge allows easy storage. Cameroon's annual production of these foods is growing and has reached about 1,187,531 tons for millet/sorghum, 43,963 tons for sesame and 3,182,184 tons for banana in 2010 (Minader, 2012).
Conventionally, before being fed to infants, foods are generally transformed into flour for purée (also called porridge or gruel depending on the consistency) production, whose flow properties are not often mastered. Taking into account the reduced nature and low activity of infant gut (Sanogo, 1994;Giamarchi and Trèche, 1995; *Corresponding author. E-mail(+237) 675 006 441.
Author(s) agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Laurent, 1998;Mouquet et al., 1998), it is important to master the rheological properties of gruels as they significantly affect the mechanism of absorption and digestion (Sanogo, 1994). Several rheological and textural studies on different flour-based products have been presented in the literature. Matalanis et al. (2009) studied the textural and thermal properties of sorghum starch pastes; Onyango et al. (2011) presented the effect of cassava starch on the rheological and crumb properties of sorghum-based batter and bread respectively. Abdelghafor et al. (2015) studied the effects of sorghum flour addition on rheological pasting properties of hard white winter wheat; Mahajan and Gupta (2015) compared the elasticity behaviors of sorghum and wheat flour doughs. Other authors studied the rheological behaviour of banana purée (Guerrero and Alzamora, 1997), suspensions made of banana and wheat flours (Mohamed et al., 2010), semi-solid sesame paste (Abu-Jdayil, 2003;Çiftçi et al., 2008) and sesamebased products (Razavi et al., 2007;Akbulut et al., 2012). However, the results obtained cannot accurately predict the rheological behaviour of infant gruels, due to the difference in consistencies between infant gruels and other flour-based products. Although some rheological studies have been carried out on infant gruels (Mouquet and Trèche, 2001;Trèche and Mouquet, 2008;Gnahé Dago et al., 2009;Nwakego Ayo-Omogie and Ogunsakin, 2013), they involved specific operating conditions with neither models presented to predict the effect of temperature and concentration on the rheological parameters, nor curves to study the time dependency of the gruels.
This study was therefore undertaken to evaluate the flow properties of sorghum, sesame and banana gruels, present models to describe the effects of concentration and temperature on the rheological parameters and study the effect of time on the flow properties.
Raw materials for gruel production
The raw materials considered for gruel production were sorghum (S. bicolor cv. Safrari), white sesame (S. indicum), both obtained from the Institute of Agricultural Research for Development (IRAD) at Maroua (Far-North Region, Cameroon) and ripe banana (Musa acuminata, Cavendish) obtained from a local market in Ngaoundere (Adamawa Region, Cameroon).
Sample preparation
The sorghum and sesame were separately treated as described in the literature (Elmaki et al., 1999;Elkhalifa and Bernhardt, 2010;Tizazu et al., 2010) sorted, winnowed, and washed twice with distilled water. The cleaned grains were soaked in distilled water for 24 h at 22 ± 1°C with the soaking water renewed at the twelfth hour. After soaking, the seeds were then drained using a sieve, spread on a wet tissue that was made to imbibe water every 24 h, and allowed to germinate in the dark over a period of 72 h at 22 ± 1°C (Hahm et al., 2009). The germinated seeds were dried at 40 ± 1°C for 48 h. After cutting-off and discarding their cyanide-containing radicles (Traoré et al., 2003), the dried seeds were ground and sieved to obtain small particle-sized flour (< 1 mm).
The bananas were peeled, cut into slices of about 5.0 ± 0.5 mm thickness and subjected to a combined dewatering-impregnationsoaking process/blanching as described by Jiokap Nono et al. (2002). They were then dried (40 ± 1°C for 72 h) to reduce water activity, limit protein denaturation/browning reactions and then ground to small particle sizes (< 1 mm).
The flours were mixed for 5 min with water at 45°C (quantity depending on the required concentration) and the mixture (1 litre) was placed in a stainless steel pot (2 litres capacity) and cooked with gentle heat using a two burner gas stove for 10 min at atmospheric pressure, after reaching 95°C. The mixture was slowly stirred during cooking using a stainless steel spoon. As mentioned by Trèche (1995), this procedure leads to the production of low viscosity purées and as such most appropriate for infants.
Physicochemical analysis
The waste content (Wc) of cereals was calculated as presented in equation 1, where M (10 g) is the mass of sample and Mg, the mass of good grains in the sample. The cleanliness of the grain was evaluated according to the CODEX (1989) with maximum admissible value for sorghum equal to 8%.
Wc=[(M-Mg)/M]. 100
(1) The rate of germination (Gr) was determined through a germination test using 100 good grains initially soaked in distilled water. The soaked grains were then spread on a wet filter paper, put in a petri dish maintained at 22±1°C and the filter paper was watered every 12 h. The germinated grains were counted each day till stabilization, corresponding to the time of germination. In Equation 2, Ng and N0 are respectively the number of germinated grains at the end of germination and the number of initial good grains.
Gr = (Ng/N0).100 (2) The mass of 1000 grains (Mm) which gives an idea on the quantity of matter and especially starch available in the grains was calculated as shown in Equation 3. Mg is the mass of good grains in the sample, N0 the number of good grains in the sample and MS the mass of dry matter in 100 grams of good grains.
Mm= [(Mg.1000. MS)/(N0 .100] (3) The length of the bananas was measured using a tape while the diameter was obtained using a Mitutoyo digital caliper. The classification of banana ripening was done using a colour index (Aurore et al., 2009).
The water content was determined by the AOAC (1990) method, the ash content by AFNOR (1981) method and the total nitrogen by the Kjeldahl method (AFNOR, 1984); the nitrogen content was multiplied by 6.25 to obtain the protein content; the colorimetric technic of Devani et al. (1989) was used for the chemical dosing and the protein content was determined using the conventional conversion coefficient of 6.25 (AOAC, 1975). The determination of reducing sugars was done by the DNS (3,5 dinitro salicylic acid) colorimetric method of Fisher and Stein (1961) and the total available sugars were determined in the same way after hydrolysis of the sugars by hydrogen sulfate (H2SO4, 1.5 N).
Experimental procedures
Rheological analyses were conducted using a Brookfield DV-III Ultra rheometer (model HBDV-III Ultra, 8534447, Brookfield Engineering Lab., Massachusetts, USA). The disk-shaped spindle HA/HB-2 of 133 mm height; 47.12 mm diameter and 1.65 mm thickness was used.
Three gruels were prepared at different flour concentrations (dry matter): 15, 25 and 35% w/w. After cooking, 500 ml of each was put in a graduated beaker and gently stirred while cooling in a temperature-controlled bath at different temperatures (30, 40, 50 or 60°C). Analyses for each experiment were conducted in triplicate with a scanning speed ranging from 0.01 to 250 rpm. The concentrations of 15, 25 and 35% were chosen in view of the fact that the average dry matter concentration of infant gruel is around 25%. A study of the effect of the concentration shows the cases of dilutions (15%) where swallowing is easy and nutrients are insufficient; and cases of very viscous porridge (35%) where nutrients would be sufficient and swallowing difficult. The usual consumption temperature is around 45°C. While feeding the child, this temperature may drop and reach room temperature. A study of the effect of the temperature up to 60°C allows us to have at least three points which will be used to determine the activation energy.
Determination of rheological parameters
The apparent viscosity was calculated as described by Anonymous (1998) with a dimensionless factor of the spindle equals to 3200/N, where N (rpm) is the rotation speed. For the disk-shaped spindle N°2, the shear rate ̇ (s -1 ) was determined as presented in Equation 4 (Mitschka, 1982): Where (%) and (Pa.s) are respectively the torsion torque and the apparent viscosity for each value of the rotation speed.
The threshold shear stress , the flow index (n) and the consistency index (k) were determined by adjustment, either using the Herschel-Bulkley model (Equation 5) or using the power law model (Equation 6): The model with a better coefficient of determination and p-values less than 0.05 was chosen.
Evaluation of the effect of concentration and temperature
In literature, there is limited information regarding models presenting a correlation between rheological parameters and substrate concentration for the case of gruels. However, for other types of food pastes, an exponential model (Equation 7) has been presented to describe the consistency index behaviour in function of the concentration (Arslan et al., 2005). (7) For each operating temperature, the relationship between consistency index and substrate concentration was studied using Equation 7.
The dependency of apparent viscosity on temperature was evaluated using an Arrhenius-type equation (Equation 8): Where is the absolute temperature in kelvin, in the range 30 -60°C; (Pa.s) is the Arrhenius constant; (J.mol -1 ) is the Taga and Nono 973 activation energy and (J. K -1 .mol - ) is the perfect gas constant. Measurements were conducted at a constant shear rate of 100 rpm (3.8 s -1 ).
Study of the time effect
The effect of time on each gruel was studied at 30°C by monitoring the evolution of gruel viscosity (30% gruel) with time at a constant shear rate of 100 rpm (3.8 s -1 ). The hysteresis curves were obtained by increasing, directly followed by reducing, the rotating speeds. This procedure for the forward and backward curves was done without interruption.
Model fitting and statistical analysis
The fitting of the models was done using the Sigmaplot © Software Version 11 (wpcubed, GmbH, Germany) while the mean comparison was carried out with Duncan's multiple range test (P < 0.05) using IBM SPSS Statistics software version 20.0.0.
Raw material characterization
The average length and diameter of the bananas were respectively 18±1 cm and 3.9±0.2cm while the colour index according to the commercial peel colour scale was located between 6 and 7. This classification of banana ripening was done using a colour index as presented by Aurore et al. (2009). Table 1 presents the physical characteristics and germination rates of sorghum and sesame. According to the CODEX (1989), the percentage of waste obtained for sorghum and sesame are low (1.62 and 0.41% respectively), reflecting the good quality of the grains. The weight of 1000 grains shows that sorghum grains are on average twice as heavy as, and more uniform than sesame grains. The weight of 1000 grains gives an indication of the quantity of matter (mainly starch in the case of cereals) that can be extracted from the different seeds. The observed difference in grain weight could be essentially due to variations in grain dimensions (Purseglove, 1972), growing conditions of the plant or storage conditions after harvest (FAO, 1989). Under the tested experimental conditions (22±1°C and saturated atmosphere), the stabilization phase during germination occurred at the third day with a respective rate of 95 and 99% for sorghum and sesame. It was also observed that, the germination rate of sesame was higher than that of sorghum throughout the germination period. The observed low percentage germination of sesame compared to that of sorghum can be due to the fact that, radicals grow during germination mainly by using carbohydrate reserves and sorghum has more carbohydrates than sesame. The results were different from that of Hahm et al. (2009) who obtained a sesame germination rate greater than 99% after four days of germination at 35°C and in a saturated atmosphere. This observed difference in the germination time could be attributed to differences in germination temperatures and absence of an initial soaking step (24 h soaking at 22°C in our case). In addition, several authors have shown the importance of the soaking step in the efficiency of the germination (Elmaki et al., 1999;Eneje et al., 2004).
Table 2 presents the physico-chemical characteristics of the raw materials and the derived flours. The results show that for sorghum and banana, the carbohydrates occupy more than 77% of the dry matter, followed by proteins (more than 3%), while for sesame, lipids come first (57%) followed by proteins (22%). These results are similar to those reported by Onyango et al. (2011) for sorghum; Forster et al. (2003) and Abbas (2009) for banana and Elleuch et al. (2007), Ciftçi et al. (2008) and Hahm et al. (2009) for sesame. The germination presents a significant effect (P<0.05) on the carbohydrate content of sorghum and on the lipid content of sesame, as also reported by Hahm et al. (2009). Compared to total sugars, soluble sugar contents are much lower for all the biological materials. However, the soluble sugar content was observed to be higher after germination, due to the increase in α-amylases activity (Elkhalifa and Bernhardt, 2010) resulting in a corresponding increase in starch hydrolysis. The osmotic dehydration applied to banana explains the higher soluble sugar content in the dried fruits compared to the fresh fruits (Jiokap Nono et al., 2002). This is advantageous as the presence of soluble sugars in flour destined for infant gruel increases the energy intake of the child (Gerbouin, 1996;Joshi and Verma, 2015). All the treatments applied do not have significant effect on the ash and protein contents; this is important for weaning foods where proteins have an important role (Elkhalifa and Bernhardt, 2010).
Effects of temperature and concentration on the rheological behaviour of the gruels
Sorghum purée at 15 and 25% w/w presented a twophase behaviour, the first at shear rates less than 1.2 s -1 and the second at shear rates greater than 1.2 s -1 , while that of 35% showed a single phase behaviour (Figure 1). Similar to the first phase of 15 and 25% w/w concentrations, the 35% concentration showed a pseudoplastic behaviour (decrease of the viscosity with the spindle's rotation speed) throughout the range of the shear rate. The observed pseudoplastic behaviour could be due to the progressive breakdown of inter-molecular forces resulting from the breakdown of hydrogen bonds that maintains the main structural component of sorghum (Steffe, 1996;Guerrero and Alzamora, 1997). Concerning the dilatant behaviour observed at lower concentrations, it could be accounted for reformation of already broken bonds at high shear rates. These behaviours were observed for the four tested temperatures (30°C, 40°C, 50°C and 60°C). Very few studies have been carried out on the rheological properties of sorghum gruels and those presented in the literature relate to the rheological properties of sorghum starch during gelatinization (Vallons et al., 2009;Matalanis et al., 2009;Onyango et al., 2010;Onyango et al., 2011) but not after cooking as it is the case in the present work.
Unlike sorghum gruels, all the three concentrations of sesame gruels presented a single phase pseudoplastic behaviour throughout the tested range of shear rates (Figure 2). The decrease in resistance to flow could be due to structural deformation, bursting of lipid droplets (main component of sesame) and breakdown of primary and secondary bonds by shear-induced hydrodynamic forces (Arslan et al., 2005). Similar to the case of sorghum, this behaviour was observed for the four tested temperatures. Similar behaviours have been observed by other authors on sesame pastes, mixed or not mixed with other products (Arslan et al., 2005;Razavi et al., 2007;Çifçi et al., 2008;Akbulut et al., 2012) and on oil/water emulsion of protein isolates and sesame oil (Lokumcu Altay and Ak, 2005). Banana gruels showed a similar behaviour to that of sesame gruels (Figure 3). Similar behaviour of banana purée was also observed by Guerrero and Alzamora (1997). The viscosity values for the banana gruels were relatively high, compared to those of sesame and sorghum for each given temperature and concentration. This could be attributed to the differences in composition between the products (Table 2). For all the tested concentrations of the banana gruel, the viscosity as well as the consistency index globally decreased with increase in temperature, which can be attributed to the rupture of intermolecular bonds by thermal energy, leading to a decrease in the torque at a given speed of rotation. Ahmed and Ramaswamy (2007) also observed a decrease of the consistency index with temperature. Concerning the effects of concentration, the viscosity of the banana gruels increased with concentration while the flow behaviour index decreased, explained by the increase in the solid matter with concentration. Similar observations were obtained by Mohamed et al. (2010). Sorghum and sesame gruels presented similar evolution of viscosity, consistency and flow behaviour indices with temperature and concentration. However, the pseudo-stationary viscosity of banana gruels increased by a factor relatively high compared to those of sorghum and sesame gruels, as the concentration increased from 15 to 25 to 35%. Moreover, the flow index values obtained with sorghum gruels at 35% are due to the fact that this concentration (unlike the sorghum gruels at 15 and 25%) didn't present a two-phase behaviour.
Mathematical models for predicting the rheological parameters
The flow curves of all the three gruels showed a good fit with the Ostwald de Waele model (p-values less than 0.01), while the Herschel-Bulkley model was less suitable as it produced negative shear-stress thresholds. The corresponding values of the flow and consistency indices as well as the model statistical parameters are presented in Table 3. However, in the case of banana purée, the values of the flow index (0.18 < n < 0.82) were relatively higher than those presented in the literature (Guerrero and Alzamora, 1997) and this could be due to differences in raw material composition and treatment procedures. For each substrate concentration, the values of the consistency index for banana gruels were relatively high compared to those of sorghum and sesame gruels. The effects of concentration on the consistency index were conveniently described by the exponential model (Equation 7), with R² values ranging from 0.991 to 0.999 (Table 4) and Figure 4 Figure 5. Test of the time sensitivity of sorghum, sesame and banana gruels (at 30% w/w each) at 100 rpm (3.8 s -1 ) and 30°C.
followed the Arrhenius model (Table 5) with adjusted R² values ranging from 0.820 to 0.990. The value of the constant (A) varied with substrate, ranging from 514 to 1,406 Pa.s for sorghum purée, 158 to 1373 Pa.s for sesame purée, and 81 to 34,484 Pa.s for banana purée. This constant showed no trend regarding its variation with substrate concentration in the three different gruels. These differences could be attributed to the physico-chemical nature of the gruel, mainly their lipid and protein contents for sesame, and carbohydrate content for sorghum and banana; as well as their ease of forming hydrogen bonds. E a , which measures the sensitivity of the purée viscosities to temperature, was highest for sesame purée (13,225 -21,070 J.mol -1 ), followed by banana purée (10,233 -18,825 J.mol -1 ) and then by sorghum purée (964 -14,144 J.mol -1 ). The value of Ea was observed to increase with flour concentration for all the purées. These results show that the energy required for the fluid to flow increases with concentration (Table 5) and this could be explained by the fact that the number of inter-molecular bonds involved in maintaining the structure of a substrate in a milieu increases as the concentration of the milieu increases. This trend was equally observed by Arslan et al. (2005) on sesame paste.
Effect of time on the rheological behaviour of the gruels
At a constant share rate of 3.8 s -1 , the shear stress decreased progressively with time but showed no significant drop for sorghum and banana gruels (Figure 5). To confirm the time dependency of the gruels, a "loop test" was conducted. The forward-backward curves of the gruels (Figure 6) indicate the absence of a hysteresis loop for sorghum (Figure 6a), confirming thereby the timeindependency of sorghum gruel. A hysteresis loop existed for sesame and banana gruels, with ahigher amplitude for the former than for the latter (Figures 6b and c). Lokumcu Altay and Ak (2005) also observed a hysteresis loop on tahin and attributed this behaviour to the thixotropic nature of the substrate. Abu-Jdayil (2003) and Habibi-Najafi and Alaei (2006) also noticed a thixotropic character on sesame-based products. Gruels prepared from infant flours are rich in starchy and protein foods and have a viscosity that increases very rapidly as a function of their dry matter concentration. This makes the gruels difficult to swallow, digest and absorb by children due to reduced activity and capacity of their organs. The rheological study of each of the constituents could make it possible to orient the formulated mixture of these foods, and also to envisage fluidification treatments for the manufacture of infant flour. The gruels derived from these flours should have rheological properties which facilitate the ingestion by the children, while taking into account the nutritional aspects.
Conclusion
This study evaluated the rheological properties of infant gruels produced from sorghum, sesame or banana as base constituents. The sorghum gruels showed dilatant properties at high shear rates and pseudoplastic properties at low shear rates, while the sesame and banana gruels were pseudoplastic fluids throughout the range of shear rates. A good correlation was obtained between the consistency coefficient and concentration for each temperature. For all the gruels, the viscosity reduced with temperature and increased with concentration. The effect of temperature on the gruels' viscosity followed the Arrhenius law. The activation energies were highest for sesame gruels, followed by banana gruels and then sorghum gruels. The later presented a timeindependent behaviour, whereas banana and sesame gruels had a time-dependent behaviour. The results of this study could be exploited for the formulation and improvement of derived products, as well as for the dimensioning of equipment and for the conception of production units.
CONFLICT OF INTERESTS
The authors have not declared any conflict of ); T, Absolute temperature (K).
Figure 1 .
Figure 1. Effects of the rotation speed on viscosity of sorghum gruels at different concentrations and temperatures.
Figure 2 .
Figure 2. Effects of the rotation speed on the viscosity of sesame gruels at different concentrations and temperatures.
Figure 1 :
Figure 1: Effects of the rotation speed on viscosity of sorghum gruels at different concentrations and temperatures.
Figure 2 :Figure 3 .
Figure 2: Effects of the rotation speed on the viscosity of sesame gruels at different concentrations and temperatures.
Figure 3 :
Figure 3: Effects of the rotation speed on the viscosity of banana gruels at different concentrations and temperatures.
Figure 5 :Figure 6 .
Figure 5: Test of the time sensitivity of sorghum, sesame and banana gruels (at 30% w/w each) at 100 rpm (3
Table 1 .
Percentage of waste, weight of 1000 grains and germination rate of sorghum and sesame.
Table 2 .
Proximate analysis of the raw materials and corresponding flours.
Table 3 .
Parameters of the power law model ( ̇ ) at different temperatures and substrate concentrations. For each substrate and on the same column, data with the same superscript letter are not significantly different according to the Duncan test (P<0.05).
Table 4 .
Effects of substrate concentration on the consistency index of the gruels at different temperatures: Model parameters for the equation: .
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Domain: Biology Agricultural and Food Sciences
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Effects of severity of apical shoot harvest on growth and tuber yield of two sweet potatoes varieties
Leaf harvesting of sweet potato during vegetative stage is common in most parts of Liberia. There is little information on the effects of severity of apical shoot harvesting on tuber yield of sweet potato. Experiments were conducted in 2017 at the Federal University of Agriculture, Abeokuta to determine the effects of severity of apical shoots harvest on growth and tuber yields. Experiment consisted of two varieties (SHABA and SPK-004) and three levels of cutting severity; no cutting, cutting of 15 and 30 cm long apical shoots at 4-weeks. Treatments were arranged in split plot with variety as the main plot and cutting severity as sub-plot arranged in (RCBD) with three replications. Data were collected on vine length, number of leaves per plant, number of branches per plant, leaf area, and leaf area index, fresh and dry apical shoots weight, tuber, unmarketable yield, marketable yield and total number of tuber. Data collected on growth, yield parameters were subjected to analysis of variance and mean values separated using standard error at (p<0.05). In cutting severity, vegetative growth and tuber yield of SHABA were significantly higher than those of SPK-004. Cutting at 15 cm long apical shoots gave higher total tuber yields in SHABA than SPK-004. Cutting at 30 cm long apical shoots increased fresh apical shoot weigh in SPK-004 than in SHABA. For SHABA and SPK-004 had more tuber weight than 30 cm long apical shoots. Therefore, sweet potatoes whose apical shoot was cut at 15cm long for 4 weeks are recommended.
INTRODUCTION
The herbaceous dicot sweet potato plant (Ipomoea batatas Lam.) is a native of tropical and subtropical region of America and belongs to the Convolvulaceae family. Many parts of the plant are edible, including leaves, roots, and vines, and varieties exist with a wide range of skin and flesh colour, from white to yelloworange and deep purple (CIP,1999). In Sub-Saharan Africa, sweet potato is the third most important root (tuber) crop after cassava (Manihot esculenta) and yam (Dioscorea spp) (Ewell and Mutuura, 1994). This crop plays an important role in household food security and income generation among farmers and supplies substantial amount of nutritional diets that can greatly reduce risk of heart disease, stroke, and even cancer (Carey et al., 1999;Helen, 2012). It yields about 60% industrial starch in Japan and also used as a sweetener in local drinks in Nigeria (Collins, 1993;Agbo and Ene, 1994). In some countries such as Ghana and Liberia, vine tops are used as vegetables and dry forage during scarce grazing periods (Abindin, 2004), Leaf harvesting has been reported to have some detrimental effect on tuberous root yield of sweet potato. Dahniya
Pre cropping and soil analysis
Soil sample (0-20 cm) was randomly taken before planting and bulked to form a composite sample. This sample was air dried pass through 2 mm sieve and laboratory for both physical and chemical analysis to determine soil texture and soil fertility
Experimental design and planting methods
The entire plot was measured as 28 m 2 length × 19 m 2 widght giving an area of 532 m 2 and the plots size was 5 m × 3.5 m each. There was walk way of 1 m each between two plots; 1m walk way was also maintained around the perimeter of the entire plot. Five ridges were constructed in each plot with dimension of 35 cm ridge. Planting was done on the ridges in each plot at an inter-row spacing of 0.5 m and intra-row spacing of 1 m. Thus, there were 7 plants on each ridge and 35 plants per plot. This gave a plant population of 630 plants (equivalent to 20,000 plants per hectare). There were two sweet potatoes varieties (SHABA and SPK-004) and three levels of cutting severity, (no cutting, cutting 15 and 30 cm long apical shoots at 4 weeks interval); the experiment was arranged in split-plot with variety as the main plot and cutting severity as subplot,in romdonmized complete block designed (RCBD). Weeding was done manually with hoe to minimize weeds infestation to the sweet potato plants. Four weedings were done at 4,8,12,16 (WAP). Earthling up was done on all ridges to establish a desirable soil bulk for root expansion and moisture conservation.
Data collection
Data were randomly collected from 5 plants in the three mid-rows on the following parameters.
Vine length (cm)
Vines length was determined in centimeter using rope to tread from the base of the plant to the tip of five selected plants from the three middle inter rows at 5,9,13,17 (WAP).
Number of leaves per plants
The total number of leaves per plant was counted from 5, 9, 13, 17 (WAP) and recorded as sample from the field.
Number of main branches per plant
The number of main branches per plant was counted and recorded at 5, 9, 13, 17 WAP.
Fresh apical shoot weight
The vine fresh apical shoots were weighed on an electronic balance scale and recorded in gram.
Dry apical shoots weight per plant in gram
The total weight obtained from the apical shoot was oven-dried in the laboratory at 70°C and expressed in gram.
Leaf area per plant (LA) (cm2)
Leaf area was obtained by using meter rule to measure the length and breadth of the leaf. 90 samples of leaves of different sizes which were traced on graph sheet. The length and breadth measured were regressed on the leaf area as derived by Olasantan and Salau (2008 Where Y = leaf area, X = leaf breadth. The Leaf Area Index (LAI) was calculated as leaf areas of all plants divided by number of plants/ plot size.
Number of tuber per plant
The number of tuber harvested per plant was counted and recorded.
Fresh tubers weight per plant during harvest
The fresh tubers' weight was recorded and measured in kilogram and expressed in metric tons per hectare
Numbers of marketable tubers per plant
The number of marketable fresh tuber weight per plant was sampled and sorted for tuber sizes; tuber above 1.5 cm was considered as marketable tuber; disease free tuber, and non-rotten tubers were considered and recorded
Number of unmarketable tubers
The numbers of unmarketable tubers, fresh weight per plant were sorted out; signs of being damaged by disease tubers, rough skin tubers and those eaten by rats and below 1.5 cm were recorded as unmarketable yield and expressed in tons per hectare. The total tuber yield per plot was measured on the field in kilogram and expressed in ton per hectare.
Tuber dry weight per hectare
The tuber weighed were oven dried to constant weight of 70°C and recorded.
Statistical analysis
All sweet potato plants were harvested at 6-7 months and number of tubers and their weight, and fresh tuber yield per hectare were recorded. Statistical analyses were conducted using the analyses of variance procedure according to spilt-plot design of statistical analyses system institute (1990). Treatment means were presented with the associated standard error of the means (S. E.) at 5% probability.
Weather data during the study in 2017 at Alabata Road in Abeokuta
Total rainfall at the Federal University of Agriculture, Abeokuta was 894 mm in 2017. The total rainfall during the period of the experiment (April-November) was 843.3 mm in 2017 (Table 1). Higher rainfall was recorded in July (156.1 mm) while the lowest rainfall was recorded in September (50 mm). Minimum temperature was between 12.12 and 22.8°C, from April to November 2017, while maximum temperature was between 32.4 and 33.47°C, relative humidity was 77.38 in April but decreased to 63.24% in November. Higher sunshine rate per hour during the period of the experiment was 5.64 h recorded in April 2017 and the lowest was 1.28 h which was recorded in August.
Experimental site of soil analysis
The soil used for the experiment was sandy loam, slightly acidic (pH 5.6). The soil was moderate in nitrogen content (0.15 %) but very high in Phosphorus and Potassium contents (40.36 respectively) ( Table 2).
Vine length of sweet potato as affected by variety and cutting severity
The vines of SPK-004 was longer than that of SHABA at 9 WAP, while from 9-17 WAP, the two vines were similar ( Figure 1). The control plant was significantly (P ≤0.05) longer than those plants whose apical shoots were cut at 15 and 30 cm long throughout the period of the experiment ( Figure 2). Vine length of plant whose apical shoots were cut at 30 cm long were similar to plants whose apical shoot were cut at 15 cm long at 5 and 9 WAP. At 13 and 17 WAP, however sweet potato plants whose apical shoot was removed at 30 cm long was longer than sweet potato plant whose apical shoot was cut at 15 cm long. The vine of SPK-004 control plant was significantly (P≤0.05) longer than SPK-004 whose apical shoot was removed at 15 cm long and in (Figure 3). At 5 and 17 WAP, the vine length of SPK-004 control plant was significantly (P≤0.05) longer than sweet potato plant whose apical shoot was cut 30 cm long at 5-9 WAP. However at 13 and 17 WAP SPK-004 control plant whose apical shoot was removed at 30 cm long was similar. At 5 WAP SHABA control plant whose apical shoots were removed at 15 and 30 cm long were at similar, while at 9 and 17 WAP SHABA control plant was significantly (P ≤ 0.05) longer than sweet potato whose apical shoot was removed at 30 cm long; but at 13 WAP control plant and SHABA whose apical shoot was cut at 15 cm long have similar vine length. At 5 to 17 WAP SHABA plant whose apical shoots were removed at 15 and 30 cm long produced similar vines ( Figure 3).
Number of leaves of two sweet potato varieties (SHABA and SPK-004) as affected by cutting severity
The result in Figure 4 shows the varietal effect on number of leaves of SHABA and SPK-004. At 5-17 WAP the number of leaves on SPK-004 plant was significantly (P≤0.05) highest than the number of leaves on SHABA; however both varieties have similar number of leaves at 9 WAP, whereas at 13 WAP SHABA produced higher number of leaves than SPK-004. There was a gradual increase in the production of leaves of two sweet potato with respect to time. At 5 to 17 WAP, number of leaves of control plant was significantly (p ≤ 0.05) higher than sweet potato plant whose apical shoots were cut at both 15 and 30cm long. However the number of leaves on sweet potato plant whose apical shoots were removed at 15 and 30cm long were also similar at 5-9 WAP; although at 13 -17 WAP, the number of leaves on sweet potato plant whose apical shoot were removed at 15cm long were more than sweet potato plant whose apical shoots were removed at 30 cm long ( Figure 5). At 5-17 (WAP), leaves produced by SHABA control plant were more than those produced by plant whose apical shoots were removed at both 15 and 30 cm long. However, for SPK-004 number of leaves produced by control plant was similar with that of plant whose apical shoots were removed at both 15 and 30 cm long at 5 WAP; at 13 WAP control sweet potato plant and plant whose apical shoot was removed at 15 cm long had more number of leaves than sweet potato whose apical shoot was removed at 30 cm long. However, control plant, cutting at 15 and 30cm long have similar vine length at 17 WAP .
Varietal effects on number of branches of two sweet potato variety
SPK-004 produced more branches than SHABA from 5-13 WAP, but from 15-17 there was significant (P≤ 0.05) increase in number of branches produced by SHABA as compared to SPK-004 ( Figure 6). At 5 WAP, control plant produced higher number of branches than sweet potato plant whose apical shoots were removed at 15 and 30 cm long ( Figure 7); however both sweet potato plant whose apical shoots were cut at 15 and 30 cm long were similar, whereas at 9 -17 WAP control plant had significantly (P≤ 0.05) highest number of branches than sweet potato plant whose apical shoots were removed at 30 cm long. At 9 WAP control plant had significantly (P≤ 0.05) higher number of branches than sweet potato plant whose apical shoot was cut at 15 cm long, whereas at 13 -17 WAP control plant produced more number of branches than sweet potato plant whose apical shoot was removed at 15 cm long, although at 17 WAP sweet potato plant whose apical shoot was removed at 15cm long produced higher number of branches than sweet potato plant whose apical shoot was removed at 30 cm long. (Figure 8).
At 5-13 WAP SHABA control plant and cutting severity at 15cm long had similar number of branches, however at 5-17 WAP SHABA control plant significantly (P≤ 0.05) produced higher number of branches than sweet potato plant whose apical shoot was removed at 30cm long ( Figure 9); at 9 WAP control plant produced higher number of branches than sweet potato plant whose apical shoot was removed at 15 cm long. At 13 WAP cutting severity at 15 cm long had higher number of branches than sweet potato plant whose apical shoot was removed at 30 cm long and control plant. At 5-9 WAP SPK-004 control plant had similar number of branches with sweet potato plant whose apical shoots were cut at 15 and 30 cm long and at 13 WAP SPK-004 control plant produced higher number of branches than sweet potato plant whose apical shoots were removed at 30 and 15 cm long; although at 17 WAP sweet potato plant whose apical was cut at 15cm long significantly (P≤ 0.95) produced higher number of branches than sweet potato plant whose apical shoot was removed at 30 cm long and control plant ( Figure 9).
Leaf area index of two sweet potato as affected by variety and cutting severity
The leaf area index of SHABA had more leaf index cover than SPK-004 at 5 WAP; however at 9 -17 WAP SHABA plant produced significantly (P≤ 0.05) more leaf area index cover than SPK-004 plant ( Figure 10). The leaf area index of two sweet potato as affected by cutting severity (Figure 11). Between 9 and 17 WAP, the leaf area index of sweet potato with control plant significantly (P≤ 0.05) produced higher leaves area index cover than that of plant whose apical shoots were removed at 15 and 30 cm long. However, there was no significant difference between sweet potato plants whose apical shoots were removed at 15 and 30 cm long at 5-17 WAP ( Figure 11). Between 5-17 WAP, the leaf area index of SHABA control plant produced higher leaf area index cover than SHABA plant whose apical shoots were removed at 15 and 30 cm long (Figure 12). At 5-13 WAP, sweet potato plants whose apical shoot was removed at 30 cm long produced more leaf area index cover than SHABA control plant, whereas at 17 WAP SHABA control plant produced higher leaf area index cover than that of sweet potato plant whose apical shoots were cut at 30 cm long. At 5-9 WAP control plant and sweet potato plant whose apical shoots were removed at 15 and 30 cm long have similar leaf area index cover and at 13 WAP SPK-004 control plant and sweet potato plant whose apical shoots were cut at 15 cm long were similar. But both control plant and sweet potato plant whose apical shoot was cut at 15 cm long produced more leaf area index cover than SPK-004 whose apical shoot was removed at 30 cm. However at 17 WAP sweet potato whose apical shoot was cut at 15 cm long produced more leaf area index cover than control plant and sweet potato whose apical shoot was cut at 30 cm long (Figure 1).
The fresh apical shoot weight as affected by cutting severity
Fresh apical weight of SHABA was higher than that of SPK-004 at 9 WAP, but at 17 WAP, SPK-004, fresh apical shoots decreased at 21 WAP ( Figure 13). Between 5 to 9 WAP, the fresh apical shoots weight of sweet potato plant whose apical shoots were removed at 15 -30cm long was similar except at 13 to 21 WAP ( Figure 14). Sweet potato plants whose apical shoot was removed at 30cm long produced significantly (P≤ 0.05) higher fresh shoots weight than sweet potato plant whose apical shoot was removed at 15 cm long ( Figure 14). Between 5-19 WAP, SHABA plant whose apical shoots were removed at 15 cm long produced significantly (P≤ 0.05) higher fresh apical shoot weight than SHABA whose apical shoots were removed at 15 cm long ( Figure 15). At 9, 13 and 19 WAP, SPK-004 plant whose apical shoots were removed at 30 cm long produced significantly more fresh apical shoot weight than SPK-004 plant whose apical shoot was cut at 15cm long. However, at 5 and 9 WAP, SPK-004 plant whose apical shoot was cut at 15 and 30 cm long produced similar fresh apical shoot weight. Whereas at 13, 17 and 19 WAP, SPK-004 plant whose apical shoot was removed at 30cm long produced more apical shoot weight than SPK-004 plant whose apical shoot was removed at 1m long ( Figure 15).
Dry apical shoot weight of two sweet potato as affected by variety and cutting severity
At 5 WAP, SPK-004 produced higher dry apical shoots weight than SHABA; however at 9 WAP SHABA and SPK-004 produced similar dry apical weight. While at 13-21 WAP SHABA plant produced higher dry apical weight than SPK-004 plant (Figure 16). At 5, 9 and 21 WAP, sweet potato whose apical shoot was removed at 30 cm long produced more dry apical shoot weight than sweet potato plant whose apical shoot was cut at 15 cm long; however at 13 -17 WAP sweet potato plant whose apical shoot was cut at 30 cm long produced higher apical shoot weight than sweet potato plant whose apical shoot was removed at 15 cm long (Figure 17). At 5, 9 and 21 WAP SHABA plant whose apical shoots was cut at 30cm long produced similar dry apical shoot weight, whereas, at 13 and 17 WAP, SHABA plant whose apical shoot was cut at 30cm long produced significantly (P≤ 0.05) more apical shoot than SHANA plant whose apical shoot was removed at 15 cm long (Figure 18). At 5-9 WAP SPK-004 whose apical shoots were cut at 15 and 30cm long produced similar dry apical shoots. However at 13, 17 and 21 WAP SPK-004 plant whose apical shoot was cut at 30cm long produced drier apical shoots weight than 15 cm long (Figure 18) .
Total fresh and dry apical shoot weight of sweet potato as affected by variety and cutting severity
The interaction between variety and cutting severity showed that SHABA whose apical shoots were cut at 30 cm long had significantly (P≤0.05) higher total fresh weight than SHABA cut at 15cm long. SPK-004 whose apical shoots were cut at 30cm had significantly (P≤0.05) higher total fresh shoots weight than SHABA cut at 15cm long (Table 3).
SHABA whose apical shoots was removed at 30cm long had significantly (P≤0.05) higher total dry apical shoot weight than SHABA removed at 15 cm. However, SPK-004 whose apical shoots were cut at 15 and 30 cm long was similar with respect to total dry apical shoot weight. Total dry apical shoot weight of SHABA was significantly (P≤0.05) higher than that of SPK-004. Total fresh and dry apical shoot weight of sweet potato plant whose apical shoots was cut at 30 cm long was significantly higher than those cut at 15 cm long.
Unmarketable, marketable and total tuber weight of sweet potato as affected by variety and cutting severity
The interaction between variety and cutting severity significantly (P≤ 0.05) affected unmarketable, marketable and total tuber weight of sweet potato (Table 4). SHABA plant cut at 15 and 30 cm long produced similar unmarketable and marketable tuber weight. However, SHABA plant whose apical shoot was harvested at 15 cm long produced significanly (P ≤0.5) highest total tuber weight, followed by SHABA control plant and the least was recorded in SHABA plant whose apical shoot was removed at 30 cm long. The SPK-004 plant without cutting produced highest unmarketable tuber weight than SHABA whose apical shoots were removed at 15 and 30 cm long. SPK-004 plant without cutting and those plants whose apical shoots were removed at 15 and 30 cm long produced similar marketable and total tuber weight. The effects of variety on unmarketable, marketable and total tuber weight of sweet potato was not significant at (P≤t0.05). However, both SHABA and SPK-004 plant produced similar unmarketable, marketable and total tuber weight. The weight of unmarketable tuber in sweet potato as affected by cutting severity was significant. Table 4 shows that the unmarketable tuber weight of the sweet potato control plant was significantly (P ≤ 0.05) more than those plant whose apical shoot was removed at 30 cm, while the weight of marketable tuber and total tuber weight were similar in control plant, at 15 and 30 cm long (Table 4) Unmarketable, marketable and total tuber number of sweet potato as affected by cutting severity There was no significant difference between SHABA without cutting and those whose apical shoots were removed at 15 and 30 cm long with respect to unmarketable, marketable and total tuber number (Table 5). Similarly SPK-004 without cutting and those whose apical shoots were cut at 15 and 30 cm long had similar unmarketable, marketable and total tuber number. SHABA produced significantly (P≤0.05) similar number of unmarketable and marketable tubers than SPK-004. But total tuber, number of SHABA was more than that of SPK-004 (Table 5). The number of unmarketable, marketable and total tuber of sweet potato as affected by cutting severity is shown in Table 5. The number of unmarketable and total marketable tubers were similar in sweet potato without cutting and those whose apical shoots were cut at 15 and 30 cm long.
Unmarketable, marketable and total tuber yield of sweet potato as affected by cutting severity
The interaction between variety and cutting severity as it affects unmarketable, marketable and total tuber yield of sweet potato is shown in Table 5. Yield of unmarketable and marketable tuber of SHABA without cutting and those whose apical shoots were removed at 15 and 30 cm long were similar. However, SHABA whose apical shoots were removed at 15cm long produed more tuber yield than SHABA without cutting and SHABA whose apical shoots were removed at 30cm long. SPK-004 without cutting had significantly highest unmarketable tuber yield than SPK-004 plant whose apical shoots were removed at 15 and 30 cm long. SPK-004 plant without cutting and those whose apical shoots were removed at 15 and 30 cm long were similar in their marketable and total tuber yield.
Variety did not significantly influence unmarketable, marketable and total tuber yield sweet potato yield of unmarketable, marketable and total tuber yield of SHABA was similar compared to that of SPK-004. The yield of unmarketable, marketable and total tuber of sweet potato as affected by cutting severity is shown in Table 6. The control plant produced more marketable tuber compared to plant whose apical shoots were removed at both 15 cm and 30 cm long; however, unmarketable tuber yield was more in apical shoot cut at 15 cm long than apical shoot cut at 30 cm long. There was no significant difference between control plant and sweet potato whose apical shoots were cut at 15 and 30 cm long in marketable and total tuber yield.
Correlations between yield parameter
Total marketable weight was significantly correlated with total tuber weight; total tuber weight positively correlated with total unmarketable weight and total dry apical shoot weight was significantly correlated with total fresh apical shoot weight under both treatments of cutting severity and frequency of cutting (Table 7).
DISCUSSION
There was high rainfall in May which was maximum in July, while high amount was also recorded in October after a period of low rainfall in August and September. This indicates tri-modal pattern of rainfall. This was against the bi-modal pattern of rainfall reported by Adejuwon and Odekunle (2006). Sweet potato crop grows on negligible soils with partial inputs. It has the capability to tolerate harsh soil and climatic conditions and yet give satisfactory yield. It grows well in fertile and high organic matter, well-drained, light, and medium textured soils. The relatively low fertility status of the soil of the study location is a peculiar characteristic of most soil in South-western Nigeria. This low fertility status could be attributed to the degraded state of most tropical soil Agboola (1973) wrote about some of the farmers in the south who have refused to apply fertilizer to any farmland used in yam production because they have noticed that using fertilizer to grow white yam changes the colour of the yam to brown during pounding. Also this could be as a result of soil erosion and nutrient mining as a result of continuous cropping.
The results obtained in this experiment showed that variety does not influence vine length and number of leaves of sweet potato. However, SHABA variety produced significant higher (P ≤ 0.05%) leaf area index. Severity of cutting affected the growth of sweet potato. Sweet potato without cutting had longer vine length, number of leaves, branches and leaf area and leaf area index. This influence of cutting severity on sweet potato shows that harvesting of sweet potato leaves affect growth. This was in line with result by Olorunnisomo (2007) who reported that leaf harvest intensity influences the branching intensity in sweet potato crop. Better growth performance of SHABA variety cut at 15cm could be as a result of the better ability of the variety response to cutting severity . The dry matter yield and total yield of sweet potato was enhanced by variety. Better performance obtained in the SHABA variety could be attributed to the efficiency of the variety in utilization of photosynthates and soil nutrients. Cutting 15cm long apical shoot generally gave higher total tuber yield than cutting at 30cm long apical shoot at 4 weeks. However, better performance was recorded in SHABA variety cut at 15 cm than SPK=004. This indicated that minimal vine cutting in sweet potato does not adversely affect yield of the variety.
Higher nutrient content was recorded in the SPK-004 than SHABA. The higher nutrient content in the less vigorous variety could be as a result of less dilution effect with respect to moisture accumulation by the vigorous variety. Cutting severity at 30 cm had higher nutrient content and the response of each variety to severity of cutting indicated both varieties cut at 30 cm had higher nutrient content. Harvesting of forage at regular intervals is a potent agronomic tool used in maintaining a balance between yield and quality in forage species (Hong et al., 2003). The result obtained in this study on effect of variety on growth of sweet potato shows that variety affects vine length, number of leaves, number of branches and leaf area index. SHABA variety was more vigorous than SPK-004 vine length and Leaf area index.
Conclusion
Severity of apical shoot harvest had effects on the growth of the sweet potato varieties with the best cutting severity being the control with respect to vine length and number of leaves while cutting at 15 cm was the best for number of branches and leaf area. However, cutting at 30 cm increased shoot yield while cutting at 15 cm increased root yield and nutritional value. Furthermore, cutting severity had effect on the growth performance of the sweet potato varieties; SHABA had the best growth performance with respect to vine length, number of branches and leaf area while variety SPK-004 had the best growth performance with respect to the number of leaves. In the study, variety SPK-004 performed better than SHABA with respect to fresh shoot yield while SHABA performed better than SPK-004 with respect to total tuber yield.
Recommendation
Cutting sweet potato apical shoots at 30 cm is recommended for cultivation intended for optimum shoot production while cutting at 15 cm is recommended if the root yield is of interest. Variety SHABA is recommended for production intended for optimum tuber yield while variety SPK-004 is recommended as shoot yield of interest. A repeat of this study is recommended for the purpose of validation, especially in regions where both sweet potato shoot and root production are of significant economic importance.
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Domain: Biology Agricultural and Food Sciences
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Development of liquid rhizobial inoculants and pre-inoculation of alfalfa seeds
Application of liquid microbial inoculants on legume seeds is a sustainable agricultural practice that can improve plant nutrient uptake and increase crop productivity. Inoculants should provide long-term survival of rhizobia in the final product and after application, to legume seeds. Ten different medium formulations of microbial inoculants were examined (yeast mannitol broth with the addition of agar, sodium-alginate, calcium chloride, glycerol or ferric chloride and combinations thereof) for the survival of the efficient nitrogen-fixing rhizobium, Sinorhizobium (Ensifer) meliloti L3Si strain. The most suitable liquid inoculant for survival of L3Si during a storage time of 150 days was the medium formulation containing glycerol in combination with agar or sodium-alginate. Alfalfa seeds were pre-inoculated with four formulations (yeast mannitol broth (YMB), YMB with agar (1 g L-1), YMB with 1 or 5 g L-1 sodium-alginate) for up to three months. Seeds pre-inoculated and stored for one month produced successful alfalfa plants. The nitrogen content in alfalfa obtained from pre-inoculated seeds one month before sowing was adequate and ranged from 3.72-4.19%. Using S. meliloti-based liquid inoculants for alfalfa and application of the pre-inoculation technique can increase the quality of alfalfa crops and reduce cultivation cost.
INTRODUCTION
Current agricultural practice worldwide gravitates towards environmental sustainability based on the use of microbiological inoculants instead of mineral fertilizers and pesticides [1]. Microbiological fertilizers containing nitrogen-fixing bacteria have been proven to be the cheapest source of nitrogen, especially for leguminous plants. This type of biofertilizer has the longest history of use in agriculture [2]. Nitrogen-fixing bacteria and other microorganisms that are capable of converting atmospheric nitrogen into compounds usable by plants are called diazotrophs, and the process is known as biological nitrogen fixation (BNF) [3]. Diazotrophs are either free-living (non-symbiotic) or symbiotic. Non-symbiotic microorganisms fix nitrogen as freeliving organisms in the soil (Azotobacter, Beijerinckia, Clostridium, etc.). Symbiotic nitrogen-fixing bacteria, collectively called rhizobia, can establish symbiosis with plant roots from the family of Leguminosae and fix atmospheric nitrogen to the benefit of the plant [4]. The inoculation of leguminous seeds is a wellknown procedure in many agricultural systems and is a simple and beneficial way of introducing effective rhizobia to the rhizosphere of legumes. By establishing a symbiotic relationship and performing nitrogen fixation, rhizobia improve the nitrogen content and crop yield [5][6][7][8].
In the industry of microbiological inoculants, peat is the most widely-used carrier due to its good properties that successfully support rhizobial growth and survival. It can maintain high numbers of rhizobia during storage at temperatures up to 28°C [9]. However, problems with the use of peat include high sterilization costs, a significant amount of processing (drying, milling), difficulty in large-scale field application, as well as the inaccessible dumpsites of true peat in certain areas. These problems have stimulated the development and application of liquid inoculant formulations to solve the problems associated with the application and processing of solid inoculants [1,10].
For field applications, the liquid inoculant is required in an appropriate formulation and the viability of the inoculant for a certain prolonged time is important for commercialization of the technology [11,12].
Liquid inoculant formulations can include single or numerous rhizobia cultures amended with agents that promote cell survival in the commercial products during storage and after application to seed or soil. Legume seed inoculation can occur prior to sowing or prior to seed sale (pre-inoculation) [13]. Thus, the prolonged survival of rhizobia on pre-inoculated seeds should be provided. This is a gentle technique and works under ambient conditions when cell damage is reduced to a minimum [14]. Pre-inoculated seeds, prepared for days or months in advance of sowing [15,16], should have similar properties to the seeds treated prior to sowing. Application of pre-inoculated seeds contributes to simplification of the sowing process for farmers in the field.
Essentially, liquid inoculants are microbial cultures or suspensions, mainly in water, but also in mineral or organic oils, which are amended with various substances. The roles of applied additives is to improve inoculant quality, such as increased stickiness, stabilization, and surfactant and dispersal abilities [11,17,18], as well as to provide a protective niche for microorganisms and ensure viability over a prolonged period of storage [14]. Applied supplements should also confer survival of rhizobia cells on pre-inoculated seeds in stressful conditions during storage [1,9]. Advantage should be given to nontoxic and biodegradable polymers in the soil [1]. Polymers soluble in the liquid inoculant formulation are also more convenient for batch processing of microbial inoculants. Different organic polymers for inoculant production have been tested, including chitin, chitosan, gellan gum and polyvinyl alcohol [14,19,20]. Using natural polymers such as agar, alginate, carrageenan and cellulose and its derivatives, collagen and gelatin, is becoming more frequent [14]. Polymers such as sodium-alginate, gum arabic and polyvinyl alcohol are normally used as adhesives when they are applied to seed [17].
Sinorhizobium (Ensifer) meliloti is a fast-growing rhizobium capable of fixing atmospheric nitrogen in symbiosis with legumes from the genera Medicago, Melilotus and Trigonella [21]. Symbiotic association of alfalfa (Medicago sativa L.) with S. meliloti is one of the most efficient interactions between nitrogen-fixing bacteria and legume plants that usually fix 140-210 kg ha -1 of nitrogen per year in the field [22]. In this way, alfalfa contributes to the incorporation of nitrogen in the soil, with a consequent economic and ecologic benefit, helping to reduce the application of synthetic N fertilizers. The S. meliloti L3Si strain showed good nitrogen-fixing properties in alfalfa when used in a solid peat inoculant and for inoculation at the time of sowing [23,24]. In addition, this strain has not been previously used in liquid inoculant formulations.
The aim of this study was to develop liquid inoculant formulations for alfalfa by adding various supplements to the rhizobium growth medium. In these liquid formulations, the growth and survival of Sinorhizobium meliloti L3Si strain were evaluated during a five month period, as well as their nitrogen fixation efficiency in alfalfa plants, observing parameters such as shoot dry weight (SDW) and nitrogen content. In addition, we examined the effects of pre-inoculation of alfalfa seeds with the L3Si strain on plant nodulation, nitrogen content and alfalfa shoot yield after a storage period of up to 3 months.
Rhizobium culture
A working rhizobium culture was prepared using Sinorhizobium meliloti L3Si strain. This is the nitrogenfixing strain for alfalfa selected from the Collection of the Institute of Soil Science (ISS WDCM375-Collection of Bacteria, Institute of Soil Science, Department of Microbiology). The L3Si strain was grown in Erlenmeyer flasks in yeast mannitol broth (YMB) on a rotary shaker (125 rpm) at 28°C for 48 h [25].
Preparation of media formulation
The basal medium for liquid inoculant formulation contained: 0.5 g L -1 of K 2 HPO 4 , 0.2 g L -1 of MgSO 4 x 7H 2 O, 0.1 g L -1 of NaCl, 0.2 g L -1 of CaCO 3 and 100 mL of 30 g L -1 fresh yeast extract. Ten different medium formulations of liquid inoculant were prepared by adding mannitol as the source of carbon (1 or 10 g L -1 ) and the following additives: agar, sodium-alginate, CaCl 2 , glycerol and FeCl 3 (Supplementary Table S1). The additives were added separately or in combination.
Liquid inoculant preparations and rhizobium survival evaluation after prolonged storage
Liquid inoculants were prepared by adding S. meliloti L3Si (which was growing in YMB) to 50 mL of various media at a ratio 1:50 (v:v) and in duplicate. All liquid inoculants were placed in a rotary shaker (125 rpm) at 28°C for 48 h. All treatment samples were stored at 22°C for 150 days. The number of viable bacterial cells after the incubation and after each 30 days of storage was determined by dilution plating. Additionally, pH values were measured in all treatments after the expiration of storage time. The effects of time and medium formulation on rhizobium survival were evaluated by one-and two-way ANOVA followed by post-hoc Duncan's test to consider the differences between each treatment.
Testing the efficiency of S. meliloti L3Si-based liquid inoculants
After 120 days of storage, liquid inoculants were tested with host plant alfalfa (Medicago sativa variety K28) in a light chamber experiment. Alfalfa seed inoculation was prepared by adding 25 μL of particular inoculants to 0.2 g of seeds. After drying, the seeds were sown. The sowing was carried out in glass tubes (250 mm×20 mm) filled with 30 mL of Jensen's medium agar [25]. Nodulation, plant height, shoot dry weight (SDW) and N content in SDW were determined in ten replicates (10 plants per treatment). The results were compared with two controls. One control represented non-inoculated seeds grown in Jensen's medium agar (ØØ), and the second was a control with nitrogen (ØN), i.e. non-inoculated seeds grown in Jensen's medium agar provided with 0.05% KNO 3 .
Testing pre-inoculation effects on alfalfa seed
The pre-inoculation of alfalfa seed was performed by adding 50 μL of selected treatment to 0.4 g of alfalfa seed. The selected fresh treatments (without storage) were: YMB, YMBA1, YMBSA, YMBSA * (10 times concentrated treatment). Dried seeds were stored during a three month period (at 22°C) and 10 seeds were sown every month in glass tubes with Jensen's medium agar. The pre-inoculation efficiency was evaluated by examination of nodulation, plant height, SDW and N content in SDW. The results were compared with the two controls described above. The effects of preinoculation of alfalfa seeds were evaluated by two-way ANOVA followed by post hoc Duncan's test to examine the differences between each treatment.
Supplement influence on S. meliloti L3Si strain growth
The effects of five additives (agar, sodium-alginate, CaCl 2 , glycerol and FeCl 3 ) and of different concentrations of mannitol on the viable count of S. meliloti L3Si cells after 48 h of incubation were estimated (Table 1). There were only a few adverse effects of supplements on the number of viable cells in ten different media. The concentration of 1 g L -1 of agar and sodium-alginate in the medium (YMBA1 and YMBSA respectively) was slightly unfavorable for growth of the rhizobium. Their viable count was slightly below 1×10 9 cell mL -1 . In other treatments, the viable counts ranged from 1.12×10 9 cell mL -1 (YMBA2) to 2.56×10 9 cell mL -1 (YMBG) (Table 1). Statistical analysis also showed no significant differences between the tested supplements and the growth of the L3Si strain (Table 1).
Survival of S. meliloti L3Si strain in liquid media during storage
During storage from 30 to 150 days, the survival rate of rhizobium L3Si strain was monitored in ten different treatments (liquid media), and the results are presented in Fig. 1. Compared to the initial number of viable cells in all formulations (Table 1), the number of viable L3Si significantly decreased after one month of storage (Fig. 1). After that period, the number of rhizobia during storage times between 30 and 150 days in each treatment was more or less constant.
The number of viable rhizobia declined slightly in all treatments during storage times from 30 to 150 days and varied between 1.25×10 8 (YMBSA medium) and 7.81×10 8 cells mL -1 (YMBG medium) at the end of 150 days. Maximum cell survival ranged from 37.89% in the YMBC medium formulation to 4.06% for YMBSA5 (Fig. 1) after 150 days of storage.
Medium formulation and storage time had a very significant effect on L3Si stain survival (P<0.001). Interaction between these parameters was not significant (Table 2). The YMBG, YMBGA and YMBSA treatments had the highest number of viable cells during the entire storage period and were significantly different compared to YMB, YMBA1, YMBA2, YMBSA and YMBSA5 (Fig. 1). Therefore, these three treatments (YMBG, YMBGA and YMBSA) could be considered as the best formulations for cell survival during storage at 22°C. In YMB, YMBA1, YMBA2, YMBSA and YMBSA5, the number decreased below 1×10 8 cells mL -1 (Fig. 1).
S. meliloti L3Si-based liquid inoculant efficiency in alfalfa plants
After 120 days of storage, liquid inoculants were applied to alfalfa seeds and the effectiveness of microbial fertilizers was evaluated. The obtained values of nodulation, plant height, SDW and N content in SDW are presented in Table 3. Nodulation was 100% except in YMBSA5 and YMBGSA treatments. In all treatments, SDW and N content were higher when compared to the control without nitrogen (ØØ). SDW ranged from 10.43 to 18.10 mg plant -1 , while the N content ranged from 3.01 to 3.35% in SDW, and in most of the inoculated treatments they were significantly higher compared to control plants (ØØ), indicating a good nitrogen-fixation efficiency of the stored liquid inoculants (Table 3).
Pre-inoculation effects on alfalfa seed
In the nodulation test with stored pre-inoculated seeds, nodulation was detected in all tested treatments (YMB, YMBA1, YMBSA and YMBSA * ) on alfalfa roots, except for the YMBSA treatment in the seed sample after 90 days of storage. The percentage of nodulation for the seeds inoculated on the day of sowing and pre-inoculated seeds stored for one month was 100%, all the sown seeds gave nodulated plants.
After that period, the nodulation percentage decreased (Fig. 2). Slightly higher nodulation (a higher number of nodules) after 60 days of storage was observed in the treatments with agar (YMBA1) and sodium-alginate (YMBSA * ) as coating polymers (Fig. 2).
The effects of media formulations and 30-day storage of pre-inoculated seeds on the number of nodules, plant height, SDW and N content in SDW were evalu-ated by one-and two-way ANOVA (Table 4). Media formulation and 30 days of storage time had a significant effect on SDW. Compared to the control (ØØ), all medium formulations used for seed pre-inoculation significantly increased SDW of pre-inoculated seeds stored for one month before sowing. The efficiency of applied treatments ranged from 3.33-to 4.96-fold higher than the control without N (ØØ) according to SDW (average for the both storage times).
The N percentage or N content in alfalfa SDW varied from 3.99% (YMBA1) to 4.21% (YMBSA * ) (average values for both storage times, respectively; Table 4). Slightly higher N% was obtained in plants of the YMBA1 treatment in relation to the control with N (ØN, 3.92%). In addition, this medium formulation had the highest influence on N% in alfalfa plant (Table 4). On the other hand, the other three media formulations showed an equal impact on N%.
Seed pre-inoculation with the same medium formulation (YMBA1) showed that the N content was 4.23% and 4.19% when using seeds inoculation on the day of sowing and pre-inoculated seeds stored for one month, respectively. Thus, seed pre-inoculation provided an improvement of about 11% and 4% in N content when seeds were inoculated on the day of sowing and the pre-inoculated seeds were stored for one month, respectively. Bearing in mind all the observed parameters: plant height, SDW and in particular the N content, the YMBA1 medium formulation had the best effect over a one-month storage time on the preinoculated seeds (Table 4).
DISCUSSION
The appropriate material for maintaining microorganisms in liquid medium has to offer special properties, such as lack of toxicity to microbes, and must be environmentally safe. Additionally, these materials should have near neutral or readily adjustable pH and be available locally at a reasonable cost. Some media formulations such as YMB1, YMBGSA, YMBGA and YMBC, for potential normal culturing conditions of rhizobia had no adverse effect on rhizobium L3Si strain growth (Table 1) in comparison to the common YMB medium. Out of 10 different applied liquid media, only YMBA1 and YMBSA had a slightly negative effect on L3Si strain growth. A negative effect of different sodium-alginate concentrations (1 to 5 g L -1 ) on the growth of various species of the rhizobium was also observed (Bradyrhizobium japonicum USDA110, Azorhizobium caulinodans IRBG23, Rhizobium phaseoli TAL1383, S. fredii HH103 and Mesorhizobium ciceri USDA2429) [16]. According to statistical analysis that showed no significant differences between the tested supplements and growth of the L3Si strain, the selected additives in this research are suitable for the growing media of L3Si strain.
For the commercialization of liquid inoculants, the viability of the rhizobial inoculant in a prescribed formulation for a certain period with preservation of strain characteristics is required [27]. During storage time, the number of cells in the liquid inoculant must not drop below 1×10 8 cells mL -1 , according to the local legislation [28]. Fig. 1 represents the effects of the length of storage on the viability of Sinorhizobium meliloti L3Si in various formulations, the biggest reduction during 150 days of storage occurring in the YMBSA5 treatment, probably due to the negative effect of the high concentration of sodium-alginate on the growth of L3Si strain.
Storage time significantly influenced the survival of the L3Si strain. Besides storage time, there were statistically significant differences (P≥0.05) in the survival of the rhizobium between medium formulations. Consequently, the number of rhizobia was significantly higher in the YMBGSA, YMBG, YMBGA, YMBC and YMB1 treatments, as compared to the remaining five treatments. Formulations containing glycerol demonstrated increased viability during storage, which was also previously mentioned in literature [14]. Also, using concentrations of sodium-alginate between 0.5 and 1 g L -1 showed good survival of Rhizobium sp G58 strain during 60 days of storage [29]. In addition, the pH value is important for rhizobia survival in inoculants. The optimal pH for L3Si strain growth ranged from 6 to 8.5, and was also optimal for other S. meliloti strains [30]. In all treatments, the pH values were in the optimal range and therefore this parameter was not a limiting factor in rhizobia survival.
In addition, inoculant storage at low temperatures is generally more suitable for bacterial survival, but it is not practical [31][32][33]. Thus, the main disadvantage of liquid inoculants is that they cannot be stored at room temperature for a long time without compromising the viability of bacteria and their effectiveness. The physical and chemical properties of applied polymers should protect cells against desiccation, sedimentation and cell death [34]. In addition, the use of sodiumalginate (1 g L -1 ) in the inoculant formulation provided successful survival during 60 days of storage at 28°C [34]. Because of this, room temperature was selected and a satisfactory survival of rhizobia was obtained. In previous research, inconsistent results were obtained. Ben Rebah et al. [9] studied the survival of the S. meliloti A 2 strain on waste sludge, peat and sludge-peatbased carriers as substrates for growth of rhizobia. The temperatures of the storage period of 130 days were 4°C and 25°C. After 120 days of storage, the numbers of viable rhizobia declined and remained lower than 1×10 8 cells g -1 in the following samples: sludge carrier at 25°C, peat and sludge-peat-based carriers at 4°C [9]. On the other hand, the survival of S. (Ensifer) fredii SMH12 strain and B. japonicum USDA110 strain in mannitol-supplemented liquid inoculants stored at 25°C supported more than 5×10 9 and 1×10 8 cells mL -1 after 90 days of storage, respectively [35].
The formulation of inoculants, the method of application and storage for an extended time period are critical for the success of the biological product [36]. A short shelf life, lack of suitable carrier materials, susceptibility to high temperature, transport and storage problems are bottlenecks in the manufacturing process of microbial fertilizers. After testing the media formulations during five months of storage on rhizobium L3Si survival, the effectiveness of the stored inoculants was examined. The 120-day-old liquid rhizobial inoculants were used to test their efficacy with alfalfa, since this is the optimal time that passes between production and use of an inoculant. In previous research, Sehrawat et al. [37,38] examined the efficiency of 90-day-old liquid rhizobial inoculants of Rhizobium sp. MB1503 strain and Rhizobium sp.strain MB703, respectively.
All tested microbial fertilizers (applied after 120 days of storage) had a positive effect on all parameters of alfalfa growth (observed as nodulation, plant height, shoot dry weight (SDW) and N content in SDW). SDW and N content are the most important parameters for estimating liquid inoculant efficiency [39]. According to SDW, effective treatments were YMB1, YMBC, YMBG, YMBGA and YMBGSA. One liquid inoculant is effective if a sample's SDW shows 2.5-fold higher values compared to the control without nitrogen [40]. In addition, the N content was satisfactory in all tested treatments because it was higher than 3%.
The technique of seed pre-inoculation is carried out to avoid seed inoculation during sowing. In this way, the transport of seeds from producer to farmer is simplified and facilitates the work of farmers in the field. Pre-inoculation of alfalfa seeds with agar and sodium-alginate as coating polymers can be justified because they create a suitable microenvironment for the rhizobium. The nodule number per plant decreased with storage time and this reduction was connected to rhizobia survival. Nodulation was slightly higher than in the samples with YMB medium. The nodule number per plant decreased with storage time and this reduction was associated with rhizobia dying over time.
In addition, the protective nature of biopolymers, such as sodium-alginate, comes from its ability to limit heat transfer and it also has high water activities [31]. In this case, these might be mechanisms that improve the survival of rhizobium L3Si on pre-inoculated alfalfa seeds. The survival leads to nodulation and nitrogen fixation in the field, and liquid inoculants compete with peat-based inoculants [26]. On the other hand, the application of pre-inoculated seeds has benefits in terms of lower cost, the use of small amounts of liquid inoculants for preinoculation and an eco-friendly approach as compared to mineral N fertilizer application. In addition, adding mineral fertilizer in amounts that are in excess of the optimum does not increase yield and crop quality [39].
Based on two factorial variance analysis, we observed that medium formulation had a highly significant (P<0.001)effect on all tested parameters (number of nodules, plant height, SDW and N content). Interaction between the medium formulation and storage time had a highly significant effect on plant height, SDW and N content. The medium formulation had a highly significant effect on all tested parameters. On the other hand, storage time did not have a significant effect on the number of nodules and plant height, but had a significant effect on SDW (P<0.01) and N content (P<0.001).
A storage time of one month was selected according to the observed 100% nodulation of alfalfa plants in pre-inoculated seeds. Two months after storage of pre-inoculated seeds, the percentage of plant nodulation was less than 80%, and after three months it was about 20% and less. In addition, after the selected storage time of 30 days SDW did not change or was significantly increased (P<0.01). Plant SDW is the best parameter to evaluate the symbiotic nitrogen efficiency of legume-rhizobium associations [39]. All used liquid inoculates for pre-inoculation had a significant positive effect on SDW after one month of storage. In that period, the survival of L3Si strain was the highest according to plant nodulation. The percentage of the N content in alfalfa SDW in all treatments was adequate or slightly higher according to Bergmman [41], where the optimal content of N was 3-5%. Therefore, a large number of cells remained viable over time. Additionally, in the present study, the technique of seed pre-inoculation was equally efficient as a plant inoculation procedure with regard to N content. Delić et al. [42] reported an N content of 3.70% in the SDW of the same alfalfa cultivar (K-28) inoculated with the same strain (L3Si). This corresponded to an N content of 3.92% that was obtained in the YMB treatment in our study. The results indicate that the storage time of one month did not prevent the L3Si strain from providing an adequate percentage of N in the host plant in the process of nitrogen fixation.
CONCLUSION
The additives agar, sodium-alginate, calcium chloride, glycerol and ferric chloride did not affect the growth of S. meliloti L3Si strain, but they showed a significant positive effect on its survival during storage. Treatments with glycerol had the highest positive effect on survival of rhizobia in liquid inoculants during 150 days of storage, as well as on their efficiency after application of a 120-day-old liquid Sinorhizobium inoculant. The pre-inoculation technique yielded good results with YMB, YMBA1, YMBSA and YMBSA * treatments. The alfalfa SDW significantly increased in pre-inoculated seeds stored for one month, and the content of nitrogen reached adequate values, ranging from 3.72 to 4.19% with the pre-inoculation technique. Therefore, application of pre-inoculated seeds could provide double benefits in agricultural productioneasier application of seeds in the field and a higher N content in alfalfa plants.
Fig. 1 .
Fig. 1. Survival of meliloti L3Si strain in ten different liquid media formulations during storage. Data are presented as the mean±SD of two independent experiments. The different colors of columns denote different storage times of the liquid inoculants. Values followed by the same letter in each treatment are not significantly different (Duncan's test, P<0.01).
Fig. 2 .
Fig. 2. The effect of storage of pre-inoculated seeds on nodulation and the number of alfalfa nodules. Data are presented as the mean±SD of ten independent experiments. The interrupted line represents nodulation after 60 days, and the solid line after 90 days of storage of the pre-inoculated seeds. The different colors of columns represent different times of pre-inoculated seed storage.
Table 1 .
The effect of medium formulation on rhizobium growth during 48 h.
Values present mean values of two replications ±SD. Values followed by the same letter in the column are not significantly different (Duncan's test, P<0.05)
Table 2 .
Analysis of variance for the survival of Sinorhizobium meliloti L3Si strain during storage from 30 to 150 days.
Table 3 .
Efficiency of liquid rhizobial inoculants in alfalfa plants: nodulation, plant height, SDW and N in SDW after inoculants storage of 120 days.
Table 4 .
Effects of seed pre-inoculation on alfalfa growth and its nitrogen-fixing efficiency. Values followed by the same letter in the column are not significantly different (Duncan test, P<0.05); ns -not significant (P≥0.05);** Significant at P<0.01, *** Significant at P<0.001, respectively; ØØ-control without nitrogen; ØN-control with nitrogen (0.05% KNO 3 ); One-way ANOVA shows mean values of ten replications ±SD; Two-way ANOVA shows F-values.
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Domain: Biology Agricultural and Food Sciences
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A Method to Improve the Embryogenesis Rate of Banana Somatic Cell Embryogenesis
Getting embryogenic callus is the first step for banana transformation. Enhancing embryonic-callus-rate is important for banana molecular breeding. In this paper, WIND1, an AP2/ERF transcription factor was added into the embryonic-callus-inducing medium with poly-Arg and nuclear localization signal (NLS). The embryogenic-callus-induced rate of the immature male florescence cultured on the media containing the recombinant protein Arg9-NLSWIND1 was significantly higher than that of the immature male florescence cultured on the control media. Medium containing 0.01% of the protein Arg9-NLS-WIND1 had the best effect for inducing the embryogenesis of the immature male inflorescence. On the medium containing 0.01% of the protein Arg9-NLS-WIND1, 11.5% of the immature male flowers can develop embryonic callus. On the medium containing Arg9-NLS-WIND1, the protein might be transferred into cytoplasm of the explants by endocytosis due to the interaction between the poly-Arg transduction domain and the plasma membrane. After it entered the nucleus guided by NLS and bind with the target DNA domain, the somatic cell embryogenesis reactions were initiated and the embryonic callus formed.
Introduction
Banana is an important crop and fruit in tropical and subtropical countries. American Journal of Plant Sciences However, banana production is always hindered because of lacking good varieties.
Breeding new banana varieties through transgenic method is inevitable [1].
However, although some papers about banana transformation had been published, the transgenic method for banana is still a tough work for many laboratories. The main reasons are 1) banana transformation needed embryogenic suspension cell as initial material due to the worrying about chimera, which required much banana embryogenic callus [2]; 2) The embryogenesis rate of banana somatic cells is very low according to the methods used now [3] [4].
Therefore, if banana embryogenesis rate can be improved significantly, banana transformation might become easier.
WOUND INDUCED DEDIFFERENTIATION 1 (WIND1) is an AP2/ERF transcription factor. It participates in the regulation of cell dedifferentiation in Arabidopsis [5]. After WIND1 was over-expressed in Arabidopsis, typical callus with disorganized mass of cells formed on the surface of leaves, roots and hypocotyls [5]. As a transcription factor, WIND1 acts as a key molecular switch to initiate wound response to the control of cell dedifferentiation [6]. If WIND1 can be over-expressed in banana plant, the embryogenic callus of banana might be induced easily. However, transferring foreign genes into banana genome is a hard work itself. If the foreign transcription factor itself can be transferred into the nucleus of banana cells directly and played roles, this problem might be resolved.
Transactivator of transcription (TAT) protein transcription domain (PTD) is a special 10 -20 amino acid sequence derived from HIV TAT protein [7]. Once added into the culturing meida, TAT-fusion protein can rapidly enter the cells.
Within cells, the TAT-fusion protein is either degraded or refolded by the cellular machinery into functional protein [8]. Most of the TAT PTD contained Arg and Lys, which have strong positive charge, that lead to that these amino acid sequences could bind with cell membrane and be transferred into cell quickly [9] [10]. Poly-Arg transduction domain is an artificial sequence according to the sequences of transduction domains found. Its transduction efficiency is higher than TAT PTD derived from HIV [11]. Experiments showed that it can be used to lead the foreign protein to transfer the cell membrane [12].
In this paper, the transcription factor gene WIND1 was linked with poly-Arg and a nuclear localization signal or sequence (NLS), which is an amino acid sequence that "tags" a protein for import into the cell nucleus by nuclear transport [13]. Typically, NLS consists of one or more short sequences of positively charged lysines or arginines exposed on the protein surface [14]. And then, the artificial sequence was over-expressed in bacteria and the recombinant protein was extracted and purified. After the purified protein was sterilized by passing filter membrane, it was added into the embryogenic-callus-induced media. It was found that the embryogenic-callus-induced rate of the banana immature male florescence on the media containing the recombinant foreign protein was significantly higher than those on the control media. This indicated that after the recombinant protein touched with the explant cells, poly-Arg transduction 533 American Journal of Plant Sciences domain can lead the followed amino acids pass through the cell membrane. The NLS can lead the followed protein enter the nucleus. In the nucleus, the transcription factor WIND1 can bind with the target DNA sequence and the somatic cell's dedifferentiation responses were initiated and callus formed. According to our knowledge, this is the first paper that reported that transcription factor can be guided into nucleus of plant cells and can induce the embryogenesis response of plant somatic cells. This might play potential roles in the transformation of crops whose trangenic systems are hard to be constructed.
The PCR product was purified and digested with Bam HI and Xho I. The plasmid of pET28a was also digested with Bam HI and Xho I. The digested PCR product and digested pET28a were ligated using T4 DNA ligase at 4˚C for 24 hours. The ligated mixture was transformed into E. coli Top10. The positive clones were amplified using primer 1 and primer 2. The clones from which the responding DNA band could be amplified using primer 1 and primer 2 were sequenced. Plasmids were extracted and transformed into E.coli Rosseta (DE3) using the standard heat shock method.
In a sterile hood, immature male flowers which localized from position 16 to 6 (1 being the immature flower closest to the meristematic dome) were isolated from the surface-sterilized buds and cultured on M2 medium. The cultures were kept in total darkness at 28˚C. Seven to nine mongths later, the embryonic callus induced was calculated and the rates of embryogenesis were assayed.
After the artificial sequence Arg9-NLS-WIND1 (Figure 1) was synthesized, it was inserted into the vector pET28a and transformed into E. coli BL21(DE3) (Figure 2). The transformed bacterial cells were lysed and electrophoresed as described in material and methods. Since the length of WIND1 cDNA was 1 kb.
The 30˚C, a remarked protein band whose molecular weight was 39 kD was found (Figure 3 and Figure 4). This indicated that the artificial sequence could be expressed well at 30˚C.
2) The embryogenic-callus-induced rate of the immature male florescence cultured on the media containing the recombinant protein Arg9-NLS-WIND1 was significantly higher than that of the immature male florescence cultured on the control media.
Although immature male inflorescence is the best candidate for inducing embryogenic callus, the embryogenic-callus-induced rate is still very low. According to our experiment, the rate of embryogenic callus induced (Brazil) is only about 2.1%. After the recombinant protein Arg9-NLS-WIND1 was filter-sterilized by passing through MILLIPORE membrane (0.22 µm), it was added into the culturing media. Results showed that there was significantly more embryogenic callus induced from the immature male inflorescence cultured on media containing the recombinant protein than those induced from the immature male inflorescence cultured on media without the artificial protein (Figure 5 and Figure 6). The embryogenic-callus-induced rate of the immature male florescence cultured on the media containing the protein Arg9-NLS-WIND1 was higher than that of the immature male florescence cultured on the control media. Adding the artificial protein Arg9-NLS-WIND1 can improve the embryogenic-callus-induced rate of the immature male florescence.3) Medium containing 0.01% of the protein Arg9-NLS-WIND1 had the best effect for inducing the embryogenesis of the immature male inflorescence.
After a series of the protein Arg9-NLS-WIND1 was added into the culturing media, immature male florescence was cultured on the media and put in darkness. Three months later, some crisp callus began to form. Results showed that the immature male florescence cultured on the media containing 0.01% of the protein Arg9-NLS-WIND1 formed the most embryogenic callus among the treatments. The embryogenic-callus-induced rate was about 10.2% (Figure 7).
The embryogenic-callus-induced rate of the immature male florescence cultured on the media containing more Arg9-NLS-WIND1 was less than that of the immature male florescence cultured on the media containing 0.01% of the protein Arg9-NLS-WIND1. This suggested that media containing 0.01% of the protein Arg9-NLS-WIND1 is the best treatment for enhancing the embryogenic-callusinduced rate of banana immature male florescence.
Discussion
Protein transduction referred to that some proteins were transferred into cells and play roles directly. Many researchers recognized protein transduction as a promising vehicle for delivery of macromolecular drugs [16]. It has been widely used in medical experiments [7] [17]. Some proteins with special roles have also been transferred into plant cells successfully [18] [19] [20] [21].
Protein transduction has been found when transactivator of HIV transcription (TAT-1) experiments was done. TAT-1 is a protein composed of 86 amino acids, which binds to the transacting response element (TAR) of the viral RNA to transactivate the viral promoter. TAT-1 can be internalized into cells after it was added into the culture medium [22] [23]. Later, researchers found that not only Figure 7. The embryogenic-callus-induced rates of the immature male flowers cultured on media containing different amount of Arg9-NLS-WIND1. 1 to 6 referred to the final concentration of Arg9-NLS-WIND1 in the medium was 0.004%, 0.006%, 0.008%, 0.01%, 0.012% and 0.014% respectively.
TAT peptide, but also various arginine-rich oligopeptides poses very similar characteristics in translocating proteins into cells [11].
The process of plasma membrane invagination in animal cells has also been found in plant cells [24] [25]. Poon et al. found that some specific Arabinogalactan proteins were isolated and incorporated into tissue culture medium, cotton somatic embryogenesis can be promoted [26]. This also indicated that exogenous proteins can be transformed into plant cells and play roles directly.
Banana transformation is a hard work for many laboratories. The important reason is that it is difficult to get embryogenic callus. If some proteins which can induce embryogenesis can be transferred into banana somatic cells directly and play roles, this hard work might become much easier. WIND1 is an important transcription factor that participated in plant somatic cell embryogenesis [5]. If WIND1 was over-expressed in Arabidopsis, there was much embryogenic callus can be found on the roots, hypocotyls and cotyledons of the transgenic plants [6]. Only over-expressing WIND1 is enough to induce and maintain the status of cell embryogenesis [5]. Excess expression of WIND1 might be the most important step for inducing plant somatic cell embryogenesis [6]. If WIND1 protein can be transferred into banana somatic cells, the embryogenic-callus-induced rate of banana somatic explants might be enhanced significantly.
Immature male florescence is the best candidate for inducing banana emnryogenic callus. However, on usual medium, the embryogenic-callus-induced rate of banana immature male florescence is only about 4.3% [3] [4]. Considering the pollution from bacteria or fungi, this rate will become even lower. After the artificial protein Arg9-NLS-WIND1 was added into the medium, the embryogenic-callus-induced rate of banana immature male florescence was increased significantly. At descent dose, such as 0.01% of Arg9-NLS-WIND1, this rate can be enhanced to 11.5%. This demonstrated that the artificial protein Arg9-NLS-WIND1 can improve banana somatic cell embryogenesis. This might be due to the following mechanism. After the protein was added into the culturing medium, it was transferred into cytoplasm because of endocytosis due to the interaction between the poly-Arg transduction domain and the plasma membrane [11]. In the cytoplasm, the protein was guided to pass through the nucleus pore by NLS and enter the nucleus, in which the protein bind with some specific DNA domain and the somatic cell embryogenesis reactions were initiated. Auxin production or its response was induced and embryogenic cells formed.
Altogether, in this paper, we found that after the artificial protein was added into the medium, the embryogenic-callus-induced rate of banana immature male florescence can be improved significantly. According to our knowledge, this is the first paper reported that banana somatic cell embryogenesis can be induced by adding artificial WIND1 protein into culturing medium. Considering the difficulties for banana transformation and the importance of banana production in many tropical and subtropical countries, this finding might play potential roles in banana breeding and banana production in the future.
Figure 1 .
Figure 1. The sequence of Arg9-NLS-WIND1. The capital letters underlined referred to the sequence of Arg9. The bold capital letters showed the sequence of NLS (nuclear localization signal). The lowercase letter referred to the sequence of WIND1. ATGGTACGTCGCCGTCGTCGCCGTCGTCGCCGTGAAGAAGAAGAGGAAGGTGGC atggcagctgctatgaatttgtacacttgtagcagatcgtttcaagactctggtggtgaactcatggacgcgcttgtaccttttatcaaaag cgtttccgattctccttcttcttcttctgcagcgtctgcgtctgcgtttcttcacccctctgcgttttctctccctcctctccccggttattacccg gattcaacgttcttgacccaaccgttttcatacgggtcggatcttcaacaaaccgggtcattaatcggactcaacaacctctcttcttctcag atccaccagatccagtctcagatccatcatcctcttcctccgacgcatcacaacaacaacaactctttctcgaatcttctcagaccaaagcc gttactgatgaagcaatctggagtcgctggatcttgtttcgcttacggttcaggtgttccttcgaagccgacgaagctttacagaggtgtga ggcaacgtcactggggaaaatgggtggctgagatccgtttgccgagaaatcggactcgtctctggcttgggacttttgacacggcggag gaagctgcgttggcctatgataaggcggcgtacaagctgcgcggcgatttcgcccggcttaacttccctaacctacgtcataacggatct cacatcggaggcgatttcggtgaatataaacctcttcactcctcagtcgacgctaagcttgaagctatttgtaaaagcatggcggagactc agaaacaggacaaatcgacgaaatcatcgaagaaacgtgagaagaaggtttcgtcgccagatctatcggagaaagtgaaggcggagg agaattcggtttcgatcggtggatctccaccggtgacggagtttgaagagtccaccgctggatcttcgccgttgtcggacttgacgttcgc tgacccggaggagccgccgcagtggaacgagacgttctcgttggagaagtatccgtcgtacgagatcgattgggattcgattctagct
Figure 6 .
Figure 6. Immature male flower cultured on the medium without the recombinant protein Arg9-NLS-WIND1.
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Domain: Biology Agricultural and Food Sciences
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Effects of Different Oils on the Production Performances and Polyunsaturated Fatty Acids and Cholesterol Level of Yolk in Hens*
In order to understand the effects of different oils on the production performances and polyunsaturated fatty acids and cholesterol level in the yolk. 160 Hexices hens at 42 wks were divided into four groups randomly. Each group fed with control diet (CG), control diet+5% fish oil (FG), control diet+5% palm oil (PG) and control diet+5% soybean oil (SG), respectively. After three weeks’ experiment, the results showed that: different oils showed no significant effect on feed/egg weight, egg white weight, body weight, C16, C18:3 n-6 and C20:4 n-6 contents in the yolk (p>0.05). But the egg mass of PG was higher than SG (p<0.05), the average egg weight of CG was lower than FG (p<0.05), and the of PG was lower than FG (p<0.05), during the experiment, FG gained more than SG (p<0.05), the cholesterol level in yolk of FG was lower than PG and CG (p<0.01), meanwhile the C20:5 n-3 content of FG was higher than CG and SG (p<0.01), and no C20:5 n-3 was detected in PF, as far as C22:6 n-3 in the yolk was concerned, FG was higher than PG (p<0.01), the C18:1 n-9 content of SG was lower than PG (p<0.05), the C18:2 n-6 content of SG was the highest than other three groups (p<0.01), and CG was the lowest, showed significant to FG (p<0.05), the C18:3 n-3 content of FG was higher than SG and PG (p<0.05), and the C20:1 n-9 content of FG was higher than other groups (p<0.01). The results demonstrated that fish oil could decrease the cholesterol and increase the n-3 fatty acids content in the yolk, and increase the n-3/n-6 level. (Asian-Aust. J. Anim. Sci. 2004. Vol 17, No. 6 : 843-847)
INTRODUCTION
Egg is the most nutritious, unadulterated, natural food. Egg will supply about 6.5 g of wholesome protein of high biological value, 5.8 g of emulsified, easily digestible fat, rich in phospholipids needed for brain and other nervous tissue growth and health and supplies only 80 kcal energy. Egg is also a rich source of all essential amino acids, minerals and vitamins (except vitamin C) (Narabari, 2001). To increase egg consumption as a part of healthy eating, scientists are constantly searching for methods to nutritionally enrich the egg. The total protein, fat and sugar content of the egg cannot be alter much but it is possible to manipulate fatty acid composition and levels of minerals, vitamins and certain non-nutrient chemicals (like pigments and antioxidants) in eggs by dietary means (Nash et al., 1995;Ayerza and Coates, 1999).
A number of epidemiological and controlled experiments have reported an inverse relationship between ω-3 acid consumption, and risk of cardiovascular, some autoimmune disorders, diabetes, and some types of cancer aside from their important role in neuronal (Bang et al., 1980;Leaf and Kang, 1998;Simopoulus, 2000). Animal studies showed that lack of ω-3 fatty acids can lower learning and visual abilities of animals.
A number of researchers have showed that inclusion high level polyunsaturated fatty acids (PUFAs) of plant oils into hen's feed can increase the PUFAs content in the yolk, especially ω-3 PUFAs content, but the results varied. This study was conducted to investigate the effects of fish oil, soybean oil, palm oil on the production performances and PUFAs contents in the egg yolk.
illuminating time was 16 h per day and luminance was 10 lx.
Production performances measurements
Eggs were collected by replicates per day, and egg mass and egg weight were recorded, production performances and feed consumption were calculated each week, each hen was weighted at the end of the experiment. At the last two days of each week, eggs were sampled, and marked group number and laying date on the egg shell, and selected 40 eggs from each group at random, other days sampled 6 eggs per day at each group, and all sampled eggs were stored in refrigerator at 4°C for chemical analysis.
Chemical analysis
The contents of crude protein (CP), calcium and phosphorus in diets were analyzed according to standard procedures described by the Association of Official Analytical Chemists (1990). Energy values of the diets were calculated as metabolizable energy (ME) according to the methods described by Krogdhal (1985).
Cholesterol in egg yolk was determined spectrophotometrially in Encore Chemistry System (Baker Instruments, UK), using Cholesterol Enzumatique PAP 100, kit. Ref. 61244 from bioMeriedux (France). Fatty acid composition of egg yolk was determined by GLC procedures according to the methods described by Ulbreth and Henninger (1992) for extracted/methylated samples. The fatty acid methyl esters were determined on a Perkin Elmer Autosystem gas chromatograph (Perkin Elmer Corp., Norwalk, CT) with a SGE capillary column no. 5QC/3bpx 70, 0.25, 25+25 m (SGE International Pty. LTD, Ringwood, Victoria, Australia). The results are presented as relative distribution of the individual fatty acids (g 100 -1 of total fatty acids) determined by the percent area.
Statistical analysis
All data were analyzed by using the General Linear Model Procedures of SAS (1989). Comparison of treatment means was based on Duncan's multiple range test. A significant level of p<0.05 was applied in all case.
Egg production
Hen weight, weight gain, average egg weight, feed to egg ratio, yolk weight, egg mass, egg white weight were presented in Table 3. No significant difference (p>0.05) in feed to egg ratio, egg white weight, end body weight were found among treatments. Egg mass of palm oil group was higher than soybean oil group (p<0.05) and the average egg weight of fish oil group was higher than control group (p<0.05), the yolk weight of fish oil group was higher than palm oil group (p<0.05) and hen weight gain of fish oil group was higher than soybean oil group (p<0.05). 2.60 0.13 0.06 C 22:6 n-3 (DHA) 9.31 0.01 0.12 C 20:5 n-3 (EPA) 11.01 ND ND Table 4 showed the fatty acid composition and the cholesterol content of egg yolk. The cholesterol level of fish oil group was lower than control and palm oil groups (p<0.01). No significant differences among treatments were found for C 16:0, C 16:1, C 18:3 n-6 and C 20:4 n-6 (p>0.05). Among control group, fish oil group and palm oil group, no C 20:2 n-6 was detected in the yolk, while in soybean oil group were higher. The C 20:5 n-3 in fish oil group was higher than control and soybean oil group (p<0.01), but no C 20:5 n-3 was detected in palm oil group, the C 22:6 n-3 in fish oil group was higher than palm oil group (p<0.01), the C 18:1 n-9 content of soybean group was lower than palm oil group (p<0.05), as far as C 18:2 n-6 was concerned, soybean oil group was higher than other group (p<0.01), and the control group was the lowest, show significant difference to fish oil group (p<0.05), and the C 18:3 n-3 content of fish oil group was higher than soybean oil and palm oil group (p<0.05), also the C 20:1 n-9 level of fish oil was higher than other groups (p<0.01).
DICUSSION
In this study, the hens were fed with isocaloric and isonitrogenous experimental diets, and the oil supplemental levels were all 5%. The results showed that different oils did not affect feed to egg ratio, egg white weight and body weight (p>0.05), these results were similar to Chen et al. (2003), who found that when supplemented 2% tallow and 2-6% refined cod liver oil in duck diets, no significances were found for feed efficiency, body weight, but when the refined cod liver oil supplemental level was 6%, the yolk weight was lighter than 2-5% oil groups, the plausible reason for this difference was the basal composition and breed. Zhang et al. (1997) reported when added 8% palm oil, the egg mass was higher than 8% soybean oil and control group, this was similar to this result, they reported that the palm oil could improve the egg production and feed conversion rate was the result of soybean oil contains antinutrients (such as trypsin inhibitors, phytohaemagglutinins). Van Elswyk et al. (1994) reported that the hypolipodmic effect of fish oil might have reduced the hepatic lipogenesis and lipid transport from blood into the developing ova. Oh et al. (1994) reported that hens diets isoenergetically supplemented with 5% fish oil for 8 weeks did adversely influence feed efficiency, body weight or egg production. Kjos et al. (2001) reported that when supplemented 1. 8, 8.8, 18.8 and 24.8 g fish silage per kg hen diet, the feed intake decreased significantly to control diet with the fish silage level increased, and when supplemental level was above 8.8 g, the egg mass and egg weight decreased (p<0.01). Baucells et al. (2000) reported even when the fish oil supplemental level was 40 g/kg diet, the egg's production performances did not changed.
The cholesterol level in the yolk of fish oil group was significantly lower than control and palm oil group (p<0.01), this was similar to Yu et al. (1998) andzhang et al. (1997). Ricardo Ayerza and Wayne Coates (2001) reported that when flaxseed supplemental levels were 2, 2.5 and 5%, no significances were found among control group and experimental groups for cholesterol in egg yolks. Herstad et al. (2000) found that with the fish oil increased, the cholesterol in the yolk decreased. Jiang and Sim (1992) found that when fed rats with n-3 fatty acid-enriched chicken egg, the plasma and serum cholesterol levels were decreased. In this experiment, with the egg weight improved, the cholesterol level was decreased, which was similar to Choi et al. (2001). Several factors affect egg composition and lipid profile including bird age, strain and breed. Nevertheless, dietary manipulation still yields the most significant changes to yolk lipid profile (Leskanich and Noble, 1997). Graded levels if dietary saturated and monounsaturated fats have minor effects on the relative egg fatty acid profile (Baucells et al., 2000). In contrast, dietary polyunsaturated fats can cause major changes (Noble et al., 1990) thus allowing for manipulation of yolk lipids to better meet human nutritional requirements. As far as the n-3 polyunsaturated fatty acids levels were concerned, its level in the yolk was proportional to the level in the diet, in this experiment we verified this theory. It is interesting to note that in spite of the higher concentrations of eicosapentaenoic acid (EPA) relative to docosahexaenoic acid (DHA) in fish oil, the concentration of latter found in yolks from hens fed fish oil diet is much greater than the former. This was similar to the results of Gonzále-Esquerra et al. (2000) and Nash et al. (1996). The explanation to this finding possibly relates to the birds metabolism of n-3 fatty where conversion of EPA from DHA and vice versa along with tissue specific preterential DHA deposition might occur as reported in mammals (Sprecher et al., 1995). Hargis et al. (1991) reported when the fish oil inclusion was 3% could significantly improve the EPA and DHA concentrations in the egg yolk.
Several studies have shown that an appropriate ω-6:ω-3 ratio must be provided in human diet. Nutritional recommendation suggest a dietary ω-6: ω-3 ratio of 5:1 (British Nutrition Foundation, 1992;FAO, 1994), or 4:1 (Ministry of Health and Welfare of Japan, cited by Okuyama et al., 1997), or even lower (Simopoulos et al., 1998). The ω-6:ω-3 ratios in the yolks produced by the enriched feeds used in the current trial greatly improve the nutritional quality of the eggs, compared with those of the laying hens fed control diet. The deposition of n-3 fatty acids in the yolk is a gradual process, Yu et al. (1987) reported that n-3 fatty acids concentrations could maintain at a relatively stable level in the yolks after hens fed with salmon fish oil for 8 days, the explanation to this might be the formational time of yolk in the hen need 9 days (Huang et al., 2001). In this study we also found the same phenomenon.
CONCLUSION
n-3 polyunsaturated fatty acids enriched egg can be produced by supplementation with 5% fish oil without negative effect on laying performances. These eggs may server as viable dietary alternatives to fish, fish products to provide significant amounts of n-3 PUFAs in human diet.
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Domain: Biology Agricultural and Food Sciences
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Evaluation of the productivity and plant health of pruned coffee intercropped with annual crops
Alley intercipiente with annual crops is a usual practice in coffee cultivation, especially in periods of renewal of the crop by pruning. Its purpose is to make better use of the area, decrease costs of implantation and renovation, mainly in coffee plantations with open lines, through the production of subsistence food with generation of additional and immediate income of the producer. Therefore, the objective of the present study was to evaluate the productivity and plant health of pruned coffee crop in consortium with annual crops in different spacings. The experiment was carried out at the Instituto Federal de Educação, Ciência e Tecnologia do Sul de Minas Gerais – Campus Muzambinho, in the 2016/17 and 2017/2018 crop years, in a coffee plant of Catuaí Vermelho cultivar 144, 12 years old, pruned in 2014. Three intercrops (corn, chia and beans) in two spacings (30.0cm and 60.0cm) plus two additional treatments without intercropping (slashing or applying herbicide) were implanted in the soil. In the crop year 2016/17, a delay in the fruit maturation was observed in the treatment with intercropping spaced at 30.0cm, when compared to the same crops at 60.0cm spacing. The maturation of the fruits in the 2017/18 crop year was delayed in the treatments of consorts spaced at 60.0cm, when compared with the additional treatment. There was an expressive increase of cercosporiosis with cropping culture spacing 60.0cm. It was also observed that the average yield of coffee in the 2016/17 and 2017/18 crop years was affected by the interplant cultures implanted in the spacing of 30.0cm. In general, regardless of the spacings, intercropping negatively influenced the productivity of coffee in both 2016/17 and 2017/2018 crop years.
Introduction
Brazil, as the largest coffee producer and exporter (Coffea spp.), occupies the second position in consumption behind the United States of America, becoming increasingly influential in agribusiness at the international level. High costs of agricultural inputs combined with inadequate crop management practices increase the cost of production, making coffee farmers seek new alternatives to reduce these costs and, consequently, increase profitability (NADALETI, 2017).
Intercropping is a usual practice in coffee growing, especially during periods of planting or renewal by pruning (ASTEN, 2011). The initial phase of implantation of the coffee plant has a very high cost, in addition, its economic return begins only at the third year (OUMA, 2009). The renewal of the crop interrupts the production of coffee up to two years, but the situation is complicated by very dense crops, which require periodic pruning from coffee growers (CARVALHO, 2010).
The main purpose of intercropping is to make better use of the area, to decrease the costs of implantation and renovation, especially in crops with open lines, through the production of subsistence food with the generation of additional and immediate income to the producer (SANTOS et al., 2008;CHUNG et al., 2013) through better use of the area, especially in small properties. The coffee consortium can also provide other benefits, such as improvements in soil moisture conservation conditions, reduction of damage caused by winds (DaMATTA; RAMALHO, 2006;PEZZOPANE et al., 2010), possibility of improving soil fertility (VAAST et al., 2005), reduction in the occurrence of spontaneous plants (SILVA et al., 2013), improvement in the use of labor (APARECIDO et al., 2014) and favoring financial return.
Crops intercropped with coffee are intended to favor the main crop. In its adoption, agronomic, economic, and ecological aspects of the production system are considered. However, depending on the species and management, they may bring additional benefits or undesirable losses, directly influencing the potential of the crop (SANTOS et al., 2008).
The adherence to this cultivation system must be based on technical criteria that involve the analysis of several factors, such as the choice of the appropriate species/cultivar, the level of shading, fertility, irrigation, altitude, and climate. Although intercropping has some advantages, both intercropping and coffee cultivation must be well planned for the success of using this practice (CARVALHO et al., 2007). However, the difficulties of mechanization and execution of phytosanitary treatments, the competition of intercalary crops for water, nutrients and light, in addition to the consequent reduction in the growth and production of coffee trees (PAULO et al., 2004), make the recommendation of cultures intercalations in coffee plantations controversial.
Among the intercrop crops most planted in coffee plantations, rice, beans, corn, soybeans, and peanuts stand out. As for the number of rows of intercropping, it basically depends on the species to be introduced and the spacing of the coffee plantation, with a free strip of planting with a width of half a meter, in addition to the projection of the coffee canopy on each side of its lines (SANTOS et al., 2008).
Adopting techniques for growing intercrop crops in coffee plantations based on the most up-to-date recommendations, such as varieties, stands and spatial arrangements, the present study aims to evaluate the intercropping of coffee received with intercrop crops (chia, beans and corn) in different spacing and to evaluate their effect on coffee productivity and plant health in the region of Muzambinho, for two harvests of the coffee crop.
Material and methods
The experiment was developed at the Coffee Industry Sector of the Federal Institute of Education, Science and Technology of the South of Minas Gerais -Campus Muzambinho, in an area with geographical coordinates of 21º20'32.64 "South and 46º32'00.99" West, average altitude of 1,023 meters, humid temperate climate with dry winter and moderately hot summer (Cwb), according to Köppen (SÁ JUNIOR et al., 2012).
The experiment was conducted in the crop years 2016/17 and 2017/2018, in an area cultivated with coffee (Coffea arabica L.), of the cultivar Catuaí Vermelho IAC-144 12 years of age, with 3.8m x 1.0m spacing, and received in September 2014.
A randomized block design was adopted, in a factorial scheme 3 x 2 + 2 in plots subdivided in space and with 3 replications, with 3 types of intercropping crops (corn, beans and chia) in two interrow spacing of the crops (30cm and 60cm), plus two additional treatments (dried with glyphosate or just brushed).
The spacing factor was randomized in the plots and the interim crop factor and the additional ones in the subplots, totaling 8 treatments (combinations of the types of interim crops and the spacing plus the additional ones) and 24 plots. Each plot consisted of 18 plants (3 lines with 6 plants each), the useful plot consisting of 4 plants from the central line, and the others, from borders.
Initially, a soil sampling from the experimental field was carried out in order to characterize its fertility, the fertilizations of the coffee tree and the intercropping of the crop year 2016/17 were made according to the analysis of the soil in depth from 0 to 20cm (TABLE 1) and the fertilizations for the 2017/18 crop year were made according to soil analysis in depth from 0 to 20cm (TABLE 2). Both analyzes were carried out at the Soil and Leaf Laboratory of the Federal Institute of Education, Science and Technology of the South of Minas Gerais -Campus Muzambinho. It was not necessary to apply lime in the experimental area to correct the soil. The soil preparation was carried out in a conventional manner, using a roto-enchanter and leveling harrow, in order to leave the soil in suitable conditions for sowing. For crop year 2016/17, interim crops were established on December 14, 2015 and for crop year 2016/17, they were implanted on December 06, 2016.
The fertilization of corn and chia was carried out according to Raij et al. (1997), and the chia fertilization was based on the culture of mint and spearmint, as they are from the same botanical family. Fertilizers of coffee and beans were made according to Ribeiro et al. (1999), considering level 3 of technology for the fertilization of common bean. The phytosanitary management of coffee followed the pattern carried out by the coffee sector of the Federal Institute of Education, Science and Technology of the South of Minas Gerais -Campus Muzambinho.
The population density of chia and beans, in the spacing of 30.0cm, was 6 plants m -1 and in the spacing of 60.0cm, it was 12 plants m -1, whereas the density for corn culture in the spacing of 30.0cm was 1,65 plants m -1 and in the spacing of 60.0cm it was 3.3 plants m -1. Regardless of the spacing between the consortiums, they were all implanted at a minimum distance of 50.0cm from the projection of the coffee canopy.
To evaluate the maturation of the fruits, 100.0mL of coffee fruits were harvested from each useful plant of the evaluated plots, totaling 400.0mL of fruits per sample, in the months of June and July 2016 and in the months of May, June and August of 2017. The fruits were quantified and qualified as: Green (fruits with green and greenish exocarp, until the stage of physiological maturity), Ripe (fruits with reddish, red and dark red exocarp) and dried (fruits that had already passed physiological maturity, with brown exocarp and / or dehydrated aspect).
The coffee productivity was evaluated right after the harvest of the experimental plots, carried out in July 2016 and August 2017, quantifying the total fruits harvested in each useful plot in liters, disregarding the sweeping coffee. The values were transformed into productivity, using as a reference the value of 450 liters of "da roça" coffee fruits for each 60kg bag of processed coffee (11% b.u.) (NADALETI, 2017).
The coffee yield was obtained by the ratio between the weight of the processed coffee (11% b.u.) and the volume of "da roça" coffee in liters. For this purpose, 10 liters of "da roça" coffee fruits from each plot were put to dry in suspended terraces until reaching the recommended humidity, later they were benefited and calculations were made to transform the values into yield (NADALETI, 2017).
The physical classification as to the type and intrinsic defects was made according to Brasil (2003). In the presence of more than one defect class in the same grain, the one with the highest equivalence was considered. Defective grains were individually weighed for all defect classes. The granulometric classification of the grains was made in samples of 100 g and was obtained by the percentage of grains retained in the circular sieves (18, 17, 16, 15, 14 and 13) for flat grains and oblong sieves (13,12, 11, 10 and 9) for round grains (mocha) (SILVA et al., 2010).
In order to monitor the dynamics of pests and diseases in coffee plants, evaluations were carried out from January to June, both in the crop year 2016/17 and in the crop year 2017/18, totaling six assessments per year. The sampling was carried out in the middle third of the plant, 3 plagiotropic branches were chosen at random on the north face, plus 3 random branches on the south face, evaluating the 3rd and 4th pair of leaves, which were classified by level of incidence, that is, the presence or absence of pests and diseases in the plant tissue.
The evaluations were made monthly in order to monitor pests and diseases such as: Bicho Mineiro (Leucoptera coffeella), Cercosporiosis (Cercospora coffeicola), Rust (Hemileia vastatrix), Phoma spot (Phoma spp.), Aureolada spot (Pseudomonas syringae pv. Garcae) and Phoma Tarda (Ascochyta coffeae). It was considered present leaves that have the pathogenic agent already installed on them and absent leaves free of infestations or with an onset of attack not yet developed (ROCHA, et al., 2013).
For statistical analysis, analysis of variance was performed for the response variables, with a significance level of 5% (p-value). For the variables that had a significant effect of the intercropping factor or the interaction "spacing and intercropping", the averages were subjected to the Scott-Knott test at the 5% probability level. For variables that had a significant effect of the spacing factor and/or additional treatments, the means were separated according to the F test, at a significance level of 5% (p-value). All procedures for carrying out statistical analyzes were performed using software R version 3.4.1 (R CORE TEAM, 2017).
Results and discussion
In crop year 2016/17, based on analysis of variance, significant effects were observed only for the maturation variable and, in crop year 2017/18, there were significant results for the parameters maturation, grain size and incidence of cercosporiosis.
Fruit ripening in crop year 2016/17 was influenced by the spacing factor of the intercrop crop, with a higher percentage of green fruits in treatments in which the consorts were spaced 30cm apart and a higher percentage of ripe fruits in the consorts spaced 60cm (FIGURE 1).
The difference in the results is probably due to the fact that in treatments with consorts spaced at 30cm, that is, with 6 crop lines between those of the coffee tree, there was a greater light interception than in the treatment of consorts spaced at 60cm, with only 3 rows of intercropping. Therefore, this greater shading in the coffee tree may have contributed to the delay in maturation. These results corroborate the studies carried out by Carvalho et al. (2007), at which the authors determined the number of rows and the fertilizer dose of beans intercalated with dense coffee, reporting a shading of the coffee according to the increase in lines of the intercropping, decreasing production and increasing the diameter of the coffee stem. The bars of the treatment averages are within the confidence intervals (95%). Thus, the means in which the confidence interval bars overlap are statistically equal and those that do not overlap are different.
Source: Elaborated by the authors (2017).
In the maturation of the fruits of the 2017/2018 crop year, the percentage of green and ripe fruits showed significance between the additional treatments and the treatments with intercrop crops, with a higher percentage of green fruits in the treatments with consorts spaced 60cm and higher percentage of ripe fruits in the additional grazing treatment (FIGURE 2).
The explanation for this result is that possibly in the cleared plots, there was no interference in the maturation process, since the coffee, being cleared, does not suffer interference from shading, leading to greater maturation, as seen by Pezzopane et al. (2010). In the 60 cm treatment, with three rows of intercropping, a shadier environment was registered, which may have contributed to the delay in maturation. The grain size classification for the 2017/2018 crop year showed significant results for the intercrop species grown, with a higher percentage of small flat grains observed in treatments implanted with corn than in those implanted with beans (FIGURE 3), regardless of the spacing of interim crops.
The difference in results is probably due to the fact that the corn crop is more demanding than the bean crop, that is, it has greater demands for water, nutrients and light, so there was a greater interspecific competition with the coffee tree, which directly interfered in the size of the coffee beans (AMARAL FILHO et al., 2005). The bars of the treatment averages are within the confidence intervals (95%). Thus, the means in which the confidence interval bars overlap are statistically equal and those that do not overlap are different.
Source: Elaborated by the authors (2017).
Regarding the dynamics of coffee pests and diseases, there was a significant effect of treatments only in the month of May of the 2017/2018 crop year on the incidence of cercosporiosis for the different spacing, a period that coincided with the high crop year. As shown in Figure 4, the incidence was higher in treatments with 60cm than in those with 30cm. This can be explained by the fact that crops at 60cm allowed an environment with greater insolation than in treatments with 30cm, and high insolation promotes ideal conditions for the development of cercosporiosis (SILVA et al., 2013). Considering the averages of productivity and yields for the sequenced crop years (2016/17 and 2017/18), significant results were observed.
It was observed that the average productivity for this period was influenced by the treatments, with significantly higher averages being reached for glyphosate management compared to intercrop crops (FIGURE 5). Both interim crops implanted in the 60cm spacing and those implanted in the 30 cm spacing negatively influenced the average coffee yield, which can be explained by the greater competition of intercrop cultures for water, nutrients and light (PAULO et al., 2004). It was observed that the average yield for this period was influenced by the spacing, as the yield was lower in treatments with spans spaced at 30cm, than in those of 60cm (FIGURE 6), demonstrating that where there was a greater number of lines of interim crops, greater competition was observed with coffee. For this reason, there was a need for a greater quantity of coffee in natura to produce a 60kg bag of processed coffee (11% b.u.). Similar results were found by Pezzopane (2010), who obtained a lower yield of coffee combined with macadamia. The bars of the treatment averages are within the confidence intervals (95%). Thus, the means in which the confidence interval bars overlap are statistically equal and those that do not overlap are different.
Conclusion
In the crop year 2016/17, there was a delay in fruit maturation in treatments with consorts spaced at 30cm, when compared to the same consorts at 60 cm spacing. In the evaluation of the ripening of the fruits of the 2017/18 crop year, there was a delay in the treatments of consorts spaced at 60cm, when compared with the additional treatment.
The corn crop compared to the bean crop in the 2017/18 crop year negatively interfered in the coffee grain size, as it resulted in a higher percentage of small flat beans. The incidence of cercosporiosis in the month of May of crop year 2017/18 was more severe in interim crops with 60cm than in those of 30 cm.
It was also observed that the average coffee yield in crop years 2016/17 and 2017/18 was affected by the interim crops implanted in the spacing of 30cm. Regardless of the spacing, the intercrop crops negatively influenced the average coffee productivity in crop years 2016/17 and 2017/18, requiring further studies on these crops in relation to coffee cultivation.
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Domain: Biology Agricultural and Food Sciences
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Identification of zygotic and nucellar seedling of Harumanis mango through molecular markers and morphological approach
1 Horticulture Research Centre, Malaysian Agricultural Research and Development Institute (MARDI) Sintok, 06050 Bukit Kayu Hitam, Kedah, Malaysia. 2 Industrial Crop Research Centre, Persiaran MARDI-UPM, Malaysian Agricultural Research and Development Institute (MARDI) Headquarters, 43400, Serdang, Selangor, Malaysia. 3 Biotechnology and Nanotechnology Research Centre, Malaysian Agricultural Research and Development Institute (MARDI) Headquarters, Persiaran MARDI-UPM 43400 Serdang, Selangor, Malaysia. 4 Department of Crop Science, Faculty of Agriculture, Universiti Putra Malaysia (UPM), 43400 Serdang, Selangor, Malaysia.
INTRODUCTION
Harumanis mango (Mangifera indica) is a mango variety that is economically important and classified as one of the sought-after mango variety in Malaysia (Farook et al., 2013). The demand for Harumanis mango is increasing yearly due to the exquisite taste and aroma of the fruit (Khalid et al., 2017). However, the plant is only cultivated in the Northern region of Peninsular Malaysia such as in Perlis and Kedah since the weather is suitable for the growth of the Harumanis mango (Muhamad Hafiz et al., 2019;Rosidah et al., 2010). As recorded in 2019, there was about 6,373 ha of mango cultivation in Malaysia with 15,766 metric tons production (Department of Agriculture, 2019). This valuable crop is generally propagated by the means of grafting rather than via seedlings to ensure true-to-type planting material (Ahmad Hafiz et al., et al., 2020). This method of propagation however is laborious, costly, requires skilled worker, time-consuming and dependable on availability of seeds for rootstock; which explain the deficit in supply of Harumanis mango planting materials in Malaysia. An alternative method of mass production of true-to-type mango planting material is via cutting, which is reported to be more cost effective, efficient and uniform planting materials (Deependra et al., 2018). This method however is hindered in mango due to the polyembryonic attributes of the mango seed.
Harumanis mango is one of the several mango fruit trees that possess polyembryonic genotype (Mohd Asrul et al., 2018). Generally, cultivars originating from Southeast Asia, as well as tropical Latin America are polyembryonic, while those originating from Florida and India are largely monoembryonic (Vasanthaiah et al., 2007). Polyembryony is defined by the development of more than one seedling in a single seed, and one may be zygotic and the rest are nucellar (Simon et al., 2010;Ravishankar et al., 2004), in some reports all could be nucellar (Degani et al., 1993). This trait is genetically controlled, and in mangos, it is linked to a single dominant gene (Aron et al., 1998). The number of seedlings per seed varies with the cultivar and environmental conditions (Aline et al., 2014). The nucellar embryos in mango trees are developed in the nucellar tissue that covers the embryo sac, and the seedlings derived from these embryos are genetically identical to the parent plant (Aron et al., 1998). In contrast, the zygotic embryo is derived from fertilization by selfpollination or by cross-pollination. It is the objective in breeding programs for the selection of superior genotypes and variability achievement (Aline et al., 2014).
The identification of nucellar seedlings by morphological criteria is impossible or hard to be performed (Desai, 2004). Different morphological and biochemical markers had been used to distinguish nucellar from zygotic seedlings, but none was as efficient as molecular marker (Elisa Del et al., 2012). Thus, the use of molecular or isoenzymatic markers is necessary to observe the Sabdin et al. 93 differences. Since it was difficult to select nucellar seedlings in seeds of polyembryonic mangos using morphological characteristics, several researchers had reported on the usage of genetic markers to identify zygotic and nucellar embryos. Some of the genetic markers used were Amplified Fragment Length Polymorphism (AFLP) (Eiadthong et al., 2000), Random Amplified Polymorphic DNA (RAPD) (Ravishankar et al., 2000;Elisa Del et al., 2012) and Inter Simple Sequence Repeat (ISSR) polymorphism (Aline et al., 2014). However, seedling industries find difficulties in identifying these markers and continuously choosing plants according to their morphological characteristics. To make identification of nucellar seedlings an easy task in selecting of cutting source, germination sequence and vigour of Harumanis seedlings were investigated in this study, to determine whether these characteristics exhibit relationship to their genetic origin. The objectives of this work were to identify the genetic origin, zygotic or nucellar of seedlings from Harumanis mango polyembryonic seeds by using SSR molecular markers and thereafter relating it to the seedling germination sequence and vigour.
Plant and growth conditions
Seeds for the experiment were collected from Harumanis mango fruits obtained from Malaysian Agriculture Research and Development Institute (MARDI) Station, Sintok, Kedah on May 2019. Forty-five mature fruits of Harumanis mango were chosen. The flesh and seed coat were removed and washed with clean water and soaked in 0.2% Benomyl before the seeds were sown in the sandy seedbed. The seedbed was shaded with black netting (70%) and watered daily.
Germination sequence and seedling vigour determination
Seedlings that germinated from each seed were colour-tagged according to their germination sequence until 30 days of germination period. At 30 days after germination, plant height, stem diameter, number of leaves and leaf area of each seedling were determined. Measurement of plant height was taken from the soil surface to the highest shoot tip using a measuring tape. Stem diameter was measured at the lowest part of stem using Electronic Digital Caliper (Model SCM DIGV-6) while the leaf number was manually counted based on fully expanded leaves. Leaves areas were measured using an automatic leaf area meter (MODEL LI-300, LI-COR) and recorded as a total leaf-area per plant ( Figure 1).
DNA extraction
Genomic DNA was extracted following the method described by Mace et al. (2003) with some modifications in term of incubation time. Leaf samples of each seedling at 30 days after germination was ground using the Tissue Lyser (Qiagen, Netherlands) before incubated with extraction buffer (2% CTAB, pH 8, 100 mMTris-HCl, 20 mM EDTA, 1.4 M NaCl, 0.05% β-mercaptoethanol) at 65°C for 1 h. Then, an equal volume of cold isopropanol was used to precipitate the DNA before being washed with 70% ethanol. The DNA pellet was air-dried before being eluted in 50 μl of TE-RNase buffer. The DNA concentration and integrity was measured using Epoch Biotek (Thermo Scientific, USA), and a 0.8% agarose gel, respectively.
SSR genotyping
The SSR markers were selected from Ravishankar et al., (2011) for the assessment of nucellar and zygotic seedlings presented in Table 1. The genotyping process followed the protocol as suggested by Schuelke (2000) by concatenating each of the forward primer with M13 sequence. The PCR was conducted in final volume of 10 µl reaction mixtures containing 10 × Invitrogen PCR Buffer, 2.5 mM MgCl2, 1 μl genomic DNA (20 ng/μl), 2 μM dNTPs (0.2 μM/μl), 10 μM of each primer pair (0.5 μM/μl), 5 μM of fluorescent dye (FAM/NED/PET/VIC) (0.5 μM/μl) and 1 Unit of Taq polymerase (Invitrogen, California). The amplification of the target region was performed using the Applied Biosystem Gene Amp (Thermo Fischer Scientific, California). The PCR profile was set with an initial denaturation for 2 min at 95°C followed by 35 cycles of denaturation at 94°C for 30 s, annealing at 45 to 60°C for 30 s and extension at 70°C for 45 s before being terminated with a final extension for 5 min at 70°C. Then, the PCR products were resolved using an ABI3730xl DNA Analyzer (Thermo Fischer Scientific, California) with Gene Scan TM 500 LIZ (Applied Biosystems, California) used as the DNA ladder.
Scoring and data analyses
The output files (fsa. file) from ABI3730 DNA Analyzer were analyzed using Gene Mapper 5.0 (Applied Biosystems). The allele peaks were identified and scored as suggested by Arif et al., (2010). The seedlings alleles were compared with the parental alleles. POWERMARKER (Liu and Muse, 2005) was used to calculate the genetic distance. Number of seedlings germinated in for each seed was analyzed using descriptive analysis while the responses (zygotic or nucellar) was analyzed using logistic regression. The logistic regression model was used to estimate the sequence of germination for a certain odd ratio of nucellar: zygotic seedlings. Pearson correlation coefficient (r) was determined between the sequence of germination and all the vegetative parameters at p ≤ 0.05%.
Polyembryony in Harumanis mango
The results in this study revealed that there were 3.07 seedlings per Harumanis mango seed on average; ranging from 1 to 7 seedlings. Most of the seeds, 51.1% had 2 or 3 seedlings per seed while only 4.4% had 7 seedlings (Figure 2). The results in this study are in accordance with Zakaria et al. (2002) that reported 2 to 6 seedlings per seed in M. indica (cultivar Sala and Tangkai Panjang) while 1 to 2 seedlings per seed in Mangifera foetida and Mangifera caesia. The difference between the numbers of seedlings in species of Mangifera has been reported by Cordeiro et al. (2005) and is due to the genetic differences between the species.
Molecular analysis
The analysis of 136 seedlings which were derived from 45 seeds using microsatellite markers showed a total of four zygotic seedlings and they were identified in seeds 1, 15, 29 and 43 representing 8.9% of seeds evaluated ( Table 2). Most of the zygotic seedlings were found towards the end of germination sequence except for seed number 15. This was in agreement with the study conducted by Aline et al. (2014) on Uba cultivars. However, details and depth study are required to understand the sequence order and the occurrence of the zygotic seedling in Harumanis seed. Meanwhile, the remaining seedlings were considered as nucellar seedlings as the SSR based DNA profile match with the parent DNA profile of Harumanis mango. In the present study, seedlings that showed polymorphism at least by one primer were also considered as zygotic since this study was using codominant marker system. However, in this study, zygotic seedling from seed 15 showed the least polymorphic across all nine. As we were using codominant marker, we can directly and perfectly identified the heterozygote seedling which happened due to the pollination with the external pollen source. Hence, the seedlings with the presence of heterozygote allele form even at one marker were considered as zygotic seedling. Figure 3 describes the allelic segregation of nucellar and zygotic seedling on locus. The number of polymorphic marker was different between the zygotic seedlings which might be caused by receiving a different pollen donor. There is the successful study on identification of pollen donor in European Plum using microsatellite markers (Meland et al., 2020). Unlike previous studies which were using dominant marker system such as RAPD and ISSR, the researcher was required to set at least three polymorphic primers in order to consider the seedling is zygotic (Aline et al., 2014). Since the dominant marker system such RAPD and ISSR is unable to differentiate between homozygote and heterozygote of allele which lead to numerous number of marker is needed in order to identify the occurrence of zygotic and nucellar seedlings (Miah et al., 2013). The present study also showed there was no occurrence of double zygotic seedling in a single seed (Policaulismo).
Relationship of genetic material in accordance with germination sequence and vigour
In computing the probability of getting nucellar seedling for source of true-to-type cutting material based on germination sequence, the logistic regression model fitted from the data of germination sequence and genetic material (nucellar or zygotic) is: where Y is the outcome of nucellar or zygotic (nucellar (1) or zygotic (0)) and X is the germination sequence. Based on the parameter estimate in Table 3, the estimated odd ratio of *Numbers underlined on the germination sequence column are zygotic seedlings; not underlined are nucellar getting a nucellar seedling is . This means that as the germination sequence increases by one unit, the odds of getting nucellar seedlings reduces by 57%.
Based on the fitted logistic regression, the predicted sequence to obtain 90% nucellar seedlings is 5.47. This means that, the germination sequence of less than6 has 90% chances of getting nucellar seedlings compared to zygotic seedlings. Figure 4 shows the logistic regression plot for the germination sequence vs. predicted probability of getting a nucellar seedling.
Using germination sequence as a reference in getting nucellar seedlings for source of cutting materials serve some challenges as the nurserymen have to closely monitor germination and tag the seedlings. Alternatively, morphological characteristics such as leaf number, stem diameter and leaf area could also be used as reference with germination sequence. The results showed that there were significant (p<0.01) relationships between germination sequence and all the growth variables ( Figure 5). All growth variables were negatively correlated with germination sequence, with the strongest relationship . Zygotic seedling received an extra allele (125 bp) from external pollen source. S1G1 (seed 1, germination sequence 1), S1G6 (Seed 1, germination sequence 6). (r= -0.39; p ≤ 0.01) was recorded between germination sequence and stem diameter. This was followed by plant height (r= -0.38; p ≤ 0.01), leaf number (r = -0.29; p ≤ 0.01) and lastly leaf area (r = -0.27; p ≤ 0.01) (Figure 3).
However, leaf number showed positive correlation with plant height (r = 0.49; p ≤ 0.01), stem diameter (r = 0.43; p ≤ 0.01) and leaf area (r = 0.43; p ≤ 0.01). These results showed that there was an increase in leaf number with the increase of plant height, stem diameter and leaf area. The same result was obtained by Shaban (2010) who found that leaf number was correlated positively with leaf area and plant height and leaf area for Zebda mango seedlings from Egypt. In term of plant height, there was a strong positive correlation with stem diameter (r = 0.76; p ≤ 0.01) and leaf area (r = 0.64; p ≤ 0.01). The stem diameter also had strong positive correlation with leaf area (r = 0.65; p ≤ 0.01). Zakaria et al. (2002) found that the seedlings from different variety of Mangifera species seeds differ in terms of vigour, plant size or height depending on whether they are nucellar or zygotic in origin. Zakaria et al. (2002) and Muralidhara et al. (2015) suggested that the removal of seed coat might had given a superior response in all initiation of plant height, stem diameter, number of leaves per plant and leaf area. At the same time, the different response growth of seedlings produced after germination and emergence that may be caused by competition between seedlings for nutrient uptake, light and space.
This suggested that in order to have 90% chances of getting nucellar seedling (germination sequence below 6), the seedling needs to exhibit several morphological characteristics; big stem girth, tall plant, high leaf number and large leaf area. These are morphological characteristics of vigor seedlings. Generally, the most vigorous seedling from each seed are used by the nurserymen for the production of rootstock however the nucellar seedling is not always the most vigorous, which results in uneven orchards (Simon et al., 2010). In 'Uba' mangos, 60% of the seedlings tested were discovered as zygotic, and not correlated with the vigorous character tested (Aline et al., 2014). In addition, 90% of the most vigorous seedlings from seeds of 'Rosinha' mangos collected in 2002 and 2003 were of zygotic origin, while seedlings from seeds harvested in 2004 were mostly identified as nucellar, indicating no relationship between the type of embryo and seedling size (Cordeiro et al., 2005).
Conclusions
Harumanis is a polyembryonic mango with average 3 seedlings per seed. Based on SSR molecular markers, zygotic seedlings were found towards the end of germination sequence with 8.9% of total seeds evaluated. DNA marker system was proven to be the ideal approach in identifying zygotic and nucellar seedlings as the identification is not influenced by environmental factor and agronomic practices. Choosing vigour seedling will increase the chances of getting nucellar seedlings, which can be used as cutting source for true-to-type planting material or for breeding purposes.
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Domain: Biology Agricultural and Food Sciences
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Application of the GGE Biplot as a Statistical Tool in the Breeding and Testing of Early and Extra-Early Maturing Maize in Sub-Saharan Africa
In this paper is reviewed some aspects of the research conducted in subSaharan Africa in which the genotype main effect plus genotype by environment interaction (GGE) biplot was employed for the analysis and interpretation of the data. GGE biplot has been found quite effective in analyzing genotype × environment interaction, genotype × trait (GT) interaction, interpretation of diallel and line × tester data, and evaluation of the efficiency of testers in hybrid production. Application of GGE biplot to genotype by environment data from several studies has helped to identify outstanding varieties, inbreds and hybrids of early and extraearly maize in terms of yield performance and stability under stress and non-stress environments. The use of GT biplot analysis has resulted in the identification of ear aspect (EASP), plant aspect (PASP), anthesissilking interval (ASI), and number of ears per plant (EPP) as the most reliable traits for selection for yield under drought, low-N, high-N and well-watered environments. Studies comparing GT with path-coefficient analyses revealed that both methods identified EASP, plant height (PLHT), and ASI as the most important traits directly contributing to yield under drought stress. GT biplot identified EASP, EPP, and Striga damage as the most reliable traits for indirect selection for improved grain yield under Striga infestation. The biplot graphical analysis allowed visual display of the general combining ability (GCA) of the parental inbreds and specific combining ability (SCA) of the hybrids used in Griffings diallel mating design. In addition, information on the best mating partners, identification of proven testers and tester groups, and heterotic groups have been provided graphically. The disadvantages of the GGE biplot include limited number of entries, only two heterotic groups are handled by the method, and only fixed statistical model can be used. More attention needs to be focused on test of hypothesis and QTL analyses. Open Access Received: 26 February 2020 Accepted: 01 June 2020 Published: 11 June 2020 Copyright © 2020 by the author(s). Licensee Hapres, London, United Kingdom. This is an open access article distributed under the terms and conditions of Creative Commons Attribution 4.0 International License. Crop Breed Genet Genom. 2020;2(3):e200012. [URL]20200012 Crop Breeding, Genetics and Genomics 2 of 39
INTRODUCTION
The climatic, edaphic, and management variability in sub-Saharan Africa (SSA) is great and too formidable to be dealt with ordinarily. For example, the soil variability goes, as it were, from foot-to-foot and it is necessary that crops be able to cope with the variation. Similarly, crop plants vary a great deal in their response to environmental conditions. Therefore, genotype × environment interaction (GEI) has been defined as the degree of variation in response of a genotype across environments [1]. Genotype × environment interaction has been measured more by c. provide guidance that are very reliable in selecting the best genotypes or agronomic treatments suitable for planting at new locations and in the coming years [2].
Observable uniqueness in ensuring the interaction between the environment and the genetic make-up is called phenotype. Phenotypes could be assessed, observed, estimated, and arranged in groups according to features that they have in common. Environmental factors may be regarded as locations, growing seasons, years, nitrogen levels, rainfall, temperature, all of which could have positive or negative effects on genotypes [3]. Wu and O'Malley [4] described two classes of environments with detailed differences in their information: microenvironmental differences that cannot clearly be forecast such as yearly differences in drought conditions, rainfall, and level of insect damage; and macro-environmental differences which can be forecast, such as practice. According to the authors, the G × E variance can only be projected for the macro-environmental state.
There are difficulties in determining the varietal performance evaluated in experiments containing genotypes (G), locations (L), and years (Y) due to the genotype × location × year (G × L × Y) interactions not being easy to classify [5]. The complications resulting from G × E interactions can best be avoided by identification of stable genotypes that are adapted across crop production environments. To ensure the maintenance of broader adaptation and yield stability, superior experimental varieties have been selected based on the performance across contrasting environments. For example, the Regional Drought achieve this, we re-examined target testing environments in WA for their uniqueness as it was believed that some environments could never provide unique information, because of similarity to some other environments in separating and ranking genotypes without losing valuable information on genotypes. Furthermore, it was felt that stratification of maize evaluation environments could help improve heritability of measured traits, accelerate the rate of genetic gain from selection, and strengthen the potential competitiveness for seed production and maximize grain yields of farmers [11]. It was therefore very important to develop an in-depth understanding of the target agroecologies used for the evaluation of drought tolerant cultivars in WA and to determine if it could be subdivided into different mega-environments to facilitate a more meaningful cultivar evaluation and recommendation. [12]. The seed catalogue contains the list of varieties whose seeds could be produced and commercialized within the territories of the 17 member countries of ECOWAS and is an aggregate of the varieties registered in the national catalogues of the Member States. The catalogue offers a unique opportunity for the movement of good quality seeds of improved maize varieties and hybrids across the borders of the ECOWAS countries for production and marketing. As a result of these new developments and the implications of global warming, desertification, and recurrent drought in the sub-region, there was a need for a reexamination of the mega-environments in WA and the identification of core testing locations in each of the mega-environments in WA used for the evaluation of the three different regional trials in WA. A number of studies was therefore conducted to determine the representativeness, discriminating ability and the repeatability of the test locations used for the evaluation of the DT Regional Early Variety Trials and to identify core testing sites to facilitate testing, seed production and commercialization of drought tolerant cultivars in WA. Therefore, using the GGE Biplot statistical tool, Badu-Apraku et al. [13] examined the mega-environments in WA employed for testing the Regional extra-early maturing varieties.
The test locations Zaria, Ilorin, Ikenne, Ejura, Kita, Babile, Ina, and Angaredebou were identified as the core testing sites of the three megaenvironments for testing the Regional Uniform Variety Trials-Extra-early.
In another study, involving the testing sites for the Regional Early Trials, test environments were classified into four mega-environments [14].
Four test locations were highly correlated in their ranking of the genotypes in group 1, suggesting that a promising early maturing cultivar selected in one of these locations in one country will also be suitable for production in the other locations within the same mega-environments in different countries [14]. Similarly, eight test locations were highly correlated in their rankings of the genotypes in group 2 and therefore, a promising cultivar identified in one of these locations were likely to be adapted to the other locations. It was concluded that selecting a cultivar out of these two locations would likely result in varieties adapted to other locations within the same mega-environment. The identification of the core testing sites was expected to facilitate the selection of high yielding and stable cultivars in the four different regional trials of WA [Regional Uniform Variety Trial (RUVT)-early, RUVT-extra-early, Drought Tolerant (DT) Regional Early and the DT Regional Extra-early variety Trials] and seed production and marketing across the countries of WA. [15], only test locations with high discriminating ability were useful and only those that were also representative could be used in selecting superior genotypes. The repeatability of genotype ranking across years within test locations was also an essential aspect in test location evaluation. Using the GGE Biplot method, the GEI of the testing sites of the RUVT early and extra-early varieties in West and Central Africa (WCA) were studied and the test locations characterized and stratified into mega-environments and core testing sites to facilitate efficient and less costly testing of varieties [13,14]. On the other hand, the testing sites of the Regional Drought Tolerant Trials which were confined to the drought-prone locations in the four partner countries of the Drought Tolerant Maize for Africa (DTMA) project, namely, Nigeria, Ghana, Benin and Mali had not been studied. Therefore, it was believed that information on the representativeness, discriminating ability and repeatability of the testing sites of the DT Regional Variety Trials in WA would facilitate better understanding of the responses of drought tolerant maize genotypes in target drought environments and would be invaluable in designing an efficient and economic selection strategy for the International Institute of Tropical Agriculture (IITA) Maize Breeding Program. However, there was limited information on the representativeness, discriminating ability and repeatability of the testing sites of the Regional DT Trials which were largely in the drought prone locations in the four partner countries of the DTMA project, Nigeria, Ghana, Benin and Mali (Table 1). Therefore, twelve early maturing maize cultivars were evaluated for 3 years at 16 locations in WA to determine the representativeness, discriminating ability and the repeatability of the testing sites and to identify core testing sites using the GGE biplot method [16]. The results revealed that and not repeatable and would not be useful for evaluating early maize cultivars for drought tolerance.
Beyond analysis of a MET data where significant GEI is singularly partitioned into eigen values in principal component analysis to obtain information on stability and adaptability of genotypes, as well as discriminativeness and representativeness of the environments, it is interesting to note that GGE biplot is very appropriate for the analysis of any other data that can cast into a 2-way table. This facilitates the use of GGE biplot in graphical analysis of traits relationship and genetic data obtained from factorial mating designs as well as QTL studies [17].
In the rest of this paper, we address the issue of stability of performance by comparing varieties, using different statistical methods.
Specifically, our objectives are to (i) compare GGE biplot with other statistical analytic methods, (ii) determine the effect of genotype × trait interaction, (iii) test GGE biplot for analysis of genetic data using diallel and line × tester designs, (iv) evaluate the efficiency of testers in hybrid production, (v) discuss the strengths and weaknesses of the GGE biplot statistical tool, and (vi) give future directions.
GGE BIPLOT COMPARED WITH OTHER ANALYTICAL METHODS
Stability studies have allowed researchers to identify broadly adapted cultivars for use in breeding programs and have been helpful in recommending new varieties to farmers [18]. Different concepts leading to different definitions of stability have been proposed over the years [19,20]. Lin et al. [19] identified three types of stability concepts: The methods proposed by [24]) and [25] are examples of Type 3 concept.
Becker and Léon [20] stated that all stability procedures based on quantifying GEI effects belong to the dynamic concept. This includes the procedures for partitioning the GEI of Wricke's [26] ecovalence and Shukla's [23] stability of variance, procedures using the regression approach such as those proposed by Finlay and Wilkinson [22], Eberhart and Russell [24], and Perkins and Jinks [25] as well as non-parametric stability analyses such as rank summation index.
Lin and Binns [19] proposed Type 4 stability concept based on predictable and unpredictable non-genetic variation. The predictable component relates to locations while the unpredictable component relates to years. These researchers suggested the use of a regression approach for the predictable portion and the mean squares for years × location interaction for each genotype as a measure of the unpredictable variation.
The procedure involving combined analysis of variance is the earliest and the most used analysis method to measure the existence of GEI from METs with replicates. In recent times, however, a wide range of methods have been proposed to study GEI that were broadly divided into four groups: analysis of variance, stability or parametric, qualitative or nonparametric, and multivariate methods. We will consider three multiplicative methods here; that is, cluster analysis, additive main effect and multiplicative interaction (AMMI), and genotype and genotype by environment interaction (GGE) effects.
Cluster Analysis. Cluster analysis is a numerical classification
technique that defines groups of similar individuals. There are two types of classification. The first is non-hierarchical classification, which assigns each item to a class. The second type is hierarchical classification, which groups the individuals into clusters and arranges these into hierarchies for the purpose of studying relationships in the data. Comprehensive reviews of the applications of cluster analysis to study GEI can be found in [19]. The report from cluster analyses by Shaibu et al. [27] revealed the genetic diversity among the genotypes and identified genotypes that can be selected for hybridization and improvement of maize.
Additive Main Effects and Multiplicative Interaction (AMMI).
Stability methods have been used in both univariate and multivariate statistics [19]. Among the multivariate methods, the additive main effects and multiplicative interaction (AMMI) analysis are widely used for GEI investigations. This method has been effective because it captures a large portion of the GEI sum of squares, clearly separating the main and interaction effects, and often provides meaningful interpretation of data to support a breeding program [2]. The AMMI model combines ANOVA for the genotype and environment main effects with Principal Components Analysis of GEI [28,29]. Therefore, based on the AMMI model (IPCA1 and IPCA2) the AMMI stability value (ASV) has been used Crop Breed Genet Genom. 2020;2(3):e200012. [URL]20200012 Crop Breeding, Genetics and Genomics 11 of 39 [30]. The ASV is comparable with the methods used by Shukla [23] and Eberhart and Russell [24] for genotype stability [30].
The AMMI method can be used more effectively to analyze METs than ANOVA and PCA. According to Zobel et al. [28], ANOVA fails to detect a significant interaction component, PCA fails to identify and separate the significant genotype and environment main effects, while linear regression models account for only a small portion of the interaction sum of squares. The AMMI method takes care of the flaws in these methods and is used for three main purposes: a. The model diagnosis. AMMI is more appropriate in the initial statistical analysis of yield trials, because it provides an analytical tool of diagnosing other models as subcases when these are better for particular data sets [31].
b. AMMI clarifies the GEI by summarizing patterns and relationships of genotypes and environments [2,28].
c. It improves the accuracy of yield estimates. Gains have been obtained in the accuracy of yield estimates that are equivalent to increasing the number of replicates by a factor of two to five [28]. Such gains may be used to reduce testing cost by reducing the number of replications, increasing the number of treatments (e.g., varieties) in the experiments, or improving efficiency in selecting the best genotypes. It has proven useful for understanding complex GEI. The results can be graphed in a useful biplot that shows both main and interaction effects for both the genotypes and environments.
AMMI combines ANOVA into a single model with additive and multiplicative parameters. The model equation is: where Yij is the measured mean of ith genotype in jth environment; Yj is the grand mean; λ1 and λ2 are the singular values for PC1 and PC2; Ei1 and Ei2 are the PC1 and PC2 scores for genotype i; ϒj1 and ϒj2 are the PC1 and PC2 scores for environment j and εij is the error term.
The combination of ANOVA and PCA in the AMMI model, along with prediction assessment, is a valuable approach for understanding GEI and obtaining better yield estimates. The interaction is explained in the form of a biplot display where PCA scores are plotted against each other thereby providing a visual inspection and interpretation of the GEI components. Integrating biplot display and genotypic stability statistics enables genotypes to be grouped based on similarity of performance across diverse environments. Yield-stability statistic (YSi) was also used to recommend varieties for commercialization [32]. Kang [32] proposed an improved superior stability index (I) that is free from all the aforesaid drawbacks. A new approach, known as genotype selection index (GSI), was used by taking into consideration the AMMI stability value and mean yield for quantification of stability [33].
GENOTYPE AND GENOTYPE BY ENVIRONMENT INTERACTION (GGE)
Yan et al. [34] proposed a methodology known as GGE biplot for graphical display of GEI patterns. It allows visual examination of the relationships among test environments, genotypes and GEI. It is an effective tool for: (i) mega-environment analysis (e.g., "which-wonwhere" pattern), where specific genotypes can be recommended to specific mega-environments [35,36]; (ii) genotype evaluation (the mean performance and stability); and (iii) environmental evaluation (the power to discriminate among genotypes in target environments) [37]. ii) it can only identify two distinct heterotic groups in a genetic study where even more exist; iii) it cannot estimate genetic variances, covariances, and heritability; and iv) there is limited literature on its application to molecular data.
One recent study compared 15 methods of stability analysis using 17 varieties of maize evaluated in four years with several locations within the year for a total of 21 environments [41]. Spearman's rank correlation coefficient was used to rank the varieties ( Table 2). Many of the methods had no significant correlation with each other (Table 2) Another study was conducted to examine the effect of G×E on the performance and stability of 18 early maize cultivars and to identify core test sites and mega-environments at 15 locations in five countries of WA [14]. Results of the GGE biplot classified the locations into four megaenvironments, regardless of their countries and Kita (KX, lat. 13°05' N, long. 09°25' W) in Mali was identified as the ideal location, and Zaria (lat. 13°05' N, long. 09°25' W) in Nigeria was close to the ideal location ( Figure 4). In addition, variety 2004 TZE-W Pop STR C4 was identified in the study as the ideal cultivar because it had highest grain yield and was the most stable cultivar. Genotype-by-trait (GT) analysis presents the results of trait relationship by graphical display of the genetic relationships among traits [42]. It also provides information that helps to detect less important (redundant) traits and identify those that are appropriate for indirect selection for a target trait. The GGE biplot model equation for the genotype-by-trait analysis is as follows:
Genotype × Trait Analysis
Where Yij is the genetic value of the combination between inbred i and trait j; μ is the mean of all combinations involving trait j; βj is the main effect of trait j; λ1 and λ2 are the singular values for PC1 and PC2; gi1 and gi2 are the PC1 and PC2 eigenvectors, respectively, for inbred i; e1j and e2j are the PC1 and PC2 eigenvectors, respectively, for trait j: dj is the phenotypic standard deviation (with mean of zero and standard deviation of 1); and εij is the residual of the model associated with the combination of Inbred i and trait j. For the GT biplot analysis, the data were not transformed ("Transform = 0") but were standard deviationstandardized ("Scale = 1"), and trait-centered ("centering = 2"). Therefore, the outputs are appropriate for visualizing the relationships among genotypes and traits.
In order to validate consistency of the results of GT biplot with other multivariate techniques such as stepwise multiple regression analysis and path coefficient analysis, a study was conducted to compare the results of the GT biplot and path analyses by Badu-Apraku et al. [47].
Results revealed that both methods identified EASP, PLHT, and ASI as important traits directly contributing to yield under drought stress.
Similarly, Oyekunle and Badu-Apraku [48] reported that the two methods The ASI, EPP, EASP, and PASP were identified as most reliable traits for simultaneous selection of drought and low-N tolerant genotypes.
Diallel analysis
Adequate knowledge and understanding of genetic variability, modes of inheritance and heterotic response in a germplasm are very crucial for determining appropriate methods to employ for improving the genetic resources. Backcrossing, inbreeding, hybridization, and the S1 recurrent (Figure 8). Tester TZEI 3 was the closest to the ideal tester while Entry TZEI 7 had the highest GCA effects across stress environments (Figure 9).
In summary, analysing diallel data using GGE biplot is very fascinating and it provides more genetic information beyond just the combining ability of the parents and hybrids. It gives additional information on the relationship among parents, identify testers, assess efficiency of testers, display relationships among testers, identify tester groups, reveals best mating partners, and most importantly, identify heterotic groups. These additional information are not readily available in conventional analysis of diallel data.
A major limitation to the use of diallel mating design is that there is a limit to the number of parents that can be involved. Results of only a few parents can be clearly displayed. As the number of parents to analyse increases, the results of GGE biplot become clustered and both entry and tester labels overlap and the biplot graphical views appear clumsy. In a breeding program where hundreds of inbred lines have to be analyzed, diallel analysis using GGE biplot becomes impracticable.
Line × Tester Analysis
Because of the shortcomings of diallel design in handling large number of parents, line × tester analysis was proposed by Kempthorne Data were generated from 63 newly developed inbred lines crossed to four extra-early elite testers (TZEEI 13, TZEEI 14, TZEEI 21 and TZEEI 29) evaluated under multiple stress and stress-free environments. Using GGE biplot, an ideal tester could not be identified under stress environments.
However, testers TZEEI 13 and TZEEI 14 were the closest to the ideal tester under nonstress environments ( Figure 10) [56]. Inbred TZdEEI 34 was identified as outstanding in terms of GCA effects under both stress and nonstress environments. Testers TZEEI 13, TZEEI 21 and TZEEI 29 were found to be very efficient across stress environments based on their discriminating power while testers TZEEI 21 and TZEEI 29 were the best across nonstress environments ( Figure 11).
Challenges encountered with the application of GGE biplot for analysing data from line × tester are similar to those of the diallel.
However, because the number of testers used in line × tester analysis is usually less than in diallel (where number of parents is considered as the number of testers), the graphical display of the results of line × tester study is better than that of diallel. Interpretation of results of line × tester is also easier and simpler than that of the diallel.
North Carolina design II (NCDII) is the third factorial mating design that could be analysed using GGE biplot analysis. However, there is no report in the literature where GGE biplot has been used for analysis of data from NCDII. One reason could be because larger number of parents can be accommodated in NCDII compared to the diallel and for better organization, males nested within set is considered as a factor in the statistical model rather than male factor.
We recommend that for GGE biplot to have a wider application in analysis of genetic as well as agronomic data, the proponents should consider incorporating features that will be appropriate for analyses of random and mixed model data and data from nested type of mating design. Crop
Evaluation of the Efficiency of Testers in Hybrid Production
An important prerequisite for the development of high-yielding commercial hybrids is the availability of efficient testers, which could successfully discriminate, classify inbred lines into appropriate heterotic groups, and combine well with other inbred lines, open pollinated varieties or hybrids. An effective tester should be able to rank inbred lines correctly for performance in hybrid combinations and increase the differences between testcrosses for efficient discrimination [57].
Furthermore, such testers must have improved agronomic characteristics, resistance to diseases and tolerance/resistance to prevailing biotic and abiotic stresses such as drought, low-N and Striga. which were adopted for early and extra-early maize germplasm [58].
Over the years, several testers have been developed in the early and extra-early maturity group to facilitate the development of superior hybrids for SSA. This has necessitated identification of a few efficient testers for use in classifying the available inbred lines into heterotic groups as well as inbred lines for the development of outstanding commercial hybrids for production in SSA.
The GGE biplot tool has the potential for identifying efficient testers even though its use for such analysis has not been adequately explored.
Several early maturing inbred lines, including TZEI 10, TZEI 17, TZE 23, TZEI 129 and ENT 13 have been identified as potential testers in the IITA Maize Improvement Program (MIP) using the GGE biplot statistical tool.
The GGE biplot has been used to identify the most efficient testers among the five inbred lines. As described by Akinwale et al. [17] and Yan [59], the efficiency of a tester (testers were used to replace environments) is determined by the relationship among the testers and the length of the tester vector. The smaller the angle between any two testers, the more closely related the testers are while testers with longer vectors show high discriminating power or its ability to assess the grain yield of the crosses.
Badu-Apraku et al. [53] The GGE biplot is a superior data-visualization tool widely used in several major areas of agronomy, plant breeding and for analysis in genetic studies involving GEI, test location evaluation, genotype evaluation, mega-environment investigation and identification of parental inbreds for hybrid development [61]. This tool allows researchers to graphically extract and utilize information from METs data and other types of two-way data [35]. However, the full potential and shortcomings of this powerful tool are not completely understood by breeders, geneticists, agronomists, ecologists, entomologists and pathologists. The limited use of this tool could be attributed to lack of understanding of its potential capability on the part of many researchers.
Furthermore, the major weaknesses as well as potential useful areas of Breeders focus more attention on estimating genetic parameters since they increase the effectiveness of predicting gains from selection for the genetic enhancement of crop cultivars. GGE biplot has been extensively employed in combining ability analysis and identification of heterotic patterns using diallel data [35,53] and line × tester data [56]. Badu-Apraku and Akinwale [56] when the results of interrelationships among traits using GT biplot and sequential path analysis were compared [46].
In its application to analyze genetic data, classification of genotypes into heterotic groups has been based on the SCA effects only, which is represented by the projections of the entry vectors onto the ATC ordinate. In a situation where the GCA is preponderant over the SCA, classifying genotypes into heterotic groups based on SCA alone, as analysed by GGE biplot, will be grossly inefficient and the groups will not be distinct.
Another major challenge with diallel analysis using GGE biplot is that only two heterotic groups can be identified even when more groups are present. Other inbreds that cannot fit into the two groups become unclassified [17]. Furthermore, the proportion of parents classified are smaller relative to the total number of parents involved in the study. This is particularly of great concern especially in a standard breeding program that has committed considerable time, energy, efforts, land, funds and other resources to produce several inbred lines only to find out that just a few can be classified into heterotic groups for the purpose of hybrid development.
Another major shortcoming of GGE biplot analysis of genetic data such as diallel is that only fixed statistical model is applied. When genotype is considered as a random model where the experimenter is interested in computing genetic variances and heritability estimates, application of GGE biplot in the analysis of such data becomes limiting since the biplot has not been designed to display these parameter estimates graphically. Furthermore, GGE biplot tool has not been used in the analysis of data generated using North Carolina Designs I, II, and III and some other genetic designs. The use of GGE biplot in the analysis of the mating designs could facilitate a better understanding of the mating designs.
CONCLUSIONS AND FUTURE DIRECTIONS
GGE biplot is the most widely used multivariate analytical tool in the analysis of plant breeding data. The interpretation of GGE biplot analysis of genetic data is more comprehensive with wider applicability than the conventional statistical methods. Nevertheless, the lack of discrete statistical test of significance in its analysis has sometimes made the reliability of its results debatable by researchers. However, its results have been found to be consistent with that of ANOVA, correlation, that are yet to be fully explored especially for the tropical maize germplasm.
DATA AVAILABILITY
The dataset of the study is available from the authors upon request.
AUTHOR CONTRIBUTIONS
BBA, BF and RA conceived and designed the reviewed experiments as well as drafted the manuscript. BBA, BF, RA, BA and SAK executed the experiments. SAD and JT assisted in drafting the manuscript. All authors critically reviewed the manuscript.
CONFLICTS OF INTEREST
The authors declare that there is no conflict of interest.\===
Domain: Biology Agricultural and Food Sciences. The above document has 2 sentences that start with 'GGE biplot has been', 2 sentences that start with 'The use of', 2 sentences that start with ' [URL]20200012 Crop Breeding, Genetics', 2 sentences that start with 'For example, the', 2 sentences that start with '09°25' W) in', 2 sentences that end with 'Badu-Apraku et al', 2 paragraphs that start with 'The GGE biplot', 2 paragraphs that end with 'of the diallel'. It has approximately 4834 words, 193 sentences, and 65 paragraph(s).
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Effects of prostaglandin administration 10 days apart on reproductive parameters of cyclic dairy nulliparous goats
This study reported the effects of prostaglandin (PGF2) administration 10 days apart on reproductive parameters of cyclic artificial inseminated (AI) nulliparous Alpine (n=9) and Saanen (n=9) goats. Animals received two doses of 22.5g PGF2 10 days apart. After 1 st and 2 nd PGF2 administrations, estrus was monitored at 12 h intervals, with a buck teaser. Plasma progesterone concentration (ng/mL) was determined from blood sampled on day 0 (1 st PGF2) and the following 5, 10 (2 nd PGF2), 15, 20, 25 and 30 days. After the onset of the second estrus, females were transrectally (5 MHz probe) scanned at 4 hour intervals until at least 8h after ovulation. Pregnancy was checked through transrectal ultrasound on days 20, 25, 30, 35 and 90 after insemination. All parameters studied did not differ between breeds (P>0.05). Estrous response and interval to estrus, respectively, after 1 st (78.9% and 50.617.2h) and 2 nd PGF2 (88.9% and 50.014.8h) administration did not differ (P>0.05). Overall animals ovulating (100.0%), interval to ovulation after 2 nd PGF2 (64.519.5h) and after estrous onset (18.09.1h), ovulation rate (1.3±0.5), diameter of ovulatory follicle (8.1±1.1mm) were recorded. Embryo loss occurred before day 30 of pregnancy. Estrus can be efficiently synchronized in nulliparous Alpine and Saanen goats with two doses of prostaglandin 10 days apart.
INTRODUCTION
The concentration of herd reproductive activities has a direct impact on breeding management and handling. In this context, estrous synchronization can be a valuable tool, since it allows at any given week day activities such as artificial insemination and embryo transfer, easier estrous identification which facilitates reproductive management as well as allows birth synchronization and thus meat and milk production.
Basically there are three ways to synchronize estrus in goats. First, in anestrous goats by using a combination of progestagens, gonadotrophins and prostaglandins through light programs or melatonin implants. Second, by male effect in females previously isolated (one to three months) from the buck's presence, which may also activate the estrous synchronization cycle as well as anticipate the reproductive activity in anestrous females (transition season). The third form includes prostaglandin administration either in a single dose or in two doses 10 to 11 days apart during breeding season (Fonseca et al., 2007).
An important factor to consider in estrous synchronization and superovulation is the moment of ovulation. Its recognition and association with the interval elapsed from the administration of prostaglandin and onset and duration of estrus may generate more efficient protocols of artificial insemination, based on estrous detection and at a fixed time. The moment of ovulation was initially recognized by laparoscopy (Chemineau, 1983) and nowadays may be easily identified by ultrasound (Castro et al., 1999). Soon one may monitor ovarian dynamics from the onset of estrus and identify ovulatory follicles and its rupture, and formation of the hemorrhagic body, based on each structure ecographic characteristics. Additionally, the recognition of possible differences between breeds, as well as hormonal profiles during and after synchronization may be very important.
The objective of this study was to evaluate the effects of administering two doses of prostaglandin at 10 days apart on reproductive parameters of nulliparous Alpine and Saanen goats.
MATERIALS AND METHODS
This study was conducted during the months of May and June in a location with the following coordinates: 20º45'S latitude and 42º51'WG longitude; altitude of 692.73m with CWA (dry winter and humid summer) climate, annual average temperature of 20.9ºC and annual rainfall of 1,203mm 3 . At these coordinates, the transitional portion of the seasonal breeding pattern extends from December to February, the breeding season from March (end of summer) to June (end of autumn) and the anestrous season from July to November.
The experiment used Saanen and Alpine breeds. Nine Alpine (aged 8-18 months and weighting 31.3±1.9kg,and nine Saanen, aged 8-18 months and weighting of 37.5±7.7kggoats were used. Body condition score (scale 1 to 5) was assessed and showed 2.5±0.2 e 3.0±0.7,respectively, for Alpine and Saanen goats. Goats were kept on suspended pens and fed ad libitum with corn silage and concentrated ration twice a day according to nutrient demand. Water and mineral salt were permanently available.
Animals received two doses of 22.5g synthetic prostaglandin (d-cloprostenol; Prolise , ARSA S. R. L., Buenos Aires, Argentina) intra-vulvo submucosal at 10 days interval. After the first prostaglandin administration, estrus was monitored twice daily from 06:00 to 08:00AM and 4:00 to 06:00PM, with the aid of buck teasers. Estrous detection persisted for 10 days until the administration of the second dose of prostaglandin, when all animals were evaluated through transrectal ultrasound with the help of a 5MHz probe (Aloka ® , SSD -500, Tokyo, Japan) coupled to polyethylene support. After the administration of the second dose of prostaglandin, doses were observed every four h in order to accurately identify the onset of estrus. Once estrus was identified, animals were submitted to ultrasound evaluation every four h until ovulation detection and at least for the next 8h. Ovulation was considered as having occurred when previous detected follicles collapsed and could not be observed (Ginther and Kot, 1994). Both ovary and uterus were monitored. After ovulation detection, females were inseminated with frozen-thawed semen (0.25mL straws/100x10 6 spermatozoa) of proved fertility with a minimum of 50% of progressive motility after thawing and 40% HOST reaction (Fonseca et al., 2005). All females were evaluated through ultrasound following an elapsed period of 20, 25, 30, 35 and 90 days after the second prostaglandin administration, in order to prematurely detect and confirm pregnancy.
Blood was sampled on days 0 (first prostaglandin administration), 5, 10 (second prostaglandin administration), 15, 20, 25 and 30 through jugular puncture in heparinized vacuolated tubes. After collection, the tubes were kept on ice until centrifugation in a refrigerated centrifuge at 5ºC, at 2500×g/15 min. Plasma was then aspired and stored at −20º C until analysis. Time from blood collection to plasma aspiration did not exceed 2h. Plasma progesterone concentration (ng/mL) was determined through the use of the solid phase radioimmunoassay, using a commercial kit (Coat-a-count ® progesterone kit, DPC, Diagnostic Products Co., Los Angeles, CA, USA). The mean intra-and inter-assay coefficient of variation was 7% and 8%, respectively.
The following characteristics were determined: estrous response; interval to estrus (h): time from prostaglandin administration to the onset of estrus; interval to ovulation (h): time from second prostaglandin administration to ovulation; interval to ovulation after estrous onset (h): time from estrous onset to ovulation; interval to artificial insemination (AI; h): time from second prostaglandin administration to AI; interval to AI after estrous onset (h): time from estrous onset after second prostaglandin administration to AI; percentage (%) of ovulating animals; ovulation rate; diameter of ovulatory follicle (mm): maximum diameter of follicle previously detected before ovulation; ovulation on left or right ovary (%); plasma progesterone concentration (ng/mL); number of fetuses per goat artificially inseminated; conception rate (%).
Statistical analysis included one way analysis of variance for testing the differences in variables studied between nulliparous Alpine and Saanen goats. Parametric variables were tested with the aid of Tukey test processed with SAEG. Nonparametric variables were analyzed using χ 2 procedures. Linear Pearson correlation was performed (Ribeiro Júnior, 2001). All tests were considered at minimum level of 5% significance.
RESULTS
The percentage of animals in estrus after the first and the second prostaglandin administrations did not differ between breeds (P>0.05;Table 1). Two responsive goats showing estrus 36 and 48h after first dose (one Alpine and one Saanen) displayed another estrus five days after this one. These animals did not present estrus after second prostaglandin administration. Four goats showed P4 concentration inferior to 1ng/mL on day 0; three did not come into estrus and one came into estrus 14h after first cloprostenol administration. These four goats showed lower (P<0.05)P4 (0.3±0.3ng/mL) at first cloprostenol administration when compared with those which came into estrus (5.6±2.3ng/mL). A similar pattern was observed for the second cloprostenol administration, when three goats with P4 lower than 1ng/mL did not come into estrus. These three goats showed lower (P<0.05)P4 (0.3±0.3ng/mL) at second cloprostenol administration when compared with those that came into estrus (5.8±2.2ng/mL)(Figure 1). On the other hand, goats that did not come into estrus after first or second cloprostenol administration showed higher (P<0.05)P4 on days 5 and 15 (4.6±2.1 and 4.8±0.6ng/mL)than those showing estrus (0.8±0.6 and 1.0±1.9ng/mL),respectively. Days after prostaglandin administration < 1ng/mL > 1ng/mL Figure 1. Nulliparous dairy goats submitted to estrous synchronization with two doses of 22.5g cloprostenol (PGF2) on days 0 and 10 that showed plasma progesterone concentration (ng/mL) lower or higher to 1ng/mL on the moment of the cloprostenol administration.
Arq
Ovulatory characteristic of estrous synchronized goats submitted to ultrasonography evaluation are shown in Table 3.
Negative correlation was observed between interval to estrus after the first prostaglandin administration and interval to ovulation after the second prostaglandin administration (r=-0.57;P<0.05). One Alpine goat was removed from the experiment during ultrasound evaluation because of accidental natural breeding.
In both breeds embryo loss occurred before ultrasonography on the 30 th day. Ultrasonography performed on day 20 of the studied animals (17 to 18 days after ovulation) was 94.7% efficient in order to detect pregnancy precociously (vesicles). Only one goat which showed higher progesterone (11.6ng/mL) on day 20 was not classified as pregnant through the same day ultrasound. However, this goat came into estrus five days later (day 25). One Alpine goat with 1.5ng/mL P4 on day 20 was pregnant at ultrasonography, but this goat came into estrus four days before day 20. For the other animals, those with progesterone under 0.5ng/mL (0.2±0.1ng/mL; range 0.1 to 0.4ng/mL) or higher than 1ng/mL (7.1±0.5ng/mL;range 2.8 to 8.9ng/mL) were classified as non-pregnant and pregnant, respectively, through ultrasonography on the 20 th day.2). Vesicles counts recorded 1.6 and 1.0 for Alpine and Saanen goats, respectively at day 20 after the second cloprostenol administration. From four Alpine goats with two vesicles at this time, two remained with both vesicles, one lost one vesicle, and one lost both vesicles. From seven goats with one vesicle, two Alpine and three Saanen goats lost their vesicles until the end of the study.
Goats were grouped as pregnant (vesicles present) and non-pregnant (vesicles absent) at day 20 after the second prostaglandin administration. P4 was higher (P<0.01) in pregnant than in non-pregnant at this time (Figure 3).
Embryo detection and loss are presented in Figure 4. The number of pregnant animals detected via ultrasound 20 days after the administration of the second dose of prostaglandin ( day 18 after ovulation) was 7/8 (77.8%) in the Alpine and 5/8 (62.5%) in the Saanen breed.0.23 0.28 0.17 In both breeds embryo loss occurred before ultrasonography on the 30 th day. Ultrasonography performed on day 20 of the studied animals (17 to 18 days after ovulation) was 94.7% efficient in order to detect pregnancy precociously (vesicles). Only one goat which showed higher progesterone (11.6ng/mL) on day 20 was not classified as pregnant through the same day ultrasound. However, this goat came into estrus five days later (day 25). One Alpine goat with 1.5ng/mL P4 on day 20 was pregnant at ultrasonography, but this goat came into estrus four days before day 20. For the other animals, those with progesterone under 0.5ng/mL (0.2±0.1ng/mL; range 0.1 to 0.4ng/mL) or higher than 1ng/mL (7.1±0.5ng/mL;range 2.8 to 8.9ng/mL) were classified as non-pregnant and pregnant, respectively, through ultrasonography on the 20 th day.
As can be seen on Fig. 4, the ultrasonography revealed that embryonic losses occurred before the first 30 days of pregnancy. Under the conditions of this study, embryos/fetuses that survived up to this time were not lost until 90 days of pregnancy.
DISCUSSION
The percentage of animals in estrus after the first cloprostenol administration was similar to those related by Goel and Agrawal (1998), who reported 81.8% estrous response in goats without definite breed and 75.0% in Jakhrana lactating goats. At that moment, forming corpus luteum was not susceptible to prostaglandin effects. Thus, short estrous cycle can decrease the estrous response after the second prostaglandin administration.
Furthermore, the percentage of animals in estrus after the second administration of prostaglandin was similar to the one described by Pandey et al. (1985) in cyclic Black Bengal goats (86.0%).
The same dose and intervals used in the present study were also studied by Siqueira et al. (2009) in Toggenburg goats who reported 100% estrous response after second prostaglandin administration. Previous studies reported dosages ranging from 50 to 250g of the same drug (Baril et al., 1993;Kusima et al., 2000).
Overall, the interval to estrus observed in this study after first (50.1h)and second (50.0h) prostaglandin administration was smaller than those (71.3 and 65.3h) reported by Goel and Agrawal (1998), respectively in Jakhrana and unknown origin goats. The smaller interval related in this study as compared to that could be a result of administration route. Mellado et al.
(1994) reported a smaller interval from prostaglandin administration to the onset of estrus by comparing the use of intra-vulvo submucosal to the results of intramuscular route.
Thus, it appears that the intra-vulvo submucosal route could allow a quicker access and a greater quantity of molecules to the target tissue, the corpus luteum, averting a first passage and hepatic metabolization. Siqueira et al. (2009) reported a 46h interval to estrus after second prostaglandin administration in Toggenburg goats using the same via. In Nubian goats submitted to two prostaglandin administrations 12 days apart, Romano (1998) reported interval to estrus higher after first (72h) and similar after the second prostaglandin administration (52h) than those reported in the present study.
Goat experiencing estrus later after first prostaglandin administration could probably be in growing follicular pattern and have follicles capable of ovulating if luteal progesterone was removed. This model was proposed by Menchaca and Rubianes (2004). Thus, according to interval between prostaglandins, one or two follicular waves can emerge in this period. If luteolysis is evoked during the follicular wave´s final growing phase, animals come into estrus and ovulate earlier than those animals which luteolysis occurred at the initial rump of follicles growing.
As shown in Figure 1, the presence of an active corpus luteum at the time of the prostaglandin administration is the key point to determine the success of estrous synchronization. Animals with P4 shorter than 1ng/mL at this time could have been in estrus two or three days earlier, or entering in estrus at the moment of the prostaglandin administration. Consequently, at day five pos prostaglandin administration, these animals are expected to have higher P4 when compared to those that suffer luteolysis and came into estrus in response to prostaglandin. Maffili et al. (2006), with protocol similar to a previous study but with eCG administered at sponge or CIDR removal, reported 100% goats ovulating, 1.5 ovulation per goat and 7.7mm diameter of ovulatory follicles in Toggenburg goats.
Menchaca
The higher ovulation frequency of the right ovary noted in this study corroborates the conclusion of Prasad et al. (1980). Camp et al. (1983) also noticed that the right ovary is more active than the left one. Chávez et al. (1987) noted this phenomenon in rats when they showed the asymmetry of the information carried between right and left vagal nerve to ovaries. The uni or bilateral section of the vagal nerve produced different results depending on the in situ ovary. Ovulation rate in rats unilaterally ovariectomized was smaller in the left ovary when compared to the right one (42 vs. 84%).
The authors concluded that, in the model used, regulatory compensatory systems are more likely to occur in the right ovary than in the left one. On the other hand, Romano and Abella (1997) did not find activity differences between the right and left ovaries in Nubian goats.
Higher P4 in pregnant goats 30 days after the second administration of prostaglandin was expected because non-pregnant animals could have had or were close to having luteolysis, as shown by Regueiro et al. (1999). The presence of functional corpus luteum in the moment of prostaglandin administration is required to successfully synchronize estrus. This corpus luteum must be responsive to prostaglandin activity, which happens on the fourth day of the estrous cycle in goats (Ott et al., 1980).
Pregnant animals detected via ultrasound 20 days after the administration of the second dose of prostaglandin ( day 17 to 18 after insemination) were confirmed by their P4. This proves the strong association between ultrasound and hormonal diagnostic exams, as noted by Engeland et al. (1997) It was previously reported that although confirmed pregnant and giving birth according to first estrus, an Alpine goat showed a second estrus 15 days after the first one (Fonseca et al., 2006). Thus this kind of finding appears to be repetitive for goats.
The results of this study confirm the possibility of precocious identification of goat pregnancy (<20 days). However, given embryonic mortality, it is suggested that a later evaluation (after 30 days) should be made to confirm pregnancy. Thus, there is no practical use for earlier detection of pregnancy in goats.
Finally, the central objectives of this study were to test the efficiency of estrous synchronization with prostaglandins and the identification of the ovulation moment. Due to this, goats were inseminated after ovulation, and the quality and viability of the formed embryo could be compromised. In cattle, Saacke et al. (2000) noted that insemination at the moment of estrous detection lead to better quality embryos but with a smaller fertilization rate. Inseminations performed 24h after the onset of estrus resulted in higher fertilization rates but also increased the amount of embryos with inferior quality and degenerated ones. Inseminations performed 12h after the onset of estrus resulted in intermediary results. Additionally, Motlomelo et al. (2002) observed a 52% pregnancy rate in progestagen induced estrous goats. They inferred that the probable explanation of the relatively small pregnancy rate was a consequence of insemination at the end of the estrus.
CONCLUSIONS
Estrus can be efficiently synchronized in Alpine and Saanen breeds using two doses of prostaglandin 10 days apart. The identification of the ovulation relative to the estrous onset is very important to establish effective protocols of insemination. Ultrasound supervision associated with plasma hormone (progesterone) determination is an important study tool.
Embryos may be visualized and their development may easily be followed. However, precocious diagnosis should be confirmed in more advanced pregnancy stages given the possibility of embryonic losses occurring after that.
Figure 2 .
Figure 2. Plasma progesterone concentration (ng/mL) of nulliparous Alpine and Saanen goats submitted to estrous synchronization with two doses of 22.5 g cloprostenol (PGF2) 10 days apart.
Table 1 .
Estrous response (%) of Alpine and Saanen goats submitted to administration of two doses of
Table 3 .
Ultrasonography characteristics of ovarian follicles after the second prostaglandin administration in nulliparous Alpine and Saanen goats submitted to two doses of prostaglandin 10 days apart et al. (2007)working with cyclic pluriparous Alpine goats submitted to estrous
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Domain: Biology Agricultural and Food Sciences
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Assessment of Genetic Diversity and Population Structure of Tunisian Barley Accessions ( Hordeum vulgare L.) Using SSR Markers
In barley breeding programs, information about genetic dissimilarity and population structure is very important for genetic diversity conservation and new cultivar development. This study aimed to evaluate the genetic variation in Tunisian barley accessions ( Hordeum vulgare L.) based on simple sequence repeat (SSR). A total of 89 alleles were detected at 26 SSR loci. The allele number per locus ranged from two to five, with an average of 3.4 alleles per locus detected from 32 barley accessions, and the average value of polymorphic information content was 0.45. A cluster analysis based on genetic similarity was performed, and the 32 barley resources were classified into five groups. Principal coordinates (PCoA) explained 12.5% and 9.3% of the total variation, and the PCoA was largely consistent with the results of cluster separation of STRUCTURE software analysis. The analysis of genetic diversity in barley collection will facilitate cultivar development and effective use of genetic resources.
Introduction
Barley (Hordeum vulgare L.), one of the first and earliest crops domesticated by humans, is a major cereal grain grown in temperate climates globally. It is one of the oldest crops in the world and ranks fourth after wheat, rice, and maize (Poets et al., 2015). In Tunisia, barley is mainly cultivated in arid and semiarid climates in areas with annual rainfall of below 400 mm. In less developed Mediterranean countries such as Tunisia, barley plays a key role as its grain and straws are the principal feed for livestock. Small ruminants such as sheep and goats are the main livestock in Tunisia, representing a valuable dietary contribution in rural areas and a principal economic output (Medimagh et al., 2012). Genetic improvement to increase yield is underway in the Tunisian breeding program. Yield in barley is a complex trait governed by several genes and is a result of interactions between several components. The development of high yielding varieties adapted to local conditions depends on the understanding of the existing variability and genetic relation between barley accessions. Therefore, evaluating genetic diversity of barley lines using molecular markers is important in barley breeding for successful exploration, genetic stability, and effective conservation, because morphological characters are limited in number and unstable (Azartamar et al., 2015). Amplified fragment length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), simple sequence repeats (SSR), and single nucleotide polymorphism (SNP) have been used to study genetic diversity and structure in crops (Bwalya et al., 2020;Mwangi et al., 2019). Several studies have been performed on barley to assess their genetic diversity in different germplasm collections using molecular markers. However, most studies were based on either cultivar collections (Tondelli et al., 2013) or mixtures of cultivars and landraces (Elakhdar et al., 2016). Moreover, these studies have been conducted using SSRs (Yahiaoui et al., 2014), SNPs (Cronin et al., 2007), and DArT array (Ovesná et al., 2013). SSR markers have been broadly used in plant genetic research because they are available, highly informative, and distributed throughout the genome (Varshney et al., 2005).
The main objective of this study was to analyze genetic diversity, which exist among the 32 Tunisian lines including four varieties, using 26 molecular markers. The study will facilitate cultivar development and effective use of genetic resources.
Plant Materials and DNA Extraction
Thirty-two Tunisian barley lines; including four cultivars (Rihane, Manel, Lemsi, and Kounouz), one with uncertain improvement status, and 27 landraces; were used in this study. All accessions were obtained from the U. S. National Plant Germplasm System (NPGS) international database. According to the passport data, 28 accessions were collected or donated from Tunisia between 1922 and 1972 (Table 1). Eight seeds from each accession were germinated and leaves harvested at three-leaf stage after 15 days of planting. Genomic DNA was extracted using GRS Genomic DNA kit (Grisp, Portugal) according to the instruction of the manufacturer. DNA quality and quantity were determined using a UV-Vis spectrophotometer and visual comparison of 2% agarose gel electrophoresis.
PCR Amplification of SSR Markers
All accessions were typed using 25 SSR markers and one InDel marker (HvBM5-Intr) that were reported and obtained from GrainGenes marker report ( [URL]/) ( Table 2). PCR amplification was performed in a total volume of 10 µL, consisting of 6 µL of GRS Hotstart Taq Mastermix (Grisp, Portugal), 0.25 µL of each SSR marker (10 µM), and 1 µL of DNA (50 ng). The PCR product was analyzed on a 2% agarose gel, and DNA amplification performed in a FastGene Ultra Cycler (96-well) (Nippon Genetics, Germany). The PCR was then subjected to the following conditions: initial denaturation at 95 • C for 5 min followed by 35 cycles of denaturation at 95 • C for 30 s, annealing at 52 • C to 62 • C for 30 s, and final extension at 72 • C for 30 s.
Data Analysis of Genetic Diversity and Population Structure
The number of alleles, observed heterozygosity (Ho), expected heterozygosity (He), and loci polymorphic information content (PIC) were determined using CERVUS software version 3.0.7 (Kalinowski et al., 2007). Cluster analysis of relationships between accessions based on SSR marker data was performed with the method of ward using DARwin 6.0 (Perrier & Jaccquemond-Collet, 2014). SSR marker genotyping results were used to estimate the population structure of the 32 barley accessions using STRUCTURE software. The distribution of ∆K values was determined by evaluating the logarithmic likelihood [L(K)] (Evano et al., 2005).
To determine the population structure of the studied accessions, genotyping data were processed with STRUCTURE software 2.3.4, which implements a modelbased Bayesian cluster analysis (Pritchard et al., 2000). A putative number of subpopulations ranging from K = 1 to 10 was assessed using 50,000 burn-in iterations, followed by 50,000 recorded Markov chain iterations. To estimate the sampling variance of inferred population structure, 10 independent runs were carried out for each K. The actual number of subpopulations was determined using the logarithm of likelihood for each K; ln P(D) = L(K), and the optimum value of ∆K was obtained by ∆ K = [L ′′ (K)]/SD, according to the report of Evanno et al. (2005), to determine the most likely number of groups. Based on the subpopulations inferred by structural analysis, we carried out analysis of molecular variance (AMOVA) to assess the population differentiation using GenAlEx version 6.5 (Peakall & Smouse, 2012) with 999 times boost-strapping.
Allelic Diversity of SSR Markers
In this study, we used 32 Tunisian barley lines, including four cultivars developed by the Tunisian breeding program. Twenty-six molecular markers, distributed across the seven chromosomes of barley, were used to genotype the selected lines. The number of polymorphic alleles ranged from two to five in the studied barley accessions. A total of 89 alleles were detected, with an average of 3.4 alleles per locus ( This indicates that accessions could be divided into 5 clusters. Each cluster was represented by different color ( Figure 2B).
Clustering and Population Structure
Estimated likelihood [ln P(D)] was found to be greatest when K = 5, suggesting that the population used in this study can be divided into five clusters (Figure 1). The modern cultivars, Rihane and Lemsi, were found in Cluster 1, whereas Kounouz and Manel were distributed in Cluster 2.
The average distance (expected heterozygosity) between accessions in each cluster was 0.46. The highest value of 0.53 was observed in Cluster 5, indicating greater genetic diversity within the clusters; however, Cluster 3 showed the lowest value of 0.42. Genetic differentiation (F ST ) ranged from 0.21 in Cluster 5 to 0.46 in Cluster 3, with a mean of 0.34.
AMOVA test was applied to the codominant data matrix to obtain information on the variation within and among populations using GenAlEx software. The results of the AMOVA indicated that most genetic variation was among individuals (47%) ( Table 3).
Phylogenetic Analysis
Unweighted neighbor-joining dendogram was constructed based on Nei's similarity coefficient of 32 genotypic data and revealed the genetic relationship among the accessions. The tree showed four groups of accessions ( Figure 2). All accessions collected from Kébili (south of the country) were found in Groups 3 and 4. Cultivars Unweighted neighbor-joining dendrogram showing genetic relationship among the 32 barley accessions based on the genetic dissimilarity matrix data of SSR markers alleles. All the accessions were divided into four groups. The colors of branches indicate accessions corresponding to the clusters (Cluster 1 to 5) from population structure analysis as in Figure 1. Numbers indicate accessions mentioned in Table 1. and accessions collected from the north and north west of the country were located in Groups 1 and 2. When the unrooted phylogenetic tree was compared with the clusters obtained from the STRUCTURE analysis, the phylogenic tree matched well with the cluster separation in the STRUCTURE analysis. Accessions in Cluster C4 belonged to Group A3, accessions in Clusters C3 belonged to group A2, and accessions in Cluster C5 belonged to Group A1. Accessions in Clusters C1 and C2 belonged to Groups A1, A2, and A4.
Principal Coordinate Analysis
Principal coordinate analysis (PCoA) was conducted to further assess the population structure identified using SRUCTURE. The principal coordinates explained 12.5% and 9.3% of the total variation. The PCoA was largely consistent with the results of STRUCTURE. The first principal coordinate (PCo1) clearly separated 32 barley accessions into 5 groups (Figure 3).
Discussion
Estimating the genetic diversity of plant genetic resources is one of the important prebreeding activities in crop breeding. Assessing genetic diversities is important in identifying genotypes that underlie important phenotypic and genetic shifts during domestication (Vigouroux et al., 2008) and distinct genetic groups for retention of germplasm (Agrama & Eizenga, 2008). Identification of barley cultivars, lines, and accessions of Tunisian genetic resources have been based on phenotypic traits and agromorphological data. Such methods cannot provide reliable information for calculation of genetic distance and validation of pedigree (Stanton et al., 1994). The average PIC value obtained in this study is higher than the average PIC (0.36) reported by Elakhdar et al. (2016). In general, a PIC value higher than 0.5 is useful in genetic studies because it can distinguish the polymorphism of a marker (DeWoody et al., 1994). He values demonstrate the diversity level of markers, and the values obtained in this study are high; the diversity of markers reported by Pompanon et al. (2005) is also high. He values ranged from 0.094 to 0.731, with a mean value of 0.51, suggesting that there is an extensive genetic variation within the 32 barley accessions genotyped in this study. Unrooted phylogenetic tree was compared with the clusters obtained from STRUCTURE analysis using SSR markers. The phylogenic tree matched well with the cluster separation in STRUCTURE analysis. The phylogenetic tree clearly differentiated groups according to their geographic origin. The estimation of genetic diversity and population structure of 32 Tunisian barley lines using molecular markers may provide more accurate information to barley breeders than the classical pedigree method. The 26 primer pairs used in this study may also be of potential value for further research on genetic mapping, segregation analysis, and phylogenetic status analysis of newly introduced germplasm.
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Domain: Biology Agricultural and Food Sciences
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Effect of Growth Regulators on Seed Germination and Its Significance in the Management of Aeginetia indica L . — A Root Holoparasite
Seed germination in root holoparasites depends on receipt of certain chemical signals from the host plant. It is possible to induce germination in such seeds without the association of hosts by using growth regulators under in vivo and in vitro conditions. IAA, GA3 and Kinetin have been used to induce seed germination in Aeginetia indica L. to analyse the possible ways of exploiting knowledge of germination for the management of this weed. Seeds pre treated with 50 mg·L of GA3 showed the production of aseptate, uninucleate root hair-like tubules, which probably help in the anchorage with host root. Under in vitro, GA3 (5.0 and 7.5 mg·L) has been found to induce and enhance percentage of seed germination. Therefore, it is concluded that GA3 could be used to bring suicidal germination of seeds thereby manage this parasitic weed effectively. Further production of uninucleate tubules and organisation of conventional bi-polar seedling under the influence of GA3 is being reported for the first time in this taxon.
Introduction
Aeginetia indica L. is a herbaceous root-holoparasite causing considerable yield reduction in sugar cane, rice, maize, sorghum, fodder grasses and host of other Monocotyledonous taxa in India, China, Japan, Philippines, Indonesia etc. [1]. It produces abundant minute yellow white seeds (Figure 1(A)). Longisection of the seed shows an undifferentiated embryo consisting of radicular and plumular pole without any semblance of embryonal organs like cotyledons, hypocotyl, epicotyl etc. (Figure 1
(B)).
It is established that seed germination in root-holoparasites depends on receipt of certain chemical signals from the host plant [2]. However, it is possible to induce germination in such seeds without the association of hosts in vitro [3]. Seed germination studies on Aeginetia indica are limited to Chennaveeraiah et al. [4] and French and Sherman [5]. Information on the details of seed germination and seedling morphogenesis is a prerequisite for developing methods for the management of parasitic taxa of Scrophulariaceae and Orobanchaceae [6], and hence the present study. Different growth regulators were used to induce seed germination to analyse the possible ways of exploiting knowledge of germination for the management of this parasitic weed.
Material and Methods
Seeds of A. indica were collected from Kerekatte, Chikamagalore District and Belthangady, South Canara District, Karnataka, India, during August and September. Seeds were harvested from mature capsules just before dehiscence, shade dried and stored in butter paper envelopes, under laboratory conditions. Both in vivo and in vitro culture methods were employed to know the effect of growth regulators like GA 3 , IAA and Kinetin. In vivo studies were conducted by pretreating seeds with growth regulators whereas in in vitro studies MS medium [7] was supplemented with different concentrations of growth regulators to establish their effects on seed germination.
In vivo culture was carried out in sterile petri plates lined with moist filter paper at room temperature (28˚C ± 2˚C) with constant relative humidity. Before incubation seeds were surface sterilized with nascent chlorine water and washed with distilled water. About 300 seeds were taken and arranged on the filter paper in rows to facilitate recording of mode and percentage of germination. The experiments were run in triplicates and average results were recorded.
In Vivo Studies
In vivo seed cultures raised on moist filter paper maintained under either light or dark conditions for three months failed to show any signs of germination. Seeds water washed for two, four, six and eight days gradually turned yellow to pale and when placed on moist filter paper failed to germinate even after 10 weeks in both light and dark conditions. Cold treatment showed swelling of seeds only.
Seeds pretreated with GA 3 (1 mg•L -1 ) did not show any signs of germination even after 30 days. Seeds kept in GA 3 (50 mg•L -1 ) showed 63.2% germination within 20 days, while those maintained in 100 mg•L -1 showed very low percentage of germination. During germination the cells of the embryo at radicular end become swollen and spheroidal. When transferred on to moist filter paper one to six of these spheroidal cells elongate themselves into cylindrical root hair-like embryonal tubules each measuring 600 -1240 in length & 25 to 30 in breadth (Figures 1(D) and (E)). The outgrowths remained unicellular and uninucleate (Figure 1(F)). No further growth and differentiation was seen. Seeds pretreated with Kinetin and IAA at different concentrations viz. 1 mg•L -1 , 50 mg•L -1 and 100 mg•L -1 and sown on moist filter paper did not show any signs of germination.
In Vitro Studies
In in vitro culture of seeds inoculated on M S medium supplemented with IAA (0.1 mg•L -1 ) germination occurred in 28 days. The first observable change in the germinating seeds was an increase in volume of the embryonal cells at the radicular end, which ultimately emerge out rupturing the seed coat. The emerged part developed into a mass of rounded cells called tubercle. The percentage of germination was 1.9% at the end of 40 days.51.2% of the germinated seeds developed tubercles. In 60 days the percentage of germination increased to 23.16%. In nearly 70.2% of the germinated seeds tubercles developed. Further development ceased and the embryo died by end of 10 weeks.
Seed germination within 40 days with IAA (1.0 mg•L -1 ) was 14.7%, 83.2% of which produced tubercles. In 60 days germination percentage increased to 48.16%. In 95.8% of the germinated seeds tubercles were initiated and they gradually increased in size as a mass of parenchyma cells. L. S. of the germinated seeds showed organization of the radicular and plumular meristem. In 6.2% of the seeds roots arose endogenously and are thick horn like measuring about 1 -4 mm in length (Figure 2(A)). Proliferation of the plumular pole was observed whose cells were quite distinct with dense cytoplasm and prominent nuclei.
With IAA (2.5 mg•L -1 ) 6% germination occurred in 40 days. The percentage was increased to 14% by 60 days. Tubercles were initiated in 30% of the seeds. No further growth was noticed. With IAA (5 mg•L -1 and 7.5 mg•L -1 ) only swelling of the seeds were seen without any signs of germination even after 60 days.
GA 3 (0.1 mg•L -1 ) has not shown any influence on seed germination even after 60 days except swelling of seeds. With GA 3 (1.0 mg•L -1 ), 14.3% seeds germinated in 40 days out of which 48.2% produced two to five mm long tubercle. On transfer to fresh medium they produced small mounds of new tissue. In some of them roots differentiated at the free surface. The percentage of germination increased to 52.4% by the end of 60 days. After ten weeks they turned sepia brown and died.
With GA 3 (2.5 mg•L -1 ) 29.4% seeds germinated by the end of 40 days, it was 58.3% by the 60th day.66.4% of the seeds germinated in 40 days and 83.1% in 60 days. The radicular pole emerged through the micropyle to form a golden-yellow tubercle which was globular with smooth surface. Comparatively the peripheral cells of the tubercle were rich in starch. Ultimately, a large lobed yellowish mass of varied shape resulted. Roots developed endogenously and emerged through its exposed surface. Further growth was not seen.
The germination was 6.45% in 25 days with GA 3 (5.0mg•L -1 ). By 40 days it recorded 46.3% and was 68.2% in 60 days. Of the 84.2% of germinated seeds, cells of the radicular end of embryo increased in size and protruded out through micropyle, following rupture of seed coat. It proliferated further to form a golden yellowish tubercle.
Simultaneously cells of plumular pole by cell division and cell enlargement extend out of seed coat. The shoot apex took its origin from the exposed plumular part. Due to unequal growth of the embryonal tissue, the seed coat was pushed to a side. The shoot apex developed on the lateral side. In a few cases the root developed adjacent to the shoot apex (Figure 2(B)).
Percentage of germination with GA 3 (7.5 mg•L -1 ) showed an increase at all the observation periods, with 21.6% in 30 days, 56.2% in 40 days and 71.4% in 60 days. Tubercles developed from the radicular pole of the embryo in 52.1% of the germinated seeds in 30 days, 68.8% in 40 days and 89.1% in 60 days. In nearly 10.2% of the germinated seeds branched roots without root cap and root hairs developed from the free surface of the proliferated tissue, away from the medium. Simultaneous Copyright © 2012 SciRes.
AJPS
with the growth of the radicular pole, the plumular pole also proliferated and extended out of the testa organizing an endogenous meristem on its lateral side.
Ontogenetic study of the embryo during seed germination on MS medium supplemented with GA 3 (5.0mg•L -1 and 7.5 mg•L -1 ) was made. The first noticeable change in the seeds was an increase in volume of embryonal cells at the micropylar end. As the embryo increased in size, the seed coat ruptured and exposed the epidermal cells of the radicular end. This end later developed into a mass of tissue-the tubercle by cell division and cell enlargement. On transfer to fresh medium, the tubercle produced a small mound of tissue. Sections of the germinated seeds revealed the in-situ enlargement and proliferation of the radicular pole of the embryo, while the quiescent plumular end stayed in the cup-shaped endosperm tissue enclosed by the seed coat (Figure 2(C)). As growth continued the cells of the plumular end differentiates into a shoot meristem and the seed coat is sloughed off. Initially the growth of the tubercle was uniform, later it became asymmetric. The procambium extended from the root to the shoot meristem (Figure 2(D)). The root apex consisted of densely protoplasmic cells with prominent nuclei. Further growth could not be traced as the cultures turned sepia brown and died. Anatomical observations revealed lysis of procambial elements.
On medium supplemented with Kinetin (2.5 mg•L -1 ), germination was initiated in 30 days in only 0.3% of seeds. In 40 days it was 2.2% and by 60 days it increased to 10% and this is the maximum percentage recorded for Kinetin treatment. In 91.2% of the germinated seeds the initial increase in size of the seed was followed by the rupture of the testa, exposing epidermal cells of radicular end of the embryo. Cells of radicular pole emerged out forming a protuberance of spheroidal cells. It grew both by cell division and cell enlargement to form a yellowish massive tissue of variable shapes. On transfer to fresh medium it proliferated further organizing mounds of tissue. The peripheral cells of the proliferated tissue had more of starch. Occasionally roots developed from the free end of the massive tissue. Sections of this region showed the presence of procambium in a few cases. Proliferation of the plumular cells occurred without further differentiation. Plumular differentiation occurs only after the establishment of haustoria with its host. Other concentrations of Kinetin have insignificant effect on germination (Table 1).
Discussion
The present study has revealed that seeds of A. indica germinate without any root exudate under laboratory conditions. It is apparent that host stimulus itself is not a must and could be substituted by growth regulators and minerals under light conditions. The study also showed that the light factor is essential for germination contrary to the observations made by French and Sherman (1976) who stated that light inhibits germination. Pretreatment with 5% sodium hypochlorite (NaOCl) was also not a prerequisite to break dormancy as the seeds readily germinated. Chennaveeraiah et al. [4] obtained a callused embryo when pre-soaked seeds sown on modified Whites' medium containing mineral elements supplemented with coconut milk (10%) + Kinetin (2 ppm) or both Kinetin (2 ppm) and 2,4-D (2 mg•L -1 ). They noticed occasional development of roots but no plumule morphogenesis.
Root hair-like tubular aseptate uninucleate outgrowths produced by seeds pretreated with GA 3 (50 mg•L -1 ) have not been reported in any member of Orobanchaceae earlier. These tubules probably help in the anchorage of seeds with the host root and may be compared with the sticky embryonal tubules of Balanophora abbreviata Bl. of Balanophoraceae [8]. The unicellular embryonal tubules of the present study cannot be compared with the multicellular, multiseriate structures called tendrils reported by French and Sherman [5].
1.0 mg•L -1 IAA induced development of a mulberry shaped nodule/tubercle from the protruded radicular end of the embryo from which endogenous horn like roots without root caps differentiated. The plumular pole of the embryo enclosed in the seed coat becomes active and develops into shoot only after the establishment of contact with the host root in nature. While under in vitro conditions it becomes active and develops into shoot only after the formation of tubercle and endogenous roots from the radicular pole of the embryo leading to the formation of bi-polar embryo. In the present study morphogenesis of the plumular pole led to the differentiation of shoot meristem in the proliferated plumular tissue adjoining the seed coat unlike the shoot differentiation from the mass of tissue called tubercle arising from the "germtube" at the radicular pole of the embryo of Orobanche crenata [9]. Therefore, IAA appears to induce polar growth in A. indica leading to conventional bi-polar origin of root and shoot without the intervention of host plant, a feature similar to Cistanche tubulosa [10].
Of all the growth regulators maximum percentage of germination occurred at GA 3 (5 mg•L -1 and 7.5 mg•L -1 ) and almost all the seedlings exhibited plumule morphogenesis. The organization of a conventional bi-polar seedling under the influence of GA 3 (5.0mg•L -1 ) has never been recorded earlier in this taxon. It is clear from the above findings that it is possible to grow this parasitic plant in culture without the influence of host root exudate. Therefore it is concluded that GA 3 (5.0 and 7.5 mg•L -1 ) could be used to bring suicidal germination of seeds in the management of this parasitic weed.
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Domain: Biology Agricultural and Food Sciences
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Sward structure and livestock performance in guinea grass cv . Tanzania pastures
Grazing strategy is a key element in the determination of sward structure, herbage nutritive value and animal performance. We aimed to compare the herbage characteristics and performance of livestock in pastures of Panicum maximum cv. Tanzania managed, using two rotational stocking strategies, which provided either a fixed-length rest period (FRP) of 35 days in the spring and fall and 30 days in the summer, or a variable-length rest period (VRP), determined by the time required for the canopy to achieve 70 cm in height. The pastures were evaluated in the pregrazing condition for forage mass (FM); leaf (LP), stem (SP) and dead matter (DP) percentages; and nutritive value (NV). The animals were weighed every 28 days. Pastures managed with the FRPs exhibited greater FMs, SPs and DPs and lower LPs and NVs than those managed with the VRPs. The average daily livestock weight gain was greater during the spring and summer for the VRP than for the FRP pastures, resulting in an average animal weight gain per area of 990 and 860 kg ha−1 wet period−1 for the pastures managed with the VRPs and FRPs, respectively. Thus, pasture rest periods that were maintained after the sward reached 70 cm in height reduced the animal performance on Tanzania guinea grass.
Introduction
In general, Panicum maximum cv. Tanzania (Tanzania guinea grass) has been managed under rotational grazing with fixed rest periods regardless of the season (Andrade et al., 2006) fertilization levels, (Lopes et al., 2013) and irrigation (Dupas et al., 2010). Management based on fixed and predetermined grazing and rest periods facilitates the planning of rotational grazing. However, Pedreira et al. (2007) observed inconsistent responses in Brachiaria brizantha cv Xaraes pastures when fixed rest periods were used, since, depending on the time of year and the prevailing growing conditions, these periods can be too short, leading to production losses in herbage, or too long which can result in loss of quantity and quality, as well as the degeneration of the canopy structure. Similar results were obtained by Barbosa et al., (2007) and Da Silva et al., (2009), who suggested that rest periods that are fixed and defined a priori for guinea grass pastures can restrict livestock production as these periods do not generate uniform plant physiological response patterns, resulting in variable sward structures.
The regrowth of Panicum maximum cv. Tanzania (Tanzania guinea grass) should be interrupted when the canopy is intercepting 95 % of the incident light (LI), which corresponds to a sward height of 70 cm (Barbosa et al., 2007). Growth beyond this point promotes sward deterioration, characterized by higher percentages of stem and dead material, a lower percentage of leaf and a reduced leaf-to-stem ratio. As the steer diet consists primarily of leaf blades, the presence of sheath, stem and dead material at the grazing horizon limits the bite depth (Chacon and Stobbs, 1976). Under such conditions, it is common to observe an increase in the bite duration, a reduction of the bite rate (Palhano et al., 2007) and an increase in the daily grazing time (Difante et al., 2009), resulting in inefficient harvesting and, consequently, low animal performance (Da Silva et al., 2012).
In this study we compared the sward structure, nutritive value and animal performance in Tanzania guinea grass pastures subjected to two rotational stocking strategies, one with fixed and predefined grazing and rest periods (FRPs) and the other based on a target of 70 cm for pregrazing and canopy height, or to reduce the rest period during the summer to 30 days for pastures managed with the FRPs.
Materials and Methods
The experiment was conducted during the 2007 and 2008 growing seasons in Campo Grande, in the state of Mato Grosso do Sul, Brazil (20º27' S, 54º37' W, 530 m a.s.l.). According to the Köppen classification, the climate is rainy tropical savannah of the Aw subtype, which is characterized by a seasonal distribution of rainfall and well-defined occurrence of the dry period during the colder months. Monthly rainfall and the minimum, average and maximum temperatures during the study period were recorded at the Embrapa weather station (Figure 1), 5 km from the experimental site. The average temperature and monthly precipitation were used to calculate the water balance ( Figure 2). The value used for the soil water storage capacity was 75 mm.
The Tanzania guinea grass pastures were established on an Oxisol and fertilized with 80 kg ha −1 of P 2 O 5 , 80 kg ha −1 of K 2 O and 200 kg ha −1 of N. The fertil-Sci. Agric. v.71, n.6, p.451-457, November/December 2014 izers were applied in Oct 2007, except for N that was divided into three applications, namely, Oct, Dec and Feb. Twelve modules each measuring 1.125 ha were divided into six paddocks of 0.188 ha each. A 12.0 ha-reserve pasture was also used for holding the put-and-take animals (the 'grazers') when they were not needed in the experimental units.
A randomized complete block experimental design was employed, with two treatments and six replicates. The treatments consisted of two strategies of rotational stocking as follows: one with fixed and predefined grazing and rest periods (FRP), of 7 and 35 days, during spring and autumn and 6 and 30 days in summer, respectively, and the other with a variable rest period associated with a pregrazing height of 70 cm (VRP), corresponding to the height at which the canopy intercepts 95 % of the LI (Barbosa et al., 2007). Both treatments were associated with a common postgrazing height of 35 cm.
Each experimental unit (module of six paddocks) was grazed by five Nellore tester steers, which were approximately 12 months of age when the experiment started and had an average initial weight of 290 kg. The testers were assigned randomly to experimental units; differences in the allocation weight across treatments were not significant at the beginning of the rainy season, and the animals remained in the same paddock for the entire experimental period. Ninety 'grazer' steers similar to the tester steers in weight, age, background and breeding were kept in the reserve pasture and used where needed to adjust the stocking rate.
For the pastures managed with FRPs, the grazers were added to or removed from all the paddocks as determined by the pregrazing herbage mass (HM) and the predetermined postgrazing height (35 cm). For the pastures managed with the VRPs, the decision regarding the addition or removal of the grazers was based on the pregrazing HM, the postgrazing target and the need for the animals to remain in their current paddock (variable grazing period) until the canopy height in the next paddock to be grazed reached the pregrazing target (70 cm sward height; which corresponds 95 % LI, Barbosa et al., 2007).
Throughout the regrowth period, the sward height of the pastures managed with the VRPs was monitored twice a week using a 1-m ruler graduated in centimeters to systematically perform the height measurements along five transect lines (ten measurement points per transect) in each paddock. The sward height measurements were taken from ground leaves based on the 'leaf horizon' on the top of the sward as a reference. This procedure was followed throughout the growing season, including the periods when the plants were reproductive and produced taller flowering stems. For those pastures managed with the FRPs, the sward height was only monitored when the grazing period started. The same procedure was used to determine the postgrazing height.
All pasture measurements were conducted in two paddocks for each module (experimental unit). The pre-and postgrazing herbage masses were determined from twelve samples per paddock. The herbage was cut at ground level using a 1-m 2 frame. Each sample was weighed and divided in two. One of the subsamples was oven dried at 65 ºC and weighed, and the other was separated into leaf (leaf blades), stem (stems and leaf sheaths) and dead material before each was dried at 65 ºC and weighed.
The herbage and the leaf accumulation rates were calculated as the difference between the current pregrazing and preceding postgrazing masses, considering the masses of the green (leaves and stems) or only the leaf portions, respectively, divided by the number of days between the pre-and postgrazing sampling times.
Two hand-plucked samples were taken from each paddock. The samples were oven dried, ground to pass through a 1-mm screen and analyzed to obtain estimations of crude protein, neutral detergent fiber, acid detergent lignin and in vitro organic matter digestibility via near infrared reflectance spectrophotometry (NIRS) according to Marten et al. (1985).
All steers were weighed after fasting for 16 h at 28-day intervals. The average daily gain (ADG) was cal- culated as the difference between the liveweights of the testers divided by the number of days between the weight measurements. The stocking rate was calculated according to Petersen and Lucas (1968) as the product of the average liveweights of the tester and grazer steers and the number of days that these steers remained in the experimental unit. The liveweight gain per area was obtained by multiplying the ADG of the tester steers by the number of steers (testers and grazers) retained per module and per grazing cycle. The data were grouped according to seasons of the year as follows: spring, 17 Oct to 19 Dec 2007;summer, 20 Dec 2007to 19 Mar 2008and autumn, 20 Mar to 20 May 2008. The data were subjected to an analysis of variance using the PROC MIXED in SAS (Statistical Analysis System, version 9.4). The applied model included the random effect of the blocks and the fixed effects of the management strategy, the season and the interaction between strategy and season. Where appropriate, the means were compared with Tukey's test (p < 0.05). The ADG data were analyzed via a multivariate analysis with repeated measures according to Littell et al. (2000). The correlation between sward height and herbage mass was estimated using the PROC CORR in SAS (Statistical Analysis System, version 9.4).
Results
The pregrazing sward heights remained within the planned range throughout the experiment, except for the first and last grazing cycles (Table 1) in pastures VRP. Regardless of the season, the postgrazing residue target was maintained close to the planned values (Table 1), resulting in similar (p = 0.2102) herbage masses (HMs), which averaged 2870 ± 83 kg ha −1 of dry matter (DM).
The VRP pastures reached the pre-grazing target faster in the summer than in the other seasons (p = 0.0001) ( Table 2). Additionally, the grazing period (GP) was shorter in the summer than in the other seasons (Table 2). Except in autumn, the grazing and rest periods were shorter (p = 0.0001) for the pastures managed with the variable-length rest period (VRPs) than for those managed with the fixed and predefined grazing and rest periods (FRPs) ( Table 2). There was interaction between management strategy and season for the herbage accumulation (HA, p = 0.0001), the pregrazing HM (p = 0.0074), the leaf (LP, p = 0.0001) and stem (SP, p = 0.0001) percentages, and the leaf-to-stem ratio (LSR, p = 0.0001).
Regardless of the management strategy, HA was greater in the summer than in the other seasons (Table 2). During spring and autumn, there was no difference in HA between the management strategies; however, in summer, HA was greater for the FRP pastures than for the VRP pastures ( Table 2). The pregrazing HM was lower in the autumn than in the other seasons under both management strategies. The HM was higher in summer than in spring for the FRP pastures but did not differ between summer and spring for the VRP pastures (Table 2). By contrast, the HM was similar under both management strategies during autumn and spring but higher in summer for the FRP pastures than for the VRPs ( Table 2).
Regardless of season, the FRP pastures presented lower LPs and higher SPs than the VRP ( Table 2). The FRP pastures exhibited similar LPs and SPs throughout the growing season. By contrast, the VRP had higher LPs in summer and higher SPs in autumn when compared with the other seasons (Table 2). Except in autumn, the mean leaf-to-stem ratio (LSR) was lower for the FRP pastures (Table 2).
No seasonal effect (p = 0.2683) or management strategy by season interaction (p = 0.0944) was detected for the dead matter percentage (DP); however, the mean DP was higher for the FRP pastures than for the VRP (Table 3).
There was no interaction between management strategy and season for the herbage (HAR, p = 0.7612) and leaf (LAR; p = 0.2470) accumulation rates. Moreover, no management strategy effect (p = 0.7820) was detected for the HAR; however, the VRP pastures exhibited a higher mean LAR than FRP (Table 3). Furthermore, both the mean HAR and LAR were highest in summer and lowest in autumn (Table 4).
At postgrazing, there was no interaction between management strategy and season (p = 0.0548) for the LP, SP or DP. Moreover, no management strategy effect was detected for the LP (p = 0.7852), SP (p = 0.3729) or DP (p = 0.5717), which averaged 25.9 ± 0.5 %, 28.5 ± 0.6 % and 45.6 ± 0.8 %, respectively. No seasonal effect was found for the SP (p = 0.2206) or the DM (p = 0.1132). However, the mean LP was highest (p = 0.0009) in the summer (28.2 %) compared with the spring (25.3 %) and autumn (24.4 %). In the pregrazing sward, an interaction between management strategy and season was detected for the crude protein (CP, p = 0.0001), the neutral detergent fiber (NDF, p = 0.0122) content and the in vitro organic matter digestibility (IVOMD, p = 0.0003) but not for the acid detergent lignin (ADL, p = 0.0530). During summer, the VRP pastures had higher CP contents and IVOMDs as compared with the FRPs. Higher NDF contents were observed for the pastures managed with the FRPs during spring and summer (Table 5). Lower CP and IVOMD values and higher NDF contents were observed in autumn for the VRP pastures and in summer for the FRPs (Table 5).
Overall, a higher ADL content was observed for the FRP pastures than for those managed with the VRP ( Table 3). Regardless of management strategy, the mean ADL content was higher during the autumn than during the other seasons (Table 4). There was interaction between management strategy and season for the stock- ing rate (SR, p = 0.0302) and for the average daily gain (ADG, p = 0.0001). The highest mean SR was observed during summer for both management strategies (Table 6). During the summer only, the mean SR was higher for the FRP pastures than for the VRPs (Table 6). During spring and summer, greater ADGs were observed for the VRP pastures than for the FRPs; however, the two management strategies produced similar ADGs in autumn (Table 6). Furthermore, the FRP pastures exhibited a greater ADG mean in the spring than in the other seasons; however, for the VRP pastures, a lower ADG mean was observed in autumn than in the other seasons ( Table 6). The higher SR observed in summer for the FRP pastures did not offset the lower individual liveweight gain (Table 6), resulting in a higher (p = 0.0433) animal weight gain per area for the pastures managed with the VRPs than for the FRPs, which averaged 990 and 860 ± 39 kg ha −1 growth season −1 , respectively. 63.2 Ab (0.7) Means followed by the same lowercase letter within rows or means followed by the same uppercase letter in the same column, do not differ (p > 0.05) by Tukey's test; values in parentheses are standard errors of the difference.
Discussion
During the dry period preceding the beginning of the experiment, the pastures were grazed to maintain the postgrazing height of 35 cm. Despite the welldefined dry period from May to Sept, optimal weather conditions for the growth of P. maximum cv. Tanzania were not restored until mid-November 2009 (Figures 1 and 2). However, in practical terms, the beginning of the rainy season coincides with low forage availability throughout the property and, in general, the producer has difficulty reallocating the animals to adequate pastures, making it necessary to use pastures as soon as they start recovering (Difante et al., 2009). For this reason, and because the two strategies were evaluated over the same interval, we decided to initiate grazing after the sward in the paddocks of each module had first reached an average height of approximately 50 cm, i.e., less than the targeted pregrazing height (70 cm). At this stage, P and K fertilization was applied, followed by the first N application. This procedure resulted in a pregrazing height that was below the target for the first grazing cycle in the VRP pastures (Table 1). By contrast, the below-target pregrazing heights for the cycles performed during Apr and May could be explained by the HARs reaching their lowest values in autumn (Table 4), a consequence of the water deficit recorded from Apr ( Figure 2).
Except in autumn, the pregrazing target (70 cm) was reached faster than the fixed and predefined rest periods (Table 2), which resulted one more grazing cycle in the VRP than in the FRP treatment group (Table 1) and could be a consequence of the higher HARs in spring and summer (Table 4). This result may be explained by the more favorable spring and summer climatic conditions (Figures 1 and 2) and the application of 1/3 or 2/3 of the N fertilizer by spring or summer, respectively. Similar HAR values (68 kg ha −1 d −1 ) were recorded under both management strategies. However, the higher LAR mean observed under the VRP than the FRP strategies (Table 3) is in agreement with other studies (Carnevalli et al., 2006;Barbosa et al., 2007;Da Silva et al., 2009) in which the maximum net leaf accumulation for guinea grass was obtained when the canopy was intercepting 95 % of the incident radiation. Moreover, Barbosa et al. (2007) found that 95 and 100 % light interception corresponded with canopy heights of 70 and 85 cm for Tanzania guinea grass, respectively. Based on this information, the process of regrowth was interrupted for the FRP treatment group (Table 1) when the canopy intercepted more than 95 % of the incident radiation.
In general, the prolongation of the rest period along with the interception by the canopy of 95 % of the incident light, which for Tanzania guinea grass corresponds to a 70 cm canopy height, resulted in a higher HM pregrazing (Table 2). However, this increase was primarily from the accumulation of stems (Table 2) and dead material (Table 3) because there was no difference (p = 0.1593) between management strategies in the pregrazing leaf lamina mass (3300 ± 144 kg ha −1 of DM). Note that negative correlations were observed between the canopy height and the stem mass (p = 0.0001, r 2 = 0.80) and between the canopy height and the mass of dead material (p = 0.0001, r 2 = 0.69). The interruption of regrowth at canopy heights greater than 70 cm promoted higher percentages of stem and dead material and a lower leaf percentage and leaf-to-stem ratio (Tables 2 and 3). It is probable that these circumstances contributed to an increased difficulty grazing, resulting in lower ADGs during spring and summer for the FRP pastures (Table 6). This result is consistent with the observation made by Chacon and Stobbs (1976) that the presence of stem and dead material at the grazing horizon limits the bite depth. Under such conditions, it is common to observe an increase in the bite duration, a reduction in the bite rate (Palhano et al., 2007) and an increase in the daily grazing time (Difante et al., 2009), which all influence nutrient intake and thus, animal performance (Da Silva et al., 2012).
The greater pregrazing HA mean observed for the FRP pastures during summer (Table 2) could be explained by the longer rest period imposed on those pastures (Table 2). Consequently, a higher stocking rate (SR) was also necessary in the summer to maintain the postgrazing height target (Table 6) for the FRP pastures. The longer rest period may additionally explain the lower CP and IVOMD percentages (Table 5) and the lower ADG (Table 6) for the FRP pastures during summer. The decreased nutritive value and ADG as the rest period increased were also observed by Cândido et al. (2005) for guinea grass pasture.
The changes in the grazing period for the VRP pastures (Table 2) could be explained by the variation in the HARs (Table 4), the decisions regarding the SR adjustments necessary to maintain the postgrazing tar-get ( Figure 3) and the need for the animals to remain in their current paddock until the next paddock to be grazed reached the pregrazing target. Consequently, the SR was higher in summer for the VRP pastures (Table 6).
Regardless of the management strategy, the steer liveweight increased steadily over the growth season ( Figure 3). The animals grazing the FRP pastures had similar ADGs throughout the growth season (Table 6). By contrast, for the VRP pastures, the ADG in autumn was lower than in the other seasons (Table 6) because of the autumn declines of the sward LSR (Table 2) and forage nutritive value (Tables 4 and 5). The structural sward variation in autumn could be partly explained by the flowering of P. maximum cv. Tanzania, which occurs during mid-April in Campo Grande (latitude 20º27' S). After the emergence of the inflorescence the leaf appearance ceases, stem elongation and dead material accumulation increase. Additionally, the decrease in the nutritive value of tropical pastures during the reproductive phase was observed by Minson (1990).
The number of extra animals (1.9 AU ha −1 ) used in the pastures managed with the FRP pastures during summer did not offset the lower individual liveweight gain (Table 6), resulting in lower liveweight gain per area (WGA). Thus, the shorter rest period during summer was ineffective for grazing management because this reduction was unable to control the sward structure and consequently the herbage nutritive value. In contrast, the use of the pregrazing canopy height to define the intervals between successive grazing periods dealt effectively with this variability. Thus, the WGA difference of 130 kg ha −1 during the growth period justified the monitoring of the pasture heights, the reduction of the rest period (Table 2) and the adjustments in the stocking rate ( Figure 3) that were required to maintain the target-based grazing management, especially during the months of greatest pasture growth.
Conclusions
The use of a plant-growth based criterion like sward height to define intervals between successive grazing periods results in better control of sward structure and herbage nutritive value. Intervals between successive grazing periods greater than those needed for the canopy of Panicum maximum cv. Tanzania to reach 70 cm result in reduced animal performance and consequently, in lower yield per area. Thus, the greater animal productivity justified the monitoring of the pasture heights, the reduction of the rest period and the adjustments in the stocking rate that were required to maintain the target-based grazing management.
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Domain: Biology Agricultural and Food Sciences
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Effects of feeding pomegranate peel silage on feed intake and growth performance of Turkey bred sheep
The experiment was conducted to determine the effects of feeding pomegranate peel silage with beet top silage, wheat straw, alfalfa hay, barley, cotton seed cake and mineral plus on feed intake and growth performance of Turkey bred sheep in research farm of Agriculture Faculty, Kabul University. Twelve, two and half years old turkey bred sheep with (57.240 ± 5.28) kg average initial body weight were used in a completely randomized design (CRD). Animals were caged individually in 3 groups and 4 replications. Groups included in this experiment were, first group (Control) or T1 pomegranate peel silage (PPS) 0%, second group or T2 (5% PPS) or 106 g and third group or T3 (10% PPS) or 211 g. In addition, animals were fed with 633 g barley, 633 g alfalfa hay, 211 g cotton seed cake, 106 g beet top silage, 4 g mineral plus with the same amount and wheat straw for control group or T1, T2 and T3, 528 g, 422 g and 317 g in dry matter (DM) basis, respectively once in a day at around 8 am. According to statistical analysis, there was a highly significant difference between groups in feed intake and significant difference in growth performance of sheep. According to L. S. D test, it was shown that the second group (T2) was better in feed intake and growth performance compared to other groups. The FCR of T1, T2 and T3 were 12.43, 7.88 and 15.13, respectively and the FCE were 8.05, 12.69 and 6.61 in control group, T2 and T3, respectively. Results of this study suggest that feeding (5%) pomegranate peel silage with wheat straw, alfalfa hay, cotton seed cake, barley, beet top silage and mineral plus affects the feed intake and growth performance of Tukey sheep.
Introduction
Pomegranate (Punica grantum L.) belongs to Punicaceae family and it is one of the oldest known edible fruits (Seeram et al., 2006). The edible part of the pomegranate (aril) is about 55 to 60% of total fruit weight and consists of about 75 to 85% juice and 15 to 25% seeds (Abbasi et al., 2008). Due to the potential benefits of pomegranate fruits on human health (Lansky and Newman, 2007), and the development of industrial technologies to obtain more appealing products (e.g. ready-to-eat arils or ready-made juices and extracts; Shabtay et al., 2008), there has been a great increase in the demand and production of those fruits. Consequently, the agro-industries yield large amounts of residual biomasses, the pomegranate by-products (seeds, peels and pulp). At present, the disposal of these processing wastes represents a cost, which makes imperative to find alternatives. In this regard, their use in ruminant feeding would contribute to reduce the amount of cereals fed to the animals, reducing not only the feeding cost of ruminant production but also reduce the food competition (Salami et al., 2019).
The pomegranate tree (Punica grantum L.) is important in tropical, subtropical, and Mediterranean regions (Al-Rawahi et al., 2013). Pomegranate is one of the most popular fruits in Afghanistan and the world. Kandahari pomegranate has the best quality and it is the most popular variety between 48 varieties available in Afghanistan. Production of pomegranate in Afghanistan was 181765 tons with total harvesting area of 15621 acres in 2018 (CSO, 2019).
By 2050, the world will need to feed an additional 2 billion people and require 70% more meat and milk. The increasing future demand for livestock products, driven by increases in income, population, and urbanization will impose a huge demand on feed resources. A huge quantity of fruit and vegetable wastes and by-products from the fruit and vegetable processing industry are available throughout the world that encourages to using it as a new source feeds in animal ration formulation.
In a previous study, Shabtay et al. (2008) demonstrated that dietary supplementation with fresh pomegranate peels promoted a significant increase in feed intake, with a positive tendency toward increased BW gain in bull calves. They suggested that the antioxidant and immunomodulatory properties of pomegranate peels might improve immune function, which could benefit calf health. On the other hand, Oliveira et al. (2010) found that feeding a pomegranate extract to young calves for the first 70 days of life suppressed the intake of grain and the digestibility of fat and protein, likely because of the high tannin content.
N2O is a dangerous greenhouse gas and expected to increase by 35-60% by 2030 with an increase in demand for meat and dairy products (IPCC, 2007). PP containing tannins may improve N utilization efficiency and thereby decrease the N content of manure, which, in turn, may affect N2O emissions because less N is available to the denitrifying bacteria that use the manure as substrate. The addition of saponins from PP can thus modify the C and N contents of sheep manure. Sheep (Ovis aries L.) produce 8 kg of enteric methane (CH4) gas per animal per year (Broucek, 2014) and by using PP in animals ration the amount of CH4 may reduce.
Materials and Methods
This experiment was carried out on November and December months of 2019 for 21 days at the research and experimental farm of Agriculture Faculty, Kabul University, Kabul, Afghanistan, which lies on 34º31'4.5687 latitude (N) and 69º8'18.2174 longitude (W). Twelve female Turkey bred sheep, aged two and half years old with an average live body weight of 57.240 ± 5.28 kg were divided in a completely randomized design (CRD) into three groups (Table 4), this experiment was done to know the effects of pomegranate peel silage along with beet top silage, wheat straw, alfalfa hay, barley, cotton seed cake and mineral plus, on feed intake and growth performance of turkey bred sheep. Wheat straw, alfalfa hay, Barley and cotton seed cake and Mineral plus bought from the related markets of the city, beet tops, after harvesting they cut into small pieces, sun-dried and then it was treated with urea to make silage, after 30 days the silage were ready to use. The pomegranate peels were collected from the juice shops, the peels were sun-dried and then cut into small pieces then treated with urea to make silage and was ready to use after 30 days. PPS were used in diets with different levels, 0%, 5% or 106 g and 10% or 211 g. Animals were fed 633 g barley, 106 g BTS, 4 g mineral plus, 211 g cotton seed cake, 633 g alfalfa hay with the same amount and wheat straw for control group (T1), T2 and T3, 528 g, 422 g and 317 g in DM basis, respectively once in a day at around 8 am. Fresh water and salt were available all times for animals. Daily amount of experimental ration weighed before feeding and feed residues were weighed the following morning before feeding the diet. Body weight changes were weekly recorded before they fed diet.
Collected data of feed intake and live body weight, were subjected to statistical analysis as one-way ANOVA procedure and the groups comparison done with Least Significance Differences (L. S. D) test using MS. Excel.
Average daily gain (ADG) were found by dividing the total weight gain to days of experiment. FCR were calculated by dividing the total feed intake on total weight gain and FCE calculated by dividing total weight gain on total feed intake and multiply by 100.
Economic evaluation was done using the relationship between feed costs (local market price of ingredients) and sheep live body weight gain. Economic evaluation was calculated as follow: The cost for 1-kg gain=total cost {Afghani (AF)} of feed intake/total gain (kilogram).
Results and Discussion
There was a highly significant difference between groups in feed intake and significant difference in weight gain of turkey sheep, according to L. S. D test it was shown that T2 (5% PPS) was better in both feed intake and growth performance compared to other groups. In table 6 it is shown that feed intake of control group, 5% PPS and 10% PPS were 36.008, 39.667 and 38.005 kg, respectively and the weight gain was 3.863, 5.034 and 2.513 kg, respectively. Tannins are considered to have both adverse and beneficial effects in ruminants (Makkar et al., 2003). High concentration of tannin may reduce feed intake, digestibility of protein and carbohydrates, and animal performance through their negative effect on palatability and digestion (Reed, 1995).
Pomegranate peel is rich in tannins, which were previously shown to have both adverse and beneficial effects in ruminants (Makkar, 2003). Moderate concentrations of condensed tannins (2 to 4% of DM) in the diet of sheep improve production efficiency in ruminants without increasing feed intake, as manifested by increases in wool growth, BW gain, milk yield, and ovulation rate (Aerts et al., 1999). The findings of this study were in agreement with this statement because when 10% PPS used the feed intake and weight gain was low, due to its high Tannin content.
Also the current study is in harmony with Saeed et al. (2017) showed in their study that higher dry matter intake (DMI), organic matter intake (OMI) and nitrogen intake (NI) of wheat straw by lambs fed T2 as compared T1 may due to improve rumen condition as a result of anti-oxidative property of pomegranate peel (PP) (16) (17) demonstrated that addition of PP significantly enhance feed Intake. Those workers suggested that anti-oxidative and immunomodulatory properties of PP might improve immune function, which could benefit calf health.
Result of feed intake that illustrated in table 7 recorded that when 5% PPS and 10% PPS used in rations of experimental animals, the feed consumption increased but the increase was higher when 5% PPS used compared to 10% PPS group. These results in disagreement with those reported by Sadq et al. (2016) who showed that final body weight was significantly (P<0.05) higher in Karadi lambs fed 1% or 2% pomegranate peel as compared with lambs fed 4%. In addition, incorporation DPP at level of 1 or 2% significantly (P<0.05) decreased dry matter intake.
Result of weight gain that illustrated in table 7 showed that using 5% PPS group, gained higher body weight in comparison to 10% PPS group. The result of this study is in agreement with Abarghuei et al. (2013), who stated that the tendency to similar live weight gain in all groups can be attributed to an internal mechanism related to lambs, but the inclusion of PP as half of the forage had a clear negative effect on the lambs. Abarghuei et al. (2013) suggest that PP contains high concentrations of saponin, which reduces protein digestibility due to negative effects on digestion, and decreases feed consumption by reducing the palatability. However, result of the current study is in disagreement with Kotsampasi et al. (2014) who stated that the addition of PP to the total mixed ratio (TMR) at concentrations of 0, 120, and 240 g kg -1 did not significantly affect live weight, live weight gain, DM consumption, and feed utilization. Omer et al. (2019) who stated in their study that dried pomegranate peel can be used safely in sheep feeding at level of 1% because this level realized the best growth performance and depressed the price of ration cost and recorded the best relative economic efficiency (Omer et al., 2019).
In addition, these results were in agreement with those found by Denek and Can (2006); Omer and Abdel-Magid Soha (2015) who noted that the use of agro-industrial by-products in sheep rations has been successfully adopted as a strategy to reduce feeding costs and also to cope with the need to recycle waste material.
Conclusion
From the data illustrated in the current study we found that supplementation of 5% PPS with wheat straw, alfalfa hay, barley, cotton seed cake, beet top silage and mineral plus had a significant effect on the feed intake and growth performance of turkey bred sheep. In addition, adding 10% PPS in the experimental animal ration was low compared to the second group, which may be due to the high amount of tannin available in pomegranate peel and the amount of urea used in PPS, which may change the taste of the diet. From the findings of this study it is recommended to use 5% PPS in ration of turkey bred sheep.\===
Domain: Biology Agricultural and Food Sciences. The above document has 2 sentences that end with 'or 211 g',
2 sentences that end with 'day at around 8 am',
2 sentences that end with 'performance compared to other groups',
2 sentences that end with 'performance of turkey bred sheep',
2 sentences that end with 'to 10% PPS group',
2 paragraphs that end with 'et al., 2019)'. It has approximately 2049 words, 80 sentences, and 22 paragraph(s).
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Influences of dietary herbal blend and feed restriction on growth, carcass characteristics and gut microbiota of growing rabbits
Abstract The objective of the present study was to determine the effect of feed restriction systems, herbal mixture and their interactions on growth performance, carcass traits, and microbial aspects of growing New Zealand White (NZW) rabbit kept from 5 to 13 weeks of age. A 3 × 4 factorial arrangement was performed, including three feed restriction systems (ad-libitum, 90%, and 80% of ad-libitum) and four dietary supplementation levels of herbal mix (0, 0.30%, 0.50% and 0.70%). A total number of 120 rabbits (male and female ratio 1:1) at five weeks of age were randomly allotted into twelve experimental groups (n = 10 each). Results showed a significant decrease in body weight, body weight gain and feed intake in restricted-fed rabbits compared to the control group (ad-libitum). HERBS levels significantly influenced the growth performance and carcass traits of rabbits. The herbal blend had a positive effect on reducing the population of pathogenic microorganisms and increasing the population of lactic acid bacteria. Conclusively, it could be concluded that the feed restriction system has beneficial effects in the improvement of feed conversion ratio (FCR), weight gain, and carcass traits. In addition, HERBS supplementation to the growing rabbits resulted in significant improvements in growth performance, carcass characteristics, and microbial aspects of rabbits kept from 5 to 13 weeks of age. Highlights: This work investigated the effect of feed restriction systems (FRS), herbal mix (HERBS), and their interactions with rabbits. Restricted feed decreased live body weight during all ages studied. Feed conversion ratio, weight gain and carcass traits were improved due to FRS. The HERBS improved the growth, carcass traits, and microbial aspects of rabbits.
Introduction
In rabbit production, feeding is the main cost, so feed intake control could adjust the diet and nutritional requirements to manage the growth performance (Yakubu et al. 2007;Bergaoui et al. 2008). In commercial farms, growing rabbits are usually fed ad-libitum (Maertens, 2009). After weaning in the rabbit, the early-life fast growth rate is accompanied by several problems, mainly high incidence of metabolic disorders and high mortality rate (Hassanabadi and Moghaddam 2006) and high incidence of skeletal diseases (Bovera et al. 2008).
In the growing rabbits, early feed restriction applied around post-weaning age could be of interest to improve feed efficiency (Tumova et al. 2002;Tumova et al. 2003;Yakubu et al. 2007;Gidenne et al. 2009;Gidenne et al. 2012), to induce compensatory growth (Tumova et al. 2002;Foubert et al. 2008), to reduce carcass fat deposition (Tumova et al. 2004), and to improve the digestibility of nutrients during the restricted feeding period (Tumova et al. 2004). Feed restriction suppresses growth during the restriction period, but the reduced growth can be compensated with greater future intake (Di Meo et al. 2007).
Digestive disorders are the leading cause of morbidity and mortality in growing rabbits, and are responsible for important economic losses in commercial rabbit farms (Ebeid et al. 2012). Therefore, early feed restriction could be a useful tool to improve biological and economic performance (Tumova et al. 2007), which is consequently involved in reducing production costs (Yakubu et al. 2007).
On the other hand, feeding can be restricted during the post-weaning period to improve feed efficiency and standardise growth curves in rabbits with different feed ingestion levels (Cavani et al. 1991) or to control the appearance of digestive disorders (Gidenne et al. 2009;Gidenne et al. 2011). The application of feed restriction during the fattening period of rabbits, without compromising too much the growth, maybe a good strategy for rabbit management because it may decrease the feeding cost and reduce the health risk (Foubert et al. 2008;Gidenne et al. 2012). Feed restriction during two and three weeks did not uniformly affect the carcass's parts. Internal organs percentages increased with the increased length of feed restriction time respectively for 14 and 18 weeks, while the abdominal fat percentage significantly reduced to the length of time feed restriction at 14 weeks. (Sena et al. 2015).
Herbal extracts are potentially beneficial as growth promotors in diets and play a good role as therapeutic agents to treat certain diseases and disorders. They can replace antibiotics, enhancing immunity, and fight pathogenic bacteria and viral infections (Alimon 2009). Beneficial impacts of herbs in poultry nutrition may include the enhancement of appetite and feed intake, the stimulation of endogenous digestive enzyme secretion, activation of immune response, antioxidant, antibacterial, and antiviral properties (Abd El-Hack et al. 2016). Suganya et al. (2016) summarised that herbal extracts have a wide range of activities that stimulate feed intake and endogenous secretions and have antimicrobial, coccidiostats, or anthelmintic activity. A major field of herbal application is protecting animals and their products against oxidation. The presence of colonies of Clostridium perfringens and Escherichia coli in the colon content could be reduced by adding extracts of plants with capsaicin (1.98 g/100 g) as with using of avilamycin in birds (Jamroz et al. 2003).
On the other hand, Alagawany et al. (2016) concluded that garlic supplementation (2, 4, and 6 g/kg) did not linearly and quadratically affect growth performance in rabbits while improving the immunity responses and lowered the lipid profile in blood and lipid peroxidation in liver and increased hepatic antioxidant activity in treated rabbits. Herbal mixture supplementation reduced plasma total cholesterol and triglycerides concentrations, whereas high-density lipoprotein HDL-cholesterol and glutathione peroxidase (GPX) were increased in broilers significantly. Furthermore, supplementation of the herbal mixture increased plasma levels of total protein and antibody titres for the Newcastle disease virus before and after the infection (Saleh et al. 2018).
Recently, Mossa et al. (2019) showed that medicinal plants are widespread in poultry feed as antibiotics alternatives. Several studies also confirmed the beneficial effects of phytogenic additives on growth performance, nutrient retention, gut health, intestinal microflora, reduced susceptibility to diseases, enhanced immunity function, and improved carcass yield and quality in poultry (Ashour et al. 2014;Ashour et al. 2020aAshour et al. , 2020bAshour et al. , 2020c.
Since feed restriction and the herbal mixture had positive impacts in growing rabbits, the objective of the present study was to investigate the impact of feed restriction, herbal mix and their interaction on the growth rate, carcass traits, several microbial aspects, and antioxidant activity of the growing rabbits, from 5 to 13 weeks of age.
Microbial strains
The gram-positive bacteria (G þ ) (Staphylococcus aureus, Bacillus cereus, and Listeria monocytogenes) and the gram-negative bacteria (G -), (Klebsiella pneumoniae, Escherichia coli, and Salmonella typhi) besides, fungi (Candida tropicalis, Candida albicans, Candida glabrata, Aspergillus flavus, Aspergillus fumigatus, and Aspergillus niger) were used to study the antimicrobial activity of herbal mixture extract on these microorganisms in the current study.
Antimicrobial and antifungal activity
Disc assay Antibacterial and antifungal activity of herbal mixture methanolic extract were estimated by disc diffusion method (Gulluce et al. 2007). The methanolic extract was dissolved in dimethyl sulfoxide (DMSO) to obtain (1-9 mg/mL) concentrations, then 100 mL of bacterial inoculum (1 Â 10 8 CFU/mL) was spread on Mueller Hinton agar plates surface (MHA), and disc from fungal mycelium was seeded on the centre of potato dextrose agar (PDA) plates surface, then discs (6 mm) saturated with different concentrations of the herbal extract mixture solution and placed on both sides of MHA and PDA plates. Control was discs saturated with DMSO. The plates were incubated at 37 C for a day (bacteria) or at 28 C for five days (fungi). The experiments were conducted in triplicates to determine significant differences. The ruler was used to measuring the resultant inhibition zones (mm). The inhibition zones greater than 8 mm witnessed the antibacterial or antifungal activity of methanolic extract (Rahmoun et al. 2014;Akl et al. 2020).
Minimum inhibitory concentration (MIC), minimum bacteride concentration (MBC) and minimum fungicidal concentration (MFC) estimation The MIC was estimated by the micro-dilution broth method following European Committee on Antimicrobial Susceptibility Testing (Eucast 2003). Each concentration of tested herbal mixture extract (1-9 mg/mL) was dissolved in 10% DMSO. 30 mL of herbal mixture extract concentrations were added to tubes containing 9 mL of Mueller Hinton broth or potato dextrose broth. 100 mL of bacterial (1.5 Â 10 8 CFU/mL) and standard size of fungal spore suspension (3 Â 10 3 CFU/ml) were added to tubes. Control was Mueller Hinton broth and potato dextrose broth tubes. The tubes were incubated for a day at 37 C and five days at 28 C. The MIC was the least herbal extract concentration that prevents bacterial and fungal growth. The herbal extract with high activity has lower MIC, the lowest concentration that kills the microorganisms called minimum bactericidal concentration (MBC) for bacteria and minimum fungicidal concentration (MFC) for fungi (CLSI 2016;El-Saadony et al. 2019). The MBC and MFC were estimated by taking a loopful from each MIC tubes and spread on Mueller Hinton plate and PDA and then incubated at 37 C for 24 h (bacteria) and 28 C for 5 d (fungi) and observed the bacterial or fungal growth (Usman et al. 2014;El-Saadony et al. 2020).
Animals and experimental design
This experiment was carried out in the Rabbit farm of Animal and Poultry Production Department, Faculty of Technology and Development, Zagazig University, Zagazig, Egypt. All experimental procedures for the present study were conducted following the Local Experimental Animal Care Committee and subsequently approved by the Institutional Ethics Committee, Department of Poultry, Faculty of Agriculture, Zagazig University, Zagazig, Egypt (ZU-IACUC/2/F/94/2018). The experimental period lasted for eight weeks, from September to October 2020. A 3 Â 4 factorial arrangement was performed including three systems of feed restriction (ad-libitum, 90% and 80% of ad-libitum) and four dietary supplementations consisted of the basal diet as a control group and HERBS additives groups (3.0, 5.0 and 7.0 g HERBS/kg diet where a herbal mixture containing [(300 g garlic [Allium sativum] þ 300 g [Capsicum annuum] hot red pepper þ 300 g Thymus vulgaris þ 300 g Salvia rosmarinus þ 150 g Pimpinella anisum þ 150 g Mentha spicata þ 300 g Nigella sativa) before that, these herbals were mixed and ground together in a powder form. A total number of 120 rabbits (male and female ratio 1:1) of weaning aged 5 weeks with 642.94 ± 13.68g as a general mean of body weight were randomly divided into twelve experimental groups (n ¼ 10 each) in five replicates each of two rabbits which housed in commercial wired cages (two animals per cage) with floor space of 0.15 m 2 /rabbit under 16 h photoperiod daily.
Housing, feeding and environment
Animals were housed in galvanised wired cages batteries (60 Â 50 Â 40cm), in good natural ventilation through the windows (open housing system). Simultaneously, temperatures and humidity ranged between 22 to 35 C & 50 to 64% during experimental periods, respectively. The rabbits were fattened until 91 d of age (13 weeks). Cages were supplied with feeders and stainless steel nipples for feeding and drinking. Freshwater was available ad-libitum. Rabbits were examined daily for their healthy and clinically free from internal and external parasites, vaccinated against common diseases (inactivated clostridial vaccines at 6 weeks of age, inactivated viral haemorrhagic disease at 9 weeks of age, and inactivated Pasteurella multocida (bacterial haemorrhagic disease at 11 weeks of age) and kept under the same managerial and hygienic conditions. The experimental diets were formulated to be isonitrogenous (16.54% CP) and iso-caloric (10.82 MJ DE/ kg diet). Rabbits were fed pelleted commercial diets formulated to meet recommended nutrient requirements of rabbits, according to De Blas and Wiseman (2020), and the chemical composition of the basal diet was analysed according to Association Of Analytical Chemists (AOAC) (1995), as shown in Table 1. The Chemical characterisation of total phenolic, flavonoids and DPPH activity of solo or mix of herbal as shown in Table 2 .
Measurements
Growth performance Initial body weight, body weight at 7, 9, 11 and 13 weeks of age, body weight gain, feed intake and feed conversion ratio were recorded. Also, the mortality rate was recorded during experimental periods. The weighing was carried out before offering the morning meal. Total body weight gain and feed conversion ratio were calculated biweekly, while feed conversion ratio (FCR) was calculated as feed to gain (g feed/g gain) ratio. Feed was supplied once per day (ad-libitum, 90% and 80% of ad-libitum).
Carcass traits
At the end of the experiment (13 weeks of age), five rabbits were randomly chosen for slaughtering from each group (one rabbit from each replicate) around the group's mean after being fasted for 12 h. Slaughter procedures were carried out as described by Blasco and Ouhayoun (1996) by severing the carotid arteries and jugular veins, skinned, and eviscerated for measuring carcass parameters. Following the removal of the visceral organs and head, the remaining part of the carcass was weighed as carcass weight, which was then expressed as a percentage of the fasted weight to obtain the dressing percentage. as follow: Dressing % ¼ (hot carcass weight/fasted weight)  100. The carcass was separated into three cuts: (1) fore part Table 2. Total phenolic (TP, mg GAE g À1 extract) and total flavonoid (TF, mg QE g À1 extract) contents and DPPH activity (SC 50 ; mg mL À1 ) of the methanolic extract acquired from Thymus vulgaris (including thoracic insertion muscles), (2) mid part (including the abdominal wall and the ribs after the 7 th thoracic rib), and (3) hind part (including the sacral bone and the lumbar vertebra after the 6 th lumber vertebra). The relative weights of the liver, kidneys, and heart (Giblets) were determined using the formula: Giblets weight, % ¼ Giblets weightðwtÞ=PreÀslaughter weight of rabbit  100, Dressing weight, % ¼ Carcass weight þ giblets weight=PreÀslaughterwt  100:
Microbial count in diet and caecal samples
The feed samples were microbiologically examined at an interval of 0, 2, 4, 6, and 8 weeks. Dietary samples were mixed with sterile saline peptone solution 1:10 (w/v) at the screw bottle and homogenised for three min. Different media were used to calculate microorganisms. Total bacterial count (TBC) was counted at plate count agar at 30 C for 2 d. The total yeasts and moulds count (TYMC) were estimated on Rose Bengal Chloramphenicol agar for five days at 25 C. Total coliforms were counted on violet red bile agar after 24 h incubation at 37 C (Harrigen and Mccance-Margart 1976). Escherichia coli was counted on eosin methylene blue agar plates after incubation for 24 h at 37 C (Oxoid 1982). All plates were examined for typical colony types and morphological characteristics associated with each culture medium. On the other hand, the microbial counts in rabbit caecum were estimated as in diet. Caecal samples (five-replicate) were homogenised in a screw bottle with sterilised saline peptone solution (1:10, w/v). Decimal serial dilutions up to 10 7 were prepared. The different microorganisms were counted on specific media (Abdelnour et al. 2020;El-Saadony et al. 2020). Total bacteria were counted as per Sheiha et al. (2020) and Reda et al. (2020) on Plate count agar (PCA). Total coliforms were counted on violet red bile agar after 24 h of incubation at 37 C (Harrigen and Mccance-Margart 1976). Escherichia coli was counted on eosin methylene blue agar plates after incubation for 24 h at 37 C (Oxoid 1982;Richard 1986). Salmonella spp. was calculated at S. S. agar as per Edwards and Hilderbrand (1976); black colonies indicate Salmonella spp., found. Yeasts and Moulds were enumerated as Kurtzman and Fell (1984). MRSmedium was used to count Lactic acid bacteria, according to Argyri et al. (2013). Enterococcus spp. was counted in Chromocultfi enterococci agar (Miranda et al. 2005).
Statistical analysis
Data were statistically analysed using SPSS (2014) according to Sendcor and Cochran (1982) as the following model: Where: Y ijk ¼ an observation, m ¼ the overall mean, A i ¼ effect of restricted grades (i¼ad-libitum, 90% and 80% to 3), S j ¼ effect of herbals mixture level (j ¼ 0, 0.30, 0.50 and 0.70%), AS ij ¼ the interaction between restricted grades and herbal mixture level and e ijk ¼ random error.
Data of the effect of herbal mixture on the caecal bacterial count and bacterial count reduction in the diet were analysed using one-way ANOVA using SPSS (2014).
Data presented as means ± S. E. Means were considered significant at p > .05. According to Duncan's multiple tests, significant differences between treatment means were tested (Duncan (1955).
Antimicrobial activity of herbal mixture extract
The antibacterial effect of the methanolic herbal mixture on pathogenic bacteria is shown in Table 3. G-ve bacteria more resistant to herbal extract than G þ ve bacteria. The inhibition zones diameter (IZD) significantly (p .05) increased with the increment of herbal mixture extract concentration. Staphylococcus aureus is a more sensitive G þ ve bacteria to the herbal mixture (0.9 mg/mL) with IZD 33.7 mm with a relative increase of about 20% over Bacillus cereus and 25% above L. monocytogenes. On the other hand, Klebsiella pneumonia more susceptible than Escherichia coli and Salmonella typhi, with a relative increase of about 5 and 10%, respectively. MIC expressed the least concentration inhabit the bacterial population, but MBC was the lowest concentration of herbal mixture kills the bacteria. Herbal mixture extract MIC was higher against Listeria monocytogenes with 0.8 mg/mL followed by B. cereus and S. aureus. On the level of G-bacteria Salmonella typhi needs a high level of the herbal mixture with 0.95 mg/mL than other G-bacteria. MBC ensures the bacterial kill. The bacteria were ordered in descending, S. typhi, E. coli, K. pneumonia, L. monocytogenes, B. cereus, and S. aureus according to MBC. Table 3 showed considerable antifungal activity against C. tropicalis, C. albicans, C. glabrata, A. flavus, A. fumigatus and A. niger. The highest herbal methanolic extract MIC and MFC ere against C. tropicalis followed by A. niger with 0.99/1.90 and 0.9/1.75 mg/ml.
The herbal mixture of thyme, anise, mint, rosemary, and black seed showed the highest antibacterial methanolic extract with inhibition zones ranged (19-25 mm), (14-18 mm) for S. aureus and K. pneumonia, respectively besides, the highest synergistic effect on S. aureus and K. pneumonia (Table 4). Table 5 showed a significant decrease in live body weight of growing rabbits with restriction feed during all ages studied (7, 9, 11 and 13 weeks). On the contrary, HERBS addition significantly increased rabbits' body weight at the level of 0.50% (2211 g/animal) at the end of the experiment (13 weeks). From the view of the interaction, there was a significant influence due to the main factors studied. The highest body weight was obtained by ad-libitum followed by 90% and 80% of ad-libitum, respectively. The highest body weight under ad-libitum (FRS) Ã HERBS (50%) with an average of 2197 g/animal. On the other hand, daily weight gain decreased significantly with restriction feed during all experimental periods except 11-13 weeks of age (as shown in Table 6). Bodyweight gain significantly increased due to HERBS addition and the interaction effect at all interval periods. The best values were recorded with HERBS levels of 0.50 or 0.70% under different FRS. The mortality rate was not shown because there was no mortality detected during the experimental period; that is why the feed restriction system, HERBS, and their interaction did not affect the growing rabbit's mortality rates. Feed intake and feed conversion ratio Table 7 given a significant decrease in feed intake of growing rabbits due to RFS (90 or 80%) during all ages studied 5-13 weeks of age by (10.16% and 20.14%, respectively) compared to an ad-libitum group. In contrast, HERBS insignificantly influenced feed intake of growing at all experimental terms studied. Simultaneously, the interaction effect presented signifcant effects due to the main factors studied and the highest feed intake recorded at all levels and HERBS under the ad-libitum feed restriction systems studied. The feed conversion ratio (FCR) was significantly (p < .01) improved for growing rabbits with feed restriction system (FRS) (90 or 80%) as compared to ad-libitum ones during all intervals studied, as reported in Table 8. Also, the dietary addition of HERBS significantly (p < .01) improved FCR of growing rabbits at the level of 0.50% and 0.70%, which recorded 4.06 and 4.08 respectively, at the whole experiment (5-13w). The interactions between FRS and dietary HERBS significantly (p < .05) affected on FCR values at 5-7w and 7-9w intervals, while insignificantly influenced the other experimental periods (Table 8). Table 9 illustrated the significant increase in dressing %, hindquarter % and giblets % due to restriction feed of 90 or 80% as compared to ad-libitum ones, on contrary gastrointestinal % increased significantly with ad-libitum feeding as compared with other FRS (90 or 80%). The remaining carcass traits (forequarter % and lion %) were insignificantly affected due to FRS. On the other hand, HERBS addition was insignificantly influenced all carcass traits studied (Table 9). Furthermore, the interaction effect on all carcass traits examined was insignificant, except for gastrointestinal %, which increased significantly with high levels of HERBS addition (0.5% or 0.7%) under ad-libitum feeding, as shown in Table 9.
Microbial count in diet and caecum
The total bacterial (TBC), total yeasts and moulds (TYMC), coliform, and E. coli counts in rabbit diet supplemented with different concentrations of the herbal mixture (0.3, 0.5, 0.7%) after eight weeks are presented in Table 10. The results indicated that the diet supplement with herbal mixture 0.7% significantly decreased TBC by 30% after eight weeks compared to the control group. Besides, TYMC, coliform, and E. coli count decreased by 20%, 50%, and 15%, respectively. A significant decrease in the caecal microbial count was observed with the herbal mixture supplementation at different concentrations (0.3, 0.5, and 0.7%), but a considerable increase in lactic bacteria count (Table 11). The diet reduction from ad-libitum to 90% of diet decreased bacterial count from log 9.96 to 9.66. Besides, 80% diet þ0.7% herbal mixture significantly decrease about 15% of the control diet. Salmonella did not detect in 80% diet þ0.7% herbal mixture and lactic count significantly increased from log 3.5 to 4.8. The phenolic compounds and essential oil in the herbal mixture positively affected microbial count reduction.
Growth performance
This noticeable improvement in live body weight (LBW) at the whole experimental period (5-13 weeks of age) might be due to improved digestion and absorption of diet nutrients by some components of the phytogenic additives (Tables 2 and 5). However, that may be contributed to enhancing the utilisation of feed consequence, enhancing the growth rate. Prohibition of using antibiotics in poultry production due to herbs and plant medicines as feed additives to improve growth condition, its induced saliva secretion and improve digestion processes (Suganya et al. 2016;Ashour et al. 2020d. Phytogenic additives may also reduce the environmental problems produced by using antibiotics as feed additives, such as bacterial resistance (Peric et al., 2009). Also, Ahmed et al. (2002) pointed out that live body weight and daily weight gain were improved significantly by adding garlic powder to the rabbit diet.
Also, Onu and Aja (2011) reported that garlic supplementation by 0.25% produced significant (p < .05) effects on weight gain and significantly enhanced the hematological parameters of rabbits as well. The mortality rate detected during the experimental period showed no differences between all groups due to the main factors studied or the interaction, while it agreed with Foubert et al. (2008), Ebeid et al. (2012) and Abou-Kassem (2017). They indicated that feed restriction did not influence the mortality percentage of growing rabbits. Gidenne et al. (2012) showed that a more long restriction (for 2 or 3 weeks) of growing rabbits reduced mortality and morbidity from digestive troubles. While in contrast, El-Speiy et al. (2015) reported that feed restriction significantly decreased the mortality percentage in growing rabbits compared with the ad-libitum ones. Present results agreed with Ashour et al. (2020d) and Castrica et al. (2020), who found a significant difference (p < .05) in live body weight and weight gain due to the addition of the herbal mixture powder at all studied periods.
Present results agreed with Abou-Kassem (2017), who showed a significant (p < .01) increase in feed intake of growing rabbits with ad-libitum group (control) as compared with other restriction ones during all experimental periods studied (5-13 weeks of age). These results in line with Alabiso et al. (2017) summarised that the restriction feed system for a 3-week post-weaning of growing rabbits significantly decreased feed intake by (À22 to À24 g dry matter/ day) and gave a lower feed conversion ratio than adlibitum feeding. On the other hand, results disagreed with Di Meo et al. (2007), who studied the effects of ad-libitum and feeding restricted for growing rabbits from 5 to 12 weeks of age and observed no difference in the bodyweight daily weight gain results.
The dietary inclusion of Foeniculum vulgare Mill. Seeds with oregano leaves have been reported to improve feed conversion ratio. Moreover, the dietary inclusion of a mixture of Trigonella foenum-graecum L., Cassia senna L, and Lupinus albus L. played an important role as a growth promotor in rabbits (Dalle Zotte et al. 2016). Ahmed et al. (2002) pointed out that the feed conversion ratio was improved significantly by adding garlic powder to the rabbit diet. Additionally, Onu and Aja (2011) reported that garlic supplementation by 0.25% produced significant (p < .05) effects on feed intake and feed conversion ratio and also it significantly enhanced the hematological parameters of rabbits as well. Recently, Ashour et al. (2020d) reported that the best FCR recorded in chicks fed the diet supplemented with herbal mixture powder at a level of 5.0 g/kg diet, while the worst one recorded by the group fed level of 3.0 g/kg diet during the total term, 0-5 week old in broilers.
Carcass traits
Our results agree with findings by Abou-Kassem (2017), who found that dressing weight % significantly increased by feed restriction compared to ad-libitum feed for rabbits. Present results showed that gastrointestinal % increased with ad-libitum feed system as compared to other FRS. These results disagreed with Abou-Kassem (2017), who indicated that empty gastrointestinal tract on pre-slaughter live weight was significantly (p < .01) higher for restriction than ad-libitum feed of growing rabbit.
Regarding giblets %, our findings agreed with those found by Abou-Kassem (2017), who clarified that liver weight % significantly (p < .001) increased with restriction feed system as compared to ad-libitum group. Also, Alabiso et al. (2017) found that a 3-week feed restriction after weaning have a heavier carcass produced by growing rabbits fed ad-libitum (þ100 g; p < .001), and they added that production of lighter carcasss could be compensated partly by the lower feed conversion ratio gave by restricted rabbits. While these results disagreed with those obtained by Yakubu et al. (2007), who studied the effects of feed restriction on weaned rabbits and reported no difference in the liver % compared with ad-libitum group. Also, El-Speiy et al. (2015) found that fed different restriction strategies had insignificant effects on some relative organs weight of growing rabbits. No studies have been reported on the interaction between the FRS and HERBS, which contained several herbal plants as in this investigation.
Microbial count in diet and caecum
These natural microbicidal are divided into two types of phytoanticipins that inhibit microbes and phytoalexins, which are antioxidants that induce an immune response (Sukalingam et al. 2018). Cushnie et al. (2007), El-Adawi, (2012) and Awolola et al. (2014) revealed that secondary metabolites are divided into three main groups: phenolic compounds, terpenes, and alkaloids. The mechanics of action of these compounds in removing bacteria is through the destruction of the cell membrane, the production of ROS, the prevention of the formation of biofilms, the building of cellular cells, and the proliferation of microbial DNA ATP. Besides, by synergizing with antibiotics, these compounds increase the killing of disease-causing microbes. Kone et al. (2016) studied the effect of dietary supplementation with cranberry, onion, and strawberry extracts and essential oils on the bacterial count in weaned rabbit meat; the results indicated that the dietary supplementation with polyphenolic extracts and essential oils has a significant (p < .05) positive effect in reducing bacterial load against the control group.
In conclusion, no adverse effects on performance and meat quality traits were observed with the dietsupported plant extracts and essential oils. Still, the effect can be optimised by investigating higher doses. Pog any et al. (2020) showed a reduction in coliforms counts, clostridia, and staphylococci count, but lactic acid count increased in the intestine of broiler rabbits. Namkung et al. (2004) studied the effect of herbal extracts and organic acids supplementation to feed for four weeks on weaned pigs (growth performance, gut microbiota and immune response). The animals were divided into groups: the control group, 1.1% acid group 1 containing (acetic, propionic, phosphoric and citric acid) and acid two groups with 1% (acid1 þ lactic acid) and the herbal group (cinnamon, thyme and oregano) by 0.75% addition and an Antibiotic group (Lincomycin 110 ppm). The first and second groups showed the highest ADG only during the second week, while the herbal extract had the lowest ADG during the third week only. The number of coliforms was lowest on day 4 in the first and second groups, while on day 14 in the herbal extract and antibiotics groups. The number of lactobacilli decreased in the antibiotic group after two weeks. Based on PCR-DGGE, the additives affected the numbers of gut microbes, as antibiotics' addition reduced both coliform and lactobacilli. In contrast, the herbal extracts and acids supplementation to the dietary system only reduced coliforms. Mixtures of organic acids and herbal extracts can supplement the feed as an alternative to antibiotics for the first weeks.
Conclusions
It could be concluded that the feed restriction system improved feed utilisation by giving the best values of FCR. It also has a beneficial effect on improving weight gain, feed efficiency, and carcass traits. Also, HERBS dietary addition had great improvement effects on growth performance, carcass traits and microbial aspects of growing rabbits kept from 5 to 13 weeks of age. Based on the positive experimental effects obtained on rabbit's health and production, further studies are needed to maximising these positive effects with specific herbal formulations.
Ethical Approval
All experimental procedures for the present study were conducted following the Local Experimental Animal Care Committee and subsequently approved by the Institutional Ethics Committee, Department of Poultry, Faculty of Agriculture, Zagazig University, Zagazig, Egypt (ZU-IACUC/2/ F/94/2018).
Disclosure statement
No potential conflict of interest was reported by the author(s).
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Domain: Biology Agricultural and Food Sciences
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Characteristics of the Falling Speed of Japanese Orchid Seeds
Orchid seeds, which are produced in abundance, are particularly small and light. Some orchid species are anemochorous, i.e., bear seeds that are dispersed by wind. To characterize the seeds of Japanese orchids, we measured the size of seeds and embryos of 68 Japanese orchid species. Most orchid seeds had a length of 0.3 to 2 mm and a width of 0.07 to 0.2 mm. Embryo length and width were 0.1 to 0.3 mm and 0.04 to 0.2 mm, respectively. Twenty-seven orchid species produced 1000 to 350000 seeds per pod. Speeds of fall of 34 Japanese orchid species were examined in order to obtain insight into their seed dispersal ability. Falling speeds ranged from 4 to 30 cm/s. These results strongly suggest that seeds of Japanese orchids are also dispersed by wind and thus that most Japanese orchids are anemochorous species.
Introduction
The dispersal of seeds to a suitable habitat is a crucial event in the plant life cycle. Three major mechanisms spread seeds; i.e., hitchhiking on animals, floating in water, and moving in the wind (Fenner, 1985). Most anemochorous species produce particularly small and light seeds (Burrows, 1975). In some cases, seeds have wings that enable them to travel long distances (Burrows, 1973). Mathematical models have been developed to predict the dispersal distances of various anemochorous seeds (Cremer, 1977;Murren & Ellison, 1996;1998).
The orchid family is a morphologically diverse monocot. Because of their beautiful flowers, Orchidaceae plants are important not only for biologists but also in the marketplace (Sawa et al., 2006;Fukunaga et al., 2008;Ejima et al., 2011). The orchid produces dust seeds, with shapes that are adapted to the habitat of the orchid (Shimizu et al., 2012), and some orchids are considered to be anemochorous plants. However, orchid seeds have not been well characterized. Here, we characterized the seeds and embryos of Japanese orchids, and determined the speed of fall of the seeds. We discuss correlations between orchid habitat and properties of the seed, and comment on the evolution of this relationship.
Orchid Seeds
Orchid seeds were collected from their natural habitat in Japan. All seeds were dried using silica gel in a closed plastic box at least for two weeks at room temperature.
Seed Number
All of the dried seeds in one pod were weighed. Based on mass, the seeds were divided into ten groups, and the number of seeds was counted in one of these groups. The number of seeds per pod was calculated as being ten fold the counted number.
Seed Size
Seed size was examined using a stereomicroscope (Leica S8). Two-hundred seeds were evaluated per species. Mean value was shown in Table 1.
Speed of Fall of the Seeds
A triangular prism, 25 cm each side and 2 m in height, was prepared, one surface of which was constructed of paper and two of transparent vinyl. Top surface was made of paper. A hole, 5 mm in diameter, was made in the top surface. Seeds were dropped from the hole 20 times for each species, and measured the falling time. Falling speeds were calculated from the data of falling time and falling distance (25 cm). Mean value shown in Table 1.
Results and Discussion
We measured the seed size of 68 Japanese orchid species (Table 1). Orchid seeds are known to be small. In this analysis, we found that the seeds of Eria reptas had a length and width of 0.2 and 0.086 mm, respectively. This size is almost twice that of Zea mays (corn) or chestnut pollen grains. On the other hand, the seeds of the Lechanorchis group were quite long, with L. japonica producing seeds that were 4.6 mm in length and 0.15 mm in width. However, the embryo size of L. japonica was almost the same as that of other species (Table 1).
Seed number was also examined. Cymbidium goeringii produced 346900 seeds per pod (Table 1). In contrast, Amitostigma gracile, Ponerorchis kurokamiana, and Tulotis ussuriensis produced only 900 seeds per pod. We could not establish any correlation between seed size and seed number per pod.
To characterize seed dispersal ability, we measured the speed of fall of seeds from 34 Japanese orchids. The speed of fall of the seeds of Taraxacum japonicum, which is a well-known anemochorous plant, was 30.0 cm/s. Most of the seeds had slow speeds of fall (Table 1). Especially in the case of Habenaria dentata, Neofinetia falcate, and Bletilla striata, the speed was 29.8, 26.9, and 23.5 cm/s, respectively. Thus, most of the Japanese orchids could also be considered to be anemochorous plants. However, there was variation in the speed of fall of Japanese orchid seeds, which suggests variation in their method of dispersal. In particular, H. dentate and B. striata thrive in damp areas and on riverbanks, and their seeds can also be dispersed by water. The wind on the forest floor is weak, and orchids that grow in this habitat have to adapt to the weak-wind environment to disperse their seeds. Thus, the seeds of orchids living on the forest floor may have evolved to be long, as increased length reduces the speed of fall and increases the chance that the seed will be swept up by winds (Table 1; Shimizu et al., 2012).
Here we characterize the falling speed of Japanese orchid seeds. Most of the Japanese seeds can be considered as anemochorous plants. However, they seemed to evolve the seed shape to adapt their habitat environment, i.e. long and thin to reduce the seed falling speed. More detailed analysis of the seed shape will reveal how orchid adapt various environment in the world.
Table 1 .
Size and drop-speed of seeds on the native orchids in Japan\===
Domain: Biology Agricultural and Food Sciences. The above document has
* 2 sentences that start with 'Most of the',
* 2 sentences that end with 'seeds per pod',
* 2 sentences that end with 'considered to be anemochorous plants',
* 2 paragraphs that end with 'shown in Table 1'. It has approximately 953 words, 64 sentences, and 20 paragraph(s).
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Comparison of two housing systems on behaviour and performance of fattening pigs
ABSTRACT The aim of this study was to compare the effects of different housing systems on behaviour and growth performance of fattening pigs. Forty Duroc × Meishan pigs aged 100 d were assigned into two housing systems: indoor deep litter (DL) housing (4 pens with 5 pigs/pen) and indoor pen with outdoor playground (PG; 4 pens with 5 pigs/pen). Pig behaviour, body weight, and feed intake were recorded and analysed. Results showed that DL pigs spent more time exploring (DL: 231.0 vs. PG: 178.0 s/h, P < .01), while PG pigs were more aggressive (PG: 6.6 vs. DL: 0.4 s/h, P < .01) and engaged in higher levels of abnormal behaviour (PG: 20.0 vs. DL: 3.2 s/h, P < .01), specifically stereotyped behaviour and mouth-holding/biting tail. No difference was observed for the final body weight and feed conversion efficiency. The results of this study suggest that the DL system improves pig welfare at aspects of exploratory behaviour and abnormal behaviour compared with the PG housing system under the conditions studied, providing a basis for the selection and design of optimum housing systems for pigs.
Introduction
In China, concrete floor with an associative open or semiopen playground (PG) is a general rearing mode for fattening pigs (Guo et al. 2015). Alternatively, the deep litter (DL) housing system is another rearing practice for hogs and emerging rapidly in China. The deep litter bedding is generally made of rice hulls, sawdust, straw, or other agricultural by-products, which are inexpensive and easy to obtain. Pigs reared in DL system eliminate on the surface of deep litter, followed by mixing with bedding materials and either decomposition or fermentation. Compared with conventional housing systems with fully slatted floors, both DL and PG systems have potential advantages in pig welfare by partly increasing environmental enrichment for pigs (Morrison et al. 2007;Guo et al. 2015).
Previous studies have shown that diverse rearing systems can influence pig behaviour (Morrison et al. 2003;Scott et al. 2006;Botermans et al. 2015;Tozawa et al. 2016) and/ or growth performance (Morrison et al. 2007;Dourmad et al. 2009). Even though both DL system and PG system are perceived as being welfare friendly for pigs, there is limited scientific evaluation regarding the differences of behaviour, welfare, and performance for pigs reared in these two housing systems. Therefore, the purpose of this study was to evaluate the effects of two housing systems (DL vs. PG) on these indicators, providing additional information for the better design of pens. It was hypothesized that behaviours, welfare, and performance of pigs would be affected by the housing systems.
Animals
Forty Duroc × Meishan crossbred healthy pigs (20 castrated males and 20 females; BW = 20.13 ± 0.69 kg) of the same age (around 100 d) were used in this study, which was conducted on the Taicang Swine Breeding Farm, Suzhou city, Jiangsu Province, China. This study was performed from August to November (northern hemisphere autumn) 2011. All procedures involving animals were approved by the Experimental Animal Care and Use Committee of Nanjing Agricultural University.
Treatment and housing
At the beginning of this study (100 d of age), pigs were weighed and removed from their nursery rooms, and then allotted into one of the two groups: deep litter (DL) and playground (PG). Each group consisted of four identical neighbouring pens (5 pigs per pen) that were balanced with respect to body weight and gender. Pigs were fed the same commercial diet twice per day (08:00 and 15:00, respectively) by one keeper until the pigs reached marketing time (∼200 d). Pens were cleaned once per day during the feeding time in the morning by the same keeper (3-5 min). During the whole experimental period, pigs were allowed to access water ad libitum through nipple drinkers.
The DL pens were in a fully-enclosed house which had solid wood roofing and concrete walls on four sides (Figure 1(A)). The dimension of each DL pen was 4.0 × 2.5 m, which consisted of the concrete floor area (1.5 × 2.5 m) and the deep litter area (2.5 × 2.5 m). A window with 1.5 m wide and 1.8 m high was constructed at the back of each DL pen that allowed lighting and gas exchange. The DL pen had concrete walls on four sides, with the low iron fences on both sides (2.5 m wide × 1.2 m high) of the deep litter area and the front of the pen (0.62 m wide × 1.2 m high) for passage. The deep litter of about 1.5 m depth consisted of rice hulls, sawdust, and straw. Pig excrement was mixed with bedding materials allowing either decomposition or fermentation. The deep litter bedding was loosened and replenished as required every 15 d to prevent hardening and keep the hygiene condition.
All floors in the PG groups were made of concrete. The dimension of each PG pen was 6.5 × 2.5 m (Figure 1(B)). Each PG pen was divided into two parts by a low wall of 1.88 m wide: an indoor area of 4.0 × 2.5 m and its neighbouring outdoor playground of 2.5 × 2.5 m. The indoor area was in a single-row half-open house which had solid wood roofing and concrete walls on three sides, while the outdoor playground adjacent to the indoor part was in the front of the house. The PG pen had low concrete walls (1.2 m high) on four sides, with a low iron fence (0.62 m wide × 1.2 m high) at the back of the pen for passage.
Behaviour observation
Pig behaviour was continuously video-recorded in a real-time mode using a digital video recorder (DS-7816H-SNH, Hangzhou Hikvision Digital Technology Co. Ltd., China) with twelve cameras (WV-CL 350, Panasonic Corporation, Osaka, Japan) for 13 d (from 20 October to 1 November 2011 with 10 clear d and 3 cloudy d; the outside temperature ranged from 18°C to 23°C). The age of pigs during the video-recording period was 114-130 days old. Each camera was used to monitor one indoor area or outdoor playground to ensure that there was no visual blind spot. All cameras were positioned 3.0 m high above the floor. Pigs' behaviours were continuously observed through the video using the Observer XT 10.5 software (Noldus Information Technology, The Netherlands) by one experienced observer. Scan sampling method was used to collect individual behavioural data from the video. In a total of 15 behaviours for the experimental pigs were recorded in this study. The definition of each behaviour was described in Table 1. Behavioural time budget referred to the proportion of time engaged in each behaviour, which was calculated by dividing the sum of duration of each behaviour by the total time of observation.
Production performance evaluation
The weight of experimental pigs and the feed intake were measured every 30 d until the end of the experiment by pen as the unit. The average daily gain, the average daily feed intake, and the feed convention ratio (dividing feed intake by live weight) for pigs were calculated and compared between the DL group and the PG group.
Statistical analysis
Statistical analysis was performed using IBM SPSS Statistics (version 20.0, IBM Corporation). The Kolmogorov-Smirnov test was used to check the normality of the data. All the data were normally distributed and were presented as mean ± standard error of mean (SEM). The two-tailed t-test was used to analyse the behavioural and performance differences between the DL system and the PG system. Pens were treated as independent units for the statistical analyses. Pvalue less than .05 and P value less than .01 were considered statistically significant and very significant, respectively.
Effects of housing system on pig behaviours
Results of the time budget of the observed behaviours were shown in Table 2. During the continuously video-recorded observation period, pigs reared in DL pens spent more time sit-resting (P < .05) and less time stand-resting (P < .01) compared with pigs reared in PG pens. No difference was observed for the time pigs spent in lie-resting and total inactive behaviour (P > .05).
During the whole observation period, pigs in DL pens spent less time playing (P < .05) and engaged in less social and aggressive activity (P < .01). Moreover, DL pigs spent more time drinking than that of PG pigs (P < .05), and pigs housed in DL pens spent 53.0 s more time per hour exploring the pen compared with pigs housed in PG pens (P < .01). There was no difference in the time pigs spent running, walking, elimination, and comfort when housed in DL system or PG system (P > .05).
Compared with pigs housed in PG pens, a pig housed in DL pens spent 16.8 s less time per hour exhibiting abnormal behaviours (P < .01). Specifically, pigs housed in DL pens spent less time performing the stereotyped behaviour and the mouthholding/biting tail behaviour (P < .01). No time difference was observed for a pig holding/biting another pig's ear by its mouth when housed in DL system or PG system (P > .05).
Pen exploration postures for DL pigs and PG pigs
During the pen exploration process, postures of pigs were also recorded, which were classified into lying, standing, and moving. Results showed that percentage of pen exploration postures was different when pigs were housed in DL or PG system (Figure 2). The moving, standing, and lying postures for pigs housed in DL system accounted for 38.1 ± 3.34%, 38.1 ± 3.85%, and 23.8 ± 2.36%, respectively; while the moving, standing, and lying postures for PG pigs accounted for 57.1 ± 6.35%, 36.0 ± 4.52%, and 6.9 ± 2.53%, respectively. When exploration, PG pigs had higher moving posture (P Table 1. Description of behaviours recorded over the study period.
Inactive behaviours
Lie-resting Trunk is contact with ground and no weight is supported by any limb. Sit-resting Body is in an upright position, with hindquarters and two forefeet contact with ground. Weight is supported by hindquarters and two forelegs. Stand-resting Weight is supported by four limps. No movement.
Active behaviours Running
A rapid four-beat gait with forward movement. Lasting longer than 1 s. Walking A slow four-beat gait with forward movement. Lasting longer than 2 s. Playing Pig jumps in the air or runs back and forth in the pen doing buckjumps. Exploring pens Pig's snout approaches (less than 5 cm) or digs any part of the pen. Lasting longer than 2 s. Drinking Pig manipulates the nipple drinker. Elimination Pig defaecates or urinates. Comfort Rolling, rubbing body on the wall or the floor, stretching body, shaking body, or yawning. Giving social activity Rubbing or snout-touching another pig's body in the same pen. Giving aggressive activity A pig aggressively rams or thrusts other pigs with head or snout.
Abnormal behaviours Stereotyped behaviour
Chewing with nothing in its mouth, opening its mouth to hold or bite bars of the fence, or walking back and forth in a fixed route. Lasting longer than 3 s with rhythm. Mouth-holding/ biting tail Mouth-holding or biting another pig's tail.
Mouth-holding/ biting ear
Mouth-holding or biting another pig's ear. < .05) and significantly lower lying posture compared with that of pigs in DL system (P < .01).
Average daily gain, feed intake, and feed convention ratio
As shown in Table 3, during the whole experimental period (100-200 d of age), there was no difference in the average daily feed intake, the average daily gain, the feed conversion efficiency, the initial body weight, and the final body weight for pigs housed in DL system or PG system (P > .05).
Discussion
Single factor studies have been conducted to reveal the effects of environmental enrichment (Beattie et al. 2000;Tozawa et al. 2016), group size or rearing densities (Oh et al. 2010;Vermeer et al. 2014) on pig behaviour and performance. In fact, different housing systems often involve multiple factors that may affect pig behaviour, such as pen space, group size, flooring, enrichment, etc. However, there is still a lack of information concerning these factors and pig behaviour in multi-factorial experiments. In the present study, the DL housing system and PG housing system with vary space were compared under the conditions studied, in which the genetics, age, weight, group size, feed, and location were similar. Our study provides the first evidence about the effects of the DL system and PG system on behaviour, welfare and performance for pigs, indicating DL system improves pig welfare at aspects of exploratory behaviour and abnormal behaviour. In this study, ethogram observation was conducted and inactive behaviour, active behaviour and abnormal behaviour were analysed under DL or PG housing system. For inactive behaviour, pigs housed in DL pens preferred sit-resting than standresting, which might be due to the usage of soft deep litter substrates compared with the hard concrete floor. No difference was observed for lie-resting percentage and the total inactive behaviour. Meanwhile, the active behaviour containing running, walking, playing, exploring pens, drinking, elimination, comfort, giving social activity and giving aggressive activity was observed in the study. DL pigs spent more time drinking. The enclosed house environment might contribute to a higher indoor temperature (Song et al. 2013), thus resulted in higher levels of drinking behaviour.
Furthermore, pigs housed in DL pens spent more time exploring and less time giving aggressive activity and exhibiting abnormal behaviour. Exploring the environment via snouts is one of the strongest intrinsic behaviours for swine (Tozawa et al. 2016). Research has demonstrated that the appropriate environmental abundance for pigs may arouse the expression of their natural behaviours, including the exploratory behaviour, etc (Elkmann and Hoy 2009). The ability of pigs to explore their surroundings and their information-based activities reflect their welfare status (van de Weerd and Day 2009), and the expression of exploratory behaviours has been used in pig's welfare assessment (Morrison et al. 2007).
Expression restriction of natural species-specific behaviours may cause chronic psychological stress for animals (Pearce and Paterson 1993). In the barren environment, the pig still displays an inherent motivation to explore. In such cases, the exploratory behaviour is usually re-directed at the limited number of substrates available, namely pen-mates and pen components (Scott et al. 2006), leading to behavioural problems such as aggression (Beattie et al. 1996;Olsen et al. 2002;Presto et al. 2009) as well as tail-biting or other abnormal behaviour (Scott et al. 2006;Presto et al. 2009;Jensen et al. 2010). Abnormal behaviours are signs of suffering due to the thwarting of a need (Jensen and Toates 1993). In addition to increased pen exploration for DL pigs, our results indicated that PG pigs spent more time interacting with their pen-mates and there was no difference on the total time pigs spent on pen exploration and individual interaction for pigs housed in DL system or PG system. In the present experiment, PG pigs re-directed exploratory motivation towards pen-mates in the relative barren environment, resulting in higher pen-mate interaction time compared with that of DL pigs. The elevated time for pen-mate interaction in PG system provided the possibility for the increased abnormal behaviours for pigs housed in PG pens. This is consistent with previous studies that environmental enrichment may increase the time of pigs interacting with their pens, and reduce behaviours directed towards pen-mates (O'Connell and Beattie 1999). The enhanced exploratory behaviours as well as less aggressive activities and abnormal behaviours for DL pigs indicated that the DL system was more effective in improving the welfare condition compared with the PG system. Interestingly, pigs housed in PG system also showed more social activities and playing behaviour (an indicator of positive emotion), which might be related to additional playground field in PG house.
In summary, our results suggest that DL housing of pigs effectively enhanced the environmental enrichment, leading to more pen exploration and less pen-mate interactions compared with PG housing system. Moreover, lower pen-mate interactions resulted in less abnormal behaviours for pigs housed in DL pens. Although the DL or PG rearing system did not affect the production performance, the use of deep litter bedding effectively increased the environmental enrichment and partly improved pig's welfare conditions. In a time whereby increased meat-animal production is needed, determining the optimum and appropriate production regimens which also consider animal welfare and enrichment are vital. New mechanisms of pig housing, will lead to enhanced animal growth and development, especially if the physiological and psychological needs of the animals are considered.
Disclosure statement
No potential conflict of interest was reported by the authors.
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Domain: Biology Agricultural and Food Sciences
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Upregulation of TLR 4 mRNA Expression Levels in Broiler Chickens under Acute Heat Stress
Toll-like receptors (TLRs) are well-characterized in mice and rats, but little is known on their role in broiler chickens during acute heat stress (AHS). The aim of the present study was to estimate the change in TLR4 mRNA expression in the liver, kidney’s pleen, heart, and small intestine of broiler chickens under AHS. A total of 240 healthy Arbor Acres (AA) broiler chickens were randomly divided into four groups: control group (22±1°C; 0h), HS2, HS5 and HS10 (38±1°C; 2h, 5h and 10h of heat stress, respectively). As AHS duration increased, TLR4 mRNA expression slightly decreased at HS2 and HS5, but was dramatically elevated in HS10 (p<0.001) compared with the control group in the small intestine, as well as in the spleen at HS2 (p=0.001) and HS10 (p<0.001). The mRNA expression levels of TLR4 significantly increased in the liver, heart, and kidneys (p<0.001) at HS10, and in the kidneys at HS5 (p=0.003). It is found that TLR4 mRNA expression at HS10 in different organs was significantly higher (p<0.001) compared with HS2 and HS5. The results of the present study suggest that AHS may modulate the functional responses of the liver, kidney, spleen, heart, and small intestine of broilers by regulating TLR4 mRNA expression.
INTRODUCTION
Heat stress (HS) may cause mortality in broilers, and it is a major economic concern as it reduces their growth performance and feed efficiency (Ryder et al., 2004). However, many animal species typically have a protective metabolism that is adapted to the optimal temperature range of the environment in which they have evolved (Zhang et al., 2014;Sun et al., 2015). It well known that poultry species, particularly broiler chickens, are more sensitive to high environmental temperatures and humidity levels than other domestic animals because of their feather cover, lack of sudoriferous glands, and fast growth rate (Piestun et al., 2013;Di et al., 2015). When the environmental temperature exceeds the optimal temperature range, broilers show signs of heat stress, including rise in body temperature, reduced feed intake, poor reproductive and growth performance, immunosuppression, and increased mortality (Kamboh et al., 2013;Zhao et al., 2013;Zhang et al., 2014;Lasagna et al., 2015;Li et al., 2015;Sun et al., 2015). Many studies have demonstrated the damage or injury of the heart, liver, and brain tissues, as well as and angiocarpy in chickens and rats under HS (Zhang et al., 2014;Quinn et al., 2015;Ito et al., 2015). Moreover, AHS triggers the expression of heat stress-related genes, such as HSP70, HSP70s, TLR2 and TLR4 (Ju et al., 2014;Zhang et al., 2014;Richter et al., 2015;Zhang et al., 2015).
Toll-like receptors (TLRs) are a family of transmembrane-spanning proteins, which recognize molecules unique to microbes, discriminate
Upregulation of TLR4 mRNA Expression Levels in Broiler Chickens under Acute Heat Stress
self from nonself antigens, trigger appropriate immune responses, act as sentinels of tissue damage, and mediate inflammatory responses to aseptic tissue injury (Takeda & Akira, 2005;O'Neill, 2006;Marsh et al., 2009). Zhou et al. (2005) and Ju et al. (2011) have documented that the TLR4-mediated signal pathway is activated in response to stress, particularly to HS. In the present study, we hypothesized that AHS caused changes in the expression of TLR4 in different organs damaged by HS. To our knowledge, little information is available on the effect of AHS on TLR4 mRNA expression levels in various organs of broiler chickens. Thus, we investigated TLR4 gene expression in the liver, kidney, spleen, heart, and small intestine of broiler chickens subjected to AHS for different periods, providing a basis for the elucidation of the mechanism of tissue damage by AHS.
Birds
A total of 240 one-day-old healthy Arbor Acres (AA) broiler chickens were purchased from a local commercial poultry company (Henan, China). Birds were randomly divided into four groups: control group, HS2, HS5 and HS10 (0h, 2h, 5h and 10h of HS, respectively), with six replicates of 10 broilers per cage. All broilers received a conventional commercial feed and water ad libitum until the end of the experiment.
This experiment was undertaken according to the directions of the regional Animal Ethics Committee and was approved by the Institutional Animal Care and Use Committee of Henan Agricultural University.
Experimental Procedures
On day 28, the birds of control group were observed in a separate room (22±1°C), while heat-stress groups (HS2, HS5 and HS10) were allocated into another room, where the temperature was abruptly increased from 22±1°C to 38±1°C using heaters. Room relative humidity was maintained at 50±10% during the entire experiment. Six chickens were randomly selected at 0h, 2h, 5h and 10h of the experiment from each group and killed rapidly by cervical dislocation. The liver, kidney, spleen, heart and intestinal tissues (duodenum) were dissected, placed into 2.0 mL cryogenic vials, frozen in liquid nitrogen, and stored at -80°C until RNA isolation.
RNA extraction and reverse transcription
Total RNA was extracted from the liver, kidney, spleen, heart and small intestine collected from each group of broiler chickens. A final volume of 20 mL of total RNA was reversely transcribed into cDNA with a reverse transcription kit (Takara Biotechnology CO., LTD, Dalian, China), according to manufacturer's instructions at reaction temperatures of 42 °C for 40 min and 70 °C for 15 min. The agarose gel was run to identify the gene using the DL2000 marker.
Reverse transcription-quantitative PCR (RT-PCR) for mRNA expression TLR4 primers were designed by the Primer Premier Software 5.0 Version (Premier Biosoft International, USA) based on known chicken sequences (Table 1). The TLR4 primers and GAPDH were synthesized from Invitrogen Biotechnology Company (China) and stored at -80 °C until use. The reactions were performed in a 10-μL reaction mixtures containing 5 μL of SYBR GreenI mix(Takara Biotechnology CO., LTD, Dalian, China), 1.25 μL of diluted cDNA, 0.2 μL of each primer (10 μM), and 3.35 μL of Rnase-free water. The PCR procedure for TLR4 with Eppendorf Real-time PCR System Mastercycler ® ep realplex (Eppendorf'Germany) was performed: one cycle at 95 °C for 2 min, and followed by 40 cycles of 95 °C for 20 s, 60 °C for 20 s, and 72 °C for 20 s. The relative mRNA abundance was calculated according to the method of ΔΔCt, which accounts for gene-specific efficiencies and was normalized to the mean expressions of the above-mentioned parameters. In addition, melting curve analysis was determined to monitor PCR product purity in this experiment.
Statistical Analysis
All data were statistically analyzed using the SPSS statistical software for Windows (version 17.0; SPSS, Chicago, Illinois). The general linear model was applied for analysis of variance, and Duncan's new multiple range test was applied to compare the differences between treatments. A significance level of 0.05 was used. Data are expressed as means ± standard error (SE).
Melting curve analysis of the GAPDH and TLR4 genes
Melting curve analyses of the GAPDH and TLR4 genes are shown in Figures 1A and 1B. The Tm of TLR4 and GAPDH were 84.3°C and 86.5°C, respectively. Moreover, PCR products produced a melting curve with a single peak at the Tm. It was indicated that RT-PCR did not generate primer dimers and nonspecific amplification in the amplification process.
Agarose gel analysis of the quality of the TLR4 and GAPDH genes
Agarose gel electrophoresis for the TLR4 and GAPDH genes resulted in TLR4 and GAPDH amplicons of 272bp and 156bp in size, respectively, which are consistent with the anticipated fragment (TLR4: 200~300bp; GAPDH: 100~200bp, Figure 1C). Changes in TLR4 mRNA expression levels in the liver, kidney, spleen, heart and small intestine The TLR4 mRNA expressions levels in the liver, kidney, spleen, heart and small intestine of broiler chickens were investigated during AHS. Compared with the control group, the TLR4 mRNA expression level was slightly lower in the HS2 and HS5 groups, but dramatically elevated in the HS10 group (p<0.001; Figure 3A) in the small intestine, and was up to 7.139 times higher in the HS10 group than in control group. Moreover, the TLR4 mRNA expression levels in the spleen, as in the small intestine, were significantly lower (p=0.001) in the HS2 group and markedly elevated in the HS10 group (p<0.001; Figure 3D) compared with the controls. In the liver and the heart, the TLR4 mRNA expression levels in the HS10 group were significantly higher than in the control group (p<0.001;Figures 3B and 3E), being 3.012 and 3.848 times higher, respectively. Additionally, the TLR4 mRNA expression levels in the kidney were markedly higher in the HS5 (p=0.003) and HS10 groups (p<0.001; Figure 3C) than in the control group. In general, TLR4 mRNA expression levels in different organs were significantly higher (p<0.001) in the HS10 group than in the HS2 and HS5 groups. These data demonstrate that AHS significantly changed the TLR4 mRNA expression levels in the liver, kidney, spleen, heart, and small intestine of broiler chickens.
DISCUSSION
Due to global warming, environmental temperatures are predicted to increase over the next decades, likely contributing to an increase in heat stress-induced organ injury and dysfunction during HS in animals (Yu et al., 2013). This study investigated the effect of AHS on the TLR4 mRNA expression levels in the liver, kidney, spleen, heart and small intestine of broiler chickens. The results clearly show the significant effect of increasing environmental temperatures on TLR4 mRNA expression levels in different organs of broiler chickens. The TLR4 mRNA expression levels were slightly lower at HS2 and at HS5, but dramatically elevated in the small intestine at HS10 compared with the control group, not submitted to HS. Xue et al. (2011) found that the expression level of the TLR isoform TLR2 in the mucosa of the small intestine significantly increased and intestinal mucosa was damaged in rats submitted to heat stress. Fukata & Abreu (2007) demonstrated that TLR4 induces the activation of cyclooxygenase 2 and prostaglandin E2 in vitro and in vivo, which are important for cell proliferation and apoptosis in response to intestinal mucosal injury. Gu et al. (2012) indicated that HSP70 is capable of protecting the intestinal mucosa from heat stress injury by enhancing the antioxidant capacity of broilers. Furthermore, TLR4 is involved in the recognition of endogenous or exogenous products of microbes, such as heat shock protein (HSP) and lipopolysaccharide (LPS) (Ohashi et al., 2000;Vabulas et al., 2001). Fukata et al. (2005) reported that TLR4 is important for healing the injured intestinal epithelium. Hence, the markedly increased TLR4 mRNA expression levels indicate that the The liver, as a major site of metabolism and detoxification, is the system of choice in studies on toxico-proteomics, metabolic disorders, and stress effects caused by various pathobiological processes. Flanagan et al. (1995) and Kregel et al. (1995) demonstrated that the liver is a sentinel organ for thermal stress. In addition, data from HS and endotoxin shock models (Ryan et al., 1994) support the critical role of the liver in the response to thermal and endotoxin challenge. During HS, a disturbance in the microecological balance of the intestinal flora may cause bacterial translocation and induce the production of intestinal endotoxins (Gu et al., 2012;Yu et al., 2013), allowing large amounts of endotoxins to reach the liver. TLR4 is stably expressed on the surface of many cells, including the Kupffer cells of the liver (Zuo et al., 2003). Zhang et al. (2009) showed that Salvia miltiorrhizae was able to significantly down regulate TLR4 expression on the surface of hepatic cells, suppress the release of the tumor necrosis factor (TNF)-α, and mitigate the liver injury caused by excessive inflammatory reaction. In the present study, TLR4 mRNA expression level was significantly increased in the liver of HS10 broilers, indicating liver damage due to the release of TNF-α, leading to excessive inflammatory reaction. Altawheed et al. (2003) showed that HS can cause acute or chronic kidney failure in human patients. In animals, the kidneys play an important role in the maintenance of a stable inner environment, which can be easily disturbed by harsh environmental changes. Wolfs et al. (2002) and Anders et al. (2004) found that TLR2 and TLR4 are constitutively expressed in the epithelial cells of both proximal and distal tubules and in the mesangial cells of the kidneys in response to injury. In the present study, the TLR4 mRNA expression levels in the kidneys were markedly increased in the HS5 and HS10 groups compared with the control group, demonstrating that the kidneys were seriously impacted by AHS. In addition, previous research has shown a marked increase in TLR2 and TLR4 expression in renal tubular cells and in renal infiltrating cells caused by ischemia and reperfusion injury compared with control group (Kim et al., 2005;Pedregosa et al., 2011). Cunningham et al. (2004) reported that TLR4 expression in the kidneys was critical in mediating LPS-induced acute kidney failure via proinflammatory cytokine release and subsequent kidney damage. Those results indicate that TLR4 mRNA expression levels in the kidneys may be caused by renal tubular cells or renal infiltrating cells damage, or LPS induction under AHS. Further studies are required to elucidate the mechanism involved in the kidney injury induced by HS.
Upregulation of TLR4 mRNA Expression Levels in Broiler Chickens under Acute Heat Stress
It has been previously reported that the spleen, mainly composed by B and T lymphocytes, macrophages, and other immune cells, is a peripheral lymphoid organ that plays a critical role in innate and adaptive immune response against systemically-acquired antigens in the body (Mebius & Kraal, 2005;Abdul-Careem et al., 2007). Ohtsu et al. (2015) showed that HS affects spleen weight and induces spleen involution in broiler chickens. Therefore, the immune function of the spleen may be affected by heat stress. Huang et al. (2014) demonstrated that nickel chloride reduced TLR4 and TLR7 mRNA expression levels in the spleen of broilers, suggesting that nickel chloride may impair innate and adaptive immune responses in the spleen. This means that down regulated TLR4 and TLR7 mRNA expression levels in the spleen are beneficial to its immune function. On the other hand, Pedregosa et al. (2011) indicated that ischemia and reperfusion injury caused a significant increase of TLR2 and TLR4 expression levels in spleen cells. In the present study, the TLR4 mRNA expression levels in the spleen were consistent with those observed in the small intestine, which were significantly reduced in the HS2 group and markedly elevated in the HS10 group. This upregulation of TLR4 mRNA expression levels in the spleen suggests that the immune function of the spleen was damaged. However, it is interesting that the down regulated TLR4 mRNA expression levels in the HS2 group indicate that the immune function was stimulated at the beginning of HS.
In the current study, the TLR4 mRNA expression levels were significantly higher in the heart of HS10 birds compared with the controls. It is well known that the activation of TLR signaling is essential for the regulation of the innate and adaptive immune systems, and results in the upregulation of inflammatory pathways and release of inflammatory cytokines, such as TNF-α (Konner & Bruning, 2011). However, some non-immune cells, such as cardiac myocytes may also cause inflammation, due to their higher expression levels of TLRs, but the possible role of TLRs in this mechanism is not clear. Chao (2009) identified two TLR isoforms (TLR2 and TLR4) have on the myocyte surface, which were implicated in ischemic cardiac injury and reduced cardiac myocyte survival. In addition, the presence of significantly higher TLR4 levels in the myocardium than in the spleen and kidney of heatstressed broiler chickens in the present study suggest that TLR signaling was highly physiologically relevant to the heart. Moreover, Bopassa et al. (2008) and Deng et al. (2012) provided strong evidence that TLR4 signaling not only mediates myocardial inflammation and ischemic injury, but also contributes to cardiac dysfunction during metabolic disease. Therefore, HS lead to a marked increase of TLR4 mRNA expression levels, indicating that the cardiac function was disturbed by HS. In addition, de Laat et al. (2014) showed that the downregulation of TLR4 expression in the mammalian heart during metabolic dysfunction would facilitate improved management of cardiac sequela to metabolic syndrome and diabetes.
Overall, the results of the current study show that there is a positive regulation of TLR4 mRNA expression in the liver, kidney, spleen, heart and small intestine of broiler chickens, and that acute heat stress may cause organ injury via increased TLR4-metiated inflammation; however, this finding need to be further investigated in broiler chickens. In addition, the TLR4 mRNA expression was different among the organs of broilers submitted to 10 hours of heat stress (small intestine > heart > liver > spleen and kidney). In those submitted to two hours of heat stress, TLR4 mRNA expression was significantly reduced in the spleen, but not in the liver, kidney, and heart, suggesting that the immune function plays a vital role at the beginning of AHS and TLR signaling is highly physiologically relevant to the organs.
expression of HSP70 was elevated and triggered TLR4 overexpression to protect the intestine from injury by AHS.
Figure 2 -
Figure 2 -TLR4 mRNA expression levels in the liver, kidney, spleen, heart and small intestine of broiler chickens under AHS. Data are expressed as mean± SE (n=6).
Table 1 -
Gene-specific primers used in real-time quantitative RT-PCR
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Domain: Biology Agricultural and Food Sciences
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Feeding preference of Spodoptera frugiperda on different sorghum genotypes Preferência alimentar de Spodoptera frugiperda em diferentes genótipos de sorgo
Arq. Inst. Biol., v.86, 1-6, e0992018, 2019 RESUMO: A resistência das plantas às pragas é uma característica de grande importância para a agricultura, pois reduz os custos com inseticidas e promove o aumento da produtividade, resultando em maiores lucros. Este trabalho foi desenvolvido com o objetivo de avaliar a preferência alimentar da lagarta Spodoptera frugiperda por diferentes genótipos de sorgo. O experimento foi conduzido no Laboratório de Entomologia da Universidade Estadual de Mato Grosso do Sul, na Unidade Universitária de Cassilândia, no período de março a junho de 2016. O delineamento experimental foi inteiramente ao acaso, com 10 repetições. Os tratamentos foram compostos por sete genótipos de sorgo: Agromen 50A40, Agromen 50A50, DOW 1G100, DOW 1G220, DOW 1G233, XB 6022 e LG 6310. As avaliações foram realizadas com lagartas de 1o instar. Anotou-se o número de lagartas que se estabeleceram nos genótipos aos 5, 10, 15, 20, 25, 30, 60 minutos e 24 horas após a infestação. Foi estimado o índice de preferência e a massa fresca de folha consumida. O genótipo Agromen 50A40 apresentou menor atratividade para a S. frugiperda dentre todos os genótipos de sorgo avaliados.
PALAVRAS-CHAVE:
Feeding preference of Spodoptera frugiperda on different sorghum genotypes INTRODUCTION Sorghum (Sorghum bicolor) is a species that presents high adaptability to different environments and has physiological characteristics that allow it to tolerate and develop satisfactorily in water deficient conditions when compared to other crops. Because of that, the crop has potential to be cultivated in the second crop in the Brazilian ecoregion called Cerrado (ALMEIDA FILHO et al., 2010). Currently, in Brazil, approximately 622 thousand hectares of sorghum are cultivated, with mean grain yield estimated at 2,667 kg ha -1 (CONAB, 2017).
Even with these characteristics, some relevant factors have caused significant damage to their production, as pests (ELLIOT et al., 2014). The fall armyworm, of the genus Spodoptera is one of the pests that cause significant damage, it is widely distributed in the world, causing significant damage, and among the 30 species described, half are pests of sorghum and several crops of economic importance. Among them, the Spodoptera frugiperda (Smith) stands out for feeding on over 80 species of plants including cotton, corn, and soybean (POGUE, 2002).
According to BARROS et al. (2010), the S. frugiperda feed on the leaves initially, consequently starting to consume grains in the initial stage of grain filling in certain cultures. Among the factors related to the control of this insect-plague, to reduce the damage caused in crops, several insecticides are used. By misusing these chemicals, the imbalance in the environment is becoming increasingly common and resulting in higher production costs (ANDRADE et al., 2016).
Several methods have been used to control the armyworm, and the diversification of sorghum genetic material has been of great importance in controlling the pest, and the plant resistance may be the most viable (CRUZ et al., 1998). Genetic improvement of sorghum, aiming at resistance to meet future grain and forage demand, can be an effective component of the integrated pest management program (ARUNA et al., 2015).
Considering the importance of controlling this pest, the objective of this work was to evaluate the feeding preference of the Spodoptera frugiperda on different sorghum genotypes.
Location
The experiment was carried out at the Entomology Laboratory of Universidade Estadual de Mato Grosso do Sul (UEMS), at University Unit of Cassilândia, from March to June 2016. The Spodoptera frugiperda caterpillars were obtained from the National Research Center of Maize and Sorghum (CNPMS -EMBRAPA), located in Sete Lagoas (MG).
Source of Spodoptera frugiperda
The S. frugiperda caterpillars were stored in the laboratory with artificial diet (KASTEN JUNIOR et al., 1978). The breeding and reproduction methodology proposed by PARRA (1986) was used. The conditions of breeding and reproduction were: temperature of 27 ± 1°C, relative humidity of 70 ± 10% and a photoperiod of 14 hours.
Plant preparation
Seven sorghum genotypes were used: Agromen 50A40, Agromen 50A50, DOW 1G100, DOW 1G220, DOW 1G233, XB 6022 and LG 6310. Sorghum genotypes were sown in pots with the volume of 5.0 dm -3 filled with soil collected in the 0 to 20 cm depth layer. The soil used is classified as Entisol (95 g kg -1 of clay, 50 g kg -1 of silt and 855 g kg -1 of sand).
The pots were irrigated so that the soil reached the field capacity. After that, ten seeds per pot were sown. The sowing depth was 3.0 cm. Five days after emergence (DAE) a thinning was performed leaving only one plant per pot.
At 15 DAE 150 mg dm -3 of nitrogen (urea) were applied, 300 mg dm -3 of phosphorus (single superphosphate) and 150 mg dm -3 of potassium (potassium chloride). Before to application, the sources were diluted in water and then applied. For irrigation,a daily application of 180 mL of water per pot was standardized.
Evaluations
The evaluations were carried out with 1 st instar caterpillars of S. frugiperda. The design was entirely randomized with ten replicates. Each arena (repeat) was formed by a 15 cm diameter Petri dish with damp filter paper placed on the bottom of the plate. The leaves of each sorghum genotype were collected at the pre-flowering stage.
The leaves were washed and dried with paper towel. Leaf rectangles of 3.0 × 3.0 cm (9.0 cm 2 ) were then cut longitudinally and parallel with the central vein. The rectangles were arranged circularly in the arenas and fixed with pins. At the center of the plate, 140 newly hatched caterpillars of S. frugiperda were released, and the arena was then sealed with the lid of the petri dish. The number of S. frugiperda caterpillars was recorded at 5, 10, 15, 20, 25, 30 and 60 minutes after infestation. It was also noted the number of caterpillars that settled in each treatment after 24 hours of infestation.
The preference index for newly hatched caterpillars of S. frugiperda was estimated, considering as a 100% index the genotype in which the largest number of S. frugiperda caterpillars was found 24 hours after infestation. The initial (before releasing) and final (24 hours after infestation) masses were obtained with an analytical balance. From the initial and final masses, it was possible to estimate the fresh mass consumed by the S. frugiperda of each genotype.
Statistical analysis
The obtained data were transformed into (x + 0,5)^1 /2 and then submitted to analysis of variance, and F test tested the significance of the mean squares obtained in the analysis of variance at the 5% probability level. The averages for genotypes were grouped by the clustering test proposed by SCOTT;KNOTT (1974) at the 5% probability level. The attractivity data were submitted to polynomial regression analysis as a function of time. The Student's t test was applied to evaluate the significance level of the regression coefficients.
RESULTS AND DISCUSSION
The highest percentage of fresh mass consumed by S. frugiperda was observed in genotypes XB 6022, DOW 1G100 and DOW 1G220, with consumption of 19.59, 19.20 and 16.15% of leaf sections, respectively. These results are showing feeding preference of the S. frugiperda for these sorghum genotypes. The genotype Agromen 50A40 presented the lowest percentage of fresh mass consumed (7.58%), thus revealing the preference of the S. frugiperda for the other genotypes studied, to detriment of this one (Table 1).
The feeding preference and leaf consumption by S. frugiperda may be related to the plant, the insect, and the environmental factors. These are ways of manifesting resistance, according to BUENO et al. (2006), who also reports that these genetic traits of the cultivar differ in resistance and are less likely to be related to the target pest and may also be related to age and part of the plant and the physiological stage. According to SMITH (2005), a plant ceases to serve as a host for the insect when there is no the insect-plant interaction, demonstrating the potential of this pest to choose another plant as the host, thus characterizing the antixenosis.
The S. frugiperda occurs more frequently in maize, which is more susceptible when compared to sorghum (CORTEZ, 1997) due to the insect's difficulty in feeding of sorghum. When the S. frugiperda feed, its maxillary palms and labratory gustatory receptors transmit information to the nervous system, which perceive the presence of chemical, physiological and / or morphological changes in the plant and transmit commands to the central nervous system of the insect, causing it to choose other plants to feed on (SMITH, 2005).
The number of S. frugiperda caterpillars attracted by the Agromen 50A40 and XB 6022 genotypes about the time after infestation presented a linear behavior (Fig. 1), while for other genotypes the number of S. frugiperda caterpillars did not change as the time progressed (Table 2).
The number of S. frugiperda caterpillars in the leaf sections in the Agromen 50A40 genotype showed a linear behavior, with a decrease in the number of caterpillars (Fig. 1A). These results indicate the non-preference of the S. frugiperda for this sorghum genotype. For genotype XB 6022, there was a linear increase in the number of S. frugiperda caterpillars in Table 1. Initial fresh mass, final fresh mass and fresh mass consumed from leaf sections of sorghum genotypes offered to Spodoptera frugiperda caterpillars for 24 hours. Cassilândia, Mato Grosso do Sul, 2016.
Genotypes
Initial fresh mass Final fresh mass Fresh mass consumed Means followed by the same letter in the columns belong to the same group by the SCOTT; KNOTT (1974) cluster test, at 5% probability; ns not significant by the F test; *significant at 1% by the F test; CV: coefficient of variation. the leaf sections (Fig. 1B), indicating the S. frugiperda feeding preference for this genotype.
There was a reduction in the number of S. frugiperda caterpillars attracted by the Agromen 50A40 genotype when comparing the 5 th minute with the 60 th minute after infestation. The inverse occurs for genotype XB 6022. These results are a partial indication of the susceptible and resistant genotypes.
The different taste receptors of the insect can detect quantitative and qualitative variations on the chemical composition of the tested plant tissues, which depends on the different types of phytochemicals present in the plant, which have the effect of repelling the insect (SMITH, 2005).
There was no difference between sorghum genotypes regarding the number of caterpillars attracted in the first 25 minutes after infestation (Table 3). BOREGAS et al. (2013) found that in this stage of larval development of the insect there may occur interference in the adaptation and development of the insect in the host, that is, the caterpillars tend not to feed in the first minutes until finding a suitable host, and for this reason they migrate from one plant to another.
At 30 minutes, the genotypes Agromen 50A40, Agromen 50A50 and LG 6310, presented a smaller number of S. frugiperda caterpillars compared to the others. At 60 minutes, genotypes DOW 1G100 and XB 6022 presented the highest number of S. frugiperda caterpillars compared to the other genotypes. After 24 hours of infestation, the genotypes XB 6022, DOW 1G100, DOW 1G220 and DOW 1G233 presented a higher number of S. frugiperda caterpillars, indicating a higher feeding preference of S. frugiperda for these sorghum genotypes. The Agromen 50A40 genotype showed lower feeding preference for S. frugiperda after 60 minutes compared to the other genotypes.
The migration of S. frugiperda caterpillars from one genotype to another can be due to the stiffness of the leaves' Means followed by the same letter in the columns belong to the same group by the SCOTT; KNOTT (1974) cluster test, at 5% probability; ns not significant by the F test; *significant at 1% by the F test; CV: coefficient of variation.
epidermis, which increases with larger amounts of silica present in each plant, acting as a mechanical barrier, making it difficult to the S. frugiperda to feed (DJAMIN; PATHAR, 1967;CARBONARI;MARTINS, 1998).
With the feeding preference index, it is possible to verify that the S. frugiperda has a higher feeding preference for the genotypes XB6022 and 1G100, and less preference for the Agromen 50A40 genotype (Fig. 2). The lower feed preference of S. frugiperda for the genotype Agromen 50A40 can be attributed to the large amount of fiber present in the leaves (BOREGAS et al., 2013).
CONCLUSION
Among all sorghum genotypes evaluated, the Agromen 50A40 genotype showed less attractiveness by the Spodoptera frugiperda.
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Domain: Biology Agricultural and Food Sciences
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Evaluation of dry matter yield, yield components and nutritive value of selected alfalfa (Medicago sativa L.) cultivars grown under Lowland Raya Valley, Northern Ethiopia
The experiment was conducted at Raya Azebo district, which is located in Southern Tigray, North Ethiopia, with the objective to investigate the highest dry matter yield and herbage nutritive value among the selected alfalfa cultivars. The experiment was conducted by randomized complete block design with four replications and five cultivars. The experimental cultivars were FG-10-09 (F), FG-9-09 (F), Magna-801-FG (F), Magna-788 and Hairy Peruvian. Harvesting cutting intervals was taken at an average of 57.78±4.78 days of mid flowering at irrigation land. A total of 4 cutting cycles were taken from January 2016 to August 2016. The result of the study showed that dry matter (DM), organic matter (OM), crude protein (CP), neutral detergent fiber (NDF) and in vitro dry matter digestibility (IVDMD) was comparable across the five cultivars. Stand height was significant different (P<0.001) among the cultivars. Alfalfa cultivars FG-10-09(F), FG-9-09(F), Magna-788 and Hairy Peruvian had significantly (P<0.001) higher plant height as compared to Magna-801-FG (F). However, DM yield and leaf to steam ratio (LTSR) was not affected by cultivars (P>0.05). Cutting cycle significantly affected stand height, DM yield and LTSR. Plant height and DM yield were significantly different (P<0.001) among the cultivars across the cutting cycle. Cutting cycles 2, 3 and 4 had the highest stand height and DM yield as compared to cutting cycle 1 (P<0.001). But, cutting cycles 2 and 1 were significantly higher in LTSR as compared to 3 and 4 (P<0.001). Therefore, it can be conclude that all the cultivars evaluated had not shown significant difference in DMY and nutritive content, but Hairy Peruvian had relatively good DM yield and higher stand height, as a result, it is good to promote Hairy Peruvian cultivar for further demonstration and seed production.
INTRODUCTION
Feed scarcity in both quantitative and qualitative dimensions is one of the major constraints for the promotion of the livestock subsector in Ethiopia (Alemu, 1997). In many areas of the country, animals are kept on poor quality natural pasture that commonly occur on permanent grasslands, roadsides, pathways and spaces *Corresponding author. E-mailAuthor(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License between cropped plots (Tewodros and Meseret, 2013). Such low quality feeds are associated with a low voluntary intake, thus resulting in insufficient nutrient supply, low productivity and even weight loss (Hindrichsen et al., 2001). Effective methods through which utilization of low quality roughages could be improved include supplementation with energy and nitrogen sources, chemical or physical treatment, and selection and breeding of crops, each of which ultimately depends on the economic benefits and applicability (McDonald et al., 2002). One way to optimize utilization of available feed resources is strategic supplementation of crop residues with plant protein sources such as leguminous forage crops which have the potential for alleviating some of the feed shortages and nutritional deficiencies experienced in the dry season on smallholder farms (Hove et al., 2001;Teferedegne, 2000). As a result, animals with access to leguminous forage crops perform better than those kept on natural pasture in milk yield, weight gain, reproductive performances and survival rates (Elbasha et al., 1999;Norton, 1994b).
In Ethiopia, more attention, however, has been given to assessment of the environmental adaptation, herbage DM yield potential and seed bearing ability of candidate accessions, while data on their nutritive value is generally scarce (Geleti et al., 2014). Alfalfa has one of the highest crude protein contents among forage crops, but it is rapidly and extensively degraded by rumen microorganisms (Dong et al., 2009). It can produce around 25% more dry matter than pasture (Richard, 2011) and Yields of irrigated alfalfa have been shown to be up to 24 ton DM yield ha -1 year -1 (Brown et al., 2000). There are numerous cultivars of alfalfa, selected for specific abilities, such as winter hardiness, drought resistance, tolerance to heavy grazing or tolerance to pests and diseases (Frame, 2005). Selection of important cultivars in Ethiopia, has been given to assessment of the environmental adaptation, herbage DM yield potential and seed bearing ability of candidate cultivars (Geleti et al., 2014). Moreover, these five cultivars used in the current study were grown under different production systems and agro-ecological conditions in Ethiopia. As a result, testing the same cultivars in different agroecological zones has been an advantage to find suitable cultivars specifically to the study area. Therefore, this study was initiated to investigate the highest dry matter yield and herbage nutritive value among the selected five alfalfa cultivars in lowland agro ecology area of Raya value.
Description of the study area
The study was conducted at Raya-Azebo district, Wargiba research site. The area is located at a distance of 660 km from Addis Ababa (capital city of Ethiopia) to the North and 120 km far from Mekelle (capital city of Tigray regional state) to south direction. The altitude of the area is 1600 m above sea level. Geographically, it is located between 12.32-12.95°North latitude and 39.56 _ 39.98°East longitude. The temperature of the district is within the range of 22 to 26°C. The mean annual rainfall is 600 mm and within the range of 400 to 800 mm. The distribution of the rainfall is the temporal situation and shows bimodal event. The area covered a total of 85% categorized as the mid land agro ecology and 15% covers a low land agro ecology. From February to April the rainfall is commonly little rain, but the main rain season is between July and September (OARD, 2016).
Experimental design and treatments
The experiment was conducted by randomized complete block design (RCBD) with four replications and five treatments. Each alfalfa cultivars were assigned randomly for each block. The cultivars were evaluated at Alamata Agricultural Research Center, Wargba Research site at irrigated land. The experimental treatments used were FG-10-09 (F), FG-9-09 (F), Magna-801-FG (F), Magna-788 and Hairy Peruvian. The cultivars were planted in a plot size of 9 m 2 (3 m × 3 m), and spacing between rows and blocks 0.2 and 1 m, respectively. The seed rate used in the experiment was 10 kg ha -1 and sowed drilled within the row. With this after sown the soil was slightly covered carefully and 100 kg ha -1 of DAP was applied during sowing. Water was supplied every week and in every cutting hoeing applied. The other management practice like weeding, cutting and protection managements were done carefully as important.
Stand height, dry matter yield and leaf to stem ratio
Determination of stand height, dry matter yield and leaf to stem ratio data was recorded. Mean stand height of five randomly selected plants from a plot was recorded. The data of the plant height was taken at the stage of herbage biomass harvesting. Leaf to stem ratio was determined from the same sampling area of fresh biomass, after taking the sample of 300 g for dried DM yield. Then after, the harvested biomass was partitioning into leaf and stem fractions, and drying the fraction samples using similar procedures described above for herbage DM yield determination. From the total area of 9 m 2 plots, a net area of 1.8 m 2 was harvested randomly from three selected adjacent middle rows to estimate the fresh biomass yield and sample for DM yield. The fresh biomass was recorded after cutting using sickle and weighing using spring balance. To determine DM yield, 300 g sample was taken and dried in an oven at 65°C for 72 h. The harvested stage for estimation of good biomass and nutritive value was followed by Ball (1998), explained as a stage when open flowers emerge on average of 2 or more nodes and no seed pods present at the stage of full flowering stage.
Cutting intervals of herbage yield
With increasing alfalfa maturity in regrowth cycle, forage nutrient concentrations decrease while forage dry matter yield increase to about mid-flowering (Radović et al., 2009). To compromise, these yield and nutritive value, harvesting cutting intervals in this study was taken at an average of 57.78±4.78 days of mid flowering at irrigation land. A total of 4 cutting cycle were taken from January 2016 up to August 2016.
Relative feed value
Relative Feed Value (RFV) is an index used to rank feeds relative to the typical nutritive value of full bloom alfalfa hay, containing 41% ADF and 53% NDF on a DM basis, and having a RFV of 100, which is considered to be a standard score. This index is widely used to compare the potential of two or more forages on the basis of energy intake (Schroeder, 2013).
Chemical analysis
Chemical composition of the cultivars were prepared from each replication and then finally pooled as one cultivar within each cutting cycle. The dry matter (DM%), crude protein (CP%) (Nx6.25) and ash were determined using the standard procedures of AOAC (1990). The neutral detergent fiber (NDF%), acid detergent fiber (ADF%) and acid detergent lignin (ADL) fractions were analyzed according to Van Soest (1994). The modified Tilley and Terry in vitro method (Van Soest and Robertson, 1985) was used to determine the in vitro dry matter digestibility (IVDMD).
Statistical analysis
The data obtained from the experiment was subjected to analysis of variance using the General Linear Model Procedure of SAS (1998). Significant treatment mean was separated using Tukey HSD. The model used for the analysis of all parameters was: Yi jk = µ + ai + bj+ eijk where Yi jk = response variable, µ = overall mean, ai = i th treatment effect, bj = j th block effect, and eijk = random error.
Chemical composition and in vitro DM digestibility of alfalfa cultivars
Chemical composition and in vitro DM digestibility of alfalfa cultivars are shown in Table 1. The study showed that the DM content was comparable across the five cultivars. Similarly, the CP content of the present study also indicated comparable result within the treatments. The fiber (NDF, ADF and ADL) value of the experimental cultivars showed similar contents within the treatments. Likewise, the results of in vitro dry matter digestibility (IVDMD) content were also comparable across the five cultivars.
Stand height, leaf to stem ratio and dry matter yield
Stand height, dry matter yield and leaf to steam ratio of five alfalfa cultivars are shown in Table 2. The present study showed that plant height was significance differences (P<0.001) among the five cultivars. Alfalfa cultivars FG-10-09 (F), FG-9-09 (F), Magna-788-FG (F) and Hairy Peruvian had significantly (P<0.001) higher plant height as compared to Magna-801. However, DM yield and leaf to steam ratio (LTSR) was not affected by the cultivars (P>0.05).
Dynamics of forage production across cutting cycles
Cutting cycles of stand height, DM yield and leaf to stem ratio of selected alfalfa cultivars are shown in Table 3.
Cutting cycle was significantly affected by stand height, DM yield and LTSR. Stand height and DM yield were significantly different (P<0.001) among the cultivars across the cutting cycle. Cutting cycles 2, 3 and 4 had the highest stand height and DM yield as compared to cutting cycle 1 (P<0.001). This might be due to additional tillers which created an impact on the increment of DM yield included in the other cutting cycles as compared to the 1st cutting cycle. But, cutting cycles 2 and 1 were significantly higher than LTSR as compared to 3 and 4 (P<0.001).
Nutritive value of alfalfa cultivars
As Kazemi et al. (2012) reported high quality alfalfa had Means within the same rows bearing a common superscript not significantly, ***(P<0.001), **(P<0.01), *(P<0.05), DMY=Dry matter yield, LTSR= Leaf to steam ratio, SEM= Standard error of mean, NS= Not significance. Table 3. Effect of cutting cycles on stand height (cm), DM yield (ton ha -1 ) and leaf to stem ratio of selected alfalfa cultivars. to contain <40% NDF, <31% ADF and >19% CP in general, but particularly at full bloom stage alfalfa forage had to contain a CP>16%, ADF <41%, NDF <53% and RFV >100%. With this threshold of the aforementioned report, the nutritive value of the cultivars in the present study had fulfilled the full bloom stage. In addition, the fibrous content of FG-10-09(F), Magna-801-FG (F), Magna-788 and Hairy Peruvian also contains high rank quality alfalfa content unlike, the CP content. However, Hairy Peruvian cultivar in the present study had scored high quality alfalfa with the threshold content of CP%, NDF% and ADF%. The differences in nutritive value might have occurred due to many factors: harvesting management, varieties and harvest frequency. This implies that cutting at earlier stages might improve the crude protein content and decrease fiber content, but at the expense of yield (Dennis and Howard, 1993).
Cutting cycle
The current study also ranged comparable result of the quality of alfalfa hay reported by Redfearn and Zhang (2011) as the first prime NDF < 40-46, ADF < 31-40, CP% >17-19 and RFV <125-151. The cultivars FG-10-09(F), Magna-801-FG (F), Magna-788 and Hairy Peruvian had a value of NDF 39.49, 39.29, 39.74 and 38.75%, respectively which facilitates the rate of passage unlike, FG-9-09(F) cultivar resulted in 42.31% NDF with greater than the bench mark. This result was comparable with Găvan et al. (2013) where NDF levels greater than 40% begin to slow rate of passage down, creating a gutfill effect. This resulted in lower dry matter intake as higher gut-fill occurred. In general, between yield and nutritive value, the greatest impact on timing of harvests made in spring and early summer in humid environments, and in early and late summer in more arid regions led to negative association (Brink et al., 2010).
The DM content of the current study was comparable with Gashew et al. (2015), while higher DM content was indicated as compared to Geleti et al. (2014) for the same cultivars. The DM (%) content of FG-10-09(F) was comparable with the report of Walie et al. (2016), but the other four cultivars of the current study had less DM (%) content as compared to the same author. In vitro dry matter digestibility (IVDMD) ranged from 73.58 to 79.53% in this study showed less value as compared to the report of Diriba et al. (2014) which ranged from 83.07 to 87.35%, but higher value of IVDMD was recorded as compared to Walie et al. (2016) ranging from 61.58 to 62.37%. Similarly, small value of IVOMD were also reported for 14 alfalfa varieties, with values ranging from 59.15 to 66.33% (Kamalak et al., 2005) with less value as compared to the current study. The differences in IVDMD might occur from the time of harvesting. As the lignin levels increase with maturity in stems, digestibility will decrease in many forage crops such as alfalfa, because lignin concentration correlates negatively with forage digestibility (Dianging et al., 2001).
Relative feed value (RFV) has been used for years to compare the quality of legume and legume/grass hays and silages (Peter and Alvaro, 2004). As Moore and Undersander (2002a) demonstrated, forages with RFV greater than 100 are of higher quality than full bloom alfalfa hay, and forages with a value lower than 100 are of lower value than full bloom alfalfa. The RFV index of the cultivars of the current study indicated greater than the threshold of 100, which illustrated the cultivars to have higher quality standard. This RFV was proposed to reflect how well an animal will eat and digest a particular forage species when it is fed as the only source of energy (Kazemi et al., 2012). However, the RFV index of this study indicated lower value ranged from 110.88 to 123.25 as compared to Diriba et al. (2014) whose report ranged from 154.01 to 189.55 for the same cultivars. In general, the result of the current study id ranked 2nd prime standard quality classification as reported by Redfearn and Zhang (2011) as 1st and 2nd prime ranging from CP(17-19%), NDF(40-46%), ADF(31-40%) and RFV (125-151), and CP(14-16%), NDF(47-53%), ADF(36-40%) and RFV (103-124), respectively.
Stand height, leaf to stem ratio and dry matter yield
Alfalfa forage production may be related to plant density, disease resistance, cutting cycle and cultivar difference (Cook et al., 2005). The stand height of the current study was significantly different (P<0.001) among cultivars. This result was true with the report of Walie et al. (2016) and Diriba et al. (2014) for the same selected alfalfa cultivars. Hairy Peruvian showed higher stand height (79.6 cm) as compared to the other cultivars. Agreed with the study by Diriba et al. (2014) and Heuzé (2013) who reported that, Hairy Peruvian had higher stand height as compared to respective evaluated cultivars, but superior stand height for this cultivar shown as compared to the current study (86.5 cm and 1 m), respectively. On the contrary, Walie et al. (2016) had indicated higher stand height for FG-9-09(F) as compared to the other cultivars. In general, stand height of the current study lay in the range of different scholars for different cultivars (Turan et al., 2017;Walie et al., 2016;Diriba et al., 2014;Taherian, 2009).
Leaf to stem ratio (LTSR) of the current study had no significant differences (P>0.05) among the cultivars, this was comparable with the report of Diriba et al. (2014) and Afsharamanesh (2009) unlike, Gashaw et al. (2015) for the same alfalfa cultivars. While, the evaluated value of LTSR alfalfa cultivars in the present study ranged from 0.77 to 0.87 and it was inferior as compared to the value reported by Diriba et al. (2014) ranging from 0.95 to 1.21 for the same cultivars. This might have occurred due to the difference of soil type, management and harvesting stage. Similarly, Katic et al. (2006) reported that the proportion of leaves and stems in alfalfa hay can vary greatly, depending on maturity at harvest, cultivars, handling, and rain damage. Among the evaluated selected alfalfa cultivars, Magna-801 FG (F) had superior LTSR in the current study. Leaf to stem ratio is an important trait in the selection of appropriate forage cultivar as it is strongly related to forage quality (Sheaffer et al., 2000). Alfalfa leaves have significantly higher nutritive value than stems, so to advance forage quality has been to develop cultivars which possess a greater proportion of leaves than steam (Ray et al., 1999a). Because, leaves have a stable protein content that is much higher than that of the stems. Stem develops at the expense of leaves and their cell walls and lignin content increases with maturity (Veronesi et al., 2010).
Dry matter yield (DMY) of the present study does not show any significant differences among the cultivars (P>0.05), and this agreed with the result reported by Gashaw et al. (2015) for the same cultivars. Unlike this finding, other reports observed significant different among cultivars (Turan et al., 2017;Walie et al., 2016;Diriba et al., 2014). The DMY of the current study ranged from 3.96 to 4.81 ton ha -1 , which was comparable to Basafa andTaherian (2009), Geleti et al. (2014), Befekadu and Yunus (2015), and Walie et al. (2016) reported a values of 2. 84-4.23, 4.22-4.77, 4.12 and 4.00-4.87 ton ha -1 for different cultivars, respectively. But, Gashaw et al. (2015) reported inferior result (2.4-2.8) ton ha -1 for the same cultivars with the aforementioned scholars and the present study. The difference in value of dry matter yield (DMY) might be observed due to the attributed varietal or environmental and/or their interaction differences reported (Diriba et al., 2014). In this study, Hairy Peruvian showed relatively higher DMY as compared to FG-10-09, FG-9-09, Magna-788 and Magna-801, but in other scholars, FG-9-09 cultivar had scored higher DMY as compared to FG-10-09(F), Hairy Peruvian, Magna-788 and Magna-801-FG(F) (Gashaw et al., 2015;Diriba et al., 2014). This yield differences might be due to the growth stage, leaf to stem ratio, moisture conditions at harvest and processing method (Veronesi et al., 2010).
Dynamics of forage production across cutting cycles
Stand height and DMY of the present study showed highest values at cutting cycles of 2, 3 and 4 as compared to the 1st cutting. However, for LTSR there was no increment with cutting cycle increases from 1st to 4th cutting. This report quite agreed with Diriba et al. (2014), Gashaw et al. (2015), and Walie et al. (2016) who showed the values of stand height and DMY to increase as the cutting cycle increased for the same alfalfa cultivars. Different reports indicated that the optimal harvest interval for alfalfa is between 30 tand35 days (Sheaffer, 2000). But, in the current study, longer time interval was recorded, around 57 days as compared to the bench mark indicated. This could be observed due to the difference in varieties, temperature, soil texture and management. The variation of harvesting interval might be based on a compromise between yield, quality, regrowth, and persistence (Sheaffer, 2000). But, a maximum yield on alfalfa is achieved at reproductive maturity when the nutritive value of the forage is at a minimum (Collins and Fritz, 2003).
CONCLUSION AND RECOMMENDATION
It can be concluded that all the alfalfa cultivars had not shown any significant difference in DMY and nutritive content, but Hairy Peruvian had relatively good DMY, LTSR and higher stand height, as compared to FG-10-09(F), FG-9-09(F), Magna-801-FG(F) and Magna-788. As a result, it will be good to promote Hairy Peruvian cultivar for further demonstration and seed production.
CONFLICT OF INTERESTS
The authors have not declared any conflict of interests.
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Domain: Biology Agricultural and Food Sciences
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Feedlot performance, health, and carcass characteristics of beef heifers treated with Cydectin® or Dectomax® at processing
Two parasite-control products were compared in an experiment evaluating growth performance, health, and carcass characteristics. Crossbred heifers (n=1747; 837 lb average weight) were randomly assigned to receive either Cydectin® or Dectomax®. Both products were administered at processing at 1 ml per 22 lb of body weight. Cattle were randomly allotted to 12 paired pens by treatment based on source, truckload, and arrival date. Fecal egg counts taken at processing (9.74 eggs per gram) and at reimplanting (0 eggs per gram) indicated that both products were effective in eliminating adult female gastrointestinal parasites. No differences were detected in average daily gain, feed intake, feed efficiency, or most carcass characteristics. Respiratory pulls, realizer cattle, and death loss did not differ between treatments. In this experiment, similar growth performance, health, and carcass traits were observed for heifers treated with either macrocyclic lactone product.
Introduction
Internal and external parasites are a common problem in cattle. Economic losses to the U. S. cattle industry due to parasitism have been estimated to be more than a billion dollars annually. Internal parasites decrease performance by reducing feed intake, reducing available nutrients, and impairing nutrient utilization. Lice and mites also reduce cattle performance. Grubs cause losses due to hide and muscle tissue damage. Carcass and animal health can be improved with parasite control through better nutrient availability and utilization.
Several cattle products based on macrocylic lactones, a class of endectocides that control both internal and external parasites, have been marketed since 1984. The products are oral drenches, injectables, or pour-ons. Product differences also include the carrier and the active ingredient. Carriers have been either alcohol or oil based. The active ingredients come from one of two chemical families; milbemycins or avermectins. Moxydectin, the active ingredient in Cydectin ® , is a milbemycin, whereas doramectin, the active ingredient in Dectomax ® , is an avermectin. There are some differences between efficacy and persistence (post-treatment control) of the two products. Although there are differences in label claims with regard to species controlled, a number of the species such as Cooperia and Thelazia spp.are not economically import, particularly in specific locales. The five most economically important internal parasites in cattle are Dictyocaulus, Haemonchus, Nematodirus, Ostertagia, and Trichostrongyles. Table 1 lists the similarities and differences between Cydectin ® and Dectomax ® for internal and external parasite control. Numerous studies have attributed im-1 Formerly with Fort Dodge Animal Health. 2 Fort Dodge Animal Health.
proved feedyard performance to internal and external parasite control with the use of the macrocyclic lactones. This experiment was conducted to evaluate feedlot performance and carcass traits of heifers treated with either Cydectin ® or Dectomax ® for internal parasite control. The presence and control of grubs, lice, mites, and horn flies was not evaluated in this study.
Procedures
Yearling crossbred heifers (n=1747) averaging 837 lbs originated from three ranches in South Dakota and Wyoming. Approximately 24 hours after arrival at a southwestern Kansas feedyard, the heifers were processed, and each animal received a four-way modified live viral vaccine, a clostridial vaccine, an implant containing 20 mg estradiol benzoate and 200 mg testosterone, and a uniquely numbered eartag. One of each pair of heifers was assigned to either Cydectin ® or Dectomax ® according to a predetermined randomization schedule. The cattle were treated topically along the back with 1 ml of one product per 22 lbs of body weight (0.5 mg active ingredient / 2.2 lb). The cattle were blocked by origin, truckload, and arrival date and were randomly allotted to neighboring pens by treatment. Six pens per treatment were used, with 134 to 196 heifers in each pen. Numbers of heifers in paired pens differed by no more than one animal. The heifers were placed on feed October 10, 2001. The cattle were fed the same steamflaked rations, and adjusted to the finishing ration two to three weeks after arrival. Feed and water were offered for ad libitum consumption. Approximately 80 days before harvest, the heifers were revaccinated with a modified live IBR/BVD vaccine and reimplanted with a 200-mg trenbolone acetate implant. Each pair of pens was harvested on the same day at a commercial abattoir in southwestern Kansas. Days on feed ranged from 128 to 139, with an average of 133 days. Carcass data were collected after a 26-to 28-hour chill.
Fecal samples were collected at processing from one randomly predetermined animal of every 10 animals in each treatment. The samples were again collected from the same heifers at reimplanting. The number of eggs per gram of feces was determined at a commercial laboratory by using the modified Wisconsin method, a commonly used and accurate method for counting internal parasite eggs.
Individual weights measured at processing were summed by pen for use as initial weights. Pen weights measured before shipment for harvest were used as final weights after a 4% pencil shrink. Feed delivery from the feedyard closeout summary was used as feed intake. Average daily gain, feed intake, and feed efficiency were calculated with deads in.
Results and Discussion
Individual fecal samples collected at processing ranged from 0 to 124 eggs per gram with an average of 9.74 eggs per gram. Both Cydectin ® and Dectomax ® eliminated gastrointestinal parasites, as indicated by fecal evaluation at reimplanting (0 eggs per gram).
Animal performance and carcass traits are listed in Table 2. Initial body weights were similar between the two treatments, as were average daily gain, feed intake, and feed efficiency. No differences were detected for respiratory pulls, realizer animals, and death loss. Final weight, hot carcass weight, and dressing percentage were similar. No differences were observed for the quality traits of marbling score, carcass maturity, and dark cutting. The percentage of USDA Prime carcasses tended to be higher (P=0.10;3.71 vs. 2.13%) and kidney, pelvic, and heart fat was slightly greater (P=0.06;2.34 vs. 2.26%) in carcasses from Cydectin ® -treated heifers.
Backfat, ribeye area, and USDA Yield Grades were not different.
Although this study did not have an untreated control group, other research has shown the benefit of treating feedlot cattle for parasites. Research has consistently shown improved gain, feed efficiency, health, and carcass traits with the use of broad-spectrum endectocides. These benefits are the result of greater feed intake, more available nutrients, and better nutrient utilization. The incidence of grubs and mites has decreased with the use of the macrocyclic lactones. Lice continue to be a common cattle problem, and also can affect performance if not controlled. Cydectin ® and Dectomax ® supported similar feedlot performance and animal health.
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Domain: Biology Agricultural and Food Sciences
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Performance of summer sesame ( Sesamum indicum L . ) cultivars under varying dates of sowing in prevailing agro-climatic condition of North Bengal
March and three cultivars of sesame (Rama, Savitri and Tillotama) with three replications. The highest (114.66 and 115.83 cm) plant height was recorded when sesame sown on 12 th March (D4) and which was statistically at par with 2 nd March (D3). Among the varying date of sowing, the highest dry matter accumulation, leaf area index and crop growth rate was recorded in 2 nd day of March compared to the other date of sowing. Among the improved cultivars of sesame, the variety Rama recorded higher plant height, dry matter accumulation, leaf area index and crop growth rate compared to Savitri and Tillotoma. The highest yield was recorded when sesame sown on 2 nd March which was 55.99 and 40.85% higher than the crops sown on 22 nd March during 2013 and 2014 respectively. Rama also exhibited highest seed yield recording 17.70 and 12.06% higher than the cultivars Tillotama and Savitri. The date of sowing significantly influenced the yield attributes and highest yield attributes was recorded when sesame sown on 2 nd March. Improved cultivar, Rama recorded the highest yield attributing characters compared to the Savitri and Tillotoma. It can be concluded that sowing of sesame within 2 nd March to 12 th March is the optimum sowing dates of sesame to have optimum seed yield if grown as late summer crop. Result indicated that cultivar Rama can be adopted in terai zone of West Bengal during summer season, because of its highest seed yield ability.
INTRODUCTION
Sesame (Sesamum indicum L.), the queen of vegetable oils belonging to family Pedaliaceae is one of the oldest oil-rich plants in the world (Janick and Whipkey, 2002) and that originated in Africa (Brar and Ahuja, 1979;Ram et al., 1990). It is widely grown in tropical and subtropical regions. Its production is often concentrated in marginal and sub marginal lands (Ashri, 1998). India ranks among the top six world producers of sesame seed. Thus, production growth and quality improvement of oilseeds can substantially contribute to the economic development at national, regional and at family level. It is a nonleguminous annual flowering green plant cultivated *Corresponding author. E-mail: [email protected](s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License primarily for its small edible seeds rich in oil and protein of about 50 and 25% respectively (Langham et al., 2006). There are also intermediate coloured varieties varying from red to rose or from brown or grey.
The low yields coupled with problems encountered during harvesting sesame have tended to discourage growers, leading to a decline in the total area devoted to its cultivation. In general, the production constraints include poor agronomic practice, pest and disease, weed infestation, poor soil fertility, low yielding cultivars. However, crop improvement in sesame has been practiced for a long time. Yet a major breakthrough could not be made in realizing high yields in sesame varieties. One of the reasons is that there is limited genetic variability in the source material. It is a seasonal and location bound crop hence, a particular variety does not perform uniformly in all locations and in all seasons. The yielding ability of sesame crop is determined by many yield components, all of which are substantially influenced by environmental conditions and agronomic packages. The grain yield of sesame is significantly influenced by sowing date and cultivars (Hazarika, 1998). Moreover, temperature and variety affected seed yield variation by 69 and 39%, respectively (Sharma, 2005). The effect of photoperiodism on sesame has been thoroughly studied, since this is a major factor influencing seed yield. According to El-Bakheit (1985) delaying of sesame sowing increased the incidence of pests and diseases. Therefore, for successful production of crop most optimum sowing time and cultivars are indispensable (Ali et al., 2005).
In this region sesame is cultivated as a rainfed crop during pre-kharif and kharif season but it is also grown during summer season in residual soil moisture under poor management practices. Hence, the yield of sesame in this region is generally low due to use of low yielding cultivars (local) with poor agronomic management practices. Research works are limited on sowing time and cultivars under terai agro-climatic situation of West Bengal. Hence, here is a need of research effort is to be under taken to identify the sesame cultivars with desirable character. Considering the above mentioned reason, a study on growth and yield improved sesame cultivars under varying date of sowing was carried out under this region.
MATERIALS AND METHODS
A field experiment was conducted at the Instructional farm of Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, West Bengal, India during 2013 to 2014 to study the effect of dates of sowing and improved cultivars on growth and yield of summer sesame (Sesamum indicum L.). The treatment consisted of five different dates of sowing that is, 10 th February, 20 th February, 2 nd March, 12 th March and 22 nd March (symbolized as D1, D2, D3, D4 and D5, respectively) in the main plots and three cultivars that is, Rama, Savitri and Tillotama (symbolized as V1, V2 and V3, respectively) in the sub plot, in a split plot design with three replication. The farm is situated at 26°1986 N latitude and 89°2353 E longitude and at an elevation of 43 m above mean sea level. The soil of the experimental field was sandy loam in texture with pH 5.7. The results were analyzed taking consideration of pre harvest parameters like plant height (cm), dry matter accumulation (DMA), Leaf area index (LAI) calculated according to the formula given by Watson (1947).
Then the mean LAI (L) was calculated as per the formula given below.
Where, L1 and L2 are the leaf area indices at two successive occasions on time t1 and t2 respectively.
Crop growth rate (CGR) expresses the gain in dry matter production of the crop per unit land area per unit time and is expressed as gram per meter square per day (g m -2 day -1 ). It is calculated according to the formula given by Watson (1952).
Where, W1and W2 were the dry weight of the aerial plants per unit area gained at time t1 and t2 respectively. Postharvest parameters like number of branches plant -1 , number of capsules plant -1 , number of seed capsule -1 and test weight of seed [1000 seed weight (g)], seed yield (t ha -1 ), stem yield (t ha -1 ) and harvest index (%). Data were analyzed by using INDO-STAT-software for analysis of variance following split-plot design treatment means were separated by applying critical difference (CD) test at 5% level of significance.
Effect of date of sowing and improved cultivars on growth attributing characters of sesame Effect of treatments on growth
Irrespective of date of sowing and improved cultivars of sesame, plant height kept on increasing till the last observation recorded at harvest. The plant height increased with the advancement of the crop age due to its growth and reached its maximum at harvest irrespective of the treatments tried (Table 1). LAI was low at the early stages of crop growth (Table 2) and kept on increasing with advancement of crop age up to 75 DAS when reached at its peak. Thereafter it decline towards maturity of the crop touches which was stopped at the reproductive stages of the crop. Another reason may be attributed to senescence of the leaves at the later stage of crop growth. Dry matter accumulation was lowest at 30 DAS thereafter rapid accumulation of dry matter was noticed till at harvest. The rate of accumulation became slower and it reaches at its maximum value till the last Area of total number of leaves surface Leaf area index (LAI) = Ground area from which leaf sample were collected Where, L1 and L2 are the leaf area indices at two successive occasions (1.61 and 1.23 during both the years of experimentation respectively) at 75 DAS (2). Dry matter production was found to increase starting from 30 DAS onwards and continued up-to harvest with all the date of sowing. The highest (696.33 and 660.88) amount of dry matter accumulation was recorded from when sesame shown on 2 nd March (D 3 ) (Table 3). The better sink capacity might be attributed to the better dry matter production owing to better photosynthetic capacity of plant during reproductive phase of crop. The present results are in conformity with earlier findings of Pawar (1991) and Kanabur (1998).
The crop growth rate was found to be notably significant due to the effect of dates of sowing during both years of experimentation respectively. The highest (17.52 and 16.97) crop growth rate was recorded when sesame shown on 2 nd day of March (D 3 ) and lowest crop growth rate (13.76 and 13.63) was recorded when crop shown on 10 th February (D 1 ) between 60-75 DAS during both the years of experimentation respectively (Table 4).
Plant height was significantly influenced by the improved sesame cultivars at 30, 90 and at harvest in first year of experimentation (Table 1). Among the cultivars, Rama recorded highest (112.53 and 108.68 at harvest) plant height followed by Savitri and Tillotoma at all stages of crop growth. This might be due to genetic makeup and climatic condition which enhanced the growth and development of sesame. Among the improved cultivars highest (1.98 and 1.59) leaf area index was recorded in cultivars Rama followed by Savitri and Tillotoma at all stages crop growth (Table 2). This might be due to absorption and utilization of moisture, nutrients and light by crop which significantly influenced the leaf area. Similar observation was made by Pawar (1991). The highest (634.00 and 589.73) dry matter accumulation was recorded in cultivars Rama followed by Savitri and Tillotoma at all stages crop growth (Table 3). This might be due to higher translocation of photosynthetic was possible due to better sink capacity of cv. Rama than cv. Savitri and Tillotoma as indicated by higher number of capsules and seed weight plant -1 in cv. Rama. Crop growth rate not found to be significant. The highest (16.26 and 15.03 in between 60-75 DAS) was recorded in cultivar Rama followed by Savitri and Tillotoma during both the years of experimentation respectively (Table 4). The interaction effect of date of sowing and cultivars was not significant for all growth parameters.
Effect of dates of sowing and cultivars on yield components of sesame
Irrespective of sowing dates and improved cultivars, the number of branches plant kept on increasing till the last observation recorded at harvest. The number of branches plant -1 increased with the advancement of the crop age due to its growth and reached its maximum at harvest irrespective of the treatments tried (Table 5). The number of branches plant -1 was found significant due to the effect of date of sowing however, it was found nonsignificant due to the effect of cultivars. This might be influenced by the environment which could have counted for the fewer branches in sown crops because of the change in the environmental condition that forces the crop to reduce vegetative growth and commence reproductive phase as reported by Kifiriti and Deckers (2001). Peter and Yakubu (2012) reported that number of branches per plant decreased due to delay in time of sowing. The number of capsules plant -1 , number of grains capsule -1 and test weight of seed (1000 seed weight) was found significant due to the effect of date of sowing and cultivars. Among the varying date of sowing, 2 nd day of March (D 3 ) recorded significantly higher number of branches plant, number of capsule plant, number of seeds plant and test weight as compared to other dates of sowing and 10 th February (D 1 ) was recorded the lowest number of branches plant -1 , number of capsule plant -1 , number of seeds capsule -1 and test weight during both years of experimentation respectively (Table 5). The increase in the number of capsule plant -1 might be due to the environment and length of growth period has significantly influenced on number of capsule plant -1 . Similar result also made by Kifiriti and Deckers (2001). This might be due to the increased growth of crop and better utilization of light by crop. Abdel et al. (2007) reported that delaying the sowing date decreased number of capsules plant -1 and test weight (1000-seed). However, Patil et al. (1992) reported that increased number of capsule plant -1 with delaying sowing might be due to difference in genetic makeup and climatic conditions. This finding was in agreement with the result obtained by Nath et al. (2000) and Rai et al. (1999).
Among the different cultivars number of branches plant -1 , number of capsule plant -1 , number of seeds capsule -1 of sesame was found significantly influenced due to the effect of cultivars but test weight of sesame was not significantly influenced due to the effect of cultivars. However, variety Rama (V 1 ) was recorded higher number of branches plant -1 , number of capsule plant -1 , number of seeds capsule -1 and test weight as compared to Savitri and Tillotama during both the years of experimentation respectively (Table 5). This might be due to improved crop growth duration, availability of soil moisture and absorption of nutrients by crops which enhanced the crop growth, increase in yield attributing characters and ultimately yield. Increasing seed rate significantly decreased the number of capsules plant -1 and seed yield per sesame plant (Sudan Ahmed et al., 2012) and protein content (Caliskan et al., 2004). The interaction effect between date of sowing and cultivars was non-significant.
Seed yield, stem yield and harvest index
Irrespective of different sowing dates of sesame, 2 nd day seed yield (Rai et al., 1999;Saha et al., 1993). This result closely resembles to that obtained by Ieda et al. (1999) who also opined that delaying in sowing decreased seed yields of sesame. The results indicated that sowing of sesame within 2 nd March to 12 th March is the optimum sowing date for sesame to have optimum seed yield. Among the varieties, Rama (V 1 ) recorded significantly highest seed and stem yield as compared to Savitri (V 2 ) and Tillotama (V 3 ) during both the years of experimentation respectively (Table 6). However, the yield of Savitri (V 2 ) was statistically at par with Tillotama (V 3 ) (Table 6). This might be due to difference in genetic makeup of crop plants, varying date of sowing and climatic condition. These result also corroborated with the findings of several workers Suryavanshi et al. (1993) and Sarkar et al. (2007). Such differences in cultivars with respect to seed yield have been reported by Dixit et al. (1997) and Basavaraj et al. (2000).
Harvest index (HI) is another important parameter to assess the translocation efficiency. Seed yield is related to biological yield through harvest index (Yoshida, 1972). Further, it was also reported that the yielding potentiality of a cultivar is associated with increased seed to stalk ratio (HI). Harvest index was not significantly influenced due to the effect of dates of sowing. However, it was significantly influenced due to the effect of cultivars during both years of experimentation respectively (Table 6). The highest harvest index was recorded under 10th February (D 1 ) (19.96 and 19.53%) and lowest harvest index was recorded on 22 nd March (D 5 ) (15.80 and 16.86%). Ali et al. (2005) reported that harvest index significantly influenced by date of sowing. Among the varieties, Rama (V 1 ) recorded higher harvest index (19.49and 18.93%) as compared to Savitri (17.78 and 18.23%) and Tillotama recorded (17.68 and 18.11%) during both the years of experimentation respectively. Interaction effect between date of sowing and cultivars was not significant.
Conclusion
It may be inferred that the cultivar Rama can be adopted in North Bengal during summer season, because of its highest seed yield ability and 2 nd March to 12 th March is the optimum sowing dates of sesame to have optimum seed yield if grown as late summer crop.
Table 1 .
Effect of dates of sowing and improved cultivars on plant height (cm) of sesame.
observation at harvest increased at an increasing rate up to harvest and thereafter it increased with decreasing rate, irrespective of date of sowing and improved cultivars. This indicate that the initial growth rate (Table3). The rate of dry matter accumulationas measured by the dry matter accumulation was packed up as the crop passes through the seed filling and maturity stage. Crop growth rate was low at the early stages of crop
Table 2 .
Effect of dates of sowing and improved cultivars on leaf area index of sesame.
Table 3 .
Effect of dates of sowing and improved cultivars on dry matter accumulation of sesame.
Table 4 .
Effect of dates of sowing and improved cultivars on crop growth rate of sesame.
Table 5 .
Effect of dates of sowing and cultivars on yield attributes of sesame.
Table 6 .
Effect of dates of sowing and cultivars on grain yield, stover yield and harvest index of sesame.
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Domain: Biology Agricultural and Food Sciences
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Flesh Color Diversity of Sweet Potato: An Overview of the Composition, Functions, Biosynthesis, and Gene Regulation of the Major Pigments
Sweet potato is a multifunctional root crop and a source of food with many essential nutrients and bioactive compounds. Variations in the flesh color of the diverse sweet potato varieties are attributed to the different phytochemicals and natural pigments they produce. Among them, carotenoids and anthocyanins are the main pigments known for their antioxidant properties which provide a host of health benefits, hence, regarded as a major component of the human diet. In this review, we provide an overview of the major pigments in sweet potato with much emphasis on their biosynthesis, functions, and regulatory control. Moreover, current findings on the molecular mechanisms underlying the biosynthesis and accumulation of carotenoids and anthocyanins in sweet potato are discussed. Insights into the composition, biosynthesis, and regulatory control of these major pigments will further advance the biofortification of sweet potato and provide a reference for breeding carotenoidand anthocyanin-rich varieties.
as the main pigments in sweet potato because of their high antioxidant properties and the many beneficial effects on human health. In summary, sweet potato is highly diversified due to its varied flesh and skin colors attributed to the different phytochemical components.
Orange-Fleshed Sweet Potato
Orange-fleshed sweet potato is a nutrient-rich crop with appreciable amounts of carotenoids which provides the characteristic orange color. It is a cheap source of dietary antioxidants with many physiological functions including anti-inflammation, anti-mutagenic, anti-cancer, anti-oxidation, antidiabetic, and cardiovascular disease prevention properties [14,32].
Carotenoids including αand β-carotenes have been identified in orange-fleshed sweet potato but the amount of β-carotene is relatively higher compared to other carotenoid-rich fruits and vegetables such as carrot, mango, and tomato [33][34][35][36]. For example, high β-carotene content of about 20-30 mg/100 g and 276.98 µg/g has been recorded in orange-fleshed sweet potato [37][38][39]. However, the amount of vitamin A correlates with the color intensity of sweet potato, thus, the darker the orange color, the higher the β-carotene content. β-carotene has potent pro-vitamin A activity which the body converts to vitamin A. Vitamin A promotes health by boosting the immune system, improving overall skin and eye health, and for good vision [40]. About 100-150 g of orange-fleshed sweet potato may provide the daily Vitamin A needs of children and prevent night blindness [41]. Studies have revealed that the consumption of a medium-size orange-fleshed sweet potato can double the required daily needs of vitamin A. The retention capacity of about 80% β-carotene in boiled orange-fleshed sweet potato remains unmatched [42], hence described as a "superfood" that promotes health [43,44].
As a food security crop, the orange-fleshed sweet potato could supplement as an alternative source of staple food in areas with increasing population and nutritional deficits and for resource-poor farmers [45]. Hence, this biofortified food crop with a good supply of vitamin A can serve as a beacon of hope to battle vitamin A deficiency in underdeveloped countries and also scuffle malnutrition in rural communities [46]. In consequence, the orange-fleshed sweet potato has been incorporated into the vitamin A deficiency prevention program in Africa [47] due to its cheap source of vitamin A.
Purple-Fleshed Sweet Potato
Purple-fleshed sweet potato is a nutritious crop with high levels of anthocyanin which provides its distinctive skin and flesh colors. Anthocyanin is a natural hydro-soluble pigment that provides the purple, red, and blue coloration of flowers, leaves, fruits, and other plant parts. The purple and red coloration of the leaves, stem, and storage roots of sweet potato results from the accumulation of acylated anthocyanins [48]. Peonidin and cyanidin, acylated with either hydroxybenzoic, ferulic, or caffeic acids are the main anthocyanins among the 39 anthocyanins identified in purple-fleshed sweet potato [11,30]. In recent years, anthocyanins from purple-fleshed sweet potato have extensively been studied due to their potential beneficial health effects on humans. Purple-fleshed sweet potato anthocyanin has good bioactivity and scavenges free radicals [1] contributing to its diverse biological and antioxidant activities. The antioxidants also act as a good protective agent against inflammations, cancers, diabetes, tumors, and hypoglycemia [49][50][51]. Furthermore, the anthocyanin from purple-fleshed sweet potato has high heat and light stability owing to its acylated forms, hence used as natural food additives [52]. Purple-fleshed sweet potato anthocyanin reduced inflammations caused by oxidative stress and decreased oxidative stressors confirming its free radical scavenging ability, thus, the strong antioxidant ability of purple-fleshed sweet potato [16]. The robust anti-mutagenic properties of the purple-fleshed sweet potato anthocyanin attributed to its radical scavenging activity decreased the risk of hypertension and liver injury in rats [53]. Again, the purple-fleshed sweet potato anthocyanin was revealed to have resilient anti-microbial and antiinflammation properties in addition to its ability to protect against colorectal cancer, UV light, and reduce memory loss [49]. The daily intake of 400 mg beverage prepared from purple-fleshed sweet potato protected the liver from oxidative stresses [54]. Hence, the many physiological activities of purple-fleshed sweet potato make it a health-promoting functional food.
Major Pigments in Sweet Potato
Pigments are mainly produced by plants and are responsible for the color variations observed in many plant tissues. Natural pigments accumulate in different plant parts including flowers, fruits, leaves, and stems. Generally, these natural pigments provide several physiological activities with many beneficial health effects [55]. The relative quantity of pigments accumulated mainly determines the colors produced by the various plant tissues. However, the concentration of pigments correlates directly to the color intensity [56]. The storage root of sweet potato is the main repository organ of natural pigments that provide the different flesh colors (white, yellow, orange, and purple) compared to other crops [57].
Carotenoids and anthocyanins are the major sweet potato pigments which provide the yellow, orange, and purple colors. These pigments are synthesized through different metabolic pathways with different structural and regulatory genes regulating their biosynthesis and accumulation. They also accumulate in different sweet potato genotypes, the orange-and purple-fleshed respectively. As a result, it may be likely to observe no form of interaction between their biosynthesis and accumulation. However, there is evidence of transgenic sweet potato accumulating both color pigments in a single storage root. For instance, Park et al. [58] produced a dual-pigmented transgenic sweet potato through Agrobacteriummediated transformation. The transgenic plants expressing the IbMYB1 gene (a key regulator of anthocyanin biosynthesis in sweet potato), accumulated high levels of both anthocyanins and carotenoids in a single storage root. Overexpression of the gene slightly increased most carotenoid biosynthetic genes, such as phytoene desaturase, lycopene ε-cyclase, zeta-carotene desaturase, and lycopene β-cyclase in transgenic plants than in control plants, suggesting that IbMYB1 expression might affect carotenoid biosynthesis-related gene expression. The above result indicates a possible interaction in the regulation of carotenoid and anthocyanin biosynthesis but needs to be clarified through further research.
Carotenoids
Carotenoids are lipid-soluble natural pigments responsible for the yellow, orange, and red colors of flowers, vegetables, seeds, fruits, and roots [59]. They are synthesized only in photosynthetic organisms including plants, some bacteria, fungi, and algae. Carotenoids are the most abundant of all plant pigments [60]. The storage root of sweet potato has an excellent supply of carotenoids including β-cryptoxanthin, violaxanthin, β-carotene, lycopene, and zeaxanthin [26,61] among which β-carotene is the main carotenoid with the highest pro-vitamin A activity both in terms of widespread distribution and bioavailability [47,57]. Sweet potato leaves have an adequate supply of carotenoids including lutein, neoxanthin, β-carotene, and violaxanthin [19] with the composition equivalent to other plant chloroplasts [62]. Research evidence revealed that orange-fleshed sweet potato has high β-carotene content, thus about 80% of the carotenoids being trans-β-carotenes [63,64]. However, other studies also reported the presence of lutein and zeaxanthin in the orange-fleshed sweet potato which provides the orange color [65]. Inclusion of orange-fleshed sweet potato in the diet provides dietary pro-vitamin A due to the high β-carotene content [19], hence used as a model crop for many small-scale research to increase vitamin A status [45,66]. The carotenoids identified in orange-fleshed sweet potato with their concentrations reported by different authors have been presented in Tab. 1.
Functions of Carotenoids
Carotenoids are naturally occurring pigments prevalent in plants with their accumulation in flowers, fruits, and other plant parts, giving those parts the yellow, orange, and red colors [59]. Carotenoids are mostly found in the chloroplast and chromoplast with several functions in both plants and animals. In plants, these molecules serve as accessory pigments for harvesting light in photosynthetic reaction sites and inhibit photo-oxidative damage to cells and tissues. Mostly, they combine with chlorophyll to absorb blue-green wavelengths, thereby protecting cells from superfluous light, increase tolerance to herbicide and salt stress and also safeguard photosynthetic apparatus from photo-oxidative damage [18].
Generally, exposure to stress leads to the production of free radicals by reactive oxygen species (ROS) causing oxidative damage which is mostly inhibited by antioxidants [81,82]. Carotenoids exhibit antioxidant properties that inhibit the harmful effects of various environmental stresses such as high temperature, resilient light, ultra-violet radiation, and drought in plants [83,84]. This ability was reported in genes that code for carotenoid. For instance, IbPSY1, a carotenoid gene was reported to be crucial in plants' resistance to abiotic stress in vivo. In some plant species (daffodil, maize, potato), the upregulation of the PSY gene was reported to highly increase carotenoid levels [11], this then explains the ability of the IbPSY1 gene to increase tolerance to environmental stress including drought, salinity, and high/low temperature which may negatively affect growth and yield of sweet potato. Another gene observed to improve sweet potato resistance to environmental stress is IbOr, a gene involved in carotenoid accumulation, and its overexpression improves resistance to heat stress and oxidative damage [19,85]. These genes can therefore be beneficial for engineering plants with improved tolerance to abiotic stress and can be proposed to be essential in the defense response mechanism of plants to abiotic stresses. Similarly, the upregulation of the IbOr gene in transgenic potato and alfalfa increased tolerance to certain abiotic stress such as salinity, drought and heat stress [86,87].
The brightly colored parts of plants resulting from carotenoid accumulation, aids in pollination and seed dispersal by attracting pollinators and other agents of dispersal [19]. Similar antioxidant properties of carotenoids exhibited in plants have also been observed in animals that feed on plants with carotenoids. Carotenoids have positive impacts on human health as they are sources of dietary antioxidants which reduces the risk of many age-related illnesses including macular degeneration, cancer, and cardiovascular diseases [2,18]. Recently, orange-fleshed sweet potato has been on the research spotlight due to the high carotenoid content particularly β-carotene.
Orange-fleshed sweet potato is considered among the main sources of vitamin A to animals and humans that cannot synthesize vitamin A but can only obtain them through their diet [88,89]. Owing to that, several studies recommend the daily intake of orange-fleshed sweet potato which helps increase the levels of vitamin A and improve general well-being [90,91]. For instance, the daily intake of orange-fleshed sweet potato significantly elevated the vitamin A status of men, women, and children in some developing nations including Bangladeshi, Kenya, and Mozambique respectively [45,66,90]. Orange-fleshed sweet potato has also been revealed to be used to prevent blindness and maternal mortality resulting from vitamin A deficiency in most developing countries [92]. This then explains the incorporation of sweet potato as an excellent food source to fight vitamin A deficiency especially in underdeveloped countries, hence, its integration in the vitamin A deficiency prevention program [93].
Carotenoids are also used as additives to improve pigmentation and to prevent UV radiation damage. In ornamental fishes, dietary astaxanthin, a carotenoid is used on commercial bases as a natural colorant [94] and also used to improve the pigmentation of egg yolks [95]. Humans have also benefited from the photo-protective properties of carotenoids through their inclusion in cosmetic products to impede damages from UV radiation.
The lycopene cyclase genes, LCY-β, and LCY-ε are involved in the biosynthesis of the branch components of carotenoids in diverse plant species. Regulating the expression levels of these genes (LCY-ε and LCY-β) may affect the relative activity and production of cyclic carotenoid genes associated with lutein synthesis in some plants including rice, Arabidopsis, and tomato [103,104]. Both genes are interrelated in producing α and β branch carotenoids in that, the overexpression of one can suppress the other. LCY-β is reported as a vital enzyme associated with the synthesis of both αand β-branch carotenoids, like α-carotene and β-carotene.
According to Haskell et al. [26], the LCY-β gene functions to increase carotenoid content, oxidative ability, and resistance to abiotic stress in sweet potato. It also increases the production of β-branch carotenoids including zeaxanthin, β-carotene, violaxanthin, and β-cryptoxanthin. In sweet potato, the downregulation of IbLCY-ε in non-embryogenic calli of light orange-fleshed sweet potato cv. Yulmi increased the content of β-branch carotenoids resulting in an orange coloration of the ensuing transgenic calli [25].
Other research has also reported that silencing IbLCY-ε or IbCHY-β during carotenoid metabolic engineering can result in increased β-carotene and total carotenoid content in sweet potato. According to Kim et al. [18], the suppression of IbCHY-β in the catalytic hydroxylation of β-carotene to yield β-cryptoxanthin which is further converted to zeaxanthin in sweet potato increased the β-carotene and the overall total carotenoids content. Based on these results, it can be established that CHY-β and LCY-β are the primary regulatory enzymes involved in carotenoid biosynthesis in sweet potato making β-carotene the main cellular carotenoid in sweet potato. Though the pathway for carotenoid biosynthesis is well elucidated in all higher plants, it is slightly implicit in sweet potato. Therefore, the pathway for the biosynthetic of carotenoids needs to be further characterized in sweet potato.
Gene Regulation of Carotenoid Biosynthesis
A key determining factor of carotenoid content is the regulation of essential biosynthetic genes [106]. Regulation of these genes and the allelic variation of genes in the biosynthetic pathway may influence the different accumulation levels of carotenoids [107,108]. Fluctuations in the levels of these genes have been associated with the development of some crops with increased carotenoid content. Most of the genes have been reported to be involved in the regulation of the three key processes (biosynthesis, degeneration, and storage) in carotenoid accumulation at different stages of plant growth [109]. Genes coding for practically all enzymes involved in the biosynthesis of carotenoids have been isolated from bacteria, fungi, and plants ( Fig. 3) [92]. Carotenoid biosynthetic genes in sweet potato including PSY, GGPS, CrtISO, PDS, LCY-ε, ZDS, ZEP, CHY-β, and LCY-β have been cloned and characterized [18,[25][26][27].
Research evidence on the specific gene that regulates the accumulation of carotenoids in plants is limited. However, the Or (orange) gene is a gene of interest and has been researched in several plant species including sorghum, cauliflower, melon, alfalfa, potato, Arabidopsis, and sweet potato. Orange denotes an extraordinary group of regulatory genes that facilitate the accumulation of carotenoids which are highly conserved in diverse species and exhibit functions like preserving homeostasis of carotenoids, maintaining photosynthesis, and regulating carotenoid biosynthesis [110,111].
The overexpression of the Or gene increased carotenoid accumulation in plants including Brassica oleracea var. botrytis [112], Cucumis melo [113], Solanum tuberosum and Arabidopsis thaliana [114]. In sweet potato, the Orange gene (IbOr) originally cloned from the sweet potato cv. Sinhwangmi (orangefleshed) based on the BoOr sequence, induced the accumulation of carotenoids in various tissues (leaves, stem, and storage root) [25]. However, the gene expression levels vary in different parts of the various sweet potato varieties. It is highly expressed in the storage root of the orange-fleshed varieties whilst in other colored varieties (white, orange, and purple), its expression is highly observed in the leaves [110]. The IbOr functions to aid the buildup of carotenoids and regulate carotenoid homeostasis in sweet potato. The orange gene has also been reported to interact with PSY and carotenoid cleavage dioxygenases (CCDs). In sweet potato, IbOr interrelates with IbPSY to enable higher stability of IbPSY through the holdase chaperone activity of IbOr [85] which offers a substitute and complement strategy for increased carotenoid levels, chromoplast differentiation and PSY stabilization [115]. Apparently, carotenoid catabolism negatively regulates accumulation. In potato, the accumulation of carotenoid was controlled negatively by CCD1 and CCD4 [116]. However, the purple-fleshed sweet potato expressing higher levels of IbOr also contained increased levels of CCD1, CCD4, and NCED transcripts [77] which proposes the ability of carotenoid catabolism genes (IbCCD1 and/or IbCCD4) to increase carotenoid accumulation in sweet potato. This is however inconclusive and exposes us to the complex mechanism involved in carotenoid accumulation, regulated by the molecular function of the IbOr gene. The interrelation between carotenoid accumulation and catabolism needs to be further elucidated.
Anthocyanins
Anthocyanins are a subclass of flavonoid compounds and an essential water-soluble natural pigment in vascular plants, responsible for the wide-ranging colors in several plant species [117]. They are existent in diverse plant tissues including flowers, fruits, and storage organs like the root and stem. Anthocyanins occur naturally as glycosides of anthocyanidins attached to different sugar moieties [118] and are highly appreciated for their anti-oxidant activities which provide several health benefits such as anti-cancer, antiinflammatory, anti-diabetic, anti-mutagenic, and cardiovascular diseases prevention properties [119,120].
Purple-fleshed sweet potato mounts up high levels of anthocyanins in their storage roots, with anthocyanin 3-O-sophoroside and its derivatives as the major compounds [121]. Anthocyanins from purple-fleshed sweet potato are non-toxic, resource-rich, and unscented bioactive compounds with stable physicochemical properties compared to anthocyanins from other plant sources including cherry, strawberry, and grapes [122]. Because these anthocyanins are acylated, they have high stability against heat and UV radiations, hence used as natural food additives [52]. The various anthocyanins identified by different authors in purple-fleshed sweet potato are summarized in Tab. 2.
Composition of Anthocyanin
Anthocyanin, a major plant secondary metabolite is a subclass of flavonoid compounds made of monoor di-glycosylated aglycones of anthocyanidins attached to a sugar moiety [123]. The molecular structure of anthocyanin mainly exists as glycosides of poly-hydroxyl or poly-methoxyl derivative of the flavylium (2-phenylbenzopyrylium) cation which consist of a double aromatic ring [A and B], divided by a heterocyclic ring [C] (Fig. 4) [124]. The presence of a positive charge on the C-ring distinguishes anthocyanins from other flavonoids. Anthocyanins are natural plant pigments with varied and complex structures. The structural variation of the various anthocyanins is attributed to the number of the sugar moiety, type of functional group, and the natural acyl group present [49]. The basic anthocyanin structure consists of aglycone base (anthocyanidin), two (2) or three (3) chemical units, sugars, and organic acids as in acylated anthocyanins [125]. Among the over twenty-six (26) anthocyanidins discovered in nature, only six (6) main types; petunidin, cyanidin, malvidin, pelargonidin, delphinidin, and peonidin are found in plants [126,127]. The six (6) main types are mostly responsible for the diverse color variations in plants (Tab. 3). These anthocyanidins usually work together with genes and enzymes to regulate the various colors of anthocyanins. According to Tanaka and Brugliera [128], the enzymes F3′H and F3′5′H which determines the hydroxylation pattern of the B-ring by different substitution patterns at R1, R2, and R3, influences the diversity and color variations of anthocyanins ( Fig. 4; Tab. 3). At present, there are over 600 kinds of anthocyanins identified in plants [127].
Functions of Anthocyanins
Anthocyanins are among the major secondary metabolites, responsible for distinctive colors in plants [134,135]. As a water-soluble natural pigment, anthocyanin plays a significant role in both plants and animals, especially in human health. In plants, anthocyanins aid reproduction by alluring insect pollinators. The brightly colored parts of plants resulting from the accumulation of anthocyanin attract insect pollinators which aid in pollination and seed dispersal [17,136]. Although some of these insects are essential in influencing the reproductive ability of the plant, others act as pathogens that infest plants with diseases. Anthocyanins are effective in reducing the infestations from these pathogenic insects. For example, tomato fruits enriched in anthocyanin exhibited tolerance to gray mold [137]. Also, large numbers of African bollworm died and pupation delayed in tropical armyworm when fed with anthocyanin-rich leaves relative to those fed with green leaves [138].
Anthocyanins also safeguard plants against some biotic and abiotic stress which may offer them better adaptation to climatic changes [139]. Although much has been reported on anthocyanin-related stress response in diverse plant species, little information is available in sweet potato. Dihydroflavonol-4-reductase (DFR), a gene involved in the biosynthesis of several flavonoids including anthocyanins was reported to influence sweet potato tolerance to cold stress [140] with the increase attributed to the enhanced antioxidant ability. Research has reported that the enhanced antioxidant activity of purple-fleshed sweet potato was due to its resilient ability to scavenge free radicals [141]. These findings then propose the role of anthocyanins in the maintenance of ROS homeostasis as the sweet potato grows and develops.
As photo-protective agents, anthocyanins protect the photosynthetic tissues by absorbing excess visible ultraviolet radiation and also act as scavengers of free radicals [142]. Furthermore, anthocyanins accumulate in immature non-reproductive tissues and light-exposed parts of fruits to offer protection against photoinhibition and photo-bleaching under light stress without considerably affecting the process of photosynthesis [143,144].
Despite its countless roles in plants, anthocyanins have beneficial health effects on mammals owing to their antioxidant properties. Purple-fleshed sweet potato has high anthocyanin content and subsequently high antioxidant properties which influences its health-promoting functions [49]. These antioxidant properties enable the scavenging of free radicals associated with aging and degenerative diseases [145]. Typically, anthocyanins are administered to animals through their diets with dietary anthocyanins offering protection against cardiovascular diseases, cancer, and other chronic disorders [146]. Some studies have attributed the shielding effects of dietary anthocyanins to their antioxidative and anticancer properties [147,148]. In an experiment on rats, it was observed that feeding rats with purple-fleshed sweet potato anthocyanin reduced hepatoxin-induced liver injury [53]. This was reported to be due to the enhanced expression of some antioxidant enzymes like glutathione peroxidase (GPX), superoxide dismutase (SOD), catalase (CAT) in the liver.
Also, the anti-aging and anti-oxidative properties of anthocyanins make it safe for the manufacturing of natural skin-care products in the cosmetic industry [135]. This inhibits the impact of UV radiation on the skin, hence, reducing inflammations and diseases. Furthermore, anthocyanins from purple-fleshed sweet potato are used as a replacement for some synthetic pigments in cosmetic products like shampoos, rouge, creams, and lipsticks among others. In the pharmaceutical industry, purple-fleshed sweet potato anthocyanins are used as potential components for the production of pharmaceuticals such as anti-neoplastic and anti-inflammatory agents due to their antioxidant properties [57]. As a non-toxic natural pigment, purple-fleshed sweet potato anthocyanin can be used to substitute synthetic pigments in the production of colored medicines as the long-term effect of these synthetic pigments could be detrimental to the human body [149].
Purple-fleshed sweet potato anthocyanin is used as a functional ingredient in food processing industries as preservatives and sources of natural colorants with excellent color potency [150,151]. Purple-fleshed sweet potato anthocyanin exhibited high stability when added to beverages and prolonged the shelf life than pigments from grapes and blackberries [152]. Anthocyanins from purple-fleshed sweet potato can proliferate the growth of helpful bacteria especially those utilized in probiotics and as well inhibit the growth of harmful ones. According to Sun et al. [153], peonidin-based anthocyanins proliferate the Bifidobacterium spp. (bifidum, adolescentis, infantis) and Lactobacillus acidophilus whiles inhibiting the growth of Salmonella typhimurium and Staphylococcus aureus. Similarly, crude anthocyanin derived from purple-fleshed sweet potato inhibits the growth of Bacteroides, Prevotella, and Clostridium histolyticum [154] proposing the ability of anthocyanins to be involved in prebiotic-like activity by modulating intestinal microbiota. In effect, purple-fleshed sweet potato anthocyanins are essential due to their diversified functions.
Anthocyanins after their synthesis are transported from the cytosol to the vacuole for storage. Vacuolar sequestration is crucial to prevent anthocyanins from being oxidized [160] and to perform its function as bioactive pigments. Though anthocyanin biosynthesis and regulatory genes are well characterized, the mechanism involved in its translocation from the cytosol to the vacuole in plants is still debatable [161,162]. The Multidrug toxic compound extrusion (MATE) protein and ATP-binding cassette (ABC) transporters confined in the tonoplast help link anthocyanins to glutathione S-transferase (GSTF) for effective segregation into the vacuole and may also cling unto anthocyanoplasts, a pre-vacuolar segment proceeding to the vacuole [163,164]. Acylated anthocyanins accumulate at high levels inside the vacuole to form AVI (anthocyanic vacuolar inclusions) in some species [165].
DFR is an essential structural gene and its substrate specificity regulates the structure and color of anthocyanins. Characterization of IbDFR revealed its expression to be also associated with both biosynthesis and accumulation. According to Wang et al. [140], expression levels of IbDFR in the leaves, stem, and root correlated with anthocyanin accumulation in these plant parts. In purple-fleshed sweet potato, a decrease in the expression of IbDFR also impacted the flux dissemination of flavonoids like proanthocyanidins and flavonols. In Arabidopsis, the transparent testa (tt) loci encode several flavonoid (anthocyanin) biosynthetic enzymes such as CHI, DFR, and CHS at the tt5, tt3 and tt4 loci respectively. However, mutations in genes encoding these anthocyanin biosynthetic enzymes eliminate anthocyanin synthesis [166,167]. For example, Dong et al. [168] observed no pigment accumulation (anthocyanin and brown tannins in the hypocotyl and seed coat respectively) in Arabidopsis tt mutants compared to the seeds of the wild-type, suggesting the absence of biosynthetic enzyme (CHS, CHI, and DFR) activity. Introduction of IbDFR into the hypocotyls, cotyledons, and seed coat of Arabidopsis tt3 mutants gave the hypocotyls and cotyledons a purple color and restored the pigments in the seed coat, proposing the biosynthetic function of the IbDFR gene. Glutathione S-transferases (GSTs) function to detoxify xenobiotics (heterocyclic compounds) by connecting glutathione to a substrate to form a glutathione S-conjugate. According to Marrs et al. [169], these enzymes catalyze the conjugation of glutathione (GSH) to anthocyanins to form anthocyanin-GSH conjugates for onward sequestration into vacuoles by the glutathione pump, proposing that anthocyanin may be an endogenous substrate for the glutathione pump. GST plays an integral role in the intracellular transport of anthocyanin by coupling its synthesis and accumulation in the vacuole. GSTs also function as carrier proteins by physically binding to anthocyanins to facilitate the vacuolar sequestration of anthocyanin from the cytoplasm, though their functions remain indistinct [170]. For instance, Kitamura et al. [171] and Sun et al. [172] observed the localization of GSTs from other plants and Arabidopsis tt19 that accrue high proanthocyanidins than anthocyanins in the cytoplasm of undeveloped seed coats. Recently, Marrs et al. [160] and Alfenito et al. [173] reported the inability of Petunia hybrida (petunia) and Zea mays (maize) mutants to accumulate anthocyanins in their vacuoles due to the lack of GST. This suggests the function of GST as flavonoid binding protein, hence, confirming its involvement in anthocyanin accumulation. Again, GSTs are allied to high-anthocyanin producing membranes in the plant cell, possibly the vacuole and endoplasmic reticulum [172]. These results confirm the function of GSTs as carrier proteins and thus, the glutathionylation of flavonoids might not be catalyzed by GSTs due to their inability to conjugate GSH to anthocyanins [161] which therefore contrast with the report by Marrs et al. [169]. It is therefore noteworthy that, there is no evidence of anthocyanin-GSH conjugates in plants [170].
GST genes involved in anthocyanin accumulation have been identified in several plant species including strawberry, cyclamen, litchi, and grapevine [174][175][176][177]. For example, Hu et al. [175] reported the involvement of LcGST4 in the accumulation of anthocyanin in the fruit Litchi chinensis (litchi) and its overexpression in pigmented tissues. The results revealed that the expression of LcGST4 was regulated by the LcMYB1 gene. MYB gene family is a part of a larger family of transcriptional factors that regulate anthocyanin biosynthesis and accumulation in plants. For instance, the IbMYB1 gene regulates anthocyanin biosynthesis in sweet potato [58] whereas, in Arabidopsis, IbMYB1a increased anthocyanin accumulation in transgenic plants [178].
In sweet potato, a GST encoding gene, IbGSTF4 is reported to be involved in the accumulation of anthocyanin. For instance, the IbGSTF4 gene after characterization was found to be highly expressed in pigmented stems, leaves, and storage root with its expression correlating with the accumulation of anthocyanin. In the same study, the varied expression profiles of IbGSTF4 in the Arabidopsis tt19 (a knockout mutant of anthocyanin-related GST) gave the cotyledon and hypocotyl a purple color, suggesting IbGSTF4 participation in anthocyanin accumulation in sweet potato [22]. However, a dual luciferase assay pointed out that the IbMYB1 gene could not directly regulate the expression of IbGSTF4 which conflicts with the report by Hu et al. [175]. This then suggests that the regulation of anthocyanin biosynthesis and sequestration may involve other MYB regulatory factors [22], thus, proposing a complex regulatory mechanism of anthocyanin vacuolar sequestration and accumulation in sweet potato which needs to be elucidated through further research.
Gene Regulation of Anthocyanin Biosynthesis
The primary regulatory genes involved in anthocyanin biosynthesis have been studied extensively and sequestered in many plant species [124]. The transcriptional factors regulating the biosynthesis of anthocyanins are WD40-type co-regulators (WD40), R2R3-MYB protein, and a basic helix-loop-helix (bHLH, MYC) protein [179].
Structural and regulatory genes are the two main types of biosynthetic genes. The structural genes encode the enzymes which catalyze every reaction step while the regulatory genes encode transcriptional components that regulate structural gene expression [180,181]. Structural genes involved in anthocyanin biosynthesis are homogeneously expressed and their expression levels are dependent on the concentration [182]. There are two divisions of structural genes in dicot plants i.e., early (CHI, CHS, FLS, F3′H, and F3H,) and late (UFGT, ANS/LDOX, and DFR) biosynthetic genes [181]. These genes operate under the MYB-bHLH-WD40 (MBW) regulatory network made up of the MYB, basic helix-loop-helix (bHLH) and WD40 replicate families. For instance, the MYB domain C1 protein which regulates anthocyanin biosynthesis in maize requires a bHLH partner to activate the flavonoid structural genes and the dihydroflavonol reductase (DFR) promoter, although the MYB domain P protein which controls phlobaphene to stimulate the promoter lacks a bHLH partner [183]. These MYB proteins have a central responsibility of regulating the biosynthesis of secondary metabolites, signal transduction, resistance to diseases as well as growth and developmental fluctuations [181]. As reviewed in Amoanimaa-Dede et al. [20], the structurally conserved MYB genes comprise 100-160 bp DNA-binding regions with one or more replications. The R2R3 MYB genes with two repeats are the predominant group of MYB genes involved in the flavonoid pathway in plants. Therefore, the intensity of anthocyanin synthesis solely depends on the expression of structural genes that are related to a specific species [184].
In sweet potato, some structural and transcription factor genes have been characterized, with most of the genes functioning in both anthocyanin biosynthesis and accumulation. The IbMADS10 is a vital regulatory gene involved in anthocyanin biosynthesis of sweet potato [185]. Two MYB genes (IbMYB1 and IbMYB2) isolated from the storage root of purple-fleshed sweet potato cv. Ayamurasaki regulates anthocyanin biosynthesis in sweet potato [186]. According to Mano et al. [186], the IbMYB1 transcription factor from the MYB-family facilitates the accumulation of anthocyanins in sweet potato storage roots. Park et al. [58] reported that the overexpression of the IbMYB1 gene effectively caused the accumulation of anthocyanin in the storage root of an orange-fleshed sweet potato with high carotenoid content thereby increasing the radical scavenging activity.
Current research has identified some post-transcriptional modulators mostly miRNA including ib-miR164c, ib-miR160e-5p, ib-miR172e-3p, and ib-miR166m [187] to be involved in anthocyanin biosynthesis. These miRNA target genes are involved in auxin signaling. Auxin can inhibit the expression of the MBW complex which intends to regulate anthocyanin biosynthesis [36]. The ib-miR159, ib-miR319, ib-miR858, and ib-miR156 also regulated the MYB genes whereas the SPL gene was targeted by ib-miR156 and its upregulation reduced the expression of ibSPL in purple-fleshed sweet potato. The ib-miR156a-5p was also reported to cling unto ibSPL genes proposing that ib-miR156 may increase the biosynthesis of anthocyanin via structural gene regulation in the phenylpropanoid pathway.
Though there is post-transcriptional modulation of anthocyanin biosynthesis, the primary level at which anthocyanin biosynthesis is inducted or shut down in plants is controlled by the expression of biosynthetic genes [188]. From the above results, it can be deduced that the molecular regulation of anthocyanin biosynthesis and accumulation is complex both at the transcriptional and post-transcriptional levels. One keen observation made was that most molecular results were more tailored to individual research (transcriptome sequencing). This is due to the lack of a reference genome (the only one sequenced "Taizhong 6" is incomplete and inaccurate hence not representative of hexaploid sweet potato). The ability to sequence the reference genome will go a long way to improve molecular research in sweet potato.
Concluding Remarks and Perspectives
Sweet potato is a multifunctional food crop with rich nutritional composition and bioactive compounds. Several cultivated sweet potato varieties differ with flesh and skin colors (white, yellow, orange, red, and purple) of the storage root. Variations in phytochemicals and nutritional compositions, the pigments produced and the morphological traits may also distinguish the various sweet potato varieties. Carotenoid and anthocyanin are the major natural pigments in sweet potato known for their antioxidative properties which scavenge free radicals and protect both plants and animals from oxidative damage. The IbGGPS, IbLCY-ε, and IbCHY-β genes regulate carotenoid biosynthesis while IbCCD1, IbCCD4, and IbOr control its accumulation. Anthocyanin biosynthesis and accumulation are both regulated by the IbMYB, IbDFR, and IbGSTF3 genes. Besides, some post-transcriptional modulators basically miRNAs were revealed to be involved in anthocyanin biosynthesis. Further characterization of the biosynthesis and regulatory mechanism of carotenoids and anthocyanins will be beneficial to unravel the complex mechanism of carotenoid accumulation regulated by the molecular function of the IbOr gene. This will help elucidate the interrelation between carotenoid accumulation and catabolism. Although the molecular mechanism underlying the biosynthesis and regulatory control of carotenoids and anthocyanin is extensively studied in sweet potato, a lot is still unknown. Furthermore, the limited report on the role of anthocyanin in sweet potato stress response mechanism calls for further research.
Many innovative biotechniques such as CRISPR/Cas9 through synthetic transcription factor detection and gene activation, could modify the expression of targeted genes [189]. Although this technology has been used extensively for crop improvement, little is known about its application in sweet potato. For sweet potato pigmentation, transgenic technologies (genetic modification and metabolic engineering) have been used to further increase carotenoid and anthocyanin content by modifying the expression of single genes through Agrobacterium-mediated transformation. Also, the identification and development of synthetic transcription factors in sweet potato might increase the accumulation of carotenoids and anthocyanins. Therefore, CRISPR-Cas9-mediated genome editing technique may be significantly useful for the biofortification of sweet potato. The lack of a reference genome makes genetic and molecular studies very challenging, hence, whole-genome sequencing is suggested to improve molecular research in sweet potato. Overall, the complex molecular regulation of anthocyanin biosynthesis and accumulation both at the transcriptional and post-transcriptional levels due to the inconsistencies in previous reports should be addressed through further research. Understanding the biosynthesis and gene regulation of these major sweet potato pigments may provide appropriate resources and better schemes for breeding sweet potato varieties with high anthocyanin and carotenoid contents.
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Domain: Biology Agricultural and Food Sciences
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Influence of Dietary Lipid Levels on Growth Performances , Survival , Feed Utilization and Carcass Composition of African Snakehead
| Five iso – energetic diets were formulated to evaluate the effects of dietary lipids levels on growth parameters, survival rate, feed utilization and body composition of African Snakehead Parachanna obscura fingerlings with initial body weight 7.69 ± 0.14 g. Experimental diets were formulated to contain graded levels of lipid (5, 7, 9, 11 and 14 g/100 g of diet). Each diet was tested in triplicate during 8 weeks. During the experiment, any mortality was registered. Growth performances and nutrient utilization parameters of fingerlings fed on different diets varied significantly (P <0.05). Highest growth performances and nutrient utilization were obtained with fish fed on a diet containing 7% of crude lipid. According to the broken line models used to analyze the relationships between the dietary crude lipid (DCL) and the specific growth rate (SGR), the maximum dietary crude lipid requirement is 7% of the diet.
INTRODUCTION
I n order to diversify aquaculture production in the world, some neglected species like Parachanna obscura which reveal interesting aqua cultural potential have been identified (Kpoguè et al., 2013a). P. obscura has high economic value (Bolaji et al., 2011) and occupies a prominent place in the diet of people (O'Bryen and Lee, 2007). Few data are available farming on this fish. Information on nutritional requirements of major dietary components such as protein, lipid and carbohydrates is a prerequisite for the formulation of an inexpensive and balanced diet for the fish. Indeed, the chemical composition and growth performance in fish can be influenced by dietary composition (NRC, 1993). Insufficient dietary protein results in poor growth and feed conversion ratio whereas excess levels are catabolized to provide energy, being neither economically beneficial nor environmentally friendly (Sugiura and Hardy, 2000). Increasing dietary non-protein energy level, often by increasing dietary lipid, can spare protein to a certain degree by directing protein to growth rather than for energy production. Dietary lipid provides essential fatty acids, phospholipids, sterols and fat soluble vitamins necessary for proper functioning of physiological processes as well as maintenance of biological structure and cell membranes function (Ghanawi et al., 2011). Therefore, it is important to formulate diets with proper lipid levels to meet the energy and fatty acids requirements for fish (Lopez et al., 2009).
South Asian Journal of Life Sciences
July-December 2018| Volume 6 | Issue 2 | Page 37 According to the results of Kpoguè et al. (2013b), optimum dietary crude protein requirements for P. obscura fingerlings is 42.5% in formulated diets. No study has yet been conducted in it lipid requirement to date. The present study aims to investigate the effects of dietary lipid levels on growth performances, survival, feed utilization and carcass composition of African snakehead P. obscura fingerlings.
compoSition anD preparation oF tHe DietS
Five iso-nitrogenous and iso -energetic experimental diets were formulated to contain with graded lipid levels namely 5, 7, 9, 11 and 14 g/100 g of diet, respectively (Table 1). These lipid contents were chosen based on the results of the lipid requirements of other snakeheads species such as Channa striatus (Aliyu-Paiko et al., 2010). Cod, soybean and cotton seed meals were used as the main protein source. Sardine oil was used as primary lipid source and maize meal is the principal carbohydrate source. The various ingredients were ground with hammer mill, weighed and mixed. Feed was manufactured by mixing the dry ingredients with boiling water and oil until the achievement of a desirable paste-like consistency. The resulting paste was transformed into pellets of 2 mm diameter byfood blender (MFM-302-Denwa). After sun-drying at a temperature varying from 28 to 35°C during 3 days almost, the pellets were manually broken in small pieces.
experimental FiSH, rearing conDitionS anD FeeDing trial
Fish were collected from the swamp "Dra" in Takon village (South-Est of Benin). After their collection, fish were transported to the experimental Station of the Laboratory of Research on Wetlands belonging to the Faculty of Sciences and Technics of Abomey Calavi University where they were then put in circular tank for 2 weeks. During this period, fry were trained to progressively accept the formulated diet. A mixture of the different experimental diets (20% of each) was used as feed during this phase. After this conditioning period, 40 fingerlings (7.69 ± 0.14 g) were stocked per a 225 liter tank for 8 weeks. Each diet was tested in triplicate. Water in all tanks was renewed continuously (1 L/min). To prevent fish from jumping out, tanks were covered at 50% with a perforated wooden plank. During the experiment, fishes were hand fed daily every 2 h from 08:00 am to 08:00 pm up to apparent satiation. Water quality parameters such as temperature, pH, dissolved oxygen and nitrite were daily measured in each tank throughout the experimental period. These parameters mean value were 27.91 ± 0.32°C, 6.26 ± 0.23, 6.21 ± 0.07 mg/L, respectively.
At the beginning and the end of the experiment, all fish were counted and weighed per tank and 20 fingerlings were sampled forindividual weight and total length. All fish were counted and weighed every 7 days during the experimental period in order to adjust feed ration. No feed was given to the fish on the sampling day.
cHemical anD calculationS
Fishes samples were analyzed by standard methods for dry matter (oven drying) at 105°C for 24 h, crude protein (CP) (N-Kjeldahl ×6.25) and ash (oven incineration at 550°C for 12 h). Total lipids were extracted according to Bligh and Dyer (1959).
After the feeding trial, fishes were collected, counted, weighed and the different parameters were calculated as follows: Weight
StatiStical analySiS
The mean values of final weight, SGR, FE, CV, survival rate and body composition were compared between treatments by one way analysis of variance (ANOVA 1) after verifying the homogeneity of variance using "Hartley's test" for each treatment. Significant differences between treatments means (P<0.05) were determined using a Fisher's test (Saville, 1990). Results are given as means ± standard deviation.
Mathematical model (dose -response) was used to assess the effect of dietary lipid level on specific growth rate of P. obscura fingerlings. The general equation of the broken line model (Robbins et al., 1979) is y = L+U(R-XLR) where L is the ordinate and R, the abscissa of the breakpoint. R is taken as the estimated requirement (dietary lipid that guarantees the maximum specific growth rate). XLR means X less than R and U is the slope of the line for XLR. By definition, R-XLR is zero when X > R.
RESULTS
Survival rates and growth performances parameters of P. obscura fingerlings during the feeding trial are shown in Table 2. Survival rates were 100% in all the treatments. They were not therefore significantly affected by the dietary lipid level (P˃ 0.05). In return, the dietary lipid level affected significantly fish growth (P<0.05)as indicated by growth parameters values (Table 2). The final body weight, weight gain, specific growth rate and feed efficiency improved as dietary lipid level increased from 5 to 7% in the diet. The specific growth rate varied from 1.31 ± 0.02 to 1.85±0.05%/day. Variation of feed efficiency among the experimental diets is shown in Figure 1. It varied from 0.38 ± 0.07 to 0.52 ± 0.07. Inclusion of dietary lipid above 7% of the diet did not produce any improvement in growth or feed efficiency (Table 2). The highest final body weight, weight gain and specific growth rate are obtained with fish fed on the diet with 7% of lipid (P<0.05). Therefore, the lowest growth performances and feed efficiency are obtained with fish fed on the diet with 14% of lipid (P<0.05).reached the best value with diet contained 7% of lipid level. Therefore, whole body protein obtained with 7,9,11 and 14% of lipid level are not significantly different (P˃0.05). Whole body lipids increased significantly with dietary lipid levels and fish fed on the diet with 5% of lipid had the lowest (P<0.05). Diets with 11 and 14% induced the highest body lipid (P<0.05).
Whole body composition is presented in
Relationships between dietary lipid in diet and SGR have been used to estimate the maximum dietary lipid requirements for P. obscura fingerlings. According to the broken line model (Figure 1), maximum lipid requirement for P. obscura fingerlings is 7% of the diet.
DISCUSSION
In any treatments, survival rates (100%) was affected by the dietary lipid level. These results confirmed that P. obscura is a hardy and rustic species which has important aqua-cultural qualities (Kpoguè et al. 2003) according to whom, the excessive dietary lipid may reduce feed consumption and thus depress the growth of fish. Similar observation was reported by several authors. Indeed, dietary lipid serves as an energy sources and enable for protein sparing (Mishra and Samantaray, 2004). Dietary lipids are known to affect the accumulation of lipid in fish body in a number of fish species (Arockiaraj et al., 2004;Segato et al., 2005;Wang et al., 2006). Within certain limits, increased dietary lipid levels can also improve diet utilization (Du et al., 2005). However, excessive dietary lipid supplement may causes growth deceleration (Gonzalez -Felix et al., 2015) and even confer external metabolic burden to the liver (Lu et al., 2015), resulting in excessive fat deposition (Richard, 2006, Song et al., 2009;Ghanawi et al., 2011). According to Ruyter et al. (2000), excessive dietary lipid may negatively influence the ability of fish to digest and assimilate fatty acids. An increasing lipid deposition in the fish due to the excessive consumption of dietary lipid (Martino et al., 2002) can produce fatty fish that may have an unpleasant flavor and become easily rancid at postharvest (Yong et al., 2015).
CONCLUSION
The results of this study indicate that the maximum dietary lipid requirement for P. obscura fingerlings is estimated to be 7%. A further dietary lipid level damage the growth performances, feed efficiency and lead to an excessive body fat deposition.
South Asian
gain (g) = final body weight -initial body weight Specific growth rate (SGR; %/d) = 100 × [Ln (final body weight) -Ln (initial bodyweight)] / Duration of the experiment Feed efficiency (FE) = (FB+DB-IB) / FD Where IB (g) and FB (g) are the initial and final biomasses and DB (g) is the biomass of dead fish, and FD (g) is the total distributed feed quantity. Survival rate (%) = 100 × FN/IN; (IN, FN = Initial and Final Number of fish respectively). Coefficient of Variation (CV %) = Standard Deviation x 100/Mean.
Table 2 :
Growth performances, survival rate and nutrient utilization of P. obscura fingerlings fed diets containing different levels of lipid
Table 3 :
Body composition data for P. obscura fingerlings fed with diets containing different levels of lipid Means on the same line followed by different superscripts are significantly differents (P < 0.05).\===
Domain: Biology Agricultural and Food Sciences. The above document has 2 sentences that start with 'Each diet was tested in',
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2 sentences that start with 'obscura fingerlings fed',
2 sentences that end with 'et al., 2011)',
2 sentences that end with 'requirements for P',
2 sentences that end with 'lipid requirement for P',
2 paragraphs that end with 'is 7% of the diet'. It has approximately 1804 words, 100 sentences, and 31 paragraph(s).
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Genetics of drought tolerance at seedling and maturity stages in Zea mays L
Shortage of irrigation water at critical growth stages of maize is limiting its production worldwide. Breeding drought-tolerant cultivars is one possible solution while identification of potential genotypes is crucial for genetic improvement. To assess genetic variation for seedling-stage drought tolerance, we tested 40 inbred lines in a completely randomized design under glasshouse conditions. From these, two contrasting inbred lines were used to develop six basic generations (P1, P2, F1, F2, BC1F1, BC2F2). These populations were then evaluated in a triplicated factorial randomized complete block design under non-stressed and drought-stressed conditions. For statistical analyses, a nested block design was employed to ignore the replication effects. Significant differences (p≤0.01) were recorded among the genotypes for investigated seedling-traits. Absolute values of fresh root length, fresh root weight, and dry root weight lead to select two genotypes, one tolerant (WFTMS) and one susceptible (Q66). Estimates of heritability, genetic advance, and genotypic correlation coefficients were higher and significant for most of the seedling-traits. Generation variance analysis revealed additive gene action. Narrow-sense heritability [F2 ≥ 65; F∞ ≥ 79] revealed the same results. Generation mean analysis signified additive genetic effects in the inheritance of cob girth, non-additive for plant height, grains per ear row and grain yield per plant, and environmental for ear leaf area, cob length, grain rows per ear, biomass per plant, and 100-grain weight under drought-stressed conditions. For conferring drought-tolerance in maize, breeders can adopt the recombinant breeding strategy to pyramid the desirable genes. Additional key words: genetic effects; maize; morphological and seedling traits; water stress. Abbreviations used: BPP (biomass per plant); CG (cob girth); CL (cob length); CRD (completely randomized design); DRW (dry root weight); DS (drought-stressed); DSW (dry shoot weight); E (emergence); ELA (ear leaf area); FRCBD (factorial randomized complete block design); FRL (fresh root length); FRW (fresh root weight); FSL (fresh shoot length); FSW (fresh shoot weight); GPER (grains per ear row); GRPE (grain rows per ear); GYPP (grain yield per plant); HGW (100-grain weight); NS (non-stressed); PH (plant height). Authors’ contributions: Conceived and performed the experiments: NHK. Data recording: NHK and IJ. Data analysis and paper write-up: NHK and MN. Supervised and coordinated the research project: MA and HAS. Citation: Khan, N. H.; Ahsan, M.; Naveed, M.; Sadaqat, H. A.; Javed, I. (2016). Genetics of drought tolerance at seedling and maturity stages in Zea mays L. Spanish Journal of Agricultural Research, Volume 14, Issue 3, e0705. [URL]/10.5424/ sjar/2016143-8505. Received: 22 Aug 2015. Accepted: 30 Jun 2016 Copyright © 2016 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial (by-nc) Spain 3.0 Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Funding: This research work has not been funded by any agency or organization. Competing interests: The authors have declared that no competing interests exist. Correspondence should be addressed to Muhammad Naveed
Introduction
Maize (Zea mays L.), commonly known as corn, is a major staple consumed as food, feed, and raw materials in many industrial products worldwide. Its grains are a rich source of starch, protein, oil, fiber, sugar, and ash (Chaudhry, 1983). Globally, maize is grown on an area of about 183 Mha with 1021 Mt production annually ( [URL] demand in the international market, especially in developing countries, is expected to rise from 526 to 784 2 factorial randomized complete block design (FRCBD) with three repeats. Row to row and plant to plant distances maintained were 75 and 25 cm, respectively. Agronomic and crop husbandry practices were followed according to experimental needs.
Screening at seedling stage
This experiment was conducted in a glasshouse during autumn, 2010. A total of 36 seeds of each test entry were sown in all 3 replications using the same number of polythene bags (20×15 cm each) in two separate sets: Set-I, irrigation was applied to the 100% of the field capacity or crop need, while in Set-II irrigation was applied to the 50% of the field capacity.
Seven days after the sowing of seeds in polythene bags, 150 mL of water was applied to both the experimental sets. Fifteen days following the sowing, another irrigation of 150 mL of water was given just once to Set-I only. However, 21 days after the sowing and for uprooting the seedlings, 150 mL of water was applied to both the experimental sets. After uprooting and washing with tap water cautiously, the seedlings were dried by wrapping them in blotting papers for 10 minutes.
To select the desirable parents, assessment of the germplasm was done on absolute genotypic performances for investigated seedling-traits. This selection procedure had extensively been employed by other researchers (Azhar et al., 2005;Akhter et al., 2007;Iqbal et al., 2011). We measured the following plant characters by using the procedures given in Table 1: fresh root length (FRL), fresh root weight (FRW), dry root weight (DRW), emergence% (E), shoot length, fresh shoot weight (FSW), and dry shoot weight (DSW) under the contrasting conditions (Matsui & Singh, 2003;Qayyum et al., 2012). Data were recorded on 8 seedlings/genotype selected randomly and analyzed using analysis of variance (Steel et al., 1997). Phenotypic and genotypic correlation coefficients between pairs of seedling traits were calculated using individual plant data of F 2 population (Kwon & Torrie, 1964). Broad-sense heritability (Weber & Moorthy, 1952) and genetic advance (Falconer & Mackay, 1996) were also worked out for seedling-traits.
Studies at physiological plant maturity
Based on seedling-traits, two contrasting inbred lines were selected and used as parents (P 1 and P 2 ) to develop F 1 seed during autumn, 2011. P 1 was used as pollen parent while P 2 as female parent. During autumn 2012, both parents (P 1 and P 2 ) and their hybrids (F 1 ) were raised under field conditions. Some of the F 1 plants were selfed number severely (Heiniger, 2001;Farré & Faci, 2006). Drought occurring two weeks before and during silking phase reduces seed setting and kernel size, causing 20-50% significant yield losses (Schussler & Westgate, 1991;Nielsen, 2007). Negative effects of drought on crop productions are likely to increase in the outlook due to unpredictable global climatic changes (Sanderson et al., 2011). Improvement in water-use efficiency through management practices and evolution of stresstolerant crop varieties will likely play an effective role in mitigating damaging effects of abiotic plant stresses on agricultural production (Tester & Langridge, 2010).
As drought is quantitative in nature, therefore, requires an understanding of genetic mechanisms controlling various plant traits for adopting different breeding approaches (Khan et al., 2004;Ahsan et al., 2013). Assessment of crop genotypes at seedling-stage is an imperative feature of plant breeding for developing drought-tolerant cultivars. Vigorous maize seedlings lead to healthy crop and ultimately good production under water-deficit conditions. Potential variations exist in maize genetic stocks for drought-tolerance. Identification and characterization of genotypes for the said purpose is the primary step in developing droughttolerant cultivars (Chen et al., 2012;Naveed et al., 2016a). This requires an understanding of gene action controlling various seedling and morphological plant traits. Various biometrical techniques could be used for appraising genetic effects. Among these, generation mean analysis is the one which determines the type of epistasis at digenic level using scaling test, accurately and efficiently (Naveed et al., 2016b). In view of the above, we conducted this study to identify the contrasting inbred lines at seedling-stage drought-stress and to find the inheritance pattern of gene or genes involved in the drought-tolerance using six basic generations.
Plant material and other experimental details
Drought-tolerance studies in maize at seedling and maturity stages were carried out in the Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad, Pakistan during the years 2010-13. For this purpose, 40 out of 200 maize inbred lines, collected from different research organizations were selected based on characterization/information provided by the contributors.
As the screening experiments were conducted inside the glasshouse, the design used for laying out the plant material was completely randomized (CR). However, for the evaluation of six basic generations in nonstressed and drought-stressed conditions, we applied a 3 Genetics of drought tolerance at seedling and maturity stages in maize design to ignore the replication effects. Variance analysis of each character was done according to Steel et al. (1997). Generation mean and variance analyses were performed to find the type of genetic effects and components of variance associated with inheritance of traits for each regime, separately (Mather & Jinks, 1982). Mean and variances of parents (P 1 & P 2 ), backcrosses (BC 1 & BC 2 ), and segregating generations (F 1 & F 2 ) for each trait were averaged over replications before use in statistical and biometrical analyses. A weighted least square analysis was done on generation mean using simplest residual (m) model and tested for goodness of fit. If chi-square value of one-factor model [m] was significant then further models of increasing complexity [md, mdh, etc.] were tried and tested for goodness of fit. The best model was the one which had significant estimates of all the variables along with non-significant chi-square value. The parent with higher value was always taken as P 1 in the model fitting for each trait. Sum of squares (SS) for those comparisons were generated following Little & Hills (1978). Estimates of narrow-sense heritability of various morphological traits were also computed (Mather & Jinks, 1982).as a source for raising F 2 population while the remaining F 1 plants were backcrossed with P 1 and P 2 to develop BC 1 F 1 and BC 2 F 2 generations, respectively.
During autumn 2013, seeds of these six basic generations (P 1 , P 2 , F 1 , F 2 , BC 1 F 1, and BC 2 F 2 ) were planted in two sets, one under field (non-stressed) and the other under drought-stressed conditions. Per replication, 30 plants were sown of each parent (P 1 , P 2 ) and their hybrids (F 1 ), 60 of each backcross (BC 1 F 1 , BC 2 F 2 ), and 200 of the F 2 generation. To record the data in a replication, randomly guarded 15 plants were selected each of P 1, P 2 & F 1 while 30 plants each of BC 1 F 1 , BC 2 F 2 , and 60 plants of F 2 generations, separately both from non-stressed and drought-stressed experiments. Data were recorded on various plant traits, such as plant height, ear leaf area, cob length, cob girth, grain rows per ear, grains per ear row, biomass per plant, 100-grain weight, and grain yield per plant at physiological plant maturity.
Statistical analysis
Observations recorded on different plant traits of six basic generations were analyzed using nested block The roots of each selected plant were separated from the plant and fresh root weight was recorded in grams. Fresh shoot weight FSW The shoots of each plant were separated from the plant and fresh root weight was recorded in grams. Dry root weight DRW Fresh roots detached from selected seedlings were put in a kraft paper bag and dried in an electric oven at 65 ± 5 °C for 72 hours for complete drying. The dried roots were weighed in grams. Dry shoot weight DSW Fresh shoots detached from the seedlings were put in a kraft paper bag and dried in an electric oven at 65 ± 5 °C for 72 hours for complete drying. The dried shoots were weighed in grams. Plant height PH At physiological plant maturity, the lengths were measured in cm from ground level to the apex of tassels of randomly selected plants using a measuring rod (Guzman & Lamkey, 2000). Ear leaf area ELA Leaves were collected from randomly selected competitive plants in each treatment and leaf area of each was measured in cm 2 using a leaf area meter (Model CI-203 CID, Inc. USA).
Cob length CL
The length of cobs from each selected plant was measured in cm using a measuring tape.
Cob girth CG
The diameter of cobs from each selected plant was measured from base, middle and top with the help of a Vernier Caliper (Model, RS232) and averaged. Grain rows per ear GRPE These were counted from the cobs of each selected plant and averaged. Grains per ear row GPER Grains were counted from ear rows of each selected plant and averaged. Plant biomass BPP The weight of total air dried selected plants was recorded and converted into kg/ha. This, together the grain yield, was used to calculate the plant biomass.100-grain weight HGW Three sets, each comprising 100 grains, were collected from each selected plant and weighed in grams. Grain yield per plant GYPP The grains obtained from each selected plant were weighed in grams.
4
stressed environments revealed significant differences among the genotypes for the traits investigated (Table 2). The responses of genotypes varied for all the measured traits under both the experimental regimes. Inbred line WFTMS exhibited, in non-stressed vs drought-stressed conditions: the highest FRL (35.5 vs 34.0 cm), FSL (40.9 vs 30.1 cm), FRW (38.7 vs 15.2 g),
Selection of parents on the basis of seedling traits
Mean squares acquired from analysis of variance of experiments conducted under non-stressed and drought-
5
Genetics of drought tolerance at seedling and maturity stages in maize ability were high (>60%) for all the investigated traits in both conditions except for E% in the nonstressed environment which was low (<60%). The genetic advance was low for E% and moderate for FRL under non-stressed conditions, while high (>20%) for all the other traits under both the environmental conditions.
Association studies among seedling traits
In non-stressed conditions, most of the seedlingtraits exhibited positive and significant associations among each other except for DRW with FRL, FRW and FSW, and for DRW with DSW at genotypic and phenotypic levels (Table 4). Similarly in the droughtstressed regime, correlation coefficients recorded were positive and significant for most of the traits except for FRL with FSL, FSL with FRW and DSW, and for FSW with DRW (Table 5).
Drought tolerance studies at maturity stage
The selected inbred lines, WFTMS, a tolerant male parent (P 1 ), and Q66, a susceptible female parent (P 2 ), were used to develop six basic generations. The generation means for various traits indicated significant differences (p<0.01)among parents (P 1 , P 2 ), their hybrids (F 1 ) and segregating (F 2 , BC 1 F 1 , BC 2 F 2 ) populations for the traits studied under both non-stressed and drought-stressed conditions (Table 6). Filial-generation one (F 1 ) means fell outside the range of both the parents FSW (16.8 vs 11.8 g), DRW (27.3 vs 13.2 g) and DSW (4.1 vs 3.4 g). Inbred line Q66, however, presented the lowest values for FRL (20.8 vs 21.4 cm), FSL (19.8 vs 12.9 cm), FRW (10.5 vs 2.7 g), FSW (6.4 vs 1.2 g), DRW (4.9 vs 1.1 g), and DSW (0.7 vs 0.4 g). WFTMS and W64SP displayed the highest E%, while WF-9, the lowest. Some experimental lines, B34 and W187R revealed encouraging results for some traits, but not for others. Among all the tested genotypes, two inbred lines, WFTMS and Q66 appeared most divergent under both conditions, therefore, they were selected to develop breeding material for conducting genetic studies of droughttolerance. On overall basis, estimates of root length, shoot length, fresh root weight and dry root weight under drought-stressed regime were greater than the non-stressed one.
Assessment of genetic variability
Various descriptive statistics regarding genetic variability are given in Table 3. The coefficient of variability (CV) was highest for DRW (55.45%) while lowest for FRL (17.76%) in drought-stressed conditions. However, under the non-stressed regime, DSW (49.06%) and E% (5.43%) revealed the highest and least CV values, respectively. The magnitudes of genotypic variances were lesser in comparison to phenotypic variances for the traits studied. The variance estimates under drought-stressed condition were higher than the respective variances under the non-stressed regime. Estimates of broad-sense herit- 7 Genetics of drought tolerance at seedling and maturity stages in maize tive gene action with non-allelic dominance-dominance interaction.
Estimates of components of genetic variance and narrow sense heritability are given in Table 8. Under the drought-stressed conditions for plant traits such as PH, ELA, GRPE, GPER and HGW, additive [D], environmental [E], and interaction [F] components of genetic variance were important in contrast to only [D] and [E] component for CL, CG, BPP and GYPP. In non-stressed conditions, [D] and [E] variances predominated for traits like PH, ELA, CL, CG, GRPE, HGW and GYPP in comparison to three variance components [D, E, and F] for GPER and BPP. Narrow sense heritability under non-stressed conditions ranged 69% (GPER) to 92% (PH) in comparison to the range of 65% (PH) to 90% (CG, GYPP) under drought-stressed conditions. The estimates for CL, CG, HGW and GYPP were higher under drought-stressed than under nonstressed conditions. Estimates of heritability for infinity-generation (F ∞ ) were high in contrast to the F 2 population for all the traits under both non-stressed and drought-stressed environments.
Discussion
Drought is one of the leading abiotic plant stresses that affect plants at various levels of their organization (Yordanov et al., 2000). Building tolerance against it, for traits like ELA, GRPE, BPP and GYPP in droughtstressed conditions, and PH in the non-stressed regime, suggesting a transgressive segregation. Mean estimates of six basic generations for the investigated traits were higher in the non-stressed regime than in the respective drought-stressed conditions. Differences in mean values of F 1 , F 2 and backcrosses (BC 1 F 1 and BC 2 F 2 ) for all the traits were due to the parental contribution in a particular trait. These results pointed sufficient differences among the genetic material developed which led to perform generation mean analysis.
Estimates of genetic effects controlling inheritance pattern of various plants are given in Table 7. Dominance with epistatic additive-additive gene interaction was predominant in controlling PH under both the conditions, while GYPP, only under the droughtstressed regime. Epistatic additive-additive digenic effects controlled the inheritance of ELA in nonstressed conditions. The simply mean value best fitted to data of CL and GRPE under both the conditions, and of ELA and GPER in the non-stressed regime, while to data of BPP and HGW only under droughtstressed environments. Non-allelic additive-additive gene action was recorded for CG and HGW of this crossed material under the non-stressed regime. Duplicate dominance with additive-additive and additivedominance interactions was crucial in controlling GPER under the drought-stressed regime. For BPP under the non-stressed conditions, we observed addi-Table 7. Genetic effects for various morphological traits of maize under non-stressed and drought-stressed conditions.
Trait [1] Mean 8 causes of reduction in grains per ear may either be embryo abortion or delayed silk appearance under drought-stressed conditions (Wasson et al., 2000).
In the present study, assessment of various seedling traits for drought tolerance revealed significant variability among the 40 maize inbred lines. The estimates pertaining to different traits exhibited significant reduction under the drought-stressed regime in contrast to non-stressed conditions. This is in agreement to the observations of Ali et al. (2013). The selection of drought-tolerant (WFTMS) and susceptible (Q66) genotypes was done on the basis of FRL and other seedling traits under both the environments which were further used for the genetic studies of various morphological traits. The choice of the contrasting genotypes was made by considering actual performance under both the environments. The method of relative performance or percentage increase or decrease for each trait was not employed due to its ineffectiveness in selecting the potential genotypes. The reason is that the actual performance of some the genotypes were far better under both the conditions than those favored by percentage increase/decrease method. The study of genetic components for seedling-traits revealed higher values for most traits in drought-stressed than nonstressed conditions, implying that choice of criterion is vital for pyramiding drought-tolerance in maize. Components of genetic variability and association stud-therefore, requires genetic improvement of crop plants without any cost in yield potential. Plants copes the dry soils by employing different mechanisms ranging avoidance to tolerance. One way of managing adverse effects of drought is the development of deep-rooted genotypes by altering the carbon distribution models (Lopes & Reynolds, 2011). Longer roots displayed clear benefit in soils with deep water availability (Sponchiado et al., 1989). Previously, research efforts remain focused more on improving shoot traits linked with photosynthesis and stay-green characteristics than on the root traits (Lopes et al., 2011).
Drought affected maize plant right from seedling to grain filling stages (Haq et al., 2015). At seedling stage, it reduced root and shoot growth in maize (Thomas & Howarth, 2000). It increased root length and root weight (Rao & Singh, 2004) while decreased shoot length and its fresh weight (Thakur & Rai, 1984), and root and shoot dry weights in maize (Matsuura et al., 1996;Ali et al., 2011). Drought tolerant cultivars had higher fresh and dry shoot weights in comparison to susceptible ones (Ashraf, 1989). Water-stress, not only dwindled the maize plant height but also decreased ear leaf area causing reduction in ear length and grain yield (El-Hifny et al., 2003;Ross et al., 2006;Moosavi, 2012). Decrease in grains per ear row and 100-grain weight was also noticed under drought conditions (Saeed et al., 1997;Khayatnezhad et al., 2011). The (2011). Positive d indicated increase while negative l suggested decrease in plant biomass, implying that the model is complex and further progeny testing is required for the improvement of this trait. Involvement of duplicate gene action in the inheritance of GYPP under the non-stressed environments offered a complex situation and suggested delaying the plant selections to later generations. These findings are similar to the one reported by Afarinesh et al. (2005) and Kanagarasu et al. (2010). Iqbal et al. (2015) suggested usefulness of crossing among the desirable segregants in the segregating populations for those traits where early selection cannot be exercised. Dissection of total variance into D (additive), H (dominance), E (environmental), and F (interaction) components had been used previously for genetic studies (Haq et al., 2015;Iqbal et al., 2015). Contribution of additive (D) variance in contrast to other components was much higher in all the investigated traits. However, the interaction (F) variance for traits such as PH, ELA, GRPE, GPER, and HGW under drought-stressed conditions complicated their inheritance pattern. Larger and significant estimates of additive (D) variance for CL, CG, BPP and GYPP under drought and PH, ELA, CL, CG, GRPE, HGW and GYPP under the non-stressed environments indicated involvement of positive and negative alleles from the two parents in the developed genetic material (Rahman & Malik, 2008;Khan et al., 2014). Higher estimates of narrowsense heritability under both non-stressed and droughtstressed regimes are encouraging for maize breeders implying that plant selections for drought-tolerant recombinants could be conducted in the segregating progeny of this particular crossed material.
We may conclude that root traits like length, fresh and dry weights can be vital for effective screening of maize genotypes at seedling-stage drought-stress. Further, hybridization and adoption of recombinant breeding strategy could be the way forward for developing drought-tolerant genotypes.
ies suggested that traits such as FRL, FRW and DRW could be considered for developing drought-tolerant maize genotypes while for non-stressed conditions, traits like FSL, FRL, FRW and DRW might be considered.
Dissection of genetic variation into different components using biometric methods is important for a plant breeder to exploit the potential genetic resources through plant selections and hybridization schemes. The procedure of generation mean and variance analyses had extensively been used for drought-tolerance studies in cotton (Khan et al., 2014), wheat (Munir et al., 2007) and maize (Ahsan et al., 2013). Generation mean analysis for PH revealed involvement of dominance genetic effects in its inheritance under both the environments. Yadav et al. (2003) also reported such gene action for PH. However, positive [i] complicated the situation, therefore, requires further progeny testing under both conditions. Significance of only residual [m] effects for CL and GRPE under both environments while for ELA, BPP and HGW in drought-stressed conditions and for GPER in non-stressed environments suggested the potential role of environment in the inheritance of these traits. These findings are in agreement to the observations of Bernardo et al. (1992), Blum et al. (2001), Aslam et al. (2006), Jabeen et al. (2008) and Taheri et al. (2011). Additive-dominance along with epistatic (additive-additive) interaction effects were recorded for ELA under non-stressed environments. Iqbal et al. (2012) suggested postponement of plant selections till the later generations for plant traits with such type of gene action. For CG and HGW, epistatic additive-additive interaction was predominant under the non-stressed conditions in comparison to additive genetic effects under the drought-stressed environments. Similar results were reported by Chen et al. (1996), Singh et al. (2000), Tripathy et al. (2000), Malik et al. (2004) and Aslam et al. (2006). Positive values of genetic effects and epistatic interactions indicate the possibility to fix cob girth and 100-grain weight in the later generations. Dominance and epistatic [ijl] gene action for GPER under drought-stressed conditions suggested postponement of plant selections to later generations. Tabassum et al. (2007) and Jabeen et al. (2008) also made similar suggestions. Negative [dhi] values for grain per ear row under droughtstressed conditions revealed that conducting plant selections might be ineffective for this trait. Positive l suggests that dominance-dominance interaction is responsible for the increase in grains per ear row under drought-stressed conditions. For BPP, additive gene action with dominance-dominance interaction was found crucial under the non-stressed conditions. These results are in agreement to the observations of Taheri
Table 1 .
Various seedling and morphological plant traits of maize recorded under non-stressed and drought-stressed conditions.
Table 2 .
Mean performance and statistical significance for various seedling-traits in 40 maize inbred lines under non-stressed and drought-stressed conditions.
Table 3 .
Genetic parameters for various maize seedling-traits in 40 inbred lines under non-stressed and drought-stressed conditions.
Table 4 .
Genotypic and phenotypic correlation coefficients among various maize seedling-traits under non-stressed conditions.
Table 5 .
Genotypic and phenotypic correlation coefficients among various maize-seedling traits under drought-stressed conditions.
Table 6 .
Generation means for various morphological traits of maize under non-stressed and drought-stressed conditions.
Table 8 .
Genetic variance components for various morphological traits of maize under non-stressed and drought-stressed conditions.
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Domain: Biology Agricultural and Food Sciences
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Animal Husbandry, The red meat value chain in Tanzania
The red meat value chain in Tanzania includes live animals, fresh meat, processed meat products and by-products from cattle, sheep and goats. Participants in the chain include primary producers, traders in animals, in meat and in by-products, processors, butchers, other retail outlets and consumers. The chain from supply and use of inputs, via production and processing to marketing and retailing is confounded by many technical and institutional impediments. The chain is fragmented, unorganized, uncontrolled (in spite of being over-regulated) and uncoordinated. Many participants have more than one role. At various stages, goods and services include land, labour, live animals, veterinary supplies, feed supplies, transport, energy, finance and (perhaps above all and what is most lacking) institutional support. Clearly defined and enunciated standards and a regulatory framework under law are needed. Many of these requirements continue to be weak, non-existent or are not applied in Tanzania.
Introduction
The value chain describes the range of activities required to move a commodity through the various stages that bring if from the first point of production to the last point of consumption. This usually involves (an often complex) combination of physical change, inputs from various producer services, transfer of ownership and delivery [1]. Commodity value chains are increasingly recognized as providing a solid framework for the analysis of the public and private sector stakeholders players within them as well as the overall performance of particular markets.
Ruminant livestock have long been a mainstay of Tanzania's economy and one of the key livelihoods of its people. In the 50+ years between Independence in 1961 and the year 2012 the human population quadrupled, the cattle population increased 9-fold, the goat population 3-fold and the sheep population by a factor of 1.4. The Tanzania red meat value chain from supply and use of inputs, via production and processing to marketing and retailing is confounded by many technical and institutional impediments. The chain is fragmented, unorganized, uncontrolled (in spite of being over-regulated) and uncoordinated [2]. It is dominated by large numbers of small holder stock owners [3], an unknown but undoubtedly immense number of middlemen who operate across every link and a similarly unknown number of small processors and butchers who put products on the market for the consumer but who mainly lack the technical and financial ability to run it efficiently and profitably. The horizontal and vertical linkages of the value chain are generally weak and uncompetitive and in need of support to strengthen them [4].
In Tanzania the "red meat" value chain includes live animals, meat, processed meat products and by-products from cattle, sheep and goats that are sold both locally and in the export market. Primary processed meat and meat products are derived after animals are slaughtered and include carcasses, red offals (liver, lungs, tail, heart and kidneys), hides, skins and other by-products such as blood, bones, horns, hooves, hair, wool, glands, intestines, stomachs and gut contents.
Participants in the value chain include primary producers, traders in animals, in meat and in by-products, processors, butchers, other retail outlets and consumers. Most actors are not specialized, and their functions relate to various segments of the value chain. Many primary producers, for example, engage in trading of animals and some upstream actors, such as butchers, trade in animals and meat and undertake primary processing for production of higher value cuts, minced meat and sausages.
Materials and methods
This study derives from a period of study in Tanzania. A thorough review of the literature was first undertaken. Field visits were made to all the areas in the country, except the western provinces, where livestock are reared. Discussions were held with individual participants operating throughout the chain, with focus groups and with technical and administrative personnel in both public and private sectors. Analysis and report production were then carried out according to standard methods [5,6].
The value chain map
The value chain map ( Figure 1) shows that the whole is suspended from the consumer. If the link to the rest of the chain were broken the whole would be susceptible to collapse. This situation is similar for all other links in the chain. Each link takes the product from its immediate predecessor and "processes" it to an output that is used by the next link ( Figure 2). Nominally, the value of product increases at each stage until it reaches the consumer.
It is possible to provide a succinct list of most of the participants in the chain (Table 1) but pivotal roles are played by the middle links of the chain through which all products must pass. Many participants ( but especially those of slightly larger scale also act as processors and retailers. Further up the chain some processors are also wholesalers and retailers and operate in both the domestic and export markets. Primary producers may sell cattle, goats or sheep directly through a market, to a trader or to a processor or may use a combination of all three outlets. A trader can sell to another trader, directly to a wholesale or retail butcher or to a processor or, again, may broaden his option by using a combination of these channels. Processors, especially the smaller enterprises, may buy animals directly from farmers or from traders and sell the products to wholesalers or retailers. Every link in the chain relies on goods and services in order to enable it to fulfil its role(s). At the various stages, goods and services include land, labour, live animals, veterinary supplies, feed supplies, transport, energy, finance and (perhaps above all and what is most lacking) institutional support. Also required are clearly defined and enunciated standards and a regulatory framework under -and applied by -law. Many of these requirements continue to be weak or nonexistent in Tanzania.
Technology generation
Technology in livestock production includes inputs such as feed or veterinary medicine at the producer level, machinery use in slaughtering and processing and proper and hygienic presentation of products at the retail level [2]. Technology has a key role in improving competitiveness and especially vis-à-vis near neighbours operating in and competing for the same environment.
Red meat production in Tanzania is based on traditional systems that use very little modern technology. Indigenous cattle (Tanganyika Shorthorn Zebu,), sheep (undifferentiated African long-fat-tailed types although the Red Masai is recognized in the north of the country) and goats (Small East African) ( Figure 3) that are considered of limited potential for production dominate the herds and flocks [7]. Animals Traders Primary buyers, primary brokers and secondary buyer-agents operate throughout the region. Trading takes place at the point of production and at primary and secondary markets. Some long-distance trade towards the Dar es Salaam market by road transport is undertaken but most is more local Slaughterers Most slaughtering of goats and sheep is "informal" and done at the point of production. Cattle are slaughtered at rural slabs, usually small and out of date slaughterhouses at many of the larger villages and towns and at some larger municipal facilities.
Processors
Small primary processing to 'nyama kawaida' cuts is carried out on a variety of scales. Offals are processed by small scale processors who deal in both red (edible) and green (inedible) varieties usually in proximity to the point of slaughter. A private company, Sumbawanga Agricultural and Food Industries Limited (SAAFI) has the capacity to slaughter and process 150 cattle per day for the export and high-end domestic market.
Retailers
Retailing is done by usually small-scale street vendors, often unsalubrious one-man butchery operations and by rather more hygienic urban butchers. derive their feed almost in its entirety from the natural rangeland and some crop residues which are usually in low supply and for much of the year have minimal nutritional value. Most herds receive little in the way of animal health treatments such as vaccination (only 29 per cent of cattle are vaccinated regularly), protection from ticks (and the diseases they carry) or control of internal helminth parasites [8]. As a consequence, if the animal does not succumb to its miserable life style (death rates are very high in calves and may reach 70 per cent of those infected by East Coast Fever (ECF) which can be reduced to less than 30 per cent with regular dipping), reproductive rates in cattle are only about 50 per cent (a cow calves first at 4 years of age and then produces a calf only every 2 years) and overall growth rates are low (and characterized by the gain-loss-gain annual cycle). Thus, overall output is greatly reduced (annual offtake for slaughter may reach 12 per cent but is more likely to be 10 per cent) and if an animal survives to the slaughter stage (at a minimum of 4 years and often at 6-8 years) the resultant product (meat) is of very poor quality.
As can be inferred from the preceding paragraph many technological interventions are available. For the most part, however, they are not used by producers and probably not even communicated to them by technical staff. Some are, indeed, somewhat sophisticated or too expensive for use at the present state of development of the regional herd. A vaccine against ECF, for example, has recently been put on the market but is too costly for general use [9]: on the other hand, frequent and regular dipping or hand spraying (acaricides are subsidized by the public sector) would greatly reduce the incidence of tick-borne diseases, not only of ECF but heart water, anaplasmosis and babesiosis. The more widespread use of artificial insemination (AI) is often advocated as a means of improving the genetic make-up of indigenous stock but in the prevailing Tanzania conditions this technique can have only limited application and is fraught with such problems as supply of liquid nitrogen and actually getting to the cow while she is receptive to insemination [10]. Urea-or ammonia-treatment of fibrous feeds to improve their nutritional quality is a cheap, simple and very effective technique for accelerating weight gain but has little application in the country [11]. Low adoption of available technologies is caused by poor extension services, difficulties in gaining access to the technologies (cost/ location) and the low level of knowledge among most livestock keepers.
Adoption of known improved but not over ambitious management and technological practices can, however, bring about spectacular increases in the output and quality of livestock products (Table 3). Amongst such are: • strict implementation of the tick control regime recommended by the veterinary authority; • vaccination against epidemic and endemic diseases, both "trade" and "production"; • matching the stocking rate to the carrying capacity and providing preferential access of target groups (pregnant animals and young stock) to set aside dry season pasture reserves and conserved fodders; • regular (daily at least) access to water by livestock; • use of mineral and vitamin supplements to target groups including breeding males; • castration and early removal of inferior males and those unfit for service; • sale of barren and unproductive females and of over age draught animals; and.
• sale of slaughter cattle when they are in good condition early in the dry season and try to avoid "emergency" sales for immediate cash needs.
Conclusion
A plethora of reports, workshops, projects and programmes have masqueraded as -or been a proxy for -development of the livestock red meat industries. The simple fact is, however, that the ordinary people of Tanzania still do not have enough meat to eat and even were there to be enough they would not be able to afford to buy it [12]. Failure to overcome the lack of use of available, effective, cheap and simple technology will inevitably result in even further loss of competitiveness as the peers of Tanzania's livestock producers and processors in neighbouring countries, especially Kenya, are making widespread use of it [13].
Acknowledgement
Michael Winklmaier, Chief Technical Adviser of the Southern Highlands Food Systems, is thanked for his encouragement and support during the course of the original study. Peter Jimbuku, driver Source: Author's compilation Table 3. Potential improvements in red meat production with adoption of simple technology.
extraordinary and general factotum, is thanked for his assistance during the course of field work. Numerous participants in all the links of the chain were extremely helpful in providing information.
Funding details
This paper is an extract from a red meat study which was one of a series of commodity studies carried out by the Tanzania Southern Highlands Food Systems Programme. These studies were financed by the Food and Agriculture Organization of the United Nations.
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Domain: Business Economics Biology Agricultural and Food Sciences
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Performance of Two Citrus Species Grafted to Different Rootstocks in the Presence of Huanglongbing Disease in Puerto Rico
Since Huanglongbing (HLB) disease was detected in 2009 in Puerto Rico, a steady drop in citrus production has been experienced, forcing farmers to abandon their land or switch to other crops. Between 2015 and 2016, we used grafted trees from two experimental orchards (Tahiti lime and Nova mandarin), each on five rootstocks, to collect soil and plant tissue samples from each scion-rootstock combination to determine soil fertility, tissue nutrient content, and yield. The tree growth parameters (height, diameter, and canopy volume) and efficiency of the two orchards were also measured. These orchards, growing in Coto series (Typic Hapludox), were planted in 2009 and reported as heavily infested with HLB by 2011. Our results showed that soil and tissue samples from the Tahiti lime orchard exhibited benefits for tree growth parameters when grafted on Carrizo and Cleopatra rootstocks. Lower tree mortality (13%) was observed for Tahiti lime grafted on Carrizo, HRS 812, Carrizo and Rough lemon rootstocks, while 25% of the Nova mandarin trees perished on the same rootstocks. Yield was higher for Tahiti lime grafted on Swingle rootstock (35.6 fruit m−3) as compared to the other rootstocks. In general, HLB appears to have caused poor development and low production in the Nova mandarin orchard.
Introduction
Success in citrus production depends on the selection of suitable high-quality scion-rootstock combinations, which at the same time are adaptable to a wide range of environmental conditions and tolerant to various pests and diseases. No condition is more serious in citrus than Huanglongbing (HLB) disease, a lethal vector-transmitted disease. The causal agent of the disease is the bacterium Candidatus Liberibacter asiaticus (CLas), vectored by the Asian citrus psyllid (ACP), Diaphorina citri Kuwayama [1]. Citrus trees infected with HLB present asymmetric foliar chlorosis, acidic fruits, a shortened life cycle, branch dieback, and eventually tree death [2,3].
In Puerto Rico, HLB-ACP was first detected in 2001 in the Agricultural Experiment Substation (AES) of Isabela; the presence of the bacteria CLas was first identified there in 2009 [2,4], in commercial orchards of sweet orange [Citrus sinensis (L.) Osbeck] and lemon (Citrus latifolia), and in lemon orchards at AES of Juana Díaz [5][6][7]. From 2012 to 2015, the island experienced a 39% reduction (i.e., 2556 tons to 1557 tons) in citrus production [8]. In 2012 the citrus industry was ranked second among fruit commodities in PR, with over 7000 ha planted on 2800 farms (~700 producers), most of them located in the mountainous region of the island. Between 2013 and 2014, the citrus industry ranked third, with a net value of $6 million as reported by the Department of Agriculture of Puerto Rico (DAPR) [9,10]. Over 68 million fruits were produced during that year. However, since HLB and CLas were identified, the disease has spread throughout the entire island and the citrus industry has experienced a steady drop in production, forcing farmers to abandon their land and/or switch to alternate crops, such as coffee or plantains, usually facing high economic losses [11]. In addition, this decline in citrus production appears to be more severe in orange and mandarin orchards than in lime or lemon orchards [11].
The HLB problem may be compounded by the higher susceptibility to certain fungal (i.e., Phythophtora spp.) diseases that have been found in varietals grafted to Cleopatra mandarin rootstock (Citrus reshni Hort. Ex Tan) [12], which until recently (up to 2013) was the predominant rootstock used by farmers in PR [13]. After 2013, three citrus rootstocks [i.e., Swingle citrumelo, Carrizo citrange, and HRS 812 (also known as US 812)] were highly recommended to farmers by Román-Pérez et al. [14] after being evaluated for several years at different locations and with diverse scion-rootstock combinations. These three rootstocks had shown great potential in mainland USA, although little was known of their potential for commercial production in PR. A study driven by Román-Pérez and González-Vélez [13] found that the three rootstocks had similar horticultural responses to scions on Cleopatra rootstock. However, through the years, Cleopatra showed more susceptibility to P. citrophthora with increasing tree mortality, and Swingle citrumelo and Carrizo rootstocks have shown some resistance to Citrus Tristeza virus (CTV) and the scion-HRS 812 rootstock exhibited 100% survival with CTV [13].
Bowman and Rouse [15] found that HRS 812 rootstock was highly productive in Florida with high-quality fruits, and exhibited tolerance to CTV. Albrecht and Bowman [16] found in a laboratory trial that Carrizo and HRS-897 (also known as US 897) were tolerant to HLB, while HRS 812 was considered moderately tolerant to HLB compared to Cleopatra mandarin. The transmission of the HLB causal agent CLas was limited to the plant phloem, and was attributed primarily to ACP and, secondarily, to human-mediated transmission by grafting [17].
Spann et al. [18] attributed nutrient deficiencies to HLB, where infected plants had significantly lower values of the macronutrients calcium (Ca), phosphorus (P), and sulfur (S), and the micronutrients manganese (Mn), iron (Fe), and copper (Cu). Studies by Gottwald et al. [19] indicated no effective response to insecticide application to reduce ACP population, nor to an enhanced nutritional program on two trials conducted with Valencia orange in Florida. In Brazil, even though infected trees were removed and intensive ACP management practices were performed, the HLB disease has spread exponentially, causing significant yield losses [20]. Improved plant nutrition may help to slow down HLB disease progression in a tree, but will not cure them of HLB.
Scientifically-based fertilization data for overcoming damage caused by HLB is scarce. Accurate HLB detection requires DNA tests, since visual symptoms are similar to micronutrient deficiencies and other citrus diseases such as citrus variegated chlorosis, citrus cankers, etc. In terms of nutrition, most of the recommendations provided by local Agricultural Extension Services and the Puerto Rico Department of Agriculture are based on data and recommendations gathered from citrus growers from the mainland and Florida-USA Cooperative Extension Service publications [11]. However, significant differences exist in soil type, topography, and climate between Florida and Puerto Rico, and so the inherent limitations of such recommendations are a serious concern. For that reason, our objective was to evaluate the role of rootstocks on the performance of two HLB-infected mature citrus orchards established at AES in Isabela in overcoming the effect of this disease. Both experimental orchards were established as a Randomized Complete Block Design (RCBD) with four replicates. The trees were set at a distance of 4.5 by 5.9 m and each experimental plot had two trees. A supplementary drip irrigation system was installed and used as needed. Both experimental orchards were six years old at the time of our observations, had similar management practices, and showed HLB symptoms by 2011. Presence of HLB was confirmed in 2013 using a polymerase chain reaction (PCR) amplification of bands of 1160 bp corresponding CLas bacteria [6,22]. CLas titers were not determined. Orchards received 1.8 kg per tree of slow-release fertilizer (15-3-19-3) with micronutrients twice a year. Since the establishment of the two citrus experimental orchards in 2009 up to 2015, the trees were limed once a year [13]. From 2009 to 2010, the citrus trees were fertilized and managed as suggested in the "Conjunto Tecnológico para la producción de Cítricos en Puerto Rico" [23]. To overcome the effects of HLB on tree phloem, the fertilization program was modified since 2011-2016, with slow release fertilizers and supplementary foliar nutritional cocktail, both with micronutrients as recommended it by Rouse [24] in the state of Florida, USA. The supplementary foliar nutritional cocktail with micronutrients consisted of slow release nitrogen (30-0-0), Phosphite ® (0-29-26), Recover Rx ® (3-18-18) and a biological fungicide (Companion ® ) were applied in monthly basis. In addition, systemic insecticide Admire Pro ® (active ingredient Imidacloprid) was applied bimonthly from April to December 2015 and April-May 2016 to control ACP. Higher dosages (14 oz/75 gals of water) of Imidacloprid were used during peak ACP periods (April and June) and lower dosages (7 oz/75 gals of water) from August to December.
Tree variables (i.e., height, diameter, and canopy volume) and fruit yield were measured to determine citrus crop performance. Tree variables in both orchards were measured in May 2016. Tree height and diameter were measured using a telescoping-measuring pole. Tree canopy volume (TCV) was calculating using the Fallahi and Mousavi [29] equation: 0.524 × tree height (m) × tree diameter (m 2 ). Tree yield efficiency was calculated using the total average fruit number divided by TCV. Fruit production in the Tahiti lime orchard was measured seven times between April 2015 and May 2016. The Nova mandarin orchard was measured once in February 2016. Fruit production (fruit number and size) was considered for this manuscript as the total average value.
Statistical Analysis
Analysis of variance (ANOVA) followed by mean separation using Tukey's Honest Significant Difference test at p < 0.10 for the RCBD design was used to determine soil and tissue fertility and tree variables and efficiency of the different scion-rootstock combinations. Statistical analysis was undertaken using JMP Version 10 (SAS Institute, Cary, NC, USA).
Soil Chemical Properties
In the Tahiti lime orchard, the soil concentrations of Mg, Na, S, and NO 3 -N for the different scion-rootstock combinations were statistically different. Soil collected from where Carrizo rootstocks grew had higher Mg and Na concentrations than that collected around HRS 812 (Table 1). However, higher concentrations of S and NO 3 -N were found under HRS 812 and Cleopatra than the other rootstocks. In the Nova mandarin orchard, only OM and Na concentrations in the soil were statistically different. Higher OM content was found in soil collected from where Carrizo (5.05%) rootstocks were grown compared with the other (~4.58%)rootstocks. In addition, the soil collected from where HRS 896 rootstocks were grown had a higher Na (14.8 mg kg −1 ) concentration than Carrizo (10 mg kg −1 ) rootstock soil. However, no statistical differences were found for the other studied variables (Table 1).
Tissue Nutrient and Trace Element Concentrations
In Tahiti lime tissue samples, K, Mg, N, and Mn concentrations were affected when grafted to five different rootstocks (Table 2). Higher concentrations of K were found in Tahiti lime grafted to Rough lemon and Swingle compared to the other three rootstocks (Table 2). For Mg concentrations, higher significant differences were found between Tahiti lime scions grafted to Cleopatra (0.383 mg kg −1 ) rootstock than those grafted to Swingle (0.275 mg kg −1 ) rootstock. However, for N concentrations, a higher significant difference was found between Tahiti lime scions grafted to Swingle (2.88%) compared to Cleopatra (2.48%). Higher Mn concentrations were found for Tahiti lime scions grafted to Cleopatra (101 mg kg −1 ) rootstock versus those grafted to Carrizo (48.3 mg kg −1 ) (Table 2). No statistical differences were found in Tahiti lime tissue samples for Ca, Na, P, and S concentrations grafted to the five different rootstocks (Table 2).
In Nova mandarin orchards, only Ca, Mg, P, N, and Mn concentrations appeared to be affected when grafted to different rootstocks (Table 2). Higher Ca concentrations were found in Nova mandarin grafted to Carrizo and HRS 812 as compared with the other three rootstocks (Table 2). Higher N were found in Nova mandarin grafted to HRS 812 and HRS 896 (~2.6%) versus the other rootstocks (~2.3%). A lower concentration of P was observed for Rough lemon (0.105 mg kg −1 ) versus the other four rootstocks (~0.125 mg kg −1 ). However, trees grafted to Rough lemon had higher concentrations of Mg and Mn than the other rootstocks (Table 2). No statistical differences were found for the other elements in Nova mandarin grafted to the five different rootstocks (Table 2).
Discussion
The ideal soil pH for citrus trees ranges from slightly acidic soil (6) to alkaline (8). Soil acidity (pH less than 6), as found in Coto soils in Isabela, is well known as a major factor resulting in low crop yields due to Al and Mn toxicity, low concentrations of Ca and Mg, and decreasing availability of other nutrients like P [30]. Junior et al. [30] emphasized most of the response of citrus to liming agents was due to a high demand for Ca, used to regulate many processes related to both growth and responses to environmental stresses [31]. Also, liming with dolomitic limestone can satisfy the Mg demand.
Based on our results (Table 2 and data not shown (i.e., for Al, Bo, Cu, Fe, and Zn elements)), all the nutrients and elements were below the adequate ranges established by Mills and Jones [32] for lime. Although, for Nova mandarin, lower N (i.e., expected-3.0-3.5% versus the present study at 2.3-2.6%) and P (i.e., expected-0.15-0.25 versus this study <0.15) values observed were suggested by Mills and Jones [32] for mandarins.
When compared with previous results reported by Román-Pérez et al. [22] for the same orchards, the Tahiti lime trees yielded twice the number of fruit (797 v. 424), while mean fruit weight was less than half what they observed (20.4 g v. 42.7 g). For Nova mandarin orchard, quite low fruit production (~11 fruit/tree) was obtained due to HLB, most of them dried with thick skin. It is well established that HLB affects fruit production, resulting in smaller, evergreen, and dried fruit with an undesirable shape [33]. HLB can also cause severe tree deterioration until it dies [34]. Tree mortality by April 2016 was 13% for Tahiti lime grafted to HRS 812, Carrizo, and Rough lemon rootstocks, and 25% for Nova mandarin tree on Rough lemon and Carrizo rootstocks. Unfortunately, final CLas titers were not determined, as the values could have been a very useful parameter for further explaining rootstock performance.
Although the HLB caused some Tahiti lime tree mortality, Tahiti lime grafted to HRS 812 exhibited greater tree growth (height and diameter), developed greater canopy volume, and had higher tree efficiency as compared with trees on Cleopatra. However, studies driven by Piña et al. [35] found the opposite response of Tahiti lime growing in Fluventic Haplustolls soils in Venezuela and grafted to 11 different rootstocks. Also, even though our study had 13% tree mortality for Tahiti lime trees after 2013, compared with data collected in 2012-2013 by Román-Pérez et al. [33], there was a 3-fold increase in tree efficiency of Tahiti limes grafted to Carrizo, Cleopatra, HRS 812, and Rough lemon rootstocks in our study. In both studies, similar tree efficiency was found for limes on Swingle rootstock (~16.9 fruits m −3 ). Meanwhile, in the Nova mandarin orchard, tree efficiency was less than 1.
Discussion
The ideal soil pH for citrus trees ranges from slightly acidic soil (6) to alkaline (8). Soil acidity (pH less than 6), as found in Coto soils in Isabela, is well known as a major factor resulting in low crop yields due to Al and Mn toxicity, low concentrations of Ca and Mg, and decreasing availability of other nutrients like P [30]. Junior et al. [30] emphasized most of the response of citrus to liming agents was due to a high demand for Ca, used to regulate many processes related to both growth and responses to environmental stresses [31]. Also, liming with dolomitic limestone can satisfy the Mg demand.
Based on our results (Table 2 and data not shown (i.e., for Al, Bo, Cu, Fe, and Zn elements)), all the nutrients and elements were below the adequate ranges established by Mills and Jones [32] for lime. Although, for Nova mandarin, lower N (i.e., expected-3.0-3.5% versus the present study at 2.3-2.6%) and P (i.e., expected-0.15-0.25 versus this study <0.15) values observed were suggested by Mills and Jones [32] for mandarins.
When compared with previous results reported by Román-Pérez et al. [22] for the same orchards, the Tahiti lime trees yielded twice the number of fruit (797 v. 424), while mean fruit weight was less than half what they observed (20.4 g v. 42.7 g). For Nova mandarin orchard, quite low fruit production (~11 fruit/tree) was obtained due to HLB, most of them dried with thick skin. It is well established that HLB affects fruit production, resulting in smaller, evergreen, and dried fruit with an undesirable shape [33]. HLB can also cause severe tree deterioration until it dies [34]. Tree mortality by April 2016 was 13% for Tahiti lime grafted to HRS 812, Carrizo, and Rough lemon rootstocks, and 25% for Nova mandarin tree on Rough lemon and Carrizo rootstocks. Unfortunately, final CLas titers were not determined, as the values could have been a very useful parameter for further explaining rootstock performance.
Although the HLB caused some Tahiti lime tree mortality, Tahiti lime grafted to HRS 812 exhibited greater tree growth (height and diameter), developed greater canopy volume, and had higher tree efficiency as compared with trees on Cleopatra. However, studies driven by Piña et al. [35] found the opposite response of Tahiti lime growing in Fluventic Haplustolls soils in Venezuela and grafted to 11 different rootstocks. Also, even though our study had 13% tree mortality for Tahiti lime trees after 2013, compared with data collected in 2012-2013 by Román-Pérez et al. [33], there was a 3-fold increase in tree efficiency of Tahiti limes grafted to Carrizo, Cleopatra, HRS 812, and Rough lemon rootstocks in our study. In both studies, similar tree efficiency was found for limes on Swingle rootstock (extasciitilde 16.9 fruits m −3 ). Meanwhile, in the Nova mandarin orchard, tree efficiency was less than 1.
Conclusions
Even though common management strategies recommended to reduce the effect of HLB are used, we have observed different tree responses based on different scion-rootstock combinations. The Tahiti lime growing in Coto soils in the northwest of Puerto Rico had benefits for tree growth parameters when grafted on Carrizo and Cleopatra rootstocks. However, tree efficiency for Tahiti lime grafted to Swingle was superior to that of other rootstocks. Based on our Tahiti lime tissue results, our fertilization program covered the orchard needs and supplied most of the nutrient needs for the Nova mandarin orchard. Given the prevalence of HLB at Isabela, and our observations of higher mortality, poor development, and low yield, we do not recommend future use of Nova mandarin there.
Figure 1 .
Figure 1. Tree efficiency (fruits m −3 ) of (A) Tahiti lime and (B) Nova mandarin grafted to five different rootstocks planted at the Agricultural Experiment Substation of Isabela, Puerto Rico from April 2015-May 2016. Vertical lines represent the standard errors and lower case letters are different if there were statistical differences among the rootstocks by Tukey's test (p < 0.10) for each citrus species.
Figure 1 .
Figure 1. Tree efficiency (fruits m −3 ) of (A) Tahiti lime and (B) Nova mandarin grafted to five different rootstocks planted at the Agricultural Experiment Substation of Isabela, Puerto Rico from April 2015-May 2016. Vertical lines represent the standard errors and lower case letters are different if there were statistical differences among the rootstocks by Tukey's test (p < 0.10) for each citrus species.
Table 1 .
Soil nutrients in two citrus orchards grown in Coto series grafted in five different rootstocks in the Agricultural Experiment Substation of Isabela, Puerto Rico. - y Means followed by the same letter in a column for each soil are not significantly different by Tukey's test at p < 0.10.z OM = organic matter, Ca = exchangeable calcium, K = exchangeable potassium, Mg = exchangeable magnesium, Na = exchangeable sodium, P = available phosphorous, S = total sulphur, and NO 3 -N = nitrate.
Table 2 .
Mean concentrations of plant nutrients in two citrus orchards grown in Coto series grafted to five different rootstocks planted at the Agricultural Experiment Substation of Isabela, Puerto Rico.
Table 3 .
-Tree variables and fruit production of two citrus varieties grafted to five different rootstocks planted at the Agricultural Experiment Substation of Isabela, Puerto Rico from April 2015-May 2016.
y Means followed by the same letter in a column for each variable are not significantly different by Tukey's test at p < 0.10.
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Domain: Environmental Science Biology Agricultural and Food Sciences
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Bioherbicides in Organic Horticulture
Organic horticulture producers rank weeds as one of their most troublesome, time-consuming, and costly production problems. With the increasing significance of organic horticulture, the need for new bioherbicides to control weeds has grown. Potential bioherbicides may be developed from pathogens, natural products, and extracts of natural materials. Fungal and bacteria pathogens are two important types of microbial agents that have potential to be used as bioherbicides. The byproducts of natural sources such as dried distillers grains with solubles (DDGS), corn gluten meal (CGM), and mustard seed meals (MSMs) have shown herbicidal activities in controlling many weed species. Some essential oil extracts have shown bioherbicide potential as well. The efficacy of a bioherbicide is the main limiting factor for its application, and it may be affected by environmental factors such as humidity and moisture, the application method, the spectrum of the bioherbicide, and the type of formulation. In addition to efficacy, costs and concerns about potential human health threats are also limitations to bioherbicide use. As the integration of bioherbicide technology into current weed management systems may help manage herbicide resistance, reduce production costs, and increase crop yields, future research should involve the development of more cost-effective and efficient bioherbicides for control of weeds, as well as the optimization of production methods and cultural practices with use of candidate bioherbicides. 1. The Problem of Weeds in Organic Horticulture Weeds are the most costly category of agricultural pests, causing great yield loss and labor expense [1]. Agricultural weeds can emerge rapidly, resulting in reduction of crop plant growth and quality by competing for nutrients and water provided to crops and producing chemicals that suppress crop growth. Annual weeds reproduce through prolific seed production, and they germinate in responses to light, increased fluctuations in soil temperature and moisture, improved aeration, and accelerated nutrient release, while perennial weeds regenerate new plants from small fragments of roots, rhizomes, stolons, and other underground structures [1]. Severe weed problems present a serious threat to horticultural crop production with favorable environmental conditions, a susceptible crop, or a large weed seed bank in the soil. Current weed control in horticultural production includes conventional herbicides (pre-emergent and post-emergent), organic herbicides, physical methods (hand-weeding and mulches), and bioherbicides. Pre-emergence herbicides are effective before and during weed seed germination. When germinating seeds are in contact with the herbicide, the growth of emerging roots and/or shoots is inhibited, but pre-emergent herbicides may not …
The Problem of Weeds in Organic Horticulture
Weeds are the most costly category of agricultural pests, causing great yield loss and labor expense [1]. Agricultural weeds can emerge rapidly, resulting in reduction of crop plant growth and quality by competing for nutrients and water provided to crops and producing chemicals that suppress crop growth. Annual weeds reproduce through prolific seed production, and they germinate in responses to light, increased fluctuations in soil temperature and moisture, improved aeration, and accelerated nutrient release, while perennial weeds regenerate new plants from small fragments of roots, rhizomes, stolons, and other underground structures [1]. Severe weed problems present a serious threat to horticultural crop production with favorable environmental conditions, a susceptible crop, or a large weed seed bank in the soil.
Current weed control in horticultural production includes conventional herbicides (pre-emergent and post-emergent), organic herbicides, physical methods (hand-weeding and mulches), and bioherbicides. Pre-emergence herbicides are effective before and during weed seed germination. When germinating seeds are in contact with the herbicide, the growth of emerging roots and/or shoots is inhibited, but pre-emergent herbicides may not be effective without good contact with germinating weed seeds [2]. Post-emergent herbicides are effective after weeds have emerged from the soil, ideally at the seedling stage. Organic herbicides need to be applied either prior to crop seedling emergence or transplanting, or post-directed to established crop plantings assuring that the herbicides do not cause injury on the crop plants. Current organic herbicides include ammonium nonanoate, fatty acids, vinegar, clove oil, and D-limonene [3]. Broadcast application of vinegar and clove oil has been studied for potential use in weed management on young, actively growing sweet corn, onion, and potato [4]. Physical methods in weed control include hand-pulling and mulches (including weed discs), which is necessary with some high-value crops but it is labor intensive, time-consuming, and expensive [5]. In addition to the above methods, grazing by domestic goats has also resulted in significant control of many weed species [6]. Combining hand-weeding and spot treatment with post-emergent herbicides after pre-emergent herbicide application has provided complete weed control [5].
Organic horticulture is expanding worldwide, driven by consumer demand, resource conservation, and food security in North American and European markets [7]. Expanding organic production has implied production of nutritionally-improved food crops while using fewer external inputs and reducing environmental impacts [8]. Horticultural crops, especially fruits and vegetables, are critical components of a healthy diet. In some studies, organic foods contained more nutrients and vitamins compared to conventionally-produced ones, and have grown to play an important role in consumer purchases [9]. In 2013, the North American organic food and drink market was valued at 35 billion US dollars, and a healthy market growth rate was predicted [10]. Organic horticultural crops may be more difficult to grow than conventionally-produced crops due to organic production regulations governing the use of materials for control of insect, disease, and weed control challenges. With the high costs of pest and weed control, and the time, and labor in managing the system, organic horticulture relies on price premiums for economic viability, which may make it more profitable than conventional horticulture depending on management strengths and cultural practices.
Weed control in organic horticulture does not have simple or standard solutions. Organic farmers need to take long-term approaches to control weeds without causing yield loss. Successful organic weed control needs to begin with an ecological understanding of weeds and their roles in the farm or garden ecosystem [1]. In organic horticulture, hand-weeding and cultural methods should be integrated to prevent the occurrence of weed-induced yield losses and to keep down costs for weed control. Because organic horticulture excludes the uses of synthetic herbicides due to their potential contamination of crops and natural resources, the use of bioherbicides to control weeds through the use of natural products, extracts, and natural biological agents such as fungi and bacteria to attack weeds is becoming an effective tool [11].
Bioherbicide Approach
Biological controls have been developed for weed management using either living organisms, such as insects, nematodes, bacteria, or fungi, or natural products. Bioherbicides offer a sustainable, low cost, and environmentally-friendly approach to complement conventional methods, which helps meet the need for new weed management strategies. There are two main approaches to biological weed control: classical biological control and bioherbicide approach [12]. The classical biological approach introduces a natural enemy that spreads throughout the area where the target weed occurred [13]. However, this approach has the risk of attacking non-target plants after the introduction of the biocontrol agent in a new area [14]. The classical approach is subjected to strict regulations because of the introduction of potentially harmful pathogens to agricultural production. The bioherbicide approach relies on natural enemies present within the native range of the weed to cause significant damage to the weed and reduce the negative impact on crop yield [13]. The classical approach is based on the innate capacity of natural enemies to reproduce, while the bioherbicide approach is based on reproduction of natural enemies under controlled conditions and subsequent spread by man [13]. The bioherbicide approach is preferred over the classical approach, because it offers diverse possibilities for use in agricultural systems, lawns, and gardens. With the increasing importance of the role of bioherbicides in organic horticulture, the main objective of the following discussion is to review the effectiveness of various bioherbicide approaches.
Bioherbicides from Pathogens
There have been many microbial agents under evaluation for their potential as bioherbicides with horticultural crops, turf, and forest trees, including obligate fungal parasites, soil-borne fungal pathogens, non-phytopathogenic fungi, pathogenic and non-pathogenic bacteria, and nematodes [11]. One of the first bioherbicides registered was DeVine (Encore Technologies, Plymouth, MN, USA) with the active ingredient Phytophthora palmivora, which was developed to control strangler vine (Morrenia odorata) on citrus in Florida [15]. In the subsequent quarter century, several more pathogenic fungi and bacteria have been developed to control weeds [15]. Using plant pathogens as biocontrol agents can cause severe damage to target weed species. In order to become suitable pathogens, they must be mass-produced and their pathogenicity tested on weeds in a range of environmental conditions, followed by field efficacy and host range tests [16]. A variety of phytotoxins produced by plant pathogens can interfere with plant metabolism, ranging from subtle effects on gene expression to plant mortality [17].
Some fungal pathogens are toxic to a wide range of weed species. The early mycoherbicides ("DeVine", "Collego" with the active ingredient Colletotrichum gloeosporioides f. sp.aeschynomene, "Biomal" with the active ingredient Colletotrichum gloeosporioides) had highly virulent fungal plant pathogens that could be mass-cultured to produce large quantities of inoculum for inundative application to the weed host. These fungi infect the aerial portion of weed hosts, resulting in visible disease symptoms [11]. The rust fungus Puccinia canaliculata is a foliar pathogen of yellow nutsedge (Cyperus esculentus), and it can be mass-cultured on the weed host in small field plots or the greenhouse [18]. Applying the fungal pathogen Chonrotereum purpureum to wounded branches or stumps of weedy tree species inhibited re-sprouting and decayed the woody tissues [19]. Weidemann et al. (1992) [20] reported that the fungal pathogen Microsphaeropsis amaranthi controlled certain pigweed (Amaranthus) species, while Phoma proboscis controlled field bindweed (Convolvulus arvensis) and Colletotrichum capsici controlled morning glory (Ipomoea spp.). The naturally occurring fungus Phoma macrostoma has been studied for control of dandelion (Taraxacum officinale), Canada thistle (Cirsium arvense), chickweed (Stellaria media) and scentless chamomile (Matricaria perforata), and its effect is equivalent to the industry standard synthetic herbicide pendimethalin [21]. One group of important bioherbicide candidates, soilborne fungi, significantly reduced weed populations by causing seed decay prior to emergence or killing seedlings shortly after emergence [22]. In a study by [23], Trichoderma virens (Gliocladium virens) colonized composted chicken manure and significantly reduced the emergence and growth of redroot pigweed (Amaranthus retroflexus) and broadleaf weeds in fields of horticulture crops.
Bacteria have also been studied in order to cause diseases in weeds, such as Xanthomonas campestris that is registered to control annual bluegrass [24]. Pathogenic bacteria Xanthomonas campestris pv poannua and P. syringae pv tagetis have been developed as bioherbicides to control annual bluegrass (Poa annua) and Asteraceae weeds, respectively [25]. The phytotoxin produced from a crude extract of Pseudomonas syringae reduced root and shoot growth of weeds in newly-established "Stevens" cranberry bogs [26]. In greenhouse and field tomato studies, applying the fungus Myrothecium verrucaria as a bioherbicide did not affect tomato growth throughout the growing season but killed 90%-95% of purslane species and 85%-95% of spurge species, and the yield was the same as with conventional herbicide application [27]. Spore suspensions of Microsphaeropsis amaranthi and Phomopsis amaranthicola alone, or a mixture of both organisms, were used as potential bioherbicides and significantly reduced the weed biomass of waterhemp (Amaranthus rudis) and pigweed, thereby increasing the yield of pumpkin and soybean [28]. Two fungi isolated from the parasitic weed dodder (Cuscuta spp.), Fusarium tricinctum and Alternaria conjuncta/infectoria, significantly controlled dodder without affecting cranberry growth, and these two fungi have potential to be used as bioherbicides in organic horticulture [29].
Bioherbicides from Natural Products
The byproducts of natural sources have been developed as potential bioherbicides to control weeds. Dried distillers grains with solubles (DDGS) is a byproduct of ethanol production that is commonly used as cattle feed, and is a potential fertilizer supplement in horticultural production systems due to its high nitrogen content [30]. Applying DDGS on the surface of potting mix at 800-1600 g¨m 2 significantly reduced the number of annual bluegrass seedlings by 40%-57%, and common chickweed (Stellaria media) by 33%-58%, respectively [30]. The DDGS applied on the soil surface at 225 g¨m 2 reduced the number of emerging creeping wood sorrel (Oxalis corniculata) seedlings by 25% [31]. A byproduct from corn wet-milling showing herbicidal activity is corn gluten meal (CGM), which has the potential to be used as a natural herbicidal product to control many broadleaf and grass species [32]. The CGM suppressed 22 germinating weed species at rates of 300-1000 g¨m 2 , and it caused reductions in plant survival, shoot length, and root development of black nightshade (Solanum nigrum), common lambsquarters (Chenopodium album), creeping bentgrass (Agrostis palustris), curly dock (Rumex crispus), purslane (Portulaca oleracea) and redroot pigweed when applied on the soil surface in a greenhouse [33]. Mustard seed meal (MSM) (Sinapis alba "IdaGold", a member of the Brassicaceae) is a byproduct of the commercial mustard oil pressing process [34]. The MSM contains glucosinolates (GLS) that can be enzymatically hydrolyzed to isothiocyanates, thiocyanate (SCN ´), nitriles, and other compounds. These biologically active compounds are toxic to many weed species [35,36]. Applying MSM to the soil surface of containers at 113, 225, and 450 g¨m 2 reduced the number of annual bluegrass seedlings by 60%, 86%, and 98%, respectively [31]. With a MSM application rate of 225 g¨m 2 , the number of emerged seedlings and fresh weight of creeping woodsorrel were reduced by 90% and 95%, respectively. Post-emergence application of MSM at these three rates controlled liverwort from 83% to 97% without negative effects on plant growth [31]. However, there is a limitation to MSM use, because its application rate is 10-20-fold higher than typical granular herbicides used in nurseries [31]. Compared to nontreated controls, MSM application decreased emergence rates of kochia (Bassia scoparia), common lambsquarters, and barnyardgrass (Echinochloa spp.) by 83%, 73%, and 66%, respectively [37].
Bioherbicides from natural sources have shown great potential in organic production systems. Handiseni et al. (2012) [38] found that tomato and pepper seedling emergence in Pythium ultimum-infested soils have been improved by canola (B.napus) and mustard greens (B.juncea) seed meals. Brassicaceae seed meals (BSMs) were used to increase soil inorganic nitrogen and the yields of carrot, which had high efficacy in controlling weeds in organic production [39]. In strawberry production, after applying canola-derived BSM and MSM, the weed biomass of shepherd's purse (Capsella bursa-pastoris), Italian ryegrass (Lolium multiflorum), desert rock purslane (Calandrinia ciliata), and annual bluegrass decreased and strawberry fruit yields increased with BSM treatment, which indicated that BSMs may have potential use in organic horticulture systems as combined bioherbicides and green fertilizers [40]. Fennimore et al. [41] found that the combination of steam-disinfestation treatment with soil amendments of MSM showed improved strawberry yield as well as weed and pathogen control. In a lettuce field study, the application of meadowfoam (Limnanthes alba) seed meal suppressed weeds and increased lettuce yield and leaf nitrogen content [42]. Onions are poor competitors with weeds, which makes weed management in organically-grown onions difficult [34]. In a greenhouse study, MSM significantly decreased redroot pigweed emergence and slightly reduced total yield of onion, indicating MSM has potential to be used as a weed suppressive amendment in an organic onion production system [34]. In organically-grown broccoli and spinach, the application rate of 4.48 t/ha MSM and soybean seed meal significantly increased spinach yield, but broccoli yield was similar in all treatments [43]. However, application rates of 2.5% MSM and mustard greens seed meal significantly reduced heights and biomass in sorghum, and there was a negative effect of MSM on cotton yield [44]. Therefore, it is evident that the type, rate, and timing of seed meal applications should be considered to successfully manage weeds while producing an organic crop. As evidence, the combination of CGM, clove oil, and sweep cultivation had little impact on weed management for organic peanut production [45]. Russo and Webber (2012) [46] also reported that application of CGM and vinegar did not produce peanut pod or oil yields at levels produced with conventional weed control. Therefore, additional alternative weed control techniques and materials should be investigated for organic peanut production, as one example. In container-grown ornamentals, weed emergence was significantly reduced with DDGS application at 800 and 1600 g/m 2 to the soil surface, with no injury on Rosa hybrid "Red Sunblaze", Phlox paniculata "Franz Schubert", and Coreopsis auriculata "Nana", indicating opportunities for use of DDGS for organically-grown ornamentals [30].
Bioherbicides from Extracts
Extracts from natural sources may also have potential as bioherbicides. Five dipeptides extracted from hydrolyzed CGM inhibited root growth of germinating weeds [47]. Secondary metabolite extracts from the leaves of Ailanthus altissima had inhibitory effects on seed germination and plant of Medicago saltiva [48]. Rice hull extracts demonstrated a significant allelopathic potential. [49] reported that increasing concentrations of warm water hull extracts from selected rice cultivars resulted in inhibition of barnyardgrass germination, seedling growth, and weight. Nieves et al. (2011) [50] also reported that methanolic extracts of Everniastrum sorocheilum, Usnea roccellina, and Cladonia confusa inhibited germination and root growth of red clover (Trifolium pratense). Phenolics extracted from the lichen Cladonia verticillaris caused changes in the ultrastructure of both roots and leaves of lettuce seedlings, suggesting potential as powerful bioherbicides [51]. Black walnut (Juglans nigra) has allelopathic effects, and extracts from walnut have been commercially formulated as a bioherbicide [52]. A black walnut extract-based commercial product (NatureCur ® , Redox Chemicals, LLC, Burley, ID, USA) completely inhibited growth of horseweed (Conyza canadensis) and hairy fleabane (Conyza bonariensis) at a concentration of 33.3%, showing potential as a pre-and post-emergent bioherbicide [52].
Factors Affecting the Efficacy of Bioherbicide
The efficacy of bioherbicides is the main limiting factor for their use, often due to environmental factors. The humidity requirements for establishment and spread of many foliar and stem fungal pathogens for weed control necessitate the development of special formulations to ensure the effectiveness of agents applied in the field [11]. A long dew period is required by some pathogens for infection on the aerial surfaces of target weeds [63]. Some organisms have limited shelf lives, and they are not suited for long-term storage [64]. Xanthomonas campestris pv. Poannua, a pathogen causing bacterial wilt of annual bluegrass, was not successfully commercialized due to low performance and variability in efficacy under different environmental conditions [65]. Soil moisture can be an important factor affecting pathogens attacking weeds. Application of a jute fabric to cover soil areas treated with a Sclerotinia minor granular bioherbicide to reduce water loss significantly enhanced control of dandelion (Taraxacum spp.), white clover (Trifolium repens), broadleaf plantain (Plantago major), buckhorn plantain (Plantago major), ground ivy (Glechoma hederacea), and prostrate knotweed (Polygonum aviculare) [66]. The influence of moisture was reduced by addition of an invert oil emulsion to conidial suspensions of Colletotrichum truncatum, which resulted in 100% control of hemp sesbania (Sesbania exaltata) in the absence of moisture in the greenhouse, and in 95% control of hemp sesbania in the field [67]. Phoma macrostoma has been registered as a bioherbicide to control broadleaved weed species, and its efficacy on dandelion was significantly increased by 10%-20% by amendment with nitrogen fertilizers [68].
The bioherbicide application method should be considered for enhancing efficacy of the biocontrol agent, including attention to spray droplet size, droplet retention and distribution, spray application volume, and the equipment used [69]. The application distribution pattern and pressure are important considerations for determining the quantity of bioherbicide applied [70]. Retention of spray droplets is affected by surface characteristics and morphology of the weed, its biotypes, the adjuvants used in the solutions, travel speed, and droplet size [71]. Smaller droplet sizes of Colletrotrichum truncatum resulted in greater efficacy in controlling scentless chamomile (Matricaria perforata) [72]. Application of bioherbicides with different nozzles affected the disease incidence and development on waterhemp [73]. Innovations such as dual nozzle sprayers, and the use of compressed air rather than CO 2 to minimize the acidification of the spray solution, may have impacts on bioherbicide efficacy [69].
Other factors, such as the spectrum of the bioherbicide, whether broad or targeted to specific species, the type of formulation, and if it involves amino acid-excreting strains, can significantly affect efficacy. Broad-spectrum bioherbicides may show different efficacies in different regions. That can be altered, as the spectrum of Alternaria crassa was broadened by combining it with fruit pectin and plant filtrates [74]. Another method to broaden the spectrum of bioherbicide is to combine multiple pathogens. By combining Alternaria cassiae, Phomospsis amaranthicola and Colletotrichum dematium, weeds such as pigweed (Amaranth spp.), sicklepod (Senna obtusifolia), and showy crotolaria (Crotalaria spectablis) were effectively controlled [75]. Chandramohan and Charudattan (2003) [76] also found that a mixture of three pathogens, Drechslera gigantia, Exserohilum longirostratum, and Exserohilum rostratum, successfully suppressed the growth of seven weeds in citrus groves in Florida. Amendment of bacterial pathogen aqueous suspensions with surfactants has been studied for helping bacteria efficiently invade plant leaves and broaden host range [25]. Types of formulations using emulsions, organosilicone surfactants, and hydrophilic polymers have advantages and disadvantages in enhancing the efficacy of biotic agents and ease of application [69]. Emulsions may improve efficacy and consistency of weed control by predisposing weeds to a bioherbicide agent [69]. Organosilicone surfactants, such as Silwet L-77, facilitate direct entry of bacterial cells and small spores into weed tissues [69]. Hydrophilic polymers, including numerous types of natural and synthetic polymers, have different levels of water-holding qualities. However, formulations composed of expensive materials increase the cost of bioherbicide products. In addition, some materials used in these formulation are toxic to human health [69]. An abundant quantity of amino acids has the potential to terminate plant growth. Therefore, the selection of fungal strains that are able to produce significant quantities of amino acids is becoming a new technique to control weeds [77]. Valine excretion by mutants of Fusarium oxysporum controlled Cannabis sativa by 70%-90% compared to 25% by a wild type isolate [77].
In addition to bioherbicide efficacy, the high cost and the potential human health threats are some other limitations for use of bioherbicides. Although some pathogens are highly effective in controlling a number of weeds, they may also produce undesirable mammalian and avian toxins [11]. Myrothecium verrucaria was effective for weed control as a result of the production of herbicidal metabolites; however, the mammalian-toxic macrocyclic tricothecenes were also simultaneously produced, presenting a severe human health hazard [78]. A fungal pathogen, Fusarium tumidum, a potential bioherbicide for gorse (Ulex europaeus) and broom (Cytisus scoparius), also produced tricothecenes [79]. With the relatively small market at present, and the high cost of maintaining registration, bioherbicides may be dropped from production, like DeVine (Phytophthora palmivora) that provided 95%-100% control of strangler vine [80,81]. Although the demand for more environmentally-friendly strategies and bioherbicides for weed control is increasing, there have been few bioherbicides successfully registered and commercialized in North America due to these limitations.
Conclusions
Lacking few effective bioherbicides, the integration of biological controls into current weed management systems may be an effective alternative for organic horticultural production. Bioherbicide technology could be used as a component in integrated weed management strategies to help avoid herbicide resistance, reduce production costs, and increase crop yield in organic horticulture. While there have been significant efforts to develop bioherbicides, few have been registered for use. Future research should focus on the development of more cost-effective and efficient bioherbicides, as well as the optimization of their use in production systems.
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Domain: Environmental Science Biology Agricultural and Food Sciences
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Rootstock Effects on Water Relations of Young Almond Trees (cv. Soleta) When Subjected to Water Stress and Rehydration
Rootstocks with size controlling potential are being used in newly planted intensive almond orchards. Due to increased water scarcity, characterizing the response of these rootstocks to water deficit is required. The current work aims to assess whether the rootstock can improve their drought tolerance. We investigated the morphological and physiological response of P. dulcis “Soleta” either self-rooted or grafted on Rootpac-20 rootstock. Plant responses were evaluated during a water stress period (withholding irrigation for 20 days) and subsequent recovery in potted plants under greenhouse conditions. Self-rooted plants had a higher capacity to control vigour than plants grafted onto Rootpac-20, both under full irrigation and no irrigation conditions. Stressed plants exhibited severe dehydration, as indicated by lower leaf water potential and relative water content. Removing irrigation reduced stomatal conductance in grafted and self-rooted plants by a similar extent, suggesting an efficient stomatal control, while the reduction in the net photosynthesis rate was more marked in grafted plants compared to non-grafted plants. Self-rooted plants under water stress increased their root to shoot ratio and water use efficiency, which are positive aspects for growth and survival of these plants.
Introduction
Almond is an important crop in the USA, Australia and Spain, which are the main world almond-producing countries. In the Mediterranean area, doubts about further financial support provided by the European Union to some traditional crops, such as vineyards and olive, and the low profitability of some crops, such as cereals, has led to new almond plantations. Almond is one of the major tree crops in Spain in terms of cultivated area, 822.878 ha according to Encuesta sobre Superficies y Rendimientos de Cultivos en España (ESYRCE) 2019 [1]. In Spain, the almond production is mainly located in the Mediterranean coastal regions, but it is becoming an interesting alternative to traditional crops in other regions of Spain, such as in the interior of the country [2]. Although in the world's main almond-producing country (USA), most of orchards are irrigated, there are large areas in Mediterranean countries where rainfed conditions are not uncommon [3]. Almond (Prunus amygdalus Batsch, syn. P. dulcis (Mill.) D. A. Webb) is considered a drought-tolerant species, but its production increases enormously under full irrigation compared to rainfed conditions [4,5]. Thus, irrigation systems are being installed in newly planted orchards. However, in the context of the scarcity of water resources, full irrigation might be an unrealistic option, and deficit irrigation strategies or rainfed conditions might be advised [6][7][8]. Even in irrigated orchards, some periods of water stress could occur due to the limited availability of irrigation water in some areas [5,9]. In such a situation, managing global water resources is needed as there is an important pressure in agriculture and fruit culture to cultivate crops more efficiently by increasing water savings [10]. Given the economic importance of almonds in Mediterranean countries, the almond response to water stress has been widely studied and various physiological and morphological mechanisms developed by this crop to confront water stress have been identified: osmotic adjust, elastic properties, control over stomatal regulation, the onset of leaf abscission and the presence of a deeply penetrating root system [3,[11][12][13][14].
On the other hand, almond growing in the Mediterranean area has undergone significant changes in recent years. Current trends in almond orchards have focused on intensification and high-density plantings [2]. In this context, not only new cultivars but also new rootstocks are essential tools to achieve success in these new super-intensive or very high-density plantations. The use of rootstocks that reduce tree vigour is a common practice in modern olive orchards [15]. In contrast, knowledge about the influence of rootstock in the adaptation of this new high-density planting system of almond trees is very scarce. Selecting the right scion-rootstock combination is also important in the adaptation of the fruit tree to specific training system Recently, rootstocks with low vigour were developed in Spain, such as the "Rootpac ® " series, which opens the possibility to develop almonds under high-density planting system. The physiological and morphological responses of different scion-rootstock combinations of a commercial almond orchard under irrigation at high density planting were studied by BenYahmed et al. [16]. These authors conclude that "Rootpac-20", the most dwarfing rootstock, resulted in bad adaptation to Mediterranean conditions. The information provided was important for selecting the right scion-rootstock association for the establishment of a new orchard under irrigation conditions. Considering the actual trend of increasing the almond acreage, especially in semi-arid conditions, it is of foremost importance to increase our knowledge on the behaviour of this crop and the mechanisms underlaying the reduced vegetative growth of scions grafted on low-vigour rootstock, especially under water stress situations. Moreover, it is well known that the rootstocks may also confer tolerance to different biotic and abiotic stresses in the soil [17] and grafting is a potential approach that can mitigate negative drought effects [18]. In addition, farmers should select plant materials that have lower water requirements or are able to cope with water scarcity while maintaining yield and fruit quality [19]. In this sense, more recently, the production of self-rooted almond trees in hedges constitutes a technological innovation that opens the possibility of achieving greater profitability under non-irrigation conditions. The self-rooted plant offers the advantages of having the root system of the almond tree and its adaptation to drought, and also avoids grafting, thus lowering the cost of production in the nursery [20,21]. In woody crops, few and inconclusive studies have documented the response of these new low-vigour rootstocks [2,16,[22][23][24]. The selection of the suitable plant material is very important, particularly in limited water conditions, however, only indirect information about the water relations and drought resistance is available. In this way, the knowledge of physiological processes that promote drought tolerance can improve our understanding of the mechanisms involved in scion/rootstock interactions and also on the selection of proper rootstocks to be used under different irrigation conditions. There are several rootstocks widely used around the world and their selection is, sometimes, more dependent on availability than on the actual information about their agronomic aptitude [9]. Knowledge about the relationship between drought tolerance and the rootstock, as well as information regarding the response of almond plants grafted on low-vigour rootstock and self-rooted plants during water stress recovery, is scarce, and the physiological mechanisms involved in the recovery process remain poorly understood [25][26][27].
In the context of almond production intensification under limited water supply, the objective of this research was to study the morphological and physiological response to water stress of one-year-old almond plants (cv. "Soleta") grafted onto Rootpac-20 rootstock and self-rooted plants. The response of the plant during drought recovery was also considered, which is important when selecting plant material to be used under different irrigation conditions.
Plant Material and Experimental Conditions
The experiment was conducted during the summer of 2018 at Itacyl Research Research Station, Valladolid, Spain (41 • 42 N, 4 • 42 W, 705 m a.s.l). One-year-old almond plants (Prunus dulcis L. cv. Soleta from the breeding program of the Unidad de Fruticultura, Centro de Investigación y Tecnología Agroalimentaria de Aragón), either self-rooted or grafted on low-vigour rootstock, were used. One half of the plants was grafted on Rootpac-20 rootstock (Prunus besseyi x Prunus cerasifera from the breeding program of Agromillora), in order to assess the physiological responses to irrigation of the grafted combination (Rp) when compared to the self-rooted plants (Sr). Plants were grown in 5 L pots filled with an 8:7:1 mixture of coconut fibre:black peat:vermiculite and placed inside a plastic greenhouse equipped with a cooling system. The temperature and relative humidity were registered with a Hoboware Lite Data Logger (Escort Data Logger, Inc., Buchanan, VA, USA). The micro-climatic conditions registered to the total experiment were 26.8 • C (average) temperature, 59.68% (average) relative humidity, and 3.84 (mean maximum) and 1.66 (average) vapour pressure deficit (VPD). All the plants were watered daily for 3 weeks to field capacity prior to starting the treatments.
Treatments and Experimental Design
Almond plants were grown under greenhouse conditions and subjected to two irrigation treatments using a computer-controlled drip irrigation system from June to September 2018. The irrigation treatments were full irrigation (Control) and no irrigation (Stress). The irrigation treatments consisted of a control (C)-when substrate moisture was maintained close to container capacity, it was watered daily to 100% water holding capacity (leaching 15% (v/v) of the applied water), and a stress treatment-removing irrigation during 20 days (S). The different treatments will be named as follows: plants grafted under full irrigation (RpC); self-rooted plants under full irrigation (SrC); plants grafted submitted to water stress (RpS); self-rooted plants submitted to water stress (SrS). One drip nozzle, delivering 2 L h −1 per pot, was connected to two spaghetti tubes (one on the side of every pot) and the duration of each irrigation episode was used to vary the amount of water applied, which depended on the season and on climatic conditions. The electrical conductivity of the water applied was 0.4 dS m −1 . In plants subjected to water stress, irrigation was withdrawn from day of the year (DOY) 198 until 218 (stress period). Once the stress period was completed, the plants were exposed to a recovery period of 35 days with the same irrigation regime applied to control plants, and pots were re-watered up to field capacity until the end of the experiment (recovery period, DOY 218-253).
Growth and Plant Water Measurements
At the end of the stress period (DOY 218) and at the end of the recovery period (DOY 253) eight plants per treatment were harvested and separated into leaves, stems and roots. They were then oven-dried at 80 • C until they reached a constant weight to measure the respective dry weights (DWs). Leaf number and leaf area (cm 2 ), using a leaf area meter (Delta-T; Devices Ltd., Cambridge, UK), were determined in the same plants. In addition, the root to shoot ratio was determined in these plants and calculated by dividing root DW by leaf DW. Throughout the experiment, the plant height and trunk diameter were measured in 20 plants per treatment once a week.
To determine the maximum water holding capacity of the substrate, five samples were uniformly mixed and packed to a similar bulk density. The pot surfaces were covered with aluminium foil to prevent water evapotranspiration, and the lower parts were submerged to half the pot's height, in a water bath; then, the pots were removed and left to equilibrate overnight. The next day, the pots were removed and left to drain freely until drainage became negligible. The fresh weight was then recorded for each individual pot and considered as the weight at field capacity. At the end of the experiment, the substrate was dried in an oven at 105 • C until constant weight in order to obtain the dry weight and calculate the volumetric water content. Later, the difference between the weight at field capacity and the oven-dry weight was measured and the volumetric water content was calculated (64%), which was considered as the substrate field capacity. Evapotranspiration (ET) was measured gravimetrically throughout the experimental period in five plants per treatment, using the difference in weights (weight after irrigation and weight before irrigating again), using a balance (Analytical Sartorious, Model 2501; capacity 5.2 g and accuracy of 0.01 g, SECURA Insurance, Fox Crossing, WI, USA). Then, the difference between the fresh weight and oven-dry weight was measured, giving the volumetric water content of these monitored pots. Moreover, at the end of the stress period (DOY 216), the weights of these pots were also recorded several times during the day, giving the hourly ET throughout the day.
Seasonal changes in stem water potential (Ψ s ), relative water content (RWC), stomatal conductance (g s ) and net photosynthetic rate (P n ) were determined in 6 plants per treatment during the central hours of illumination. In addition, at the end of the stress period, the diurnal patterns of ET, g s , and P n were measured at a 2 h interval (diurnal course). Stem water potential was estimated according to the method described by Scholander et al. [28], using a pressure chamber (Soil Moisture Equipment Co, Santa Barbara, CA, USA), for which leaves were placed in the chamber within 20 s of collection and pressurised at a rate 0.02 MPa s −1 [29], while the RWC of leaves was calculated according to Barrs [30]. Stem water potentials were measured in non-transpiring leaves that had been bagged with both a plastic sheet and aluminium foil for at least 1 h before measurement in order to prevent leaf transpiration; in this way, the leaf water potential equalled stem water potential [31]. Gas exchange parameters (g s and P n ) were determined in attached leaves using a gas exchange system (LI-COR, LI-COR Inc., Lincoln, NE, USA). Water use efficiency of production (WUE) was calculated at the end of the experiment by dividing the increment in dry weight by the water used.
Statistical Analyses of Data
In the experiment, 20 plants were randomly assigned to each treatment. The data were analysed by one-way ANOVA using SPSS 17.0 software (SPSS Inc., 2002, Chicago, IL, USA). Ratio and percentage data were subjected to an arcsine square-root transformation before statistical analysis to ensure homogeneity of variance. Treatment means were separated with Duncan's Multiple Range Test. Statistical comparisons were considered significant at p ≤ 0.05.
Substrate Water Content (SWC) and Evapotranspiration (ET)
The volumetric water content of the substrate before and after irrigation reflected the different irrigation treatments and the climatic conditions ( Figure 1A). It was higher in the control plants and decreased during the water stress period in the stressed plants with respect to the controls. After irrigation, the substrate water content (SWC) in the full irrigated plants remained on average at about 0.60-0.64 m 3 m −3 , above and close to container capacity ( Figure 1A). SWC in stressed plants was lower than in the controls and decreased from container capacity to 10% in RpS and to 8% in SrS, at the end of the stress period, coinciding with the time of maximum stress. The pots had an initial mean weight of 3.9 kg when the substrate was at field capacity and those of the stressed plants lost on average 2.6 and 2.7 kg (RpS and SrS, respectively) from the beginning of the experiment to the time of maximum stress (DOY 218). The recovery of the substrate water content was very fast. One day after the beginning of the rehydration (recovery period), SrS pots recovered 77.3% of their initial weight, followed by the RpS (75.9%). After two days, both plant materials (Rootpac-20 and self-rooted plants) had recovered almost their initial weight: 96.6% and 93.8% (SrS and RpS, respectively). The evolution of evapotranspiration (ET) along the study period is presented in Figure 1B. During the experimental period (DOY 198-253), daily evapotranspiration values ranged from 375 to 1250 mL d −1 per pot in plants under full irrigation ( Figure 1B), while ET values in water stressed plants were significantly lower. The rootstock regime also affected daily ET and differences between both well-irrigated treatments were evident throughout the experimental period in this respect. Evapotranspiration was higher in RpC plants than in plants of the SrC treatment, and these differences were greater as the time progressed. In the stressed plants, when the irrigation pattern was changed, the plants increased or decreased their water consumption (ET) and adjusted to the new conditions, but with some particular characteristics ( Figure 1B). When plants were exposed to water stress conditions, SWC decreased progressively and plants of both stress treatments restricted their daily ET. During this phase, the ET of both stressed plants was similar, reaching very low ET values of below 70 mL per pot during most of the water stress period (DOY 205-217). Once wellwatered conditions were restored (DOY 218), the humidity in the substrate immediately recovered. In contrast, ET values in the stressed plants increased more slowly and were still significantly lower than that in control plants during the 30 days following the beginning of the recovery period. Only at the end of the experiment, ET in the stressed plants matched that of plants that had been exposed to full irrigation since the beginning of the experiment. The water consumption in each pot during The evolution of evapotranspiration (ET) along the study period is presented in Figure 1B. During the experimental period (DOY 198-253), daily evapotranspiration values ranged from 375 to 1250 mL d −1 per pot in plants under full irrigation ( Figure 1B), while ET values in water stressed plants were significantly lower. The rootstock regime also affected daily ET and differences between both well-irrigated treatments were evident throughout the experimental period in this respect. Evapotranspiration was higher in RpC plants than in plants of the SrC treatment, and these differences were greater as the time progressed. In the stressed plants, when the irrigation pattern was changed, the plants increased or decreased their water consumption (ET) and adjusted to the new conditions, but with some particular characteristics ( Figure 1B). When plants were exposed to water stress conditions, SWC decreased progressively and plants of both stress treatments restricted their daily ET. During this phase, the ET of both stressed plants was similar, reaching very low ET values of below 70 mL per pot during most of the water stress period (DOY 205-217). Once well-watered conditions were restored (DOY 218), the humidity in the substrate immediately recovered. In contrast, ET values in the stressed plants increased more slowly and were still significantly lower than that in control plants during the 30 days following the beginning of the recovery period. Only at the end of the experiment, ET in the stressed plants matched that of plants that had been exposed to full irrigation since the beginning of the experiment. The water consumption in each pot during the whole experimental period was 50.1 L for RpC plants and 33.2, 26.2 and 18.6 L for SrC, RpS and Sr-S plants, respectively (66.3%, 52.4% and 37.1% of the amount of water compared with RpC treatment) ( Figure 1C). RpS had 52% of the amount of water supplied in the RpC and SrS had 55% of the amount of water supplied in the SrC.
The behaviour of the evapotranspiration rate on a representative day at the end of the stress period can be seen in Figure 2A. When plants were well irrigated, the highest ET values were reached between 13:00 and 18:00 h (11:00 and 16:00 solar time) especially in grafted plants (71 mL per 60 min in RpC and 52 mL in SrC), coinciding with the highest temperature and VPD, after which, evapotranspiration decreased ( Figure 2B). In plants submitted to water stress, the transpiration curve was more stable throughout the day, independently of temperature and DPV changes. The ET of stressed plants (self-rooted and grafted) was similar and very low (coinciding with minimum water levels in the substrate, approximately of 10%). Only grafted plants (RpS) increased their ET at the end of the day. Although this did not occur in the case of SrS plants, in which ET remained low during all times of the day (from predawn to afternoon). Figure 1C). RpS had 52% of the amount of water supplied in the RpC and SrS had 55% of the amount of water supplied in the SrC. The behaviour of the evapotranspiration rate on a representative day at the end of the stress period can be seen in Figure 2A. When plants were well irrigated, the highest ET values were reached between 13:00 and 18:00 h (11:00 and 16:00 solar time) especially in grafted plants (71 mL per 60 min in RpC and 52 mL in SrC), coinciding with the highest temperature and VPD, after which, evapotranspiration decreased ( Figure 2B). In plants submitted to water stress, the transpiration curve was more stable throughout the day, independently of temperature and DPV changes. The ET of stressed plants (self-rooted and grafted) was similar and very low (coinciding with minimum water levels in the substrate, approximately of 10%). Only grafted plants (RpS) increased their ET at the end of the day. Although this did not occur in the case of SrS plants, in which ET remained low during all times of the day (from predawn to afternoon).
Plant Growth
Water deficit had a significant effect on biomass accumulation of the almond plants at the end of the stress and recovery period (Table 1). Plants submitted to water stress for 20 days reduced leaf dry weight (DW) at the end of the stress period compared with the controls. When plants were full irrigated, the leaf area was higher in grafted (RpC) than in self-rooted plants (SrC). In contrast, in non-irrigated plants, the reduction in leaf area compared with controls was more marked in RpS than in SrS, 77% and 67% in RpS and SrS, respectively. The reduction in leaf area induced by water stress was due to a decrease in the number of leaves and in the individual leaf size. Both self-rooted plants (Sr) had higher root to shoot ratios than grafted ones (Rp) at the end of stress period, being particularly marked in self-rooted plants submitted to water deficit (SrS). At the end of recovery period (DOY 253), root to shoot ratios in self-rooted plants were still higher than in grafted plants, but no differences were detected between RpS and SrS at that time. In addition, for each irrigation regime, grafted plants had higher aerial biomass production values (leaf DW, stem DW, leaf area and number or leaves) than those found for self-rooted at the end of the experimental period.
Trunk diameter and plant height increased with time in all irrigation treatments and material types. Figure 3A shows the values of the trunk diameter as a fraction of the diameter at the beginning (TD/TDi) of the experiment for each treatment. When water stress was induced, the trunk diameter accumulation decreased in the plants grafted on Rootpac-20 and at the end of the stress period, RpS had the lowest values for trunk diameter accumulation, while the well-irrigated plants grafted on Rp (RpC) had the highest values ( Figure 3A). At this time, trunk diameter accumulation also decreased as a result of water stress in the self-rooted plants, but it was less affected than in Rp plants and no significant differences were observed between well-irrigated and water stressed in self-rooted plants. During the recovery period, trunk diameter growth slightly increased in SrS plants with respect to the values observed during the water stress period, but trunk diameter increased markedly in the RpS plants.
At the end of recovery period, the plants with highest trunk diameter accumulation were those from RpC and the lowest from SrS, while the respective trunk diameter accumulations of RpS and SrS were similar. Plant height was less affected due to water stress than trunk diameter during the experimental period ( Figure 3B). No significant changes were observed in the plant height of self-rooted plants by irrigation effect, but height decreased as a result of water stress in the grafted plants at the end of the water stress period. RpC plants reached the greatest height, while the self-rooted plants had a significant reduction from the beginning of the experiment, leading to the smallest plants. At the beginning of the experiment, plant height was similar in both the RpC and RpS treatment, but it was inhibited by the latter 2 weeks after application onwards (from 2 weeks after beginning of deficit irrigation treatments). At the end of the experiment, the reductions were around 9%, 28% and 30% for RpS, SrC and SrS, respectively, compared with RpC.
Plant Water Relations and Gas Exchange Parameters
In response to the different irrigation amounts and changes in soil water content, different seasonal trends in midday stem potential (Ψs) developed in each treatment ( Figure 4A). Plants irrigated at full water requirements had Ψs values around −1.2 MPa throughout the experimental period, which was indicative that these plants were never short of water. By contrast, the nonirrigated plants had a decreasing Ψs as water stress developed with time, with minimum values at the end of stress period, when substrate water content values were lowest. The stem water potential at midday (Ψs) decreased in both stress treatments, especially in the RpS plants, in which values of −3.4 MPa were reached at the end of the stress period ( Figure 4A). This was followed by an increase in the Ψs values for water deficit treatments when the irrigation restriction ended, and all plants were Plant height was less affected due to water stress than trunk diameter during the experimental period ( Figure 3B). No significant changes were observed in the plant height of self-rooted plants by irrigation effect, but height decreased as a result of water stress in the grafted plants at the end of the water stress period. RpC plants reached the greatest height, while the self-rooted plants had a significant reduction from the beginning of the experiment, leading to the smallest plants. At the beginning of the experiment, plant height was similar in both the RpC and RpS treatment, but it was inhibited by the latter 2 weeks after application onwards (from 2 weeks after beginning of deficit irrigation treatments). At the end of the experiment, the reductions were around 9%, 28% and 30% for RpS, SrC and SrS, respectively, compared with RpC.
Plant Water Relations and Gas Exchange Parameters
In response to the different irrigation amounts and changes in soil water content, different seasonal trends in midday stem potential (Ψ s ) developed in each treatment ( Figure 4A). Plants irrigated at full water requirements had Ψ s values around −1.2 MPa throughout the experimental period, which was indicative that these plants were never short of water. By contrast, the non-irrigated plants had a decreasing Ψ s as water stress developed with time, with minimum values at the end of stress period, when substrate water content values were lowest. The stem water potential at midday (Ψ s ) decreased in both stress treatments, especially in the RpS plants, in which values of −3.4 MPa were reached at the end of the stress period ( Figure 4A). This was followed by an increase in the Ψ s values for water deficit treatments when the irrigation restriction ended, and all plants were fully irrigated. Ψ s of SrS plants recovered rapidly to values similar to those of the fully irrigated plants (DOY 218, 1 day after rehydration), while in the most stressed plants (RpS) this recovery took more time (DOY 232, 14 days after rehydration). The relative water content (RWC) showed a similar behaviour to that observed for Ψs, with plants subjected to water stress having the lowest values, especially in grafted (Rp) plants ( Figure 4B). No pronounced differences in RWC were observed between self-rooted and grafted plants under full irrigation conditions (RpC and SrC plants) during most of the experimental period, although using Rootpac-20 as rootstock affected RWC on some days of the experiment (end of July), when lower values were observed in the RpC treatment compared with the SrC treatment. The lowest value for RWC was found in RpS plants, reaching a value of 58.0% at the end of the stress period, coinciding with the lowest values of Ψs ( Figure 4A). The end of the irrigation restriction and the beginning of the rehydration period was followed by an increase in the values of RWC for stress treatments, and 1 day after the onset of rehydration period, differences with the controls were only observed in RpS plants. At the end of the experimental period, both the Ψs and RWC values of the plants that had been exposed to irrigation restriction were similar to those of the control treatments.
The values of the stomatal conductance (gs) and the photosynthetic rate (Pn) at midday during the experimental period can be seen in Figure 5. In general, gs values were highest in the control plants, while the gs values changed in the stressed treatments according to the irrigation applied in each phase ( Figure 5A). When the change in irrigation involved a restriction of irrigation, gs decreased The relative water content (RWC) showed a similar behaviour to that observed for Ψ s , with plants subjected to water stress having the lowest values, especially in grafted (Rp) plants ( Figure 4B). No pronounced differences in RWC were observed between self-rooted and grafted plants under full irrigation conditions (RpC and SrC plants) during most of the experimental period, although using Rootpac-20 as rootstock affected RWC on some days of the experiment (end of July), when lower values were observed in the RpC treatment compared with the SrC treatment. The lowest value for RWC was found in RpS plants, reaching a value of 58.0% at the end of the stress period, coinciding with the lowest values of Ψ s ( Figure 4A). The end of the irrigation restriction and the beginning of the rehydration period was followed by an increase in the values of RWC for stress treatments, and 1 day after the onset of rehydration period, differences with the controls were only observed in RpS plants. At the end of the experimental period, both the Ψ s and RWC values of the plants that had been exposed to irrigation restriction were similar to those of the control treatments.
The values of the stomatal conductance (g s ) and the photosynthetic rate (P n ) at midday during the experimental period can be seen in Figure 5. In general, g s values were highest in the control plants, while the g s values changed in the stressed treatments according to the irrigation applied in each phase ( Figure 5A). When the change in irrigation involved a restriction of irrigation, g s decreased in the plants of both stress treatments. During the stress period, these plants had lower g s values than the fully irrigated plants, regardless of the rootstock. All the plants subjected to irrigation restriction had very low values of g s at midday (bellow 50 mmol m −2 s −1 ) during most of the stress period (July, DOY 205-215). Such reductions with respect to the control plants were also observed in the photosynthesis levels at midday, although significant differences were observed between RpS and SrS ( Figure 5B). The plants of SrS treatment had higher P n values at midday than RpS plants throughout the stress period, despite having similar g s values. When irrigation was restored, g s and P n increased in the plants of both stress treatments, although the plants did not reach the values of the control plants until 7 days after the onset of rehydration period. One day after the onset of the rehydration period, P n and g s values in the plants submitted to water stress (RpS and SrS) remained lower than control plants, despite having similar substrate water content values to the well-irrigated plants. The behaviour of gs and Pn on a representative day of the end of the stress period can be seen in Figure 6. Maximum values of gs were observed between 9:00 and 12:00 (solar time) in the fully irrigated treatments, especially in the case of RpC ( Figure 6A). In both stress treatments, gs was reduced to a similar extent compared with the controls, with plants of both stress treatments having very low values of below 50 mmol m −2 s 1 throughout the day, regardless of the rootstock. The diurnal pattern of Pn also consisted of a reduction in both stress treatments compared with controls ( Figure 6B). RpC had, in the early morning (7:00-9:00), significantly higher values of Pn compared to SrC. The behaviour of g s and P n on a representative day of the end of the stress period can be seen in Figure 6. Maximum values of g s were observed between 9:00 and 12:00 (solar time) in the fully irrigated treatments, especially in the case of RpC ( Figure 6A). In both stress treatments, g s was reduced to a similar extent compared with the controls, with plants of both stress treatments having very low values of below 50 mmol m −2 s 1 throughout the day, regardless of the rootstock. The diurnal pattern of P n also consisted of a reduction in both stress treatments compared with controls ( Figure 6B). RpC had, in the early morning (7:00-9:00), significantly higher values of P n compared to SrC. Later, at midday, these differences disappeared. Similarly, RpS plants had higher P n than SrS plants in the early morning. In general, P n increased in all treatments as the evaporative demand of the atmosphere increased during the day. However, RpS showed a gradual decrease through the day, having minimum P n values at midday. At this time, Rp plants had even lower P n values than the Sr, although these differences were not statistically significant.
Later, at midday, these differences disappeared. Similarly, RpS plants had higher Pn than SrS plants in the early morning. In general, Pn increased in all treatments as the evaporative demand of the atmosphere increased during the day. However, RpS showed a gradual decrease through the day, having minimum Pn values at midday. At this time, Rp plants had even lower Pn values than the Sr, although these differences were not statistically significant.
Discussion
The different plant material studied in this work led to substantial differences in terms of growth and water relations in the cultivar Soleta, both under full irrigation and water stressed conditions. The rootstock used in our experiment influenced the growth responses of the almond plants to water stress, meaning that the plant material must be considered an important aspect when used under deficit irrigation strategies or rainfed conditions. In our study, the reduction in leaf area in response to water stress produced by withholding irrigation was much more evident in the variety grafted onto the Rootpac-20 rootstock than in self-rooted plants. The differences in the plant height and trunk diameter were also greater in grafted plants. According to these results, self-rooted plants would be more tolerant to water stress than Rp, because the vegetative growth was less affected by irrigation restriction. The reduction in leaf area due to water deficit is a common response, since expansive growth is the most sensitive process to water stress in plants and is affected even at relatively high leaf water potentials [32,33]. The inhibition of leaf growth under limited water availability is seen as an adaptative strategy, because it allows plants to reduce water consumption by restricting the evaporative surface and delaying the onset of more severe stress [34,35]. The differential provision of water to plant not only influences the aerial part but also the root system, which may be affected by drought [36]. In our conditions, exposure to irrigation water withdrawal caused a significant decrease in aerial dry mass, leaf area, number of leaves, plant height and trunk diameter, but had less effect on root mass, indicating that shoots and roots react differently to drought, influencing the dry matter partitioning between roots and shoots [37,38]. This was confirmed by the root to shoot ratio, which increased in plants under water deficit conditions, especially in self-rooted plants, which confer an advantage in water uptake. This aspect is an important factor for successful transplanting and establishment in the field, since root anatomy and structure may be decisive for plant survival [39]. Deficit irrigation has the potential to improve crop quality by increasing the root to shoot ratio, as previously reported for other woody crops by Moriana et al. [9] in pistachio, by Abrisqueta et al. [40] in peach, and by Yadollahi et al. [38] in several almond genotypes.
Under full irrigation conditions, the Rootpac-20 rootstock promoted higher vegetative growth than self-rooted plants. This greater growth during the experimental period resulted in different plant size.
Discussion
The different plant material studied in this work led to substantial differences in terms of growth and water relations in the cultivar Soleta, both under full irrigation and water stressed conditions. The rootstock used in our experiment influenced the growth responses of the almond plants to water stress, meaning that the plant material must be considered an important aspect when used under deficit irrigation strategies or rainfed conditions. In our study, the reduction in leaf area in response to water stress produced by withholding irrigation was much more evident in the variety grafted onto the Rootpac-20 rootstock than in self-rooted plants. The differences in the plant height and trunk diameter were also greater in grafted plants. According to these results, self-rooted plants would be more tolerant to water stress than Rp, because the vegetative growth was less affected by irrigation restriction. The reduction in leaf area due to water deficit is a common response, since expansive growth is the most sensitive process to water stress in plants and is affected even at relatively high leaf water potentials [32,33]. The inhibition of leaf growth under limited water availability is seen as an adaptative strategy, because it allows plants to reduce water consumption by restricting the evaporative surface and delaying the onset of more severe stress [34,35]. The differential provision of water to plant not only influences the aerial part but also the root system, which may be affected by drought [36]. In our conditions, exposure to irrigation water withdrawal caused a significant decrease in aerial dry mass, leaf area, number of leaves, plant height and trunk diameter, but had less effect on root mass, indicating that shoots and roots react differently to drought, influencing the dry matter partitioning between roots and shoots [37,38]. This was confirmed by the root to shoot ratio, which increased in plants under water deficit conditions, especially in self-rooted plants, which confer an advantage in water uptake. This aspect is an important factor for successful transplanting and establishment in the field, since root anatomy and structure may be decisive for plant survival [39]. Deficit irrigation has the potential to improve crop quality by increasing the root to shoot ratio, as previously reported for other woody crops by Moriana et al. [9] in pistachio, by Abrisqueta et al. [40] in peach, and by Yadollahi et al. [38] in several almond genotypes.
Under full irrigation conditions, the Rootpac-20 rootstock promoted higher vegetative growth than self-rooted plants. This greater growth during the experimental period resulted in different plant size. Figure 1 characterizes these differences very well, showing a much higher evapotranspiration during the experimental period for plants grafted under full irrigation compared to self-rooted plants. Rootpac-20 is considered to be a rootstock of low vigour, with 40-50% vigour reduction in comparison to GF677 [24,41]. Similarly, Ben Yahmed et al. [16], reported that Rootpac-20 exhibited a high capacity to control tree vigour, based on the growth of the trunk. In our experimental conditions, self-rooted plants exhibited even higher capacity to control plant vigour than Rootpac-20. In fact, reduced vegetative growth is a positive aspect of fruit crops in the view of getting high density orchards with reduced cultural costs associated with harvesting and pruning [22,42,43]. However, this response may be an undesirable feature if it reduces crop yield through a lower assimilation capacity [32]. Despite the increasing commercial importance of dwarf orchard trees, the mechanisms related to reduced vegetative growth of scions grafted on low-vigour rootstocks have not been clearly identified [22,44,45]. These authors suggested that control growth is associated with hormonal relationships and hydraulic conductance of roots and stem. Lliso et al. [46] suggested that the dwarfing mechanisms are related to competition between reproductive and vegetative growth. Motisi et al. [47] reported lower stem water potential and lower hydraulic conductance on dwarfing rootstocks compared to vigorous rootstocks.
Water stress, characterized by stem water potential and leaf relative water content measurements, was more severe in plants grafted on Rp than in the self-rooted plants. The level of induced water stress was similar, as described in other studies in which Ψ s values of −4 MPa have been reported for severe stress levels [11,12]. Ben Yahemed et al. [16] reported that leaf and stem water potential were lower for scions grafted on Rootpac-20 rootstocks than for scions grafted on more vigorous rootstocks. This behaviour is likely to be related to the lower water absorption capability of the root system of dwarfing rootstocks to fulfil the transpiration demand of canopy. In the present study, self-rooted plants had higher Ψs than plants grafted on Rp rootstocks (more vigorous). These variations in water status can be explained by the absence of grafting in self-rooted plants. The higher Ψ s values in self-rooted plants were likely related to the smaller transpiring plant leaf area and biomass. Although the reduction in leaf area allowed a quicker recovery of some parameters after water stress was over, it could result in a reduction in the assimilation capacity of the plant, which could affect crop yield [32]. This hypothesis should be checked in field experiments. Almond is a very drought-resistant crop that tolerates high levels of tissue dehydration with a quick capacity of rehydration [48]. The rehydration capacity is also very important in this species and the stem water potential, relative water content and leaf conductance were quickly recovered. The above results indicate that self-rooted plants had more efficient mechanisms for tolerating water deficits than Rp did. Our data suggest that self-rooted plants were apparently the most drought resistant according to plant water status and this could be linked to a higher root to shoot ratio and the ability to control water loss via transpiration.
The water consumption of the plants varied during the experiment and was closely related with environmental factors, plant size and the irrigation regime [3]. Plants are able to adapt to a reduced moisture level and, as a result, transpiration is reduced [27]. In our conditions, daily evapotranspiration varied during the experiment and depended mainly on the available water content. This indicates that P. dulcis plants regulated their transpiration when subjected to water restrictions, which is a common response of plants grown in Mediterranean climates [49]. Several works have studied the evolution of water consumption in almond plants under different environmental conditions and it is well established that transpiration decreased in the deficit treatments as compared to full irrigated trees [12,50]. Knowledge of transpiration responses under different irrigation conditions is essential to formulate irrigation strategies [3,5,51]. Determining a precise estimation of crop water requirements is also an important aspect when selecting the right scion-rootstock combination. In this sense, the water consumption of the plants under full irrigation was reduced in non-grafted plants compared to grafted plants, despite the similar levels of water in the substrate. Nawaz et al. [18] also observed this same behaviour, in which plants grafted on rootstocks absorbed more water and ions than self-rooted plants and transported these water and ions to the aboveground scion. This was confirmed by the accumulated ET which increased by 50% in plants grafted onto Rootpac-20 under full irrigation conditions compared to self-rooted plants with the same irrigation regime. Surprisingly, relatively little research has quantified irrigation requirements of woody crops with low vigorous rootstocks, although such knowledge would offer great possibility for water conservation [3,15].
Plant functioning and gas exchange parameters were affected by water deficit, as a marked decrease in P n and g s values were observed in stressed plants, especially at the end of stress period, when conditions were more stressful and SWC was lower. P. dulcis has been classified as a plant with very sensitive stomata that regulate stomata closure before reaching critical leaf water potential, which would cause cavitation events [3]. Li et al. [52] reported that water availability in the soil had a marked effect on stomatal morphology and movement that regulate plant water relations and plant growth. Shakel et al. [53] found that P dulcis plants under water stress exhibited a stem water potential of about −1.4 to 1.8 MPa, which caused a reduction of 50% in g s , suggesting that they had very sensitive stomata; this agrees with our results. As a result of the stomatal closure, net photosynthesis is unavoidably reduced due to decreased CO 2 availability at the chloroplast level [54], as seen in many other studies with different almond cultivars submitted to water deficit [3,38,55,56]. At the end of the water stress period, P n was seen to be negatively affected in plants grafted than in self-rooted plants.
The fact that such a reduction was less marked in Sr than in Rp plants confirms the higher drought tolerance compared to grafted plants.
As regards the behaviour of g s along the day, it began to increase at dawn due to stomatal opening in full-irrigated plants, and was the highest at midday, when evaporative demand was also the highest. After midday, stomata closing began, producing a decrease in the g s . The same observation was made for the evolution of evapotranspiration values. Maximum ET values were found at midday in full irrigation treatments, when hourly ET was also highest. By contrast, both stressed plants reduced g s values throughout the day. Only, RpS plants showed slight increases in the gas exchange parameters in the early morning, when DPV was still low. Similar behaviour was found by Villalobos et al. [57], who reported a displacement to the pattern of gas exchange of the stressed trees towards the early morning hours. Some species under water stress are able to take advantage of the times of the day when the trade-off between water and CO 2 is more favourable, to perform then most of their daily gas exchange. Later, at midday, when VPD was high, stomata of stressed trees were fully closed, reaching very low g s values.
The severe and prolonged inhibition of g s has been interpreted as evidence of a gradual increase in the non-stomatal limitations of photosynthesis during the stressful conditions [58]. Álvarez and Sánchez-Blanco [59] reported that if plants showed g s values below 100 mm m −2 s −1 for long periods, intrinsic water use efficiency is sharply reduced and non-stomatal limitation of P n are predominant, which could delay plant recovery or even cause permanent damage. In this sense, the subsequent recovery in gas exchange parameters that occurred in almond plants suggest that withholding irrigation during a period of 20 days did not cause damage to leaf tissue, or, at least, it was not irreversible, indicating that plant functioning was not permanently affected by the stressful conditions experienced by plants [60].
Although self-rooted plants exposed to water deficit showed lower biomass accumulation, the water use efficiency (WUE) was higher in the water-stressed plants. The advantage in the case of these plants is that controlled drought may lead to an accumulation of carbohydrate reserves in the plants and, together with an increased root to shoot ratio, could promote a more rapid resumption of growth once irrigation is restored or rainfall events start [61,62].
Conclusions
Data of vegetative growth suggest that self-rooted plants had a higher capacity to control plant vigour than plants grafted onto Rootpac-20, both under full irrigation and no irrigation conditions. Under irrigation, the Rootpac-20 may be the best rootstock since it induces the biggest leaf stomatal conductance and vigour, which are likely to be accompanied by a more productive response. This greater growth was associated with higher photosynthetic rates but involved a 50% increase in water consumption compared to self-rooted plants.
The tolerance of almond plants to drought was related to an effective mechanism of stomatal control, together with a reduction in leaf area. This is also clear from the decline and subsequent recovery of gas exchange parameters. The results show that Soleta is highly resistant to drought stress, but the morphological and physiological responses differed between self-rooted plants and plants grafted onto Rootpac-20. In the case of dryland or deficit irrigation conditions, self-rooted plants might be a good choice for their drought tolerance, as these plants are able to maintain better plant water status, which resulted in smaller reductions in leaf area. It was accompanied by an increased root to shoot and water use efficiency, which are positive aspects that would allow plants a more rapid recovery at the onset of the autumn. Based on this behaviour, self-rooted can be regarded as an interesting plant material for high-density plantations under drought conditions.
The responses found in this work should be taken into account when selecting the rootstock for the establishment of a new orchard, knowing the irrigation management that will be used. In addition, these results suggest the need to evaluate the effect of these rootstocks on the productive response under field conditions.
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Domain: Environmental Science Biology Agricultural and Food Sciences
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Soil microbial , chemical properties and crop productivity as affected by organic manure application in popcorn ( Zea mays L . var . everta )
Soil microbial population, dehydrogenase activity and chemical properties of soil under different doses of farmyard manure (FYM), leaf compost and vericopmpost were examined at the research farm of the Indian Agriculture Research Institute (IARI), New Delhi, India during 2008-09 and 2009-10. Results indicated the higher value of microbial population, dehydrogenase activity, organic carbon, available nitrogen (N), phosphorus (P), potassium (K) and lower bulk density were observed in farmyard manure applied equivalent to 120 kg N ha –1 followed by vermicompost equivalent to 120 kg N ha –1 . Grain yield of popcorn was significantly higher in the treatments of recommended dose of fertilizers and vermicompost equivalent to 120 kg N ha –1 .
INTRODUCTION
Popcorn (Zea mays L. var.everta.) is the most important crop under irrigated conditions of north India. Due to its importance as food, feed and specialty corn, it is a versatile cereal crop for both domestic consumption and for export.popcorn requires huge amounts of nutrients for high productivity. The continued application of chemical fertilizer leads to deterioration of soil health with reduced organic carbon and increased multi-nutrients de-ficiencies (Swarup, 2002). Maintenance of soil quality is of utmost importance for enhancing productivity which leads to food and nutritional security. High aboveground biomass yield is accompanied by an active root system, which releases an array of organic compounds into the rhizosphere (Fließbach et al., 2007). For maintaining good soil health, nutrient schedules should be correctly followed, including organic manures viz., farmyard manure (FYM),vermicompost and leaf compost, as these sources have the potential to supply nutrients to crops as well as prevent soil deterioration (Kumar et al., 2005). Application of these organic manures not only helps in enhancing the productivity but also have the beneficial effect on soil properties (Pathak et al., 2005). Nutrients available in organic manures are released slowly, remain in the soil for longer time and are available to plants, thereby maintaining soil fertility and enhance the soil microbial population (Belay et al., 2001). Researchers reported that application of various organic manures stimulated the plant growth, activity of soil microorganism, resulted in higher population of fungi, bacteria and actinomycetes and higher activity of soil enzymes (Krishnakumar et al., 2005;Sidhu et al., 2007;Alam et al., 2007;Knapp et al., 2010). Application of organic manures increased microbial respiration and resulted in increased carbon and plant nutrient mineralization rates in soil (Powon et al., 2005). Microbial processes are dynamic, and therefore patterns of temporal fluctuation during crop growth are of great importance in relation to the nutrient supplying capacity of the ecosystem and the crop demand. Consequently, microbial association needs to be studied along with available plant nutrients in soil and crop growth. The information regarding microbial population, soil enzymes and chemical properties of soil in relation to organic manures is limited in crops like popcorn (Meena et al., 2013). Hence, the present study was carried out to evaluate the effect of organic manures on soil chemical, and biological properties as well as productivity in popcorn.
Experimental location and climate
Field experiments were conducted during 2008-09 and 2009-10 at the research farm of Indian Agricultural Research Institute (IARI), New Delhi (latitude 28.40° N, longitude 77.12°E, 228.6 m above mean sea level). The climate of New Delhi is sub-tropical and semiarid type with hot and dry summer and cold winter and falls under the Agro-climatic Zone 'Trans-Gangetic Plains'. The mean annual rainfall is 650 mm and more than 80% generally occurs during the south-west monsoon (July-September) with mean annual evaporation of 850 mm. Minimum and maximum temperature ranged between 11.0 and 35.0°C and 10.9 and 37.9°C, with the rainfall of 582.7 and 502.7 mm during growth period of popcorn in 2008 and 2009, respectively. Meena et al. 1403
Application of organic manures/fertilizers and crop management
The required quantity of organic manures was applied 10 days before sowing of the crop as per the treatments. The FYM, vermicompost and leaf compost samples were analyzed for total N using a Kjeldahl digestion method (Jackson, 1967), while total P and K were determined using a wet digestion (Di-acid digestion) method as described earlier (Prasad et al., 2006). Concentrations of different nutrients in organic manures are given in Table 1. Recommended dose of fertilizer (N120P25K35 kg ha -1 ) was applied through urea, single super phosphate and muriate of potash, respectively. Full dose of P and K, 1 /3 of N and 25 kg ha -1 zinc sulphate were applied as basal. The remaining N was applied in two equal splits at tasseling and grain formation stage. To ensure the optimum moisture for germination, pre-sowing irrigation was applied and afterwards irrigation was given to the crop at frequent intervals for better crop establishment. The field was prepared by two cross ploughings with disc plough and one ploughing with cultivator followed by planking. Thereafter, a ridge with a spacing of 60 cm for popcorn sowing was made with the help of tractor drawn ridger. The popcorn seed was dibbled at 3-4 cm seed depth on the side of the ridges at a spacing of 15-20 cm. The optimum plant population (83,000 plant ha -1 ) was maintained by thinning of plants after one week of germination. For weed control, two manual weddings were done at 30 and 50 days after sowing and during second weeding the earthing up operation was also carried out.
Soil sampling, microbial population and dehydrogenase activity
Soil samples (0-15cm) were collected after harvest of the crop from each treatment for analysis. The samples were stored in plastic bags and taken to the laboratory, where soil was sieved (2 mm mesh size), homogenized and stored at 4°C. Organic carbon was determined by using wet digestion method (Walkley and Black, 1934), available N by alkaline permanganate (Subbiah and Asija, 1956), available P by Olsen's et al. (1954) method, available K by Flame photometer method (Jackson, 1967), and bulk density was estimated by using the core sampler method (Bodman, 1942). The fungal, bacterial and actinomycetes population were estimated by standard plate count method using Marten's for fungi (Martin, 1950), and nutrient agar medium for bacteria and actinomycetes (Allen, 1959). Microbial population was calculated and expressed as number of cells, x10 n /g soil. Dehydrogenase activity was determined by reduction of 2, 3, 5-triphenyltetrazolium chloride (TTC) (Casida et al., 1964). Individual character datasets were subjected to analysis of variance and means were separated by Duncan's multiple range test (DMRT) at the 5% level of probability using SAS version 9.3.
Microbial population of bacteria, fungi and actinomycetes
The pooled data of both the years are presented, as there was no significant difference observed among the years (Table 2). The microbial population viz., bacteria, fungi and actinomycetes significantly were affected with application of different organic N sources as compared to control. The application of 120 kg N ha -1 either through FYM (T3) or vermicompost (T5) resulted in maximum microbial population of bacteria (28.60 and 27.12 cfu10 5 g -1 soil), fungi (23.96 and 22.17 cfu10 2 g -1 soil) and actinomycetes (14.16 and 12.92 cfu10 2 g -1 soil), respectively (Table 2). The application of leaf compost equivalent to120 kg N ha -1 (T4) resulted in higher amount of microbial populations as compared to control and RDF (T2), although it was statistically at par with the application of 90 kg N ha -1 either through FYM (T6), vermicompost (T8) and leaf compost (T7). The increase in microbial population with the application of organic manure might be due to stimulated growth and activities of soil microorganism (Upadhyay et al., 2011). The crop plant secreted various types of organic acids from roots, which is an easily available source of food for soil microorganism (Dotaniya et al., 2013). The addition of organic inputs enhanced the microbial counts in soil, which might be due to carbon addition and changes in physico-chemical properties of soil. The recommended dose of fertilizer (RDF) resulted in lower values of microbial populations than organic manure treatments, but significantly higher than the control treatment. Microbial populations were more numerous in the application of 120 kg N ha -1 either through FYM (T3) or vermicompost (T5) probably due to the bioavailability of growth-promoting substances. Microbial population composition and density is an important attribute of soil organic matter quality, as it provides an indication of a soil's ability to store and recycle nutrients and energy. It also serves as a sensitive indicator of change and future trends in organic matter level. The increase in microbial biomass was proportional to the addition of nutrients and accelerated microbial activity (Masto et al., 2006).
Dehydrogenease activity in soil
Dehydrogenease activity increased significantly with application of organic manures (FYM, leaf compost and vermicompost) as compared to inorganic fertilizers and control (Figure 1). Farmyard manure equivalent to 120 kg N ha -1 (T3) resulted in significantly higher activity of dehydrogenase in soil (87 µg TPF g -1 soil 24 h -1 ). Application of vermicompost equivalent to 120 kg N ha -1 (T5) also recorded higher values of dehydrogenase activity in soil, although it was statistically at par with the application of 90 kg N ha -1 either through FYM (T6) or vermicompost (T8). From these results, it can be inferred that dehydrogenase activity is influenced more by the quality than by the quantity of organic matter incorporated into soil. Thus, the stronger effects of FYM on dehydrogenase activity might be due to more easy decomposition of FYM on the metabolism of soil microorganisms (Pancholy and Rice, 1973). It has been shown before that dehydrogenase activity was positively influenced by the total porosity of soil and higher dehydrogenase activity was obtained in soil treated with organic manures (Marinari et al., 2000).
Available N, P and K contents in soil
Available N, P and K increased significantly with application of organic manures (FYM, leaf compost and vermicompost) as compared to RDF and control (Table 3). The application of FYM equivalent to 120 kg ha -1 (T3) resulted in the highest concentration of available N (175.8kg ha -1 ), P (14.8 kg ha -1 ), and K (184.3 kg ha -1 ) followed by vermicompost equivalent to 120 kg N ha -1 although it was statistically at par with 90 kg N ha -1 added as vermicompost or FYM or leaf compost at the equivalent Table 3. Effect of various treatments on available N, P, K, organic carbon and bulk density in popcorn cultivated soil (pooled data).
Treatment
Available N (kg ha -1 ) Available P (kg ha -1 ) Available K (kg ha 120 kg N ha -1 . The available N, P and K with farmyard manures equivalent to 120 kg N ha -1 (T3) was higher by 10, 22 and 14%, respectively, over control. All the organic sources of nutrients improved the available N, P and K status of the soil as compared to the inorganic sources of nutrients and control. Higher N, P, K under organic treatments may be due to continuous application of FYM and organic sources. These sources may enhance organic matter status in soil, which further improves soil physical as well as microbiological activities and increases the availability of plant nutrients (Kumar and Dhar, 2010;Meena et al., 2014). Singh et al. (2008) confirmed the role of organic manures in releasing N and improving N availability in soil. During decomposition of organic manures, various phenolic and aliphatic acids are produced which solubilize phosphatase and other phosphate bearing minerals and thereby lowers the phosphate fixation and increase its availability (Dotaniya et al., 2014a). Available K in soil increased with the application of organic manures which is due to solubilising action of organic acids produced during FYM decomposition and its higher capacity to hold K in available form (Vidyavathi et al., 2011).
Soil organic carbon
Soil organic carbon (SOC) concentration in soil is significantly influenced by different sources of nutrients whether organic or inorganic (Figure 2). In general, postharvest soils of both years had more concentration of SOC (0.48%) than the samples taken before initiation of experiment (0.38%). The SOC contents of soil changes rapidly with addition of organic manures. The highest SOC concentration (0.52%) was observed with the application of FYM equivalent 120 kg N ha -1 (T3). The SOC was also higher with the application of 120 kg N ha - 1 through FYM (T3) followed by application of vermicompost (T5, T6) during both years. The higher value of SOC content in soil with organic manures might be due to biological immobilization and continuous mineralization of FYM on surface soil layer (Ramesh et al ., 2008). The SOC was also improved with the application of the RDF (N 120 P 25 K 35 kg ha -1 ) as compared to control plots. The increase in SOC due to inorganic fertilization could be due to higher root biomass accumulation in fertilized treatment than in control as described previously (Masto et al., 2006).
Bulk density
Bulk density (0-15 cm layer) of the popcorn cultivated soil differed significantly due to various levels and sources of nutrients (Table 3). The values of bulk density were significantly lower (1.42-1.46Mg m -3 ) with the treatments consisting of organic sources of nutrients than the treatments with inorganic fertilizer (1.51 Mg m -3 ) and control (1.53 Mg m -3 ). This could be attributed to higher organic carbon content in these treatments which had better soil aggregate and larger macro pore space (Bellaki et al., 1998). Bulk density was significantly reduced with application of FYM equivalent to 120 kg N ha -1 (T3) as compared to RDF (T2) and control (T1), but it remained statistically at par with other organic sources of nutrients during both years. Organic manures (FYM, vermicompost and leaf compost) decreased the bulk density and improved the soil physical properties due to reduced mass per unit of soil (Das et al., 2002).
Popcorn grain yield
The highest grain yield of popcorn was recorded with the application of recommended dose of fertilizers and 120 kg N ha -1 applied through vermicompost (T5) during both years (Figure 3). The popcorn yield was 51.3 and 46.4% higher with the application of recommended dose of fertilizers and vermicompost, respectively. The higher yield under inorganic fertilizer is attributed to the increased nutrient availability, which significantly increase the yield parameters such as plant height, stem girth, number of leaves, leaf area, leaf area index and dry matter accumulation (Kolawole and Joyce, 2009).
Conclusions
Integrated use of organic manure and inorganic fertilizers improved the enzymatic activities as well as microbial population of bacterial, fungal and actinomycetes of soil used for growth of popcorn. Based on a two years study, it can be concluded that FYM and vermicompost equivalent to 120 kg N ha -1 have almost equally important effects with respect to soil chemical, and biological properties. Decomposition of organic matter and recycling of carbon have substantial effect on the activity of soil enzymes evolved in mineralization of crop plant nutrients. It improved the soil health and microbial population in soil. While, grain yield of popcorn was significantly associated with either recommended dose of fertilizers or vermicompost equivalent to 120 kg N ha -1 .
Figure 1 .
Figure 1. Effect of various treatments on dehydrogenase activity. Same letters (a, b and c) between the two treatments indicate a non-significant difference at P ≤ 0.05 (DMRT-test).
a, b and c) between the two treatments indicate a non-significant difference at P ≤ 0.05 (DMRT-test).
Figure 2 .
Figure 2. Effect of various treatments on soil organic carbon. Same letters (a, b and c) between the two treatments indicate a non-significant difference at P ≤ 0.05 (DMRT-test).
Figure 3 .
Figure 3. Effect of various treatments on grain yield of popcorn. Same letters (a, b and c) between the two treatments indicate a non-significant difference at P ≤ 0.05 (DMRT-test).
Table 1 .
Nutrient concentration in applied organic manures.
Table 2 .
Effect of various treatments on microbial population in popcorn cultivated soil (pooled data).
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Domain: Environmental Science Biology Agricultural and Food Sciences
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Inoculation with Different Nitrogen-Fixing Bacteria and Arbuscular Mycorrhiza Affects Grain Protein Content and Nodule Bacterial Communities of a Fava Bean Crop
: The introduction of nitrogen fixing bacteria (NFB) and arbuscular mycorrhizal fungi (AMF) into the soil is an advisable agricultural practice for the crop, since it enhances nutrient and water uptake and tolerance to biotic and abiotic stresses. The aim of this work was to study plant nutrition, biological nitrogen fixation (BNF) and crop yield and quality, after inoculating seeds with NFBs (( Rhizobium leguminosarum, Burkholderia cenocepacia, Burkholderia vietnamiensis )) and/or AMFs ( Rhizophagus irregularis, Claroideoglomus etunicatum, Claroideoglomus claroideum and Funneliformis mosseae ) in a fava bean crop in two seasons. The composition of the nodule bacterial community was evaluated by the high-throughput sequencing analysis of bacterial 16 S rRNA genes. It was found that microbial inoculation accompanied by a 20% decrease in mineral fertilization had no significant effect on crop yield or the nutritional characteristics compared with a non-inoculated crop, except for an increase in the grain protein content in inoculated plants. None of the inoculation treatments increased biological nitrogen fixation over a non-inoculated level. The bacterial rRNA analysis demonstrated that the genus Rhizobium predominated in all nodules, both in inoculated and non-inoculated treatments, suggesting the previous presence of these bacteria in the soil. In our study, inoculation with Rhizobium leguminosarum was the most effective treatment for increasing protein content in seeds, while Burkholderia sp. was not able to colonise the plant nodules. Inoculation techniques used in fava beans can be considered an environmentally friendly alternative, reducing the input of fertilizers, while maintaining crop yield and quality, with the additional benefit of increasing the grain protein content. However, further research is required on the selection and detection of efficient rhizobial strains under local field conditions, above all those related to pH and soil type, in order to achieve superior nitrogen-fixing bacteria.
Introduction
The alarming increase in the world's population means that the need for fertilizers has also grown in an attempt to reach required food production levels. In this context, the application of nitrogen-fixing bacteria (NFB) for plant cultivation is considered one of the most promising methods for improving the management of nitrogen fertilizers and for increasing agricultural productivity [1], because it may help in achieving (i) a reduction in the external input of fertilizers; (ii) greater efficiency in the way a plant uses N; and (iii) a reduction of N leaching through the soil profile, thus preventing water pollution [2,3].
The N2-fixing symbiosis between most legumes and bacteria is a well-studied example of nodule formation and their subsequent invasion by specific Rhizobia [4]. In this process, Rhizobia fix atmospheric N and, in exchange, bacteria receive carbon compounds derived from photosynthesis [5]. In the case of Fabaceae species, phylogenetic studies have demonstrated that most rhizobial species that form nodules belong to alpha-proteobacteria (Bradyrhizobium, Mesorhizobium, Rhizobium, and Sinorhizobium), and beta-and gamma-proteobacteria (Burkholderia and Enterobacter, respectively) [4,[6][7][8][9][10]. In various legumes, besides the rhizobia that are responsible for nodulation and N2-fixation, other endophytic bacteria, called non-rhizobial bacteria, are also found in the nodules [11]. Some of these have proven beneficial to their legume hosts, as they enhance plant growth by producing plant hormones, fixing atmospheric N2, and solubilizing phosphate [12]. Currently, the 16 S rRNA gene sequencing technique is commonly used for the identification, classification and quantification of microbes and can be used to shed light on both rhizobial and non-rhizobial bacteria within the complex biological mixture found in nodules.
In addition to rhizobial species, arbuscular mycorrhizal fungi (AMF) also play an important role in uptake of soil nutrients, since they act as phosphate solubilizing microorganisms that are able to mineralize organic P and increase the availability of inorganic P [13], which is considered the least accessible nutrient for plants. This nutrient is related to nodule formation and functioning, and is essential for the action of the nitrogenase enzyme in the biological nitrogen fixation (BNF) process [14,15].
Legumes often need inoculation when they are being grown in areas where they have not been traditionally grown or have not been grown for a long time [16]. In this case, or in the absence of symbiotically linked microorganisms, the inclusion of NFB in the soil is recommended [17]. In general, the use of rhizobial inoculants based on autochthonous strains is advisable, because they have superior characteristics of competitiveness in nodule infection and occupancy and, therefore, superior BNF performance in the field, due to their genetic adaptations to the local environment [18,19]. In turn, plants subjected to tripartite symbiosis (NFB-AMF-legumes), compared with noninoculated plants or those inoculated with AMF or NFB alone, show benefits that include enhanced growth and crop yield, and an increased phosphorus and nitrogen content [20,21]. In this respect, dual inoculation has been seen to improve nodulation, nitrogenase activity, mycorrhization and nutrient content (N and P) compared with individual inoculation [22,23].
Based on these approaches, we studied the inoculation of fava bean seeds with a combination of NFBs (Rhizobium leguminosarum, Burkholderia cenocepacia, Burkholderia vietnamiensis) and a pool of AMFs to assess their effect on crop yield and quality and the biological quality of soils. We hypothesized that dual inoculation with NFB and AMF would increase crop yield and the protein content of the edible grain through enhancing biological N fixation more than NFB inoculation alone.
Cultivation Conditions and Experimental Design
This study was carried out in Cartagena, south-eastern Spain (37°41′ N 0°57′ E). The climate of the area is semiarid Mediterranean with a mean annual temperature of 18 °C and annual precipitation of 290 mm. Potential evapotranspiration surpasses 900 mm. The soil of the study site is a Haplic Calcisol (IUSS, 2014) with clay loam texture. The study was conducted in a field where cowpea had been previously grown for three months. The field experiment consisted of a complete randomized block with four replications, and each experimental plot was 10 m 2 . A cultivar of fava bean (Vicia faba L.) 'Muchamiel' was grown under drip irrigation and following conventional management practices for two consecutive seasons. The crop was subjected to eight treatments: 1. inoculation with Rhizobium leguminosarum (RL); 2. inoculation with Burkholderia cenocepacia (BC); 3. inoculation with Burkholderia vietnamiensis (BV); 4. inoculation with arbuscular mycorrhizal fungi (Rhizophagus irregularis, Claroideoglomus etunicatum, Claroideoglomus claroideum and Funneliformis mosseae) (AMF), 5. dual inoculation with Rhizobium leguminosarum and AMF (RL + AMF); 6. dual inoculation with Burkholderia cenocepacia and AMF (BC + AMF); 7. dual inoculation with Burkholderia vietnamiensis and AMF (BV + AMF); and 8. non-inoculated plants (control). A non-inoculated crop of broccoli (Brassica oleracea var. italica L.), also grown under drip irrigation, was included as the reference non-nitrogen fixing species to assess the BNF.
In both seasons, fava beans were sowed in the first week of November, flowering at the end of February, and harvested during April. Broccoli was planted in both seasons in the first week of December. The weather conditions during the first season were: a minimum air temperature of 8.3 °C, mean temperature of 13.2 °C, maximum temperature of 19.3 °C and rainfall of 9.3 mm; and for the second season, a minimum air temperature of 6.5 °C, mean temperature of 12.7 °C, maximum temperature of 17.1 °C and rainfall of 63.9 mm.
Fava bean seeds were inoculated by adding 2 g of the different NFB and 4 g of the pool of AMFs at sowing time. In the control treatment, autoclaved (121 °C for 20 min) inoculants were applied at the same rate. The inoculated and non-inoculated seeds were sown with a spacing of 100 cm between rows and 40 cm between plants (2.5 plants m −2 ). The seeding rate was 2 seeds per sowing hole, leaving only a plant after seedling emergence. Fertilizer application in the fava bean plots started three weeks after sowing, and involved adding 20 kg ha −1 of N and 20 kg ha −1 of P2O5 in the form of ammonium nitrate (33.5% N) and monoammonium phosphate (61% P2O5, 12% N), as well as 40 kg ha −1 of K2O in the form of potassium sulphate (50% K2O, 18% S) as fertigation throughout the crop cycles. Broccoli plants were transplanted two weeks after fava bean sowing, with a planting pattern of 20 cm between plants and 100 cm between rows (5 plants m −2 ) and fertilized similarly to the fava bean crop by fertigation. In the case of inoculated crops, the fertilizer application rate was reduced by 20% compared with the non-inoculated control, to check the efficacy of the inoculations to reduce the use of external fertilizers. No herbicide treatment was carried out, and the crop was kept free of weeds by hand-hoeing when necessary.
Soil Sampling and Analyses
The soil was sampled before the establishment of the trial and at the end of the experiment after harvesting the fava bean crop. All plots were sampled at 0-20 cm (Ap horizon). Three random soil samples per plot were collected, homogenized and sieved <2 mm to obtain a composite sample. The composite soil sample was homogeneous and divided into two sub-samples. One of the sub-samples was air-dried for 7 days, and stored at room temperature until chemical analysis, and the other subsamples was stored at −20 °C for molecular analysis. The following chemical parameters were measured: total nitrogen (Nt) by the Kjeldahl method [24] and exchangeable Ca, Mg, Na and K, which were determined in the BaCl2 extract for cation exchange capacity and measured using ICP-MS (Agilent 7500CE) [25].
Inoculum Preparation
The three strains of nitrogen-fixing bacteria (provided by the Universidade de Trás-os-Montes e Alto Douro, UTAD, Portugal) were isolated from the active root nodules of fava bean plants grown in Portugal, because of their growth-promoting effect in fava bean plants demonstrated in previous greenhouse studies. The bacterium was isolated [26], cultivated and maintained on yeast extractmannitol (YEM) agar medium consisting of 0.4 g yeast extract, 10 g mannitol, 0.5 g K2HPO4, 0.2 g MgSO4.7H2O, 0.1 g NaCl, 8 g agar and 0.25% Congo Red, dissolved in 1000 mL distilled water and autoclaved at 121 °C for 20 min. For inoculation, each bacterium was cultured in 250 mL Erlenmeyer flasks containing 100 mL of YEM broth medium for 3 days at 28 °C. The contents of each flask were diluted to 300 mL with sterilized distilled water in order to obtain 10 9 CFU per mL, estimated from the absorbance at 600 nm, and mixed with 1 kg of the sterilized carrier (compost soil:vermiculite 1:1 v/v), to give approximately 40% moisture in the inoculant (3 × 10 9 cells per gram of inoculum). The AMF were provided by Symbiom (Lanškroun, Czech Republic), and 1 g contained approximately 160 spores (40 spores of each fungal strain per gram).
Plant Sampling
Plants were sampled during fava bean flowering. Three plants per plot were carefully uprooted to obtain unharmed roots, and separated into root, shoot, nodules and seeds in the case of the legumes, and into root and shoot for broccoli to assess biological N fixation. An assessment of the bacterial community was only performed on nodules from plants uprooted during the second season. For this, the nodule surface was sterilized by immersing in a 95% ethanol solution for 45 s, washed with sterile water, crushed in liquid nitrogen and then stored at −80 °C until DNA extraction.
Fava bean yield (kg ha −1 ) was determined by continuously harvesting and weighing all the fresh pods in each plot. Nodules were dried in an oven at 70 °C to reach a constant weight and their dry weight per plant was determined. The weight of fresh fava bean pods was used to measure yield since in some Mediterranean countries, such as Spain, fava bean is typically consumed as a green bean. In addition, the following quality parameters were recorded: protein content in grain (%), number of pods per plant, weight of 100 seeds and pod length.
Plant Analyses
Plant samples were oven dried and ground (A11 Basic, IKA) before incinerating at 500 °C; the ashes were dissolved in 0.6 N HNO3 and analysed for P, Ca, Mg, Na and K by ICP-MS (7500 CE, Agilent). Nitrogen (N) was determined by the Kjeldahl method [24]. The protein content in grain was derived from the estimated N content by the following formula [27]: Protein content (%) = N content (%) × 6. 25 (1)
Efficiency of Biological Nitrogen Fixation
The 15 N natural abundance method was used to determine the efficiency of biological nitrogen fixation (BNF). The 15 N content of the plant samples was determined in the Stable Isotope Facility of UC-Davis, Davis, CA, USA, by CF-IRMS (Europa Scientific, Crewe, UK). This method is useful when the abundance of 15 N in the soil is higher than in atmospheric N2 (0.3663%). The differences (δ 15 N) between the 15 N abundance in each sample and in the atmospheric N were calculated using Equation (2) [28]: To calculate the proportion of N derived from air (% Ndfa), it is necessary to know the δ 15 N of the N2-fixing legume and the δ 15 N of the non-fixing reference plant (broccoli) grown in the same soil (Equation (3)
DNA Extraction, PCR Amplification and Processing of Sequencing Data
Total genomic DNA was extracted from 0.5 g of nodule from three replicates using Genomic DNA for plant (Nucleo Spin R Plant II, Macherey-Nagel, Düren, Germany) following the manufacturer´s protocol. The DNA concentration was determined using spectrophotometer (Nanodrop, 2000, Thermo Fisher Scientific The Meern, The Netherlands). The DNA was PCR amplified using the barcoded primers 515 F and 806 R [31] in three PCR reactions per sample as previously described by Žifčáková et al. [32]. The PCR reactions contained 2.5 μL of 10 × buffer for DyNAzyme DNA Polymerase; 1.5 μL of BSA (20 mg mL −1 ); 1 μL of each primer (0.01 mM); 0.5 μL of PCR Nucleotide Mix (10 mM each); 0.75 μL of polymerase (DyNAzyme II DNA polymerase 2 U/μL + PFU 200 μL D + 6.6 μL PFU); 1 μL of template DNA; and 17.75 μL of water. The cycling conditions were as follows: 94 °C for 4 min, 35 cycles of 94 °C for 45 s, 50 °C for 1 min, 72 °C for 75 s, and a final extension at 72 °C for 10 min. The sequencing of bacterial amplicons was performed on Illumina MiSeq, and the sequences were generated with a MiSeq Reagent Kit v2 in paired-end mode with sizes of 251 base pairs in the C4SYS facility at the Institute of Microbiology of the CAS, Prague, Czech Republic.
The amplicon sequencing data were processed using the pipeline SEED 2 [33]. Briefly, pair-end reads were merged using fastq-join [34], and whole amplicons were processed. Chimeric sequences were deleted using Usearch 7.0.1090 [35]. UPARSE implemented in Usearch [36] was used for clustering at a 97% similarity level. Consensus sequences were constructed for each cluster, and the closest hits at genus or species level were identified using BLASTn against the Genbank databases. Sequences identified as non-bacterial and singletons were excluded from further analyses. Sequences were upload to NCBI repository.
Statistical Analyses
Data were checked to ensure normal distribution using the Kolmogorov-Smirnov test and lntransformed when necessary to ensure normal distribution. A one-way repeated measures ANOVA, with season (1, 2) as within-subject factor, and inoculation treatment (RL, RL + AMF, BC, BC + AMF, BV, BV + AMF, AMF and CONTROL) as between-subject factors was developed. Data without normal distribution were submitted to a non-parametric ANOVA (Kruskal-Wallis test) for the factor inoculation treatment. To assess the effect of season, the paired-data Friedman non-parametric test was used. Relationships among properties were studied using Pearson's correlations. Statistical analyses were performed with the IBM software SPSS for Windows, Version 22.
Biological Nitrogen Fixation
The inoculation treatment did not significantly affect BNF in roots (Table 1), while the season did so, with significantly lower values in the second season. In addition, BNF was not significantly correlated with plant nutrients, nodule weight, crop yield or crop quality parameters (data not shown).
Plant Nutritional Characteristics
Inoculation treatment significantly affected the N content of seeds, shoots and roots. Inoculation with Rhizobium leguminosarum (RL) led to a significant increase of N in seeds compared to the control ( Table 2). In turn, dual inoculation with RL and AMF significantly increased the N concentration in shoots in both seasons compared with inoculation with RL alone (Table 3). However, the N content of roots was significantly higher after inoculation with Burkholderia cenocepacia (BC) and BC + AMF than in the control (Table 4). N content in different plant parts (seed, shoot and root) was also significantly higher after inoculation with AMF than in the control in Season 2. As a general pattern, most nutrients measured in the different plant parts (seeds, shoot and root) were higher in the second than in the first growing season. Seed Mg, Na, K and P concentrations were positively correlated with each other (R > 0.75; P < 0.01). In shoots, Ca, Mg, Na and P concentrations were positively correlated with each other (R > 0.73; P < 0.01), as were Mg, K and P concentrations in roots (R > 0.69; P < 0.01).
Crop Yield and Quality
Inoculation did not significantly influence the dry weight of nodules, crop yield, weight of 100 seeds and number of pods per plant (Table 5). However, it affected the protein content of the grain, and higher values were recorded after inoculation with NFB, AMF and the combined inoculations than in the control. Season influenced crop yield and the number of pods per plant. Crop yield and number of pods per plant were higher in the first season than in the second.
Among the different NFB inoculation treatments, Proteobacteria were the least abundant and Verrucomicrobia the most abundant in the BV treatment, while, in the AMF treatment, Bacteroidetes were the most abundant compared to the other treatments ( Figure 2). Overall, two α-rhizobia (Rhizobium and Bradyrhizobium) were detected, the most abundant being Rhizobium in all the treatments. However, Bradyrhizobium was only detected in the RL treatment. Among the non-rhizobial bacteria detected were the genera Pseudomonas, Devosia, Agrobacterium and Rhodococcus (Figure 3). Pseudomonas was more abundant in the AMF treatment, followed by RL + AMF, BC and BVAMF. Agrobacterium was only detected in RL, RL + AMF; BC, BV and; BV + AMF. Devosia was only detected in BC + AMF, BV + AMF, AMF and the Control, while Rhodococcus was detected in all the treatments except for RL and BV + AMF (Figure 3). Among the non-nodulating bacterial endophites detected were the genera Variovorax, Arthrobacter, Bacillus, Streptomyces and Ensifer. Of these, Variovorax were most abundant in AMF followed by RL + AMF, RL, BV and BV + AMF. Arthrobacter was only detected in BV, RL + AMF and BC + AMF, and Bacillus only in RL + AMF, BV, AMF and the Control. Streptomyces was highest in RL and BV and Ensifer was lower than control values in all the treatments.
As regards the other genera found (Figure 3), Flavobacteria and Dyadobacter showed the highest abundance in BV and AMF; Methylotenera and Acidovorax in BV and Chrysebacterium in the AMF treatment.
Taken together, these results suggest that microbial inoculation accompanied by a 20% decrease in mineral fertilization had no significant effect on crop yield or the nutritional characteristics compared with non-inoculated plants, except for an increase in the grain protein content in inoculated plants. Furthermore, the genus Rhizobium predominated in all nodules, both in inoculated and non-inoculated plants, suggesting the previous presence of these bacteria in the soil.
Discussion
Despite the reduction in the amount of fertilizer applied in the inoculated treatments compared to that applied in the control treatment, crop yield, quality and nutritional characteristics were maintained. This may be attributed to the beneficial properties of the inoculated microorganisms, alone or in combination, including better mineral solubilization and organic matter mineralization as a result of phytohormones, organic compounds or enzymes being released [38,39]. These microorganisms would interact with plant root exudates in the rhizosphere [40,41], inducing an improvement in plant growth [10].
The differences with regard to season observed in most plant properties could be related to differences in atmospheric temperature. As indicated in Section 2.1, the first season was warmer than the second, which may have contributed to higher crop growth and production, and the increased nutrient content. The higher rainfall during the second season had no effect on production since the crop was irrigated, and there was no water deficit at any time during the entire cycle.
Inoculation with Rhizobium leguminosarum (RL) led to a higher nitrogen and protein content in fava bean seeds than in the non-inoculated seeds, suggesting that subsequent N assimilation by the plant was enhanced with RL, and that the efficiency of protein anabolism was improved. This finding agrees with that of Alsina et al. [42] who found that some RL strains promoted protein accumulation in the seeds of two fava bean cultivars, their efficacy being dependent on interaction between strain, soil conditions and cultivar. Therefore, RL could be considered as a key bacterial symbiont to improve and protect yield components and seed quality for sustainable agricultural systems [43]. The ability of rhizobia to increase the protein content in fava beans has been observed previously, particularly when supplemented with nitrogen fertilizer [44]. In a previous study with common bean cultivated in reduced fertilization, a higher protein content was found in seeds of Rhizobium-inoculated plants in combination or without AMF than in inoculated plants, showing that AMF-inoculated plants had higher nutrient seed accumulation [45]. In our study, inoculation with AMF led to higher nitrogen content in seeds, shoots and roots in the second season than in the non-inoculated plants, indicating the influence of AMF on plant growth, due to a higher nutrient use efficiency by the plant [46]. Contrary to our hypothesis, the dual inoculation of rhizobial bacteria and AMF did not increase nitrogen fixation or crop yield in the same way as other authors showed [47,48]. This could be due to competition between the different AMF applied and those already present in the soil, which would affect the final extent of plant colonization. Moreover, Scheublin et al. [49] found that AMF colonisation of the nodules may inhibit N fixation.
Inoculation with NFB, alone or combined with AMF, did not lead to a higher amount of biologically-fixed N, which may also be due to the competition with native microbes or a higher nutrient use efficiency [50]. In this respect, Menge et al. [51] observed greater biological nitrogen fixation when N was a determinant nutrient. The high abundance of Rhizobium and other rhizobia bacteria in nodules observed in the different treatments would imply that rhizobia bacteria (an integral part of the soil microbial community) can remain viable in soil for several seasons [52], and that inoculation with specific rhizobia bacteria would not be sufficient to overcome the effect of the native ones. Thus, the differences found in the ability to colonise the nodules by the rhizobia bacteria demonstrate that specific bacterial determinants contribute to their acceptance by the host [53].
Legume nodules occupy a distinctive ecological niche, with a programme adapted to the accommodation of compatible soil microbes [4]. As our results indicate, legume nodules are often occupied by a phylogenetically diverse microbial community apart from rhizobia. However, the RL treatment induced a higher nitrogen and protein content than the other treatments. It is assumed that when legume plants are exposed to complex communities, they selectively regulate the access and accommodation of bacteria occupying this specialized environmental niche-the root nodule [54]. Furthermore, Burkholderia was not detected in nodules of different treatments, probably due to its low ability to form nodules in the presence of other native strains, which showed superior characteristics of competitiveness, and its lack of adaptation to the local environment [55]. It has been recently demonstrated that species of the genus Burkholderia have higher affinity for acidic soils, while they can be replaced by alpha-rhizobia in alkaline habitats [56], as could happen in the soil where this experiment was carried out.
In addition to rhizobia, non-rhizobial bacteria are considered to be able to infect legume nodules and multiply within. The fact that we found Pseudomonas, Devosia, Agrobacterium and Rhodococcus in nodules points to the coexistence of both types of bacteria in fava bean nodules, where they might have different roles [52,57]; for example, inoculation with Bradyrhizobium and Pseudomonas was seen to play a growth promoting role in the nodule ecosystem [58], and Agrobacterium and Rhodococus induced nodule formation [59,60].
Non-nodulating bacterial endophites (Variovorax, Arthobacter, Bacillus, Streptomyces and Ensifer) were also found in the nodules, as other authors have reported [61,62]. According to Xu et al. [63], Bacillus is the most abundant non-nodulating genus found in nodules, whereas it was only detected in three treatments in our experiment.
No significant differences were observed in the Shannon diversity index in nodules, although the BV and AMF treatments showed a slight increase compared to the other treatments and the control, but no correlation was found with crop yield and quality parameters, or with nitrogen fixation. In AMF, the genera Bacillus, Flavobacteria, Dyadobacter and Chrysebacterium were more abundant than in the other treatments; however in BV, the genera Methylotenera, Acidovorax, Bacillus, Streptomyces, Dyadobacter, Arthrobacter and Flavobacteria were more abundant than in the other treatments, while they have also been found in the nodules of other legumes [64,65].
In conclusion, the inoculation with Rhizobium leguminosarum (RL) produced the highest seed N content and protein in grain. However, none of the treatments increased biological nitrogen fixation compared with the plants grown from non-inoculated seeds. Rhizobium predominated in all nodules, and non-rhizobial and non-nodulate bacteria were also present. The lower amount of mineral fertilizers applied to the assayed soil can be considered an environmentally friendly alternative for reducing their use. Inoculation with RL was seen to be the most effective treatment, while Burkholderia sp. were not able to colonise the plant nodules. However, further studies are needed on the selection and detection of efficient rhizobial strains under local field conditions in order to obtain superior nitrogen-fixing bacteria.
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Domain: Environmental Science Biology Agricultural and Food Sciences
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NUTRITIONAL DIAGNOSIS FOR EUCALYPT BY DRIS , M-DRIS , AND CND Gualter
The evaluation of the nutritional status in eucalypt (Eucalyptus grandis W. Hill ex Maid.) forests through vegetal tissue analyses what reflects water and nutrient flows in the system, and represents a complementary tool to soil analysis can be helpful to raise and maintain the forest productivity at high levels. This study compared the use of the Diagnosis and Recommendation Integrated System (DRIS), ModifiedDRIS (M-DRIS), and Compositional Nutrient Diagnosis (CND) diagnose methods in eucalypt stands in Central-Eastern Minas Gerais State, Brazil. Data of productivity and of N, P, K, Ca, and Mg leaf contents in 993 Eucalyptus grandis stands aging between 72 and 153 months, planted on six sites in 3 × 2 m spacing, were used. The nutritional status was diagnosed by the DRIS, M-DRIS, and CND methods, and validated by the chi-square (χ) test applied to the nutrients diagnosed as primary limiting by deficiency. These three methods were compared to each other based on the diagnosis concordance frequency (DCF) derived from the fertilization response potential (FRP) by the criteria considering each nutrient separately; from all (5) to none (0); and only the primary limiting nutrients by either deficiency or excess. The diagnosis concordance level among the methods was procedure-dependent, and varied according to the nutrient concentration in trees.
INTRODUCTION
The use of the critical level for the evaluation of crops or forests nutritional status is questionable, since it does not define whether the deficiency is acute or not, nor if the nutrient is the most limiting when more than one nutrient is classified as deficient (Baldock & Schulte, 1996). Furthermore, nutrient tissue contents are influenced by dilution or concentration effects caused by variations in the dry matter yield quantity (Jarrel & Beverly, 1981).
Diagnosis methods dealing on plant tissue analyses play a key role on precise definition and interpretation of the nutritional plant status, since reveals greater constancy of nutrient relations, compared separately to each nutrient content, as well as in relation to the tissue age (Beaufils, 1973). The Diagnosis and Recommendation Integrated System (DRIS) and the modified DRIS The DRIS method, proposed by Beaufils (1973), is based on the comparison of dual relationships (N/P, P/ K, K/Ca, Ca/Mg, etc.) in samples with standard or norms values. The M-DRIS method (Hallmark et al., 1987) also considers nutrient contents, and not only their dual relationships. On the other hand, the CND method (Parent & Dafir, 1992), relies on studies developed by Aitchison (1982), which involve statistical composition data analysis, based on the establishment of multinutrient variables (z), weighed by the geometric mean of the nutritional composition.
Consistency of interpretation of plant tissue analysis increases according to the extent by which the univariate approach (of the critical level) is amplified, so that two-by-two relationships (dual relations) between nutrients are observed, (bivariate approach). Progressively, ternary relations are included, until ideally, through multivariate focus, the entire variation structure of the nutritional composition is embraced (Holland, 1966).
The most outstanding studies among the many that use DRIS diagnosis methods are eucalypt Wadt (1996) and Wadt et al. (1998a), for eucalyptus; Hartz et al. (1998) for tomato, and Reis Jr. & Monnerat (1998) for sugarcane; for M-DRIS, Hallmark et al. (1989;1990) for soybeans, (Creste et al., 2001) for maize; and for CND, Parent et al. (1994);and Khiari et al. (2001) and for potato, and (Raghupathi & Bhargava, 1999) for grape. A comparison of the nutritional diagnosis methods, with well-differentiated characteristics of index calculation and interpretations, is fundamental to identify differences between results yielded by each method, allowing an enhanced diagnosis of the nutritional state. The present study compares the use of the methods DRIS, M-DRIS, and CND in eucalypt forests, at sites in the Central-Eastern region of Minas Gerais State, Brazil.
MATERIAL AND METHODS
Data of productivity and N, P, K, Ca, and Mg contents in Eucalyptus grandis leaves were used. The trees, aging 72 to 153 months, were planted in 3 × 2 m spacing at six sites: Cocais, Piracicaba, Rio Doce, Sabinópolis, Santa Bárbara, and Virginópolis, in the Central-Eastern region of Minas Gerais State, in 993 stands. Sites' geographic location and altitude are represented in Table 1.
Data were stratified according to their sites: Cocais n = 191, Piracicaba n = 201, Rio Doce n = 54, Sabinópolis n = 198, Santa Bárbara n = 180, and Virginópolis n = 169 stands. The stand population was stratified according to age, in 12-month intervals, counted from the youngest age on. For each age class, mean and standard deviation of the mean annual stem volume in-crease (IMA) were calculated, and the population classified in stands of low and high productivity. The latter was defined as population of reference (> µ + 0.5 SD).
Diagnosis of the nutritional status were made by the methods DRIS (Beaufils, 1973) and M-DRIS (Hallmark et al., 1987), where all relationships (direct and inverse forms) were taken into consideration. The diagnosis was also made by the Compositional Nutrient Diagnosis (CND) method, according to Parent & Dafir (1992).
The norms, site-specific and regarding the reference population, consisted in: mean and standard deviation of all dual relations between the studied nutrients for the DRIS; mean and standard deviation of the N, P, K, Ca, and Mg contents and of all dual relations for the M-DRIS; mean and standard deviation of the multinutrient variables zN, zP, zK, zCa, and zMg; and g(x) (Parent & Dafir, 1992) for the method CND, calculated as: where g(x) = geometric mean of the nutritional composition; N, P, K, Ca, and Mg = respective nutrients contents (g kg -1 ); R = value of the complement to 100 g kg -1 of dry matter in relation to the sum of N, P, K, Ca, and Mg; D = number of diagnosed nutrients, including the complement (R); z i = multinutrient variable; and x i = nutrient content for which the multinutrient variable is calculated.
For the calculation of the DRIS and M-DRIS functions, Jones' equation (1981) was used as follows: where 10 = sensitivity coefficient (Black, 1993); A/B = dual relation between the "A" and "B" nutrient concentrations (g kg -1 ) of the diagnosed subpopulation; a/b = dual relation between the "a" and "b" nutrient concentrations (g kg -1 ) of the reference subpopulation; and s = standard deviation of the dual relation of the reference subpopulation. Founded on the values of all DRIS functions, the DRIS index for each nutrient was calculated as follows: where I A = DRIS index of the nutrient; = mean of the DRIS functions; f(A/B) and f(B/A) = DRIS functions in the direct and inverse forms, respectively; and n = number of DRIS functions (f). Subsequently, the mean nutritional balance index (IEN m ) (Wadt et al., 1998b) for the different sites was obtained by summing up the nutrient indices in a module and by dividing this value by the number of analyzed nutrients.
To calculate the M-DRIS functions and indices, and the dual relations, the nutrient contents were taken into account. In analogy to the establishment of the DRIS indices by means of the nutrient content functions involved in the diagnosis, the M-DRIS dry matter index was obtained too. The following equation allowed the calculation of the Iz i indices for the CND method: where Iz i = index of the multinutrient variables; Z i = multinutrient variable of the diagnosed sample; z i = mean of the multinutrient variable in the reference subpopulation; and sz i = standard deviation of the multinutrient variable in the reference subpopulation. As for the IEN m (Wadt et al., 1998b) calculated for the DRIS, this index was computed for the CND at the different sites. For the DRIS and M-DRIS methods, the function and index calculations were performed with routines developed in Excel 5.0; zN through zMg as well as the CND nutrient indices were also established with Excel 5.0.
The nutritional status diagnosis made by the three methods was statistically validated by the chi-square test (α = 0.10) based on the counted frequency of the number of times each nutrient appeared as primary limiting by deficiency. As zero hypothesis (H 0 ) was considered the one where this frequency was random-attributed, suggesting that the observed and expected frequencies did not differ from each other; the alternative hypothesis (H 1 ) indicates that there are differences between the observed and the expected nutrient frequencies. For each method, the expected frequency was equal to the total number of observations at each site divided by the total number of analyzed nutrients.
The concept of the fertilization response potential (FRP) (Wadt et al., 1998b) was adopted to interpret the DRIS and CND indices (Table 2), and the M-DRIS index (Table 3). The nutrients of the low-productivity stands at each site were therefore classified according to the fertilization response potential: positive (p), positive or zero (pz), zero (z), negative or zero (nz), and negative (n). For easier interpretation of the M-DRIS indices, the adjusted M-DRIS index -the difference between the index of a certain nutrient and of the dry matter -was calculated.
The DRIS, M-DRIS, and CND methods were compared, based on the diagnosis concordance frequency (DCF) derived from the fertilization response potential (FRP), focusing on the following situations: separate nutrients; from all (5) down to none (0); and only for the primary limiting deficient nutrient (p) and excessive nutrient (n). Moreover, low-productivity subpopulation stands were selected at Virginópolis with different nutrient concentration degrees in the trees, considering the IMS (M-DRIS) and G(X) (CND) values, in order to verify the behavior of these methods regarding the diagnose interpretation.
RESULTS AND DISCUSSION
High productivity stands presented a mean frequency of 30.61% and low variation among the sites (from 27.8% in Virginópolis to 33.3% in Rio Doce) (Table 4), reflecting the statistical criterion used to separate high and low-productivity subpopulations. The percentage increases of stem productivity in the high-productivity stands in relation to the low-productivity ones were the following: Cocais 79.7, Piracicaba 81.9, Rio Doce 51.3, Sabinópolis 73.6,Santa Bárbara 78.2, Virginópolis 52.3, and 70.9 for the all sites. Except for Rio Doce, productivities of the reference population, although not and zero (z).clonal, presented quite satisfactory values (Table 4). Although the studied eucalypt populations aged between 72 and 153 months, stratification according to age class certainly contributed to equalize possible age effect, even though small, in the referred age group. The norms determined for the DRIS, M-DRIS, and CND methods at the different sites are displayed in Table 5. Note the values of the dual relations and the multinutrient variables differed, in most situations, between high and low-productivity subpopulations, based on the variance ratio or, much more frequently, in relation to the means.
The validation of the diagnosis (norms) is traditionally carried out by factorial schemes, based on fertilization experiments (Caldwell et al., 1994;Payne et al., 1990;Elwali & Gascho, 1984;Bailey et al., 1997). Because of the lengthy time required to determine results, this kind of validation is more complicated for a crop like eucalypt. In this case, an alternative to validate results is to determine whether the frequencies of the most limiting nutrients (response p) are randomized or not.
In this study, corresponding to the one used by Wadt et al. (1998a), the test of hypothesis was carried out by the chi-square test applied to the counted frequency in which each nutrient appeared as primary limiting by deficiency (response p) at the different sites by the DRIS, M-DRIS, and CND methods. With exception of the Rio Doce site, the referred frequency cannot be attributed to coincidence, in other words, the indices obtained by these methods are viable to evaluate the nutritional status of eucalypt trees (Table 6).
A comparison among the DRIS, M-DRIS, and CND methods was performed by means of specific norms, based on the frequency of concordant diagnoses (DCF) derived from the fertilization response potential (FRP), where three different criteria were taken into consideration. The first criterion considered the nutrients N, P, K, Ca, and Mg separately in the DCF evaluation of the FRP, and mean values of 65.0% (DRIS vs M-DRIS), 86.3% (DRIS vs CND), and 68.6% (M-DRIS vs CND) were established for all sites (Table 7). However, for the second criterion, the DCF of the FRP for all sites, considering all (5) nutrients, had means of 17.4% (DRIS vs M-DRIS), 54.3% (DRIS vs CND), and 20.3% (M-DRIS vs CND) (Table 8), meaning that, 82.6%, 45.7%, and 79.7% of the stands were diagnosed differently in at least one nutrient, respectively. This kind of comparison expresses the highest level of similarity among the methods.
A third criterion of evaluation was introduced, in a less rigorous but more adequate comparison from a practical point of view. The DCF of the FRP for the primary limiting by deficiency (p), considering all sites, was 77.8% (DRIS vs M-DRIS), 90.4% (DRIS vs CND), and 79.0% (M-DRIS vs CND) (Table 9), whereas the primary limiting by excess (n) was 42.7%, 85.6%, and 43.9%, respectively.
As observed for the response "n", the concordance is smaller when M-DRIS takes part in the comparisons. This results from the fact that, in average, the stands present nutrient dilution, evidenced by the positive dry matter index values (IMS), relative to the DRIS, M-DRIS, and CND index analysis (Silva, 2001). Therefore, the concordance among the methods may vary according to the nutrient dilution (or concentration) degree in the tree according to the diagnosis method. Finally, the M-DRIS (by the IMS) and the CND (by the G(X)) provide information on the referred degree. These methods differ, however, in the aspect that M-DRIS uses the IMS as primary reference, while the G(X), in the CND, does not establish any reference for the diagnosis, at least in the way it has been used in the present study. It is therefore fair to suggest that M-DRIS is sensitive to the effects of dilution or concentration, a fact that calls for further comment.
Selected stands of the low-productivity subpopulation of the Virginópolis site with, different nutrient concentration degrees in the trees (Table 10) where thus investigated, as indicated by the values of IMS (M-DRIS) and of G(X) (CND). The M-DRIS did not detect any limi- tation by deficiency, represented by the positive response (p) or positive or zero (pz) of the fertilization response potential. The DRIS and the CND, on the other hand, presented similar performance at detecting these responses. The observation of stands where expressive nutrient concentrations (for example, IMS < -15 or G(X) > 1.5) are found, despite the low productivity, indicates that the cause of the low productivity probably lies in problems other than nutritional. On the other hand, in less concentrated stands (for example, IMS = -3.15 or G(X) = 1.196)where not all nutrient contents are greater than the mean values of the respective nutrients of the Virginópolis site (Table 5), M-DRIS did not detect any response, neither "p" nor "pz", either, whereas the DRIS and the CND gen- ) and standard deviation (s) (norms) of the N, P, K, Ca, and Mg leaf contents, of their dual relations, and the CND 1 variables in Eucalyptus grandis, subpopulation of high productivity 2 , at sites in the Central-Eastern region of Minas Gerais. Parent & Dafir (1992); 2/ > than mean + 0.5 standard of deviation; 3/ MS = dry matter; 4/ number of observations; 5/ geometric mean of contents.erally identified some nutritional limitation by deficiency. Thus, the DRIS and the CND, in the suggested situation, would recommend fertilization, but that would not happen if the diagnosis were made by the M-DRIS. Therefore, if the limitations of these stands were really of nonnutritional nature, the use of the M-DRIS nutritional di-
Sci
) of the frequency of stands considered primary limiting by N, P, K, Ca, and Mg deficiency, in leaves of the aerial part of Eucalyptus grandis, subpopulation of low productivity 1 , using DRIS 2 , M-DRIS 3 , and CND 4 , by means of specific norms for each site, in the Central-Eastern region of the State of Minas Gerais.agnosis would appear more appropriate. However, if the low productivity were a consequence of nutritional problems as well, the application of the nutritional diagnosis through the DRIS or CND could help boost the productivity, if the non-nutritional problems were also solved.
The productivity can be limited by macronutrients (S) and by micronutrients, for which the respective norms have not been calculated. Depending on the chosen form of comparison and the nutrient concentration degree in the trees, very different results can be obtained in relation to the concordance of the methods. The M-DRIS was more sensitive to identify stands with non-nutritional problems, com-pared to the other methods. In stands with lower nutrient contents and some nutrient concentrations below those of the reference population, the M-DRIS does not detect this limitation, in spite of its nutritional character. In such cases, using diagnosis methods might be more adequate, even though they have lower sensitivity to identify nonnutritional problems.(1987), using the equation of Jones (1981) for the calculation of the functions; 5/ Parent & Dafir (1992); p = primary limiting by deficiency; n = primary limiting by excess.
Table 10 -Productivity (IMA), indices of primary and mean nutritional balance (IEN m ), nutrient contents, fertilization response potential 1 of N, P, K, Ca, and Mg using the methods DRIS 2 , M-DRIS 3 , and CND 4 , applied to the leaves, in Eucalyptus grandis stands, selected from the low-productivity 5 subpopulation from the Virginópolis site, in the Central-Eastern region of Minas Gerais.
Table 1 -
Geographic coordinates and altitude of the sites.
Table 2 -
Interpretation of the DRIS and CND indices of the nutritional diagnosis in eucalypt plantations in function of the fertilization response potential (FRP).
Table 3 -
Interpretation of the M-DRIS indices of the nutritional diagnosis in eucalypt plantations in function of the fertilization response potential (FRP).
Table 4 -
Productivity, number, and mean age of Eucalyptus grandis stands in the Central-Eastern region of Minas Gerais.
Table 7 -
Frequency of stands with concordant diagnoses of the fertilization response potential 1 for N, P, K, Ca, and Mg in Eucalyptus grandis, subpopulation of low productivity 2 , in the Central-Eastern region of Minas Gerais, among the methods DRIS 3 , M-DRIS 4 , and CND 5 by means of specific norms for each site, applied to the leaves.
Table 9 -
Frequency of stands with concordant diagnoses of the fertilization response potential 1 (p and n) in Eucalyptus grandis, subpopulation of low productivity 2 in the Central-Eastern region of Minas Gerais, among the methods DRIS 3 , M-DRIS 4 , and CND 5 , by means of specific norms for each site, applied to the leaves. 1/ Wadt et al. (1998b); 2/ ≤ than mean + 0.5 standard deviation; 3/ Beaufils (1973), considering the IEN m proposed by Wadt et al. (1998b); 4/ Hallmark et al.
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Domain: Environmental Science Biology Agricultural and Food Sciences
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Effect of Aspergillus awamori-Fermented Burdock Root on Mouse Diabetes Induced by Alloxan — Prevention of Cell Apoptosis
Root of burdock contains high amounts of dietary fibers and polyphenols. To improve the functional properties, the root was fermented with Aspergillus awamori. Effect of the fermented burdock on alloxan-induced mouse diabetes was examined. A diet containing the 5% fermented burdock powers was prepared to examine effect of the burdock diet on alloxan-induced mouse diabetes. Mice fed the burdock diet and the control diet for 14 weeks. Then, alloxan (200 mg/kg of body weight) was administrated to each mouse. After 5 days from the administration, blood glucose assay and glucose tolerance test were carried out. Incidence of hyperglycemia decreased and the glucose metabolism was improved when mice fed the burdock diet. Insulin, C-peptide, biomarkers of oxidative stress in plasma and apoptosis in pancreas were examined and compared to those obtained from mice fed the control diet. It is deduced that alloxan-induced diabetes is caused to lower insulin concentration. The fermented-burdock diet improves the diabetes and prevents apoptosis in the pancreas.
Introduction
Catalase (EC1.11.1.6)has a predominant role in removing high concentration of hydrogen peroxide. The catalase deficient animal is called as acatalasemia and is sensitive to the oxidative stress [1]. Alloxan is a diabetogenic drag, and the treatment of animals caused a specific necrosis of pancreatic islets to result in an animal model of insulin dependent diabetes mellitus [2]. We indicated that a smaller amount of alloxan administration to the acatalasemic mouse developed diabetes than that to normal ones [3], and then deduced that acatalasemic animals promoted diabetes faster and more frequently than normal ones [4]. The root of burdock, Artium lappa, is taken as a vegetable in Japan and contains high amounts of dietary fibers as well as polyphenols such as chlorogenic and caffeic acids [5] [6]. However, there are few preparations to take considerable amounts of the roots as foods. To improve the functional properties, the roots were fermented with Aspergillus awamori, and the fermented burdock was prepared [7]. When rats fed the fermented burdock, their intestinal environments were improved and the obesity was suppressed. As it was known that the injury caused by oxidative stress was ameliorated by the intake of antioxidant [8] [9], we examined effect of fermented burdock as a source of antioxidants on alloxan-induced mouse diabetes.
Materials
Male mice of the C3H/AnL CS a CS a (normal) and C3H/AnL CS b CS b (acatalasemia) strains established by Feinstein, Braun, & Howard [10] were maintained on a laboratory diet (CE-2 diet, Clea Japan, Tokyo) and water ad libitum until the start of the experiments. Fermented burdock powders were prepared from burdock roots by Ahjikan Co. Ltd (Hiroshima, Japan) and Yaegaki Bio-Industry Inc. (Himeji, Japan) [7]. A fermented burdock diet was consisted of 5% of the burdock powders and 95% of AIN-93M formula diet (w/w) [11], and the control diet was AIN-93M. Pellets of these diets (1.3 cm) were prepared and stored at −20˚C until use.
Animal Experiments
Acatalasemic mice and normal mice (15 weeks old) were divided into two groups, respectively. One group was maintained on the burdock diet and another group the control diet for 14 weeks. Then, alloxan (200 mg/kg of body weight), as an oxidative stress, was intraperitoneally administrated to each mouse [8]. Mice in each group were maintained on the same diet for one more week. After five days from alloxan administration, mice were fasted, and a glucose tolerance test (GTT) was carried out. After seven days, blood was collected from each mouse heart, and heparin was used as the anticoagulant. Oxidative stress markers, as well as the insulin and C-peptide levels in plasma, were examined. Pancreas was also isolated, and the sections were prepared for microscopic studies.
Determination of Glucose in Blood
Mice were fasted. The glucose content in the blood obtained from the tail was determined. As the blood volume for the determination of blood glucose was quite small (approximately 2 μL), the glucose contents in blood were measured with a "Glucose-Test-Ace R" apparatus (Sanwa Kagaku Kenkyusho Co., Nagoya, Japan) applying a glucose oxidase method.
GTT
After fasting, a forty percent aqueous glucose solution (5 mL/kg of body weight) was intraperitoneally administered to each mouse [8] [12]. At 0 and 30 min before and 15, 30, 60, 90 and 120 min after the administration, the glucose contents in the blood were measured.
Measurement of the Oxidative Stress Markers
The measurement of 8-oxo-2'-deoxyguanosine (8-OHdG) in plasma was carried out using an ELISA KIT (JaI-CA, Shizuoka, Japan). Sample or standard solution was added to each well in a 96-well plate coated with 8-OHdG, and a monoclonal antibody for 8-OHdG was added. The mixture was reacted for 12 hrs. Then, an en-zyme-conjugated antibody was added and reacted for 1 hr. The reagent solution for the color reaction was added and reacted for 15 min. The absorbance at 450 nm was recorded. Lipid peroxidation in plasma was determined using a Bioxytech LPO-586 KIT (OXIS Health Products Inc, CA, USA). Malondialdehyde and 4-hydroxyalkenals as products of lipid peroxidation were reacted with N-methyl-2-phenylindole at 45˚C. The absorbance at 586 nm was recorded. Values of lipid peroxide in blood were calculated as malondialdehyde.
Determination of the Insulin and C-Peptide Levels in Blood
The insulin and C-peptide plasma levels were determined using Mouse Insulin and C-peptide ELISA KITs (U-type) (Shibayagi, Gunma, Japan). Each determination was carried out according to the manufacturer's instructions. Biotin-conjugated anti-insulin antibody (45 μL) was added to each well in an antibody-coated 96-well plate. To the well, 5 μL of the sample or standard solution was added and reacted for 2 hrs. Then 50 μL of peroxidase-conjugated avidin solution was added and reacted for 30 min. Chromogenic substrate solution (50 μL) was added and reacted for 30 min. The reaction was stopped and the absorbance at 450 nm (sub-wave length, 620 nm) was recorded.
Microscopic Studies of Pancreatic Tissues in the Acatalasemic Mice Treated with Alloxan
Pancreatic tissues were isolated, fixed in Bouin's fluid and embedded in paraffin. Serial sections (6 μm) were cut from each paraffin-embedded tissue block, and several sections were stained with hematoxylin-eosin and mouse anti-insulin antibody (Santa Cruz Biotechnology) using the Vectastain Elite ABC Rabbit IgG Kit for visualization by light microscopy. The islets and other cells were recorded with a FX380 CCD Camera and a microscope (Olympus, Tokyo, Japan). For apoptosis analysis, terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) staining was carried out using the In Situ Cell Death Detection Kit (Roche Diagnostics Japan) [13]. The apoptosis incidence was calculated from the TUNEL-positive cells per the total cells in the Langerhans islets.
Statistical Analysis
Student's t-test was used to evaluate the statistical significance of difference. The difference was considered significant when p < 0.05.
Catalase Activity in Mouse Blood
Catalase activity in the mouse erythrocytes was calculated as the difference between the hydrogen peroxide removal rate by hemolysate and the rate (0.73 μmol/s/g of hemoglobin) by hemoglobin [3] [14]. The catalase activity in blood at 25˚C was 0.15 ± 0.07 μmol/s/g of Hb in the acatalasemic mice and 6.77 ± 0.62 in the normal mice.
Biomarkers of Oxidative Stress in Plasma before and after Alloxan Treatment
After alloxan administration, 8-OHdG in the mouse plasma increased. The increase in blood was suppressed by feeding the burdock diet but the difference is not significant (data not indicated). On the other hand, increase of lipid peroxide marker in blood after alloxan administration was significantly suppressed by feeding the burdock diet (Figure 1).
Blood Glucose in the Mice Maintained on the Burdock and the Control Diets before and after Alloxan Administration
Before alloxan administration, the average fasting blood glucose in the normal and acatalasemic mice fed a burdock or control diet was scarcely different. After the administration, the average fasting blood glucose of acatalasemic mice was higher than that in normal mice. Furthermore, the average fasting blood glucose of mice fed the control diet was significantly higher than that of mice fed the burdock diet (Figure 2). It was also indicated the incidence of hyperglycemia in both mice fed the burdock diet was lower than that fed the control diet (Table 1).
Effect of the Burdock or the Control Diet on GTT
After alloxan treatment, GTT in the mice was examined. The difference in the blood glucose of the treated acatalasemic mice fed the burdock and control diet was indicated in Figure 3. The blood glucose in the mice fed the control diet after 60, 90 and 120 min from glucose administration was higher than mice fed the fermented burdock diet. Blood glucose in the acatalasemic mice fed the control diet after 120 min was higher than that before glucose administration.
Insulin and C-Peptide Concentrations and the Index of Insulin Resistance
The C-peptide level in the normal mouse blood (data not shown) was significantly higher than that in the acatalasemic blood, and the concentration was hardly affected by each diet. The insulin concentrations in fasting blood after alloxan administration were shown in Figure 4. The insulin concentration of mice fed the control diet was lower than the mice fed the burdock diet, and, in them, the concentration of acatalasemic mice fed the control diet was only significantly low. The index of insulin resistance was calculated according to [15] [16]. The indexes were almost constant and not affected by the diet, catalase activity and alloxan administration (data not shown).
Microscopic Examination of Pancreatic Tissues in the Acatalasemic Mice Treated with Alloxan
Pancreatic tissues after alloxan administration were subjected to immunohistochemical staining (Figure 5). The numbers of β-cells stained by insulin antibody in the islets of Langerhans were calculated. There was scarcely difference of the numbers in normal mice between the fed the burdock diet and the control one but significant difference in acatalasemic mice (data not shown).
Incidence of apoptosis was calculated using TUNEL-positive cells per the total cells in the islets of Langerhans (Table 2). The incidence in acatalasemic mice fed the burdock diet before and after alloxan administration was constant and almost a same level with normal ones, but in acatalasemic mice fed the control diet after alloxan administration was significantly high compared to that fed the burdock diet.
Discussion
Alloxan administration to mice caused to increase both oxidative stress markers in blood, and by feeding the fermented burdock diet, lipid peroxide in blood was significantly lowered than that taking the control diet. Incidence of hyperglycemia in normal and acatalasemic mice fed the burdock diet was lower than that in mice fed the control diet. GTT of the acatalasemic mouse group fed the burdock diet was improved compared to that fed the control diet. By alloxan administration, insulin concentration in blood was decreased, and insulin resistance indexes indicated that the burdock diet did not induce insulin resistance. Insulin contents in blood of acatalasemic mice fed the burdock diet were higher than those of the mice fed the control one. These results indicate that hyperglycemia and low insulin concentration induced by alloxan administration are ameliorated by feeding the fermented burdock diet. This study also indicated that acatalasemic mice fed the burdock diet did not induce TUNEL-positive cells in the Langerhans islets after alloxan treatment but acatalasemic mice fed the control diet induced them (Table 2). As oxidative stress induced by alloxan caused DNA breaks and activation of poly (ADPribose) synthetase to result deterioration of insulin synthesis in pancreas [17], it might be that the DNA breaks caused apoptosis of β-cells in acatalasemic mouse pancreas. Finally, we deduced that feeding the fermented
Figure 1 .
Figure 1. Lipid peroxide in blood after alloxan administration. Mice fed fermented burdock diet (+) or the control diet (−) for 14 weeks. Then, alloxan was administrated. After a week, lipid peroxide in blood was measured. N, Normal mice; A, acatalasemic mice. Vertical lines indicate SE. * indicates p < 0.05.
Figure 2 .
Figure 2. Blood glucose before and after alloxan administration. Mice fed fermented burdock diet (+) or the control diet (−) for 14 weeks. Then, alloxan was administrated. Black columns, means of blood glucose before alloxan administration; gray columns; means of blood glucose 5 days after alloxan administration. N, Normal mice; A, acatalasemic mice. Vertical lines indicate SE. * indicates p < 0.05.
Figure 3 .
Figure 3. Glucose tolerance test of acatalacemic mice after alloxan administration. Acatalacemic mice fed fermented burdock diet or the control diet for 14 weeks. Then, alloxan was administrated. Glucose tolerance test was carried out after 5 days. Closed circles, blood glucose of mice fed fermented burdock diet; open circles, blood glucose fed the control diet. Arrow indicates glucose loading point. Vertical lines indicate SE.
Figure 4 .
Figure 4. Insulin in blood after alloxan administration. Mice fed fermented burdock diet (+) or the control diet (−) for 14 weeks. Then, alloxan was administrated. Columns indicate means of insulin in plasma after a week. N, Normal mice; A, acatalasemic mice. Vertical lines indicate SE. * indicates p < 0.05.
Figure 5 .
Figure 5. Mouse pancreas stained with insulin-antibody after alloxan administration. Acatalasemic mice fed fermented burdock diet or the control diet for 14 weeks. Then, alloxan was administrated. After a week, mouse pancreas was isolated and stained with insulin-antibody.(a) From mice fed fermented burdock diet; (b) from mice fed the control diet.
Table 1 .
Incidence of hyperglycemia after alloxan administration.
n in parentheses indicates number of mice.
Table 2 .
Incidence of apoptosis in the pancreatic tissues after alloxan administration.
* indicates p < 0.05 compared to mice fed the 5% fermented burdock diet.
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Domain: Medicine Biology Agricultural and Food Sciences
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Oral administration of Allium sativum extract protects against infectious bursal disease in chickens
Garlic ( Allium sativum, Liliaceae) has been safely used for more than 5000 years, and research on garlic extract is rapidly increasing because of its multiple biological functions. The in vivo effects of oral administration of garlic mixture (GM, water-soluble extract) on infectious bursal disease virus (IBDV)-infected speci fi c pathogen free male white leghorn chicken were examined through histopathological, immunohistochemical, and Western blot analyses, and enzyme-linked immunosorbent assay. The results con fi rmed the protective effects of oral administration of 5 mg $ kg – 1 BW GM (Group GM1) on bursal lesions after IBDV infection. In particular, protein expression of IBDV in the bursa decreased in Group GM1, indicating that GM administration decreased IBDV replication in the bursa. Furthermore, immunoglobulin M-and A-bearing B lymphocytes signi fi cantly increased 7 days post infection in bursae in Group GM1 ( P < 0.01), suggesting that the oral administration of 5 mg $ kg – 1 GM offers moderate protection against B cell destruction after IBDV infection. During infection, the concentration of bursal interferon gamma (IFN-g ) increased and peaked in Group GM1 earlier than in Group T (IBDV-exposed), demonstrating that GM administration prompted the production of IFN-g to protect against IBDV infection.
Vaccinations and egg-yolk antibodies are effective preventive and therapeutic options for IBD. Antiviral drug usage has greatly progressed over the past decades; however, some of these drugs may induce viral drug resistance and lead to cell toxicity [14] . Garlic (Allium sativum, Liliaceae), a rich source of bioactive compounds, has been safely used as a flavoring, preventive, and curative agent for more than 5000 years worldwide [15,16] . It is also notable for its antiarthritic, antimicrobial, antithrombotic, antitumor, hypoglycemic and hypolipidemic properties [17,18] . Furthermore, garlic has antiviral activity against several viruses [19] . Flavonoids and organosulfur compounds are the two main classes of biological functional components in garlic [20,21] . Allicin, which has virucidal activities, is the predominant thiosulfinate in fresh garlic extract [22] . Moreover, garlic oil (GO), an oilsoluble garlic extract, is commercially used as a functional food worldwide [23] . The major sulfides in GO include diallyl sulfide (57%), allyl methyl (37%), and dimethyl sulfide series (6%) [15,24] . Our previous study has shown that GO plays an active role in resistance against IBDV infection [25] ; however, GO administration methods are limited because of its solubility. Therefore, we designed a method to extract the water-soluble allicin (garlic mixture, GM) from crushed garlic by steam distillation, but the antiviral effects of GM are little known. Thus, in the study reported here, we examined the effects of oral administration of GM on bursal tissue damage in IBDV-infected specific pathogen-free (SPF) male white leghorn chickens, on changes in bursal IgM + and immunoglobulin A-bearing (IgA + ) B lymphocytes, on distribution and load of IBDV in the bursa, and on changes in interferon gamma (IFN-g) concentrations in the bursal homogenate through histopathological, immunohistochemical, and Western blot analyses, and enzyme-linked immunosorbent assay (ELISA).
Ethics statement
The Beijing Municipal Committee of Animal Management and The Ethics Committee of China Agricultural University approved the protocols for animal use and experimentation. All efforts were made to minimize animal suffering.
Preparation of GM
Fresh garlic bulbs (5000 g) were peeled, washed, chopped, and homogenized with distilled water (25000 mL). After fermentation in a warm bath, the homogenized mixture was subjected to steam distillation twice, and 800 mL of the distillate was collected. A water solution of hydroxypropyl-β-cyclodextrin was added to the distillate for preparing the cyclodextrin inclusion compound of allicin. Subsequently, potassium sorbate was dissolved in a suspension of this inclusion compound, which was then diluted to 1L with distilled water. Finally, the garlic extract (pH 6.5) was passed through a filter (pore size 0.45 µm), and the allicin content was determined as 1.5 mg$mL -1 using the HPLC method.
Animals and viruses
SPF chickens (Merial Vital Laboratory Animal Technology Co. Ltd, Beijing, China) for the different experimental groups were housed in separate poultry isolators. Feed was provided ad libitum during the experimental period; the amount of water was carefully recorded everyday and provided on demand according to the experimental design. A Chinese virulent strain of the IBDV (BC6/85; CVCC AV7) was obtained from the China Institute of Veterinary Drug Control (Beijing, China). The experimental groups were inoculated with two hundred 50% mean bird infectious doses of BC6/85 or 0.01 mol$L -1 phosphate buffered saline (PBS) by eye dropper.
Experimental design
In total, one hundred and twenty 14-day-old SPF chickens were randomized into four groups of 30 chickens each: control (Group C), IBDV-exposed (Group T) and GM administered (Groups GM1 and GM2). When the chickens were 19 days old, GM was added to the water of Groups GM1 (5.0 mg$kg -1 BW per day) and GM2 (0.2 mg$kg -1 BW per day) for 9 days. Furthermore, when the chickens were 21 days old, Groups T, GM1, and GM2 were inoculated with IBDV BC6/85 by eye dropper. At 1, 2, 3, 4, 5, and 7 days post infection (dpi), five chickens in each group were individually weighed, euthanized by cutting the jugular veins after anesthesia, bled for 3-5 min, and then dissected. Group C was only intraocularly administered sterile PBS and euthanized similarly. Clinical signs of the virus-inoculated chickens were visually examined; bursae from each infected and uninfected chicken were sampled and weighed. The bursal weight index was calculated by dividing bursal weights (g) of chickens and their bodyweights (kg). Moreover, paraffin sections of bursa were prepared for the observation of histopathological lesions by hematoxylin and eosin staining and for the detection of viral antigen, IgM + and IgA + B lymphocytes by immunoperoxidase staining. The bursal homogenate was collected for measuring the IFN-g and IBDV VP2 protein concentration by ELISA and Western blot analysis, respectively.
Histopathological examination
Bursal samples of all groups were fixed in 4% paraformaldehyde, dehydrated in ascending concentrations of ethyl alcohol, cleaned in benzene, and embedded in melted paraffin wax (P3808; Sigma-Aldrich, St. Louis, MO, USA). The paraffin sections were then cut into 4-mm-thick slices and stained with hematoxylin and eosin. The stained sections were microscopically observed for assessing histopathological changes.
Extraction of cytoplasmic proteins, SDS-PAGE and Western blot analysis
At 1, 2, 3, 4, 5, and 7 dpi, all bursal samples were frozen in liquid nitrogen and ground into fine powder; 100 mg of each sample was homogenized in 1 mL cold PBS. The obtained homogenates were mixed with an SDS-PAGE sample loading buffer (P0015; Beyotime Institute of Biotechnology, Jiangsu, China), boiled, treated in an ice bath, and centrifuged. The five treated bursal samples of each group were vortexed, and the supernatant was pooled. The pooled sample was loaded onto slab gels, and the separated proteins were transferred onto a nitrocellulose (NC) membrane. Subsequently, the nitrocellulose membrane was incubated with mouse monoclonal antibody against VP2 (1:1000) or against β-actin (sc-47778; Santa Cruz Biotechnology Inc., Shanghai, China; 1:2000) and incubated with HRP-conjugated goat antibody to mouse IgG (CW0102; Beijing ComWin Biotech Co. Ltd, Beijing, China; 1:2000). After washing, the NC membrane was incubated with Clarity TM Western ECL Substrate (170-5060; Bio-Rad Laboratories Inc., Hercules, California, USA), and immunoreactive proteins were visualized on an imaging system (VersaDoc; Bio-Rad).
Preparation of bursal homogenate and ELISA
At 2, 3, 4, 5, and 7 dpi, all bursal samples were homogenized as described earlier and centrifuged. IFN-g concentrations in the clarified homogenates were measured using an R&D ELISA kit (R&D Systems Inc., Minnesota, USA) according to the manufacturer's instructions.
Statistical analysis
Statistically significant differences were analyzed using one-way analysis of variance through multiple comparisons using SPSS Statistics Base 17.0 (SPSS Inc., Chicago, USA). Data were given as mean (M) AE standard deviation (SD). P < 0.05 was considered statistically significant.
Results
After infection with IBDV BC6/85, Groups T and GM1 developed clinical symptoms and characteristic lesions at 2 to 4 dpi; these groups gradually recovered from the infection at 5 dpi. However, more serious bursal lesions appeared in Group GM2, with severe edema until 7 dpi.
Effects of GM on the bursal weight index of the IBDVinfected chickens
In particular, the mean bursal indexes of Groups GM1 and GM2 at 1 to 7 and 2 to 7 dpi, respectively, were higher than the mean bursal index of Group T. In particular, bursal indexes of Group GM1 at 3 dpi and of GM2 at 3 to 4 dpi were remarkably high (P < 0.05). Moreover, bursal indexes of Group T were significantly lower than those of Group C at 4 to 7 dpi (P < 0.05). However, a significant decrease was observed in Groups GM1 and GM2 at 5 to 7 and 7 dpi, respectively (P < 0.05) (Fig. 1).
3.2 Effects of GM on the bursal structure and changes in the B lymphocyte of the IBDV-infected chickens A few injured bursal lymphoid follicles persisted in Group T at 7 dpi, and local infiltration of lymphocytes (LIL) and fibrillation were investigated in the interfollicular areas (Fig. 2a, Fig. 2b). Many LIL and a considerable number of original bursal lymphoid follicles were detected, with orderly cortical and medullary epithelial cells and many lymphocytes in the cortex and medulla in Group GM1 at 7 dpi (Fig. 2c, Fig. 2d). In contrast, the most serious pathological changes, such as congestion, edema, hemorrhage and heterophil accumulation, were observed in Group GM2, and a remarkable number of degenerated tissues accumulated in the bursal tube, with few lymphoid follicles observed at 7 dpi (Fig. 2e, Fig. 2f).
Based on these data, the cell types in the bursae of IBDV-infected chickens were further investigated by immunohistochemical analysis. After infection, the number of bursal IgM + B lymphocytes rapidly decreased. Furthermore, at 7 dpi, a few IgM + B lymphocytes were scattered in the tissues where lesions involuted in Group T, and LIL were IgM negative (Fig. 3a, Fig. 3b). However, a high number of IgM + B lymphocytes were detected in the cortex and medulla at 7 dpi in Group GM1 (Fig. 3c, Fig. 3d). Also fewer IgM + B lymphocytes were observed up to 7 dpi in the bursal epibiotic area (EA) of Group GM2 (Fig. 3e, Fig. 3f). Similar to the changes found in IgM + B lymphocytes, IgA + B lymphocytes were observed in the cortex and medulla after 5 dpi in Group GM1, and LIL were IgA negative (Fig. 3g, Fig. 3h). Few IgA + B lymphocytes were observed up to 7 dpi in Groups T (Fig. 3i, Fig. 3j) and GM2 (Fig. 3k, Fig. 3l). At 7 dpi, IgM + B lymphocytes were enumerated in five fields/bursa/ chicken, and the number of IgM + B lymphocytes in Group GM1 and the epibiotic area in Group GM2 were significantly higher than that in Group T (P < 0.01) (Fig. 3m). The number of IgA + B lymphocytes in Group GM1 was significantly higher than that of Group T at 7 dpi (P < 0.01) (Fig. 3n).
Effects of GM on the expression of bursal viral antigens of the IBDV-infected chickens
After infection, VP2 immunoreactivities revealed an increase-decrease phenomenon, which peaked at 2 to 4 dpi. At 7 dpi, many VP2 immunoreactivities were concentrated in the medulla of injured bursal lymphoid follicles, and some diffusely distributed in the cortex and interfollicular areas in Group T (Fig. 4a). However, a few VP2 immunoreactivities exhibited diffuse distribution in the plicae in Group GM1 (Fig. 4b). Group GM2 revealed the strongest VP2 immunoreactivities, with most accumu- Fig. 2 Effect of GM on the bursal structure changes of IBDV-infected chickens. At 7 dpi, a few injured bursal lymphoid follicles were still observed in Group T, and local infiltration of lymphocytes (LIL) and fibrillation were investigated in the interfollicular areas (a, b). Many LIL and a high number of original bursal lymphoid follicles were detected with orderly cortical and medullary epithelial cells and many lymphocytes in the cortex and medulla in Group GM1 (c, d). The most serious congestion, edema, hemorrhage and heterophil accumulation were observed in Group GM2, and a high number of degenerated tissues accumulated in the bursal tube, with few lymphoid follicles (e, f). Arrows indicate fibrillation. Arrowheads indicate LIL. Fig. 1 Effect of GM on the bursal weight index of IBDV-infected chickens. Mean bursal indexes of Groups GM1 and GM2 at 1 to 7 and 2 to 7 dpi, respectively, were higher than the mean bursal index of Group T. Among them, bursal indexes of Groups GM1 and GM2 at 3 and 3 to 4 dpi, respectively, were remarkably higher (P < 0.05). Bursal indexes of Group T were significantly lower than those of Group C at 4 to 7 dpi (P < 0.05). However, a significant decrease occurred in Groups GM1 and GM2 at 5 to 7 and 7 dpi, respectively (P < 0.05). Data were given as meanAESD. Data simultaneously marked with different letters indicate significant differences (P < 0.05). negative (a, b). A high number of IgM + B lymphocytes were detected in the cortex and medulla in Group GM1 (c, d). Fewer IgM + B lymphocytes were observed in the bursal epibiotic area (EA) of Group GM2 (e, f). At 7 dpi, many IgA + B lymphocytes were observed in the cortex and medulla in Group GM1, and LIL were IgA negative (g, h). Few IgA + B lymphocytes were observed in Groups T (i, j) and GM2 (k, l). At 7 dpi, IgM + B lymphocytes were enumerated in five fields/bursa/ chicken, and a significantly higher number of IgM + B lymphocytes in Group GM1 and the epibiotic area of Group GM2 was observed than in Group T (P < 0.01) (m). The number of IgA + B lymphocytes in Group GM1 was significantly higher than that in Group T at 7 dpi (P < 0.01) (n). Solid arrows and arrowheads indicate scattered IgM immunoreactivities and IgM + B lymphocytes, respectively. Feint arrows and arrowheads indicate scattered IgA immunoreactivities and IgA + B lymphocytes, respectively. Data are given as meanAESD. **, P < 0.01 between Groups GM1 and T; ## , P < 0.01 between Groups GM2 and T. lated in the degenerated tissues (Fig. 4c). Western blot analysis (Fig. 4d, Fig. 4e) confirmed the low VP2 expression in Group GM1.
Effects of GM on IFN-g concentrations in the bursal homogenate of the IBDV-infected chickens
After infection, bursal IFN-g concentrations increased in the chickens. IFN-g concentrations in Groups T and GM1 initially increased and subsequently decreased, with a significant increase at 4 and 5 dpi in Group T and 2 to 4 dpi in Group GM1 (P < 0.05). A significantly high bursal IFNg concentration was observed in Group GM2 at 4 and 7 dpi (P < 0.05). The IFN-g concentration peaked at 3 dpi in Group GM1, 2 days earlier than in Groups T and GM2 (Fig. 5).
Discussion
IBD is an economically important, immunosuppressionrelated infectious disease of young chickens worldwide [26] . BC6/85 is a standard virulent IBDV strain and is widely used in China for evaluating the efficacy of IBDV vaccines [13] . The present study confirmed our previous reports that 3-week-old chickens infected with IBDV BC6/ 85 exhibit clinical symptoms and characteristic pathological changes [13,27] . Therefore, the above model is suitable for assessing the preventive and therapeutic effects of GM.
IBDV infection severely depletes the B lymphocytes and destroys bursal tissues, in addition to severely depleting the medulla of the follicles [27][28][29][30] . In the present study, a high number of lymphocytes were detected in the lymphoid follicles in Group GM1 at 5 to 7 dpi; therefore, we speculated that a number of B lymphocytes were preserved because of the oral administration of 5 mg$kg -1 GM. Further research revealed a markedly higher number of IgM + and IgA + B lymphocytes in Group GM1 than in Group T, suggesting that the higher bursal weight indexes in Group GM1 were associated with the high number of preserved IgM + and IgA + B lymphocytes. Our data suggest that the oral administration of 5 mg$kg -1 GM confers moderate protection against B cell destruction after IBDV infection. Similar to the present findings, Salman et al. and Milner reported that garlic derivatives activate stimulatory properties in lymphocytes [17,31] .
Garlic is favored for its antiviral properties, and our previous research indicated that the oral administration of 10 mg$kg -1 GO decreases IBDV replication [25] . Therefore, we investigated IBDV replication and distribution in the bursa. Western blot analyses and immunoperoxidase staining exhibited low IBDV VP2 expression in Group GM1, indicating the anti-IBDV effect of oral administration of 5 mg$kg -1 GM. Kim et al. indicated that IFN-g is crucial in inducing antiviral effects by immune cells such as macrophages and cytotoxic T lymphocytes [32] ; these results are consistent with our results: bursal IFN-g concentration increased in the IBDV-infected chickens, and that in Group GM1 peaked at 3 dpi, 2 days earlier than the peak observed in Groups T and GM2. Therefore, we assume that IBDV infection stimulates IFN-g production and that the oral administration of 5 mg$kg -1 GM Fig. 4 Effect of GM on the expression of bursal viral antigens of IBDV-infected chickens. At 7 dpi, many VP2 immunoreactivities were concentrated in the medulla of injured bursal lymphoid follicles, and some diffusely distributed in the cortex and interfollicular areas in Group T (a). A few VP2 immunoreactivities exhibited diffuse distribution in the plicae in Group GM1 (b). The strongest VP2 immunoreactivities were observed in Group GM2, with most accumulated in degenerated tissues (c). Western blot analysis (d, e) confirmed the low VP2 expression in GM1.
strengthens the stimulating effects. T lymphocytes were first detected at 4 dpi; 65% of bursal cells are T lymphocytes at 7 dpi [32] , and these lymphocytes play a substantial role in conferring resistance against IBDV [33] . Furthermore, our data revealed that LIL are neither IgM + nor IgA + B lymphocytes, which were the most likely T lymphocytes and were observed more frequently in Group GM1. Similar to our findings, Kim et al. reported Tlymphocyte infiltration in IBDV-infected bursa using immunohistochemical analysis [32] . Moreover, IBDV infection recruited activated macrophages into the bursa [27] . Thus, local infiltration of T lymphocytes and macrophage activation may confer antiviral activities through high IFN-g concentrations.
In the present study, IBDV infection caused bursal lesions in Groups T, GM1, and GM2. A higher bursal viral load and serious pathological lesions were observed in Group GM2 because the administered concentration of GM (0.2 mg$kg -1 ) possibly did not reach the minimal effective antiviral dose. Adverse effects of garlic, such as abdominal pain, appetite loss, bloating and bad breath, are well known; an excessive intake of garlic may also adversely affect the liver and muscle cells [19] . Our pilot studies have revealed that the oral administration of 10 mg$kg -1 GM for 4 days causes vent pecking in 60% chickens (data not shown). Therefore, we speculate the presence of some other pungent components in GM. Further studies must be conducted for optimizing a simple and low-cost extraction method for GM.
Conclusions
These results show that the oral administration of 5 mg$kg -1 GM regulates the secretion of bursal IFN-g, decreases bursal viral load, and protects the bursal structure and lymphocytes against IBDV infection. In conclusion, GM has an antiviral activity against IBDV.
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Domain: Medicine Biology Agricultural and Food Sciences
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Architectural Features of 3D Genome Organization Revealed by Counting CTCF and Cohesin Molecules
Achieving a quantitative and predictive understanding of 3D genome architecture presents a major challenge and aspiration. However, this milestone will not be achieved without quantitative measurements of the key proteins driving nuclear organization. Here we report the quantification of CTCF and cohesin, two causal regulators of topological associating domains (TADs) in mammalian cells. Within the context of the cohesin/CTCF mediated loop extrusion model and recent imaging studies (Hansen 2017), here we determine the density of extruding cohesins and CTCF boundary permeability. Furthermore, co-immunoprecipitation studies of an endogenously tagged subunit (Rad21) confirms the presence of dimers and/or oligomers. Having established cell lines with accurately measured protein abundances, we report a simple method to conveniently count molecules of any Halo-tagged protein in the nucleus. We anticipate that these tools and results will advance a more quantitative understanding of 3D genome organization, and facilitate quantifying proteins involved in diverse biological processes.
Introduction
Folding of mammalian genomes into structures known as Topologically Associating Domains (TADs) is thought to help regulate gene expression while aberrant misfolding has been associated with disease (Dekker and Mirny, 2016;Hansen et al., 2018;Hnisz et al., 2017;Lupianez et al., 2015). CTCF and cohesin have emerged as master regulators of TADs since acute CTCF or cohesin depletion causes global loss of TADs (Gassler et al., 2017;Nora et al., 2017;Rao et al., 2017;Schwarzer et al., 2017;Wutz et al., 2017). Consistent with the key role played by CTCF and cohesin, models of genome folding through cohesinmediated loop extrusion and CTCF binding have been remarkably successful in reproducing the general features of genomic contact maps at the level of TADs (Fudenberg et al., 2016(Fudenberg et al., , 2018Sanborn et al., 2015). Despite such success, these models have been limited by a dearth of quantitative data to constrain the modeling. Importantly, the molecular stoichiometry of cohesin remains unknown, further limiting our ability to test various models. Building on our recent genomic and imaging studies of endogenously tagged CTCF and cohesin (Hansen et al., 2017), here we (1) determined the density of extruding cohesin complexes and estimated CTCF boundary permeability; (2) provided biochemical evidence that at least a subset of cohesin complexes exist as dimers or higher order structures and (3) developed a simple method for obtaining the absolute abundance of any protein fused with the widely used and highly versatile HaloTag (Los et al., 2008).
Absolute CTCF/cohesin quantification and implications for 3D genome organization
To obtain a reasonably accurate count of CTCF and cohesin molecules in the nucleus of mammalian cells, we took advantage of our previously validated mouse and human cell lines where CTCF (U2OS and mouse embryonic stem cells (mESC)) and the cohesin kleisin subunit Rad21 (mESC) were endogenously and homozygously Halo-tagged (Hansen et al., 2017). To establish a standard, we purified 3xFLAG-Halo-CTCF and Rad21-Halo-3xFLAG from insect cells and quantitatively labeled the purified proteins with the bright dye JF 646 (Grimm et al., 2015). We then ran a known quantity of protein side-by-side with a known number of cells and quantified total protein abundance using "in-gel" fluorescence ( Figure 1A; Materials and Methods). Note that JF 646 -labeling is quantitative in live cells (Yoon et al., 2016). This revealed that, on average, mESCs contain ~218,000 +/-24,000 CTCF protein molecules (mean +/-std) and, if cohesin exists as a monomeric ring, ~87,000 +/-35,000 cohesin complexes ( Figure 1B; Rad21 appears to be the least abundant cohesin subunit, see Materials and Methods; these numbers are comparable to FCS-measurements from HeLa cells reported in a recent preprint ). Having previously determined the fraction of CTCF bound to specific sites in mESCs by single-molecule imaging (~49%) and the number of CTCF sites by ChIP-seq (~71,000) (Hansen et al., 2017), we can now calculate that an average CTCF binding site is occupied ~50% of the time (assuming that an "average" cycling cell is half-way through the cell cycle and contains 3 genome copies; full details in Materials and Methods). In the context of the loop extrusion model (Fudenberg et al., 2018), where cohesin extrudes DNA loops until it encounters convergent binding sites occupied by CTCF, this suggests that the time-averaged occupancy of an average CTCF boundary is ~50% ( Figure 1C-D). Likewise, we find that the density of extruding cohesin molecules is ~4.2 per Mb assuming cohesin exists as a monomeric ring or ~2.1 per Mb if cohesin forms dimers (full details on calculation in Materials and Methods). These numbers will be useful starting points for constraining and parameterizing models of 3D genome organization, though we note that they represent average values. Although it remains unclear how ChIP-Seq peak strength relates to time-averaged occupancy, the wide distribution of CTCF ChIP-Seq read counts ( Figure 1D) suggests that some CTCF binding sites will be occupied most of the time, while other sites are rarely bound (i.e. 50% is an average). Likewise, the density of extruding cohesin complexes is unlikely to be uniform across the genome (e.g., due to uneven loading or obstacles to cohesin extrusion by other large DNA binding protein complexes). It is also worth mentioning that the CTCF abundance in human U2OS cells (~105,00 +/-15,000 proteins/cell) is less than half of that seen in mESCs. Thus, cell-type specific control of chromatin looping may be achieved in part by regulating CTCF abundance. (A) Representative SDS-PAGE gel showing a titration of purified and labeled JF 646 -3xFLAG-Halo-CTCF protein as a standard (first 3 lanes) side-by-side with JF 646 -Halo-CTCF from lysed mESCs (3 replicates of 150,000 cells each from two different clones and different replicates). (B) Absolute quantification as shown in (A) of mESC Halo-CTCF abundance (in two independent clones, C87 and C59), of human U2OS Halo-CTCF (clone C32) and of mESC Rad21-Halo (C45). CTCF and Rad21 were homozygously tagged in all cell lines and by Western Blotting the expression level was equivalent to the untagged level in wild-type cells (Hansen et al., 2017). Each dot represents an independent biological replicate and error bars show standard deviation. (C) Sketch of hypothetical loop extrusion model, wherein cohesin extrudes chromatin loops until it is blocked by chromatin-bound CTCF. Below, calculation of fractional CTCF occupancy and density of extruding cohesin molecules. See Materials and Methods for calculation details. (D) Histogram of read counts at MACS2-called CTCF ChIP-Seq peaks using data for wild-type CTCF described in (Hansen et al., 2017).
Mammalian cohesin can form dimers and/or higher order oligomers
Interpreting the cohesin data described above requires an accurate count of its molecular stoichiometry. In addition to potentially engaging in loop extrusion, cohesin complexes play important roles in sister chromatid cohesion and DNA repair (Guacci et al., 1997;Losada et al., 1998;Michaelis et al., 1997;Onn et al., 2008). Perhaps most critically, cohesin is generally assumed to exist as a single tripartite ring composed of the subunits Smc1, Smc3 and Rad21/Scc1 at 1:1:1 stoichiometry (Nasmyth, 2011). However, the hypothesized ability of cohesin to extrude DNA and independently stop once it encounters CTCF at both ends of a loop (Fudenberg et al., 2018) (Figure 1C), seems intuitively more consistent with a dimeric complex (Figure 2A). Such a dimer model seems likely if direct protein-protein contacts between CTCF and a cohesin subunit are required to halt cohesin mediated extrusion, because two such interactions would presumably be necessary to stop extrusion at both CTCF-bound sites. Indeed, higher order oligomeric cohesin structures have been proposed based upon the unusual genetic properties of cohesin subunits in budding yeast (Eng et al., 2015;Skibbens, 2016). Moreover, a previous study used self coimmunoprecipitation (CoIP) of cohesin subunits to suggest a hand-cuff shaped dimer model for cohesin (Zhang et al., 2008). However, this study has remained highly controversial (Nasmyth, 2011). Since this study (Zhang et al., 2008) relied on over-expressed epitope-tagged cohesin subunits and given our recent observations that over-expression of the Rad21 subunit does not faithfully recapitulate the properties of endogenously tagged Rad21 (Hansen et al., 2017), we decided to revisit this important issue using endogenous tagging without overexpression. First, we generated mESCs where one endogenous Rad21 allele was Halo-V5 tagged while the other allele was not tagged (clone C85; Figure 2B-C; see Materials and Methods for details). In addition, we also generated mESCs where one allele of Rad21 was tagged with Halo-V5 and the other with SNAP-3xFLAG (clone B4; Figure 2B-C). If cohesin exclusively existed as a single ring containing one Rad21 subunit, a V5 IP of Rad21-Halo-V5 should not pull down the Rad21 protein generated from the other allele. However, in the C85 clonal line, the V5 co-IP clearly precipitated wild-type Rad21 ( Figure 2D). This cohesin:cohesin interaction appears to be protein-mediated rather than dependent on DNA association since benzonase treatment, which leads to complete DNA degradation ( . This demonstrates that Rad21 either directly or indirectly self-associates in a protein-mediated and biochemically stable manner consistent with dimers or higher order oligomers. To independently verify this result and to ensure that the coIP'ed Rad21 was not a degradation product of the tagged protein, we repeated these co-IP studies in the clonal cell line B4, where the two endogenous Rad21 alleles express orthogonal epitope tags. Again, a V5-IP efficiently pulled down Rad21-SNAP-3xFLAG ( Figure 2E) and, reciprocally, a FLAG-IP pulled down Rad21-Halo-V5 ( Figure 2F). As before, the Rad21 self-interaction was entirely benzonase-resistant and thus independent of nucleic acid binding as this enzyme degrades both DNA and RNA (Figure 2 - Figure Supplement 1B). Under the simplest assumption of cohesin forming dimers, using IP and CoIP efficiencies we calculated that at least ~10% of cohesin is in a dimeric state during our pull-down experiment (full calculation details in Material and Methods). This percentage is almost certainly a significant underestimate of the actual oligomeric vs. monomeric ratio in live cells, since we expect a substantial proportion of the self-interactions not to survive cell lysis and the typically harsh IP procedures. Thus, while these results cannot exclude that some or even a majority of mammalian cohesin exists as a single-ring ( Figure 2A), they do demonstrate that a substantial population exists as dimers or oligomers. Whether this subpopulation represents handcuff-like dimers, oligomers ( Figure 2A), cohesin clusters (Hansen et al., 2017) or an alternative state will be an important direction for future structural studies.
A simple general method for counting Halo-tagged proteins in live cells
Here we have illustrated how absolute quantification of protein abundance can provide crucial functional insights into mechanisms regulating genome organization when integrated with genomic and/or imaging data ( Figure 1; (Hansen et al., 2017)). The HaloTag (Los et al., 2008) is a popular and versatile protein-fusion platform that has found application in a broad range of experimental systems (England et al., 2015). Indeed, it is currently the preferred choice for live-cell single molecule imaging. Combined with the development of Cas9-mediated genome-editing (Ran et al., 2013), endogenous Halo-tagging of proteins has thus become the gold standard (Chong et al., 2018;Hansen et al., 2017;Rhodes et al., 2017aRhodes et al., , 2017bStevens et al., 2017;Teves et al., 2016Teves et al., , 2018Youmans et al., 2018), because it avoids the now wellestablished limitations and potential artifacts associated with protein overexpression (Hansen et al., 2017;Shao et al., 2018;Teves et al., 2016). Now that we have determined the absolute abundance of CTCF and cohesin in a few cell lines ( Figure 1B), determining the absolute abundance of any other Halo-tagged protein becomes straightforward: by growing your cell line of interest side-by-side with one of the cell lines characterized here (1) Grow cells expressing the Halo-tagged protein of interest together with one of the standards described here (e.g. C45; Figure 1B). (2) After labeling with a fluorophore (e.g. TMR or a JF-dye), the relative fluorescence intensity can be measured using either flow cytometry or microscopy (4) and thus the absolute abundance calculation (5). Here this is illustrated using mESC lines for Halo-Sox2 (Teves et al., 2016) and Halo-TBP (Teves et al., 2018) (e.g. C45 mESC Rad21-Halo), absolute quantification can be achieved simply by measuring the relative fluorescence intensity using either microscopy or flow cytometry (Figure 3). To illustrate this, here we compared the fluorescence intensity of mESC lines carrying homozygously Halo-tagged Sox2 (Teves et al., 2016) and TBP (Teves et al., 2018) to our mESC C45 Rad21-Halo mESC line, and determined the average protein copy number per cell to be ~235,100 +/-34,100 for Halo-Sox2 and ~47,199 +/-3,300 for Halo-TBP ( Figure 3; Figure 3 - Figure Supplement 1). The HaloTag knock-in cell lines described here will be freely available to the research community for use as a convenient standard to enable rapid absolute quantification of any Halo-tagged protein of interest.
Discussion
Our results strongly suggest that a significant subpopulation of cohesin (>10%) exists as either a dimer or higher order complexes ( Figure 2A) consistent with an earlier study that relied on over-expression of tagged molecules (Zhang et al., 2008). Along these lines, the related bacterial SMC complex, MukBEF, also forms a dimer (Badrinarayanan et al., 2012) and budding yeast cohesin exhibits inter-allelic complementation (Eng et al., 2015) consistent with a dimeric or higher order architecture. While this does not exclude that some cohesin molecules exist as single rings, it seems evident that further elucidating the molecular architecture of extruding cohesin should be an urgent goal for future studies. Moreover, although polymer-modeling of 3D genome organization is rapidly advancing (Fudenberg et al., 2018;Nuebler et al., 2017), we suggest that a paucity of quantitative data to inform us of the stoichiometries of key 3D genome organizers constrains our ability to test the various models that have been reported. We hope that the data presented here will prove useful in informing and advancing such efforts in the future.
Given that Halo-tagging has become increasingly common, we also hope that the simple method presented here for absolute protein quantification in vivo ( Figure 3) will find widespread use. To this end, we will freely share the lines described here as standards for either microscopy-or flow cytometry-based absolute quantifications of any Halo-tagged protein of interest. We also note that although Fluorescence Correlation Spectroscopy (FCS) remains a powerful complementary and orthogonal tool for measuring protein concentrations , it requires sophisticated imaging and analysis infrastructure, while conversion to absolute protein abundance depends on precise measurement of nuclear or cytoplasmic volume. Since volume scales with the cube of the radius, even small errors in measuring the radius can result in large volume errors. For these reasons, we hope that the method described here will make accurate counting of protein molecules more accessible and convenient.
CRISPR/Cas9-mediated genome editing
CTCF knock-in U2OS and mESC lines were as previously described (Hansen et al., 2017). The Rad21 knock-in C85 and B4 mESC clones were sequentially created roughly according to published procedures (Ran et al., 2013), but exploiting the HaloTag and SNAPf-Tag to FACS for edited cells. The SNAPf-Tag is an optimized version of the SNAP-Tag, and we purchased a plasmid encoding this gene from NEB (NEB, Ipswich, MA, #N9183S). We transfected mESCs with Lipofectamine 3000 (ThermoFisher L3000015) according to manufacturer's protocol, cotransfecting a Cas9 and a repair plasmid (2 μg repair vector and 1 μg Cas9 vector per well in a 6well plate; 1:2 w/w). The Cas9 plasmid was slightly modified from that distributed from the Zhang lab (Ran et al., 2013): 3xFLAG-SV40NLS-pSpCas9 was expressed from a CBh promoter; the sgRNA was expressed from a U6 promoter; and mVenus was expressed from a PGK promoter. For the repair vector, we modified a pUC57 plasmid to contain the tag of interest (Halo-V5 for C85 or SNAPf-3xFLAG for B4) preceded by the Sheff and Thorn linker (GDGAGLIN) (Sheff and Thorn, 2004), and flanked by ~500 bp of genomic homology sequence on either side. To generate the C85 Rad21-Halo-V5 heterozygous clone, we used three previously described sgRNAs (Hansen et al., 2017) that overlapped with the STOP codon and, thus, that would not cut the repair vector (see table below for sequences). To generate the B4 Rad21-Halo-V5/Rad21-SNAPf-3xFLAG tagged clone, we re-targeted clone C85 with sgRNAs specific to the "near wild-type" allele (see below) while providing the SNAPf-3xFLAG repair vector.
We cloned the sgRNAs into the Cas9 plasmid and co-transfected each sgRNA-plasmid with the repair vector individually. 18-24 hr later, we then pooled cells transfected with each of the sgRNAs individually and FACS-sorted for YFP (mVenus) positive, successfully transfected cells. YFP-sorted cells were then grown for 4-12 days, labeled with 500 nM Halo-TMR (Halo-Tag knockins) or 500 nM SNAP-JF646 (SNAPf-Tag knock-in) and the cell population with significantly higher fluorescence than similarly labeled wild-type cells, FACS-selected and plated at very low density (~0.1 cells per mm 2 ). Clones were then picked, expanded and genotyped by PCR using a threeprimer PCR (genomic primers external to the homology sequence and an internal Halo or SNAPf primer). Successfully edited clones were further verified by PCR with multiple primer combinations, Sanger sequencing and Western blotting. The chosen C85 and B4 clones show similar tagged protein levels to the endogenous untagged protein in wild-type controls ( Figure 2C).
Genomic DNA sequencing of the C85 heterozygous clone showed the expected Halo-V5targeted allele, and a "near wild-type" allele, where repair following Cas9-cutting generated a 4 bp deletion (nt 2145-2148 in the NCBI Reference Sequence NM_009009.4), expected to result in a reading frame shift replacing the 2 most C-terminal amino acids (II) with SEELDVFELVITH. The mutation was repaired in clone B4 by providing a corrected SNAPf-3xFLAG repair vector.
All plasmids used in this study are available upon request. The table below lists the primers used for genome editing and genotyping of the Rad21 knock-in clones.
Name/description
Sequence (
Western blot and co-immunoprecipitation (CoIP) experiments
Cells were collected from plates by scraping in ice-cold phosphate-buffered saline (PBS) with PMSF and aprotinin, pelleted, and flash-frozen in liquid nitrogen.
For Western blot analysis, cell pellets where thawed on ice, resuspended to 1 mL/10 cm plate of low-salt lysis buffer (0.1 M NaCl, 25 mM HEPES pH 7.5, 1 mM MgCl2, 0.2 mM EDTA, 0.5% NP-40 and protease inhibitors), with 125 U/mL of benzonase (Novagen, EMD Millipore), passed through a 25G needle, rocked at 4˚C for 1 hr and 5M NaCl was added to reach a final concentration of 0.2 M. Lysates were then rocked at 4˚C for 30 min and centrifuged at maximum speed at 4˚C. Supernatants were quantified by Bradford. 15μg of proteins were loaded on 8% Bis-Tris SDS-PAGE gel and transferred onto nitrocellulose membrane (Amersham Protran 0.45 um NC, GE Healthcare) for 2 hr at 100V. For chemiluminescent Western blot detection with HRP-conjugated secondary antibodies, after the transfer the membrane was blocked in TBS-Tween with 10% milk for at 1 hr at room temperature and blotted overnight at 4˚C with primary antibodies in TBS-T with 5% milk. HRP-conjugated secondary antibodies were diluted 1:5000 in TBS-T with 5% milk and incubated at room temperature for an hour. For fluorescence detection, after the transfer the membrane was blocked with the Odyssey® Blocking Buffer (PBS) for 1 hr at room temperature, followed by overnight incubation at 4˚C with primary antibodies in Odyssey® Blocking Buffer (PBS) and PBS (1:1). IRDye secondary antibodies were used for detection at 1:5000 dilution and 1 hour incubation at room temperature. After extensive washes, the membrane was scanned with a LI-COR Odyssey CLx scanner.
For co-immunoprecipitation experiments (CoIP), cell pellets where thawed on ice, resuspended to 1 ml/10 cm plate of cell lysis buffer (5 mM PIPES pH 8.0, 85 mM KCl, 0.5% NP-40 and protease inhibitors), and incubated on ice for 10 min. Nuclei were pelleted in a tabletop centrifuge at 4˚C, at 4000 rpm for 10 min, and resuspended to 0.5 mL/10 cm plate of low salt lysis buffer either with or without benzonase (600U/ml) and rocked for 4 hours at 4˚C. After the 4-hourincubation the salt concentration was adjusted to 0.2M NaCl final and the lysates were incubated for another 30 minutes at 4˚C. Lysates were then cleared by centrifugation at maximum speed at 4˚C and the supernatants quantified by Bradford. In a typical CoIP experiment, 1 mg of proteins was diluted in 1 mL of CoIP buffer (0.2 M NaCl, 25 mM HEPES pH 7.5, 1 mM MgCl 2 , 0.2 mM EDTA, 0.5% NP-40 and protease inhibitors) and pre-cleared for 2 hrs at 4˚C with protein-G sepharose beads (GE Healthcare Life Sciences) before overnight immunoprecipitation with 4 mg of either normal serum IgGs or specific antibodies as listed above. Some pre-cleared lysate was kept at 4˚C overnight as input. Protein-G-sepharose beads precleared overnight in CoIP buffer with 0.5% BSA were then added to the samples and incubated at 4˚C for 2 hr. Beads were pelleted and all the CoIP supernatant was removed and saved for phenol-chloroform extraction of DNA. The beads were then washed extensively with CoIP buffer, and the proteins were eluted from the beads by boiling for 5 min in 2X SDS-loading buffer and analyzed by SDS-PAGE and Western blot.
Estimate of cohesin dimer-to-monomer ratio from CoIP experiments
Assuming that a dimeric state is responsible for the observed protein-based cohesin selfinteraction, we calculated the percentage of cohesin molecules forming dimers from our CoIP experiments in the clonal cell line B4. In these cells one allele of Rad21 is tagged with Halo-V5 and the other with SNAP-3xFLAG, and the two proteins are expressed at virtually identical levels ( Figure 2C). We also assumed that V5:V5 and FLAG:FLAG dimers are formed with the same likelihood of V5:FLAG dimers, the latter being the only ones that our assay probes for. Since we observed no difference when treating with benzonase, we averaged all Western Blot results from both the V5 and the FLAG reciprocal pull-downs ( Figure 2E and F). We used the ImageJ "Analyze Gels" function (Schindelin et al., 2012) to measure pull-down and input (IN) band intensities (I) and used those numbers to calculate IP and CoIP efficiencies (%) as follows: %IP = 0.015 IP 0.1 IN %CoIP = 0.015 CoIP 0.9 IN with 0.015 being the percent of input loaded onto gel as a reference and 0.1 or 0.9 the amount of the pull-down material loaded onto gel to quantify the IP or CoIP efficiency, respectively. Within the assumed scenario, we will use the V5 pull-down of Figure 2E to illustrate our calculations. The V5 antibody immunoprecipitates Rad21 V5 monomers (M V5 ), V5:V5 dimers (D V5 ), and V5:FLAG dimers (D V5-FLAG ). The %IP (i.e., the fraction of all V5 molecules that are pulled down) is thus the sum of the three terms: %IP = M V5 + 2 x D V5 + D V5-FLAG where each D V5 contains two V5 molecules, and a D V5-FLAG contains a single V5 molecule. Since we assumed an equal likelihood of V5 and V5-FLAG dimers, the equation becomes: %IP = M V5 + 3 x D V5-FLAG Since the total number of V5 and FLAG-tagged Rad21 molecules are the same: D V5-FLAG = %CoIP thus M V5 = %IP -3 x %CoIP Finally, adjusting for the efficiency of the V5 pull-down, the total percentage of Rad21 molecules in monomers can be calculated as: % Monomeric Rad21 = M V5 / % IP and % Dimeric Rad21 = 1 -% Monomeric Rad21 After performing the calculations described above, the resulting percentages of cohesin molecules in dimers for all the experiments were: V5 IP, untreated: 11.23% V5 IP, Benzonase: 7.60% FLAG IP, untreated: 9.07% FLAG IP, Benzonase: 10.25% with an average of 9.54% ± 1.57% (standard deviation).
DNA extraction and quantification.
For DNA extraction, the CoIP supernatant was extracted twice with an equal volume of phenol-chloroform (UltraPure™ Phenol:Chloroform:Isoamyl Alcohol (25:24:1, v/v)). After centrifugation at room temperature and maximum speed for 5 minutes, the aqueous phase containing DNA was added of 2 volumes of 100% ethanol and precipitated 30 minutes at -80˚C. After centrifugation at 4˚C for 20 minutes at maximum speed, DNA was re-dissolved in 25 μl of water and quantified by nanodrop. About 100ng of the untreated sample DNA, or an equal volume from the nuclease treated samples, were used for relative quantification by quantitative PCR (qPCR) with SYBR Select Master Mix for CFX (Applied Biosystems, ThermoFisher) on a BIO-RAD CFX Real-time PCR system.
Peak fractions were pooled and incubated with anti-FLAG (M2) agarose (Sigma) and 3X molar excess fluorogenic JF 646 for 4 hr in the dark. Bound proteins were washed extensively with HSLB, equilibrated to 0.2M NaCl HGN, and eluted with 3xFLAG peptide (Sigma) at 0.4 mg/ml. Protein concentrations were determined by PageBlue staining compared to a β-Galactosidase standard (Sigma). HaloTag Standard (Promega) was labeled according to the method described above to determine the extent of fluorescent labeling.
Quantification of CTCF and Rad21 molecules per cell
The number of CTCF and Rad21 molecules per cell was quantified by comparing JF 646labelled cell lysates to known amounts of purified JF646-labelled protein standards (e.g. 3xFLAG-Halo-CTCF-His 6 or His 6 -Rad21-Halo-3xFLAG) as shown in Figure 1A. JM8. N4 mouse embryonic stem cells (either C45 mRad21-Halo-V5; C59 FLAG-Halo-mCTCF, mRad21-SNAP f -V5; or C87 FLAG-Halo-mCTCF) were grown overnight on gelatin-coated P10 plates and human U2OS osteosarcoma C32 FLAG-Halo-hCTCF cells on P10 plates. Cells were then labelled with 500 nM (final concentration) Halo-JF 646 dye (Grimm et al., 2015) in cell culture medium for 30 min at 37°C/5.5% CO 2 . Importantly, it has previously been shown that Halo-JF 646 labeling is quantitative for cells grown in culture (Yoon et al., 2016). Cells were washed with PBS, dissociated with trypsin, collected by centrifugation and re-suspended in 1 mL PBS and stored on ice in the dark. Cells were diluted 1:10 and counted with a hemocytometer. Cells were then collected by centrifugation and resuspended in 1x SDS loading buffer (50mM Tris-HCl, pH 6.8, 100mM DTT, 2.5% betamercaptoethanol, 2% SDS, 10% glycerol) to a concentration of ~10,000-20,000 cells per µL. 5-8 biological replicates were collected per cell line.
Cell lysates equivalent to 5.0 x 10 4 to 1.5 x 10 5 cells were run on 10% SDS-PAGE alongside known amounts of purified JF 646 -labelled 3xFLAG-Halo-CTCF-His 6 or His 6 -Rad21-Halo-3xFLAG. The protein standards were processed similar to the cell lysates to account for any loss of JF 646 fluorescence due to denaturation or SDS-PAGE, allowing for quantitative comparisons. JF 646labelled proteins were visualized on a Pharos FX-plus Molecular Imager (Bio-Rad) using a 635 nm laser line for excitation and a Cy5-bandpass emission filter. Band intensities were quantified using Image Lab (Bio-Rad). From the absolute protein standards, we calculated the fluorescence per protein molecule, such that we could normalize the cell lysate fluorescence by the fluorescence per molecule and the known number of cells per lane to determine the average number of molecules per cell.
Fractional occupancy and mean density calculations
Next, we calculated the fractional occupancy of CTCF in JM8. N4 mouse embryonic stem cells. Previously (Hansen et al., 2017), using ChIP-Seq we found 68,077 MACS2-called peaks in wild-type mESCs and 74,374 peaks in C59 FLAG-Halo-mCTCF/mRad21-SNAP f -V5 double knock-in mESCs. If we take the mean, this corresponds to ~71,200 CTCF binding sites in vivo. This is per haploid genome. An "average" cell is halfway through the cell cycle and thus contains 3 genomes. In total, an "average" mES cell therefore contains ~213,600 CTCF binding sites. Previously (Hansen et al., 2017), we found that 48.9% and 49.3% of Halo-mCTCF molecules were bound to cognate binding sites in the C59 and C87 cell lines (two independent clones where CTCF has been homozygously Halo-Tagged), respectively. This corresponds to a mean of 49.1%. The average number of Halo-mCTCF molecules per cell was 217,600 ± 26,000 and 218,500 ± 22,700 in the C59 and C87 cell lines, respectively (mean across biological replicates ± standard deviation). This corresponds to a mean of ~218,000 molecules per cell. Thus, the average occupancy (i.e. fraction of time the site is occupied) per CTCF binding site is: Thus, an average CTCF binding site is bound by CTCF 50% of the time in mES cells. Note, that this analysis assumes that all binding sites are equally likely to be occupied. Most likely, some of the sites will exhibit somewhat higher and lower fractional occupancy as suggested by Figure 1D.
Within the context of the loop extrusion model (Fudenberg et al., 2016;Sanborn et al., 2015), it is crucial to know the average density of extruding cohesin complexes (e.g. number of extruding cohesins per Mb). We found the average number of mRad21-Halo molecules per JM8. N4 mES cell to be ~86,900 ± 35,600 (mean across biological replicates ± standard deviation). Previously (Hansen et al., 2017), we found 39.8% of mRad21-Halo molecules to be topologically bound to chromatin in G1 phase and 49.8% in S/G2-phase. After DNA replication begins in Sphase, cohesin adopts multiple functions other than loop extrusion (Skibbens, 2016). Thus, we will use 39.8% as an estimate of the fraction of cohesin molecules that are topologically engaged and involved in loop extrusion throughout the cell cycle. The estimated size of the inbred C57BL/6J mouse genome, the strain background from which the JM8. N4 mES cell line is derived, is 2,716 Mb (Waterston et al., 2002). Importantly, using single-molecule tracking we found that essentially all endogenously tagged mRad21-Halo protein is incorporated into cohesin complexes (Hansen et al., 2017). Thus, we can assume that the number of Rad21 molecules per cell corresponds to the number of cohesin complexes per cell. Thus, we get an average density of "loop extruding" cohesin complexes of (assuming again, that an "average" cell contains 3 genomes): mRad21 = 0.398 • 86900 3 • 2716 Mb = 4.24 molecules Mb Thus, on average each megabase of chromatin contains 4.24 loop extruding cohesin molecules. We note that it is still not clear whether cohesin functions as a single ring or as a pair of rings (Skibbens, 2016). Thus, if cohesin functions as a single ring, the estimated average density is 4.24 extruding cohesins per Mb and if cohesin functions as a pair, the estimated average density is 2.12 extruding cohesin complexes per Mb. We also note that it is currently unclear whether or not the density of extruding cohesins is likely to be uniform across the genome.
Conversion based calculation of absolute abundance of Halo-tagged cell lines
To obtain the absolute abundance of the Halo-Sox2 (Teves et al., 2016) and Halo-TBP (Teves et al., 2018) cell lines, we grew them side-by-side with the C45 Halo-Rad21 knock-in cell line. We labeled them with 500 nM Halo-TMR (Promega G8251) for 30 min at 37°C/5.5% CO 2 in a tissue-culture incubator, washed out the dye (remove medium; add PBS; remove medium; add fresh medium) and then immediately prepared the cell for Flow Cytometry. We collected the cell through trypsinization and centrifugation, resuspended the cells in fresh medium, filtered the cells through a 40 μm filter and placed the cells on ice until their fluorescence was read out by Flow Cytometry (~20 min delay). Using a LSR Fortessa (BD Biosciences) flow cytometer, cells were gated using forward and side scattering. TMR fluorescence was excited using a 561 nm laser and emission read out using a 610/20 band pass filter. Finally, the absolute abundance of protein X was obtained according to: where X is the absolute abundance of the protein of interest (mean number of molecules per cell), X is the average measured fluorescence intensity of cell lines expressing protein X (in AU), Background is the average measured fluorescence intensity of cell lines that were not labeled with TMR, C45 is the average measured fluorescence intensity of the C45 cell line standard and C45 is the absolute abundance of C45 (~86,900 proteins per cell).
To quantify the abundance of Sox2 and TBP in mESCs, we performed 3 biological replicates and the measurements for each are shown in Figure 3 (A) Single-color blots corresponding to the dual-color and overlaid blots shown in Figure 2D.
(B) Effective nucleic acid digestion by benzonase nuclease in Rad21 CoIP experiments. Nucleic acids were phenol/chloroform extracted from CoIP lysates and quantified by qPCR using primers specific to the Actb gene. Error bars are SD, n=3 Raw fluorescence histograms for mESCs (background = unlabeled), C45 Rad21-Halo, C3 Halo-Sox2 and C41 Halo-TBP after forward and side-scattering gating. All 3 biological replicates are shown and the abundances are reported as the mean and standard deviation from these replicates in the main text. For full details on how the measurements were performed, please see the Materials and Methods section.
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Domain: Biology
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TORC1 modulation in adipose tissue is required for organismal adaptation to hypoxia in Drosophila
Animals often develop in environments where conditions such as food, oxygen and temperature fluctuate. The ability to adapt their metabolism to these fluctuations is important for normal development and viability. In most animals, low oxygen (hypoxia) is deleterious. However some animals can alter their physiology to tolerate hypoxia. Here we show that TORC1 modulation in adipose tissue is required for organismal adaptation to hypoxia in Drosophila. We find that hypoxia rapidly suppresses TORC1 signaling in Drosophila larvae via TSC-mediated inhibition of Rheb. We show that this hypoxia-mediated inhibition of TORC1 specifically in the larval fat body is essential for viability. Moreover, we find that these effects of TORC1 inhibition on hypoxia tolerance are mediated through remodeling of fat body lipid storage. These studies identify the larval adipose tissue as a key hypoxia-sensing tissue that coordinates whole-body development and survival to changes in environmental oxygen by modulating TORC1 and lipid metabolism.
INTRODUCTION 1 Animals often have to grow and survive in conditions where their environment fluctuates. For example, 2 changes in nutrition, temperature or oxygen availability, or exposure to toxins and stress can all impact 3 development. Animals must therefore adapt their physiology and metabolism in response to these 4 environmental challenges in order to ensure proper growth and survival 1,2 . 5 6 In most animals decreases in oxygen are particularly deleterious. Low oxygen (hypoxia) can lead to 7 rapid tissue damage and lethality, and oxygen deprivation is a hallmark of diseases such as stroke and 8 ischemia 3 . However, some animals have evolved to live in oxygen-deprived conditions and 9 consequently exhibit marked tolerance to hypoxia. For example, birds and aquatic mammals can 10 tolerate extensive periods of low oxygen without incurring any tissue damage 4,5 . Indeed, some animals 11 show quite remarkable levels of tolerance to oxygen deprivation: brine shrimp embryos have been 12 reported to recover from four years of continuous anoxia 6 , while the naked mole rat can survive up to 13 18 minutes of complete oxygen deprivation, a condition that kills laboratory rodents within about one 14 minute 7 . Understanding how these animals adapt their metabolism to low oxygen may shed light on 15 how to protect tissues from hypoxic damage in disease states. alter their physiology and metabolism to slow growth and development, and to promote survival. One 23 main regulator of these nutrient-regulated processes in Drosophila is the conserved TOR kinase 24 signalling pathway 13 . TOR exists in two signalling complexes, TORC1 and TORC2, with TORC1 being 25 the main growth regulatory TOR complex 14 . A conserved signalling network couples nutrient availability 26 to the activation TORC1 to control anabolic process important for cell growth and proliferation 14 . 27 Moreover, studies in Drosophila have been instrumental in revealing non-autonomous effects of TORC1 28 signalling on body growth. For example, nutrient activation of TORC1 in specific larval tissue such as 29 the fat body, muscle and prothoracic gland, can influence whole animal development through the 30 control of endocrine signalling via insulin-like peptides and the steroid hormone, ecdysone 9,10,15 . In 31 addition, TORC1 regulation of autophagy in the larval fat body is important for organismal homeostasis 32 and survival during periods of nutrient deprivation 16,17 . 33 34 Drosophila larvae are also hypoxia tolerant [18][19][20] . In their natural ecology, Drosophila larvae grow on 35 rotting food rich in microorganisms, which probably contribute to a low oxygen local environment. Even 36 in the laboratory, local oxygen levels are low at the food surface of vials containing developing larvae 19 . 37 Drosophila have therefore evolved metabolic and physiological mechanisms to respond to and thrive in 38 hypoxic conditions. However, compared to our understanding of the nutrient regulation of growth and 39 homeostasis, considerably less is known about how Drosophila adapt to low oxygen during 40 development. A handful of studies have shown that larval survival in oxygen requires regulation of gene 41 expression by the transcription factors HIF-1 alpha and ERR alpha, and the repressor, Hairy 21-24 . 42 Developmental hypoxia sensing and signalling has also been shown to be mediated through a nitric 43 oxide/cGMP/PKG signalling pathway 25,26 . 44 45 Here we report a role for modulation of the TOR kinase signalling pathway as a regulator of hypoxia 46 tolerance during Drosophila development. In particular, we find that suppression of TORC1 specifically 47 in the larval fat body is required for animals to reset their growth and developmental rate in hypoxia, and 48 to allow viable development to the adult stage. We further show that these effects of TORC1 inhibition 49 require remodelling of lipid droplet and lipid storage. Our findings implicate the larval fat body as a key 50 hypoxia-sensing tissue that coordinates whole animal development and survival in response to 51 changing oxygen levels. 52 53 RESULTS 54 1 Exposure of larvae to hypoxia slows growth and delays development 2 We began by examining the effect of exposing larvae to hypoxia on their growth and development. We 3 used 5% oxygen as our hypoxia conditions for all experiments in this paper. We allowed embryos to 4 develop in normoxia and then, upon hatching, larvae were either maintained on food in normoxia or 5 transferred to food vials in hypoxia chambers that were perfused with a constant supply of 6 5%oxygen/95% nitrogen. We found that hypoxia led to reduced larval growth rate and larvae took 7 approximately an extra two days to develop to the pupal stage (Fig 1a). We also found that the hypoxia-8 exposed animals had a reduced wandering third instar larval weight (Fig 1b) and reduced final pupal 9 size (Fig 1c). We found that exposure of larvae to hypoxia did not alter their feeding behaviour (Suppl 10 Fig 1), suggesting that the decreased growth rate was not simply due to a general reduction in nutrient 11 intake. Together, these data indicate that Drosophila larvae adapt to low oxygen levels by reducing their 12 growth and slowing their development. These data are consistent with previous reports showing that 13 moderate levels of hypoxia (10% oxygen) can also affect final body size 20 .
14 15 Hypoxia suppresses TORC1 signalling via TSC1/2. 16 The conserved TORC1 kinase signalling pathway is one of the main regulators of tissue and body 17 growth in Drosophila. TORC1 can be activated by dietary nutrients and growth factors such as insulin. 18 Mammalian cell culture experiments have also shown that hypoxia can suppress TORC1 activity 27-30 . 19 We therefore examined whether changes in TORC1 signalling play a role in adaption to hypoxia in 20 Drosophila larvae. We transferred third instar larvae from normoxia to hypoxia and then measured 21 TORC1 activity by western blotting using an antibody that recognizes the phosphorylated form of S6 22 kinase (pS6K), a direct TORC1 kinase target. We found that hypoxia led to a rapid suppression of 23 whole body TORC1 activity that was apparent within 10-20 minutes of hypoxia exposure (Fig 2a). This 24 suppression persisted when larvae were maintained in hypoxia for longer periods (48 hours, Suppl Fig 25 2a). We also examined how different levels of oxygen affected TORC1 activity. Third instar larvae were 26 transferred from normoxia to different levels of hypoxia (from 20-1% oxygen) for one hour and then 27 TORC1 activity measured by western blotting for phosphorylated S6K. We found that suppression of 28 TORC1 occurred at 5 and 3% oxygen but remained unchanged at higher (20 and 10%) or lower (1%) 29 levels (Fig 2b). We examined this further by exposing larvae to several different concentrations of 30 oxygen between 1 and 10%, and found that the range within which TORC1 was inhibited was between 31 2 and 6 % oxygen (suppl Fig 2C). These data indicate that larvae rapidly respond to hypoxia by 32 suppressing TORC1 signalling, and that this response occurs within a specific range of low 33 environmental oxygen rather than simply being triggered below a threshold level of low oxygen.
35
We next examined how hypoxia suppresses TORC1 activity. One of the main ways by which TORC1 is 36 activated is through a TSC1/2-Rheb signalling pathway 14 . Rheb is a small G-protein that binds to and 37 activates TOR kinase at lysosomes. TSC2 is a GTPase activating protein, and when bound to its 38 partner TSC1, it inhibits Rheb by converting it from its active GTP-bound state to an inactive GDP-39 bound state. Several diverse stimuli including nutrients, growth factors and hypoxia have been shown to 40 regulate TSC1/2 function and to control TORC1 activity in mammalian cell culture 14 . We therefore 41 explored a role for TSC1/2 and Rheb in the suppression of TORC1 kinase signalling during larval 42 hypoxia. We found that ubiquitous overexpression of a UAS-Rheb transgene (using daughterless-gal4, 43 da-gal4) was sufficient to prevent the hypoxia-mediated suppression of TORC1 signalling in larvae (Fig 44 2c). We also found that tsc1 null mutant (tsc1 Q87X ) larvae also were unable to suppress TORC1 45 signalling when exposed to hypoxia (Fig 2d). Together these data indicate that hypoxic exposure in 46 larvae inhibits TORC1 by TSC1/2-mediated suppression of Rheb.
48
Studies in mammalian cells have described how hypoxia can induce TSC-mediated TORC inhibition via 49 the classic HIF-1 alpha transcription factor. In this mechanism, HIF-1 alpha leads to upregulation of 50 REDD1, an activator of TSC1/2 31 . In Drosophila, the homolog of REDD1, Scylla, and its partner protein, 51 Charybdis, have been shown to inhibit TOR and suppress growth 32 . We therefore examined a role for 52 Sima (the Drosophila HIF-1 alpha homolog) and Scylla/Charybdis in larval hypoxia. However, we found 53 that sima mutants still showed a suppression of TORC1 signalling when exposed to hypoxia (Fig 2e). 54 Similarly, both scylla and charybdis mutants also showed a similar suppression of TORC1 signalling as 1 control larvae in hypoxia (Fig2f, Suppl Fig 2c). We also explored a potential role for AMPK in hypoxia-2 mediated TOR regulation. AMPK is activated under hypoxia in mammalian cell culture and can 3 suppress TORC1 signalling, in part by phosphorylating and inhibiting TSC2 28, 33,34 . However, when we 4 suppressed AMPK by expression of a Gal4-dependent AMPK inverted repeat transgene (UAS-AMPK 5 IR), we still saw that hypoxia exposure lead to an inhibition of TORC1 (Suppl Fig 2d). Together, our 6 data suggest that the rapid suppression of TORC1 signalling upon hypoxia exposure in larvae requires 7 TSC1/2 function but is independent of both HIF-1 alpha mediated transcription and AMPK activation. Suppression of TORC1 signalling in the fat body is required for adaptation to hypoxia 10 We next examined whether the suppression of overall TORC1 activity we observed was important for 11 animal adaptation and tolerance to hypoxia during Drosophila development. Our approach was to 12 genetically maintain TORC1 signalling in larvae exposed to hypoxia and then to examine the effects of 13 this manipulation on animal growth, development and survival. To do this, we used the ubiquitous 14 expression of UAS-Rheb with da-Gal4 since we found this condition led to larvae maintaining TORC1 15 activity under hypoxia (Fig 2c). We compared development in control (da>+) vs. Rheb overexpressing 16 (da>Rheb) animals that were grown throughout their larval period from hatching in either normoxia or 17 hypoxia. We first found that larval Rheb overexpression had no effect on overall survival to the pupal 18 stage in either normoxia or hypoxia (Fig 3a). We next examined developmental rate by measuring the 19 time to pupation. In normoxic conditions, we found that Rheb overexpression (da>Rheb) lead to a slight 20 increase in developmental rate compared to control animals (da>+, Fig 3b). When raised in hypoxia, the 21 da>+ animals had an approximately two-day delay to pupation, and this developmental delay was even 22 further exacerbated in da>Rheb animals. (Fig 3b). We also measured effects on overall body size at the 23 end of larval development. We found that da>Rheb animals exhibited an increase in both wandering 24 third instar larval weight (Fig 3c) and pupal volume (Fig 3d). These results are consistent with increased 25 growth caused by modest elevation of TORC1 signalling. However, we found that when raised in 26 hypoxia, the increase in size in da>Rheb animals was abolished (Fig 3c, d). Given that the da>Rheb 27 pupae required an additional ~2 days of larval development to reach the same size as da >+, this 28 indicates that the Rheb overexpressing animals actually had a reduced growth rate in hypoxia.
30
Finally, we examined how maintaining TORC1 activity during larval development in hypoxia affects 31 subsequent survival to adulthood. For these experiments, we maintained animals in either normoxia or 32 hypoxia throughout their larval period and then switched them to normoxia and monitored their 33 development. We first saw that animals carrying either the da>Gal4 (da>+) or UAS-Rheb (+>Rheb) 34 transgenes alone had no effect on viability in either normoxia or hypoxia (Suppl Fig 3a). We found that 35 both da>+ and da>Rheb animals grown in normoxia as larvae showed normal development to the 36 pharate adult stage. Similarly, da>+ animals grown in hypoxia as larvae also showed no significant 37 change in development to pharate adults. In contrast, da>Rheb animals that were maintained in 38 hypoxia during their larval period showed a marked lethality at the pupal stage with few animals 39 developing to pharate adults (Fig 3d). When we further examined adult eclosion, we again saw that 40 da>Rheb animals that were maintained in larval hypoxia showed almost complete lethality, but in this 41 case the da>Rheb animals raised in normoxia also showed a reduction in eclosion, albeit to a much 42 lesser extent than their hypoxia-raised counterparts. We repeated our Rheb overexpression 43 experiments with a second independent UAS-Rheb transgene and we observed similar, but slightly 44 weaker effects, where da>Rheb animals grown in hypoxia as larvae showed a significant decrease in 45 survival to adult stage compared to da>+ animals (Suppl Fig 3b). 46 47 Taken together, these experiments using ubiquitous expression of Rheb to maintain TORC1 signaling 48 indicate that suppression of TORC1 is required for larvae to reset their development and growth rate in 49 hypoxic environments, and for subsequent viable development to the adult stage.
51
The adaptation to hypoxia may reflect a cell-autonomous requirement for each cell to sense low oxygen 52 and inhibit TORC1 to promote overall development and survival. Alternatively, hypoxia may modulate 53 TORC1 in one particular tissue to control overall body growth and development. A precedent for this is 54 the nutrient regulation of larval physiology and growth. For example, nutrient-dependent changes in 1 TORC1 signalling in specific tissues such as the fat body or prothoracic gland can control whole animal 2 growth and development through non-autonomous effects on endocrine signaling. In this manner, one 3 tissue functions as a sensor of environmental stimuli to coordinate whole body responses. To examine 4 a potentially similar role in hypoxia sensing, we examined whether TORC1 suppression in a specific 5 tissue was required for hypoxia tolerance in developing Drosophila. To do this we again took the 6 approach of expressing a UAS-Rheb transgene to maintain TORC1 signaling under hypoxia, but this 7 time we restricted Rheb expression to specific larval tissues. We chose to examine effects on hypoxia 8 tolerance by maintaining animals in either normoxia or hypoxia during their larval period and then 9 measuring survival to eclosion. We tested Gal4 drivers that express in the fat body (r4-Gal4), neurons 10 (elav-Gal4), the intestine (MyoIA-Gal4), the prothoracic gland (P0206-Gal4) and the muscle (dmef2-11 Gal4). We found the most dramatic effects were seen with fat-specific expression of Rheb: r4>Rheb 12 animals grown in hypoxia during their larval stage showed a significant decrease in adult survival 13 compared to r4>+ control animals (Fig 4a). However, in contrast to ubiquitous expression of Rheb, we 14 found that fat body restricted expression did not delay larval development in hypoxia -r4>Rheb animals 15 developed slightly faster to the pupal stage in both normoxia and hypoxia compared to control (r4>+) 16 animals. Also, r4>Rheb animals showed no significant change in final pupal size compared to r4>+) 17 animals. When we performed similar experiments with expression of Rheb in either neurons, intestine 18 or prothoracic gland we saw no effect on viability (Fig 4b-d). Animals expressing Rheb in muscle 19 (dmef2>Rheb) did show reduced adult survival when grown in hypoxia as larvae, however they also 20 showed reduced survival in normoxia, making the effects on hypoxia tolerance difficult to interpret (Fig 21 4e).
23
These results suggest that the larval fat body is an important hypoxia sensing tissue that responds to 24 low oxygen by suppressing TORC1 activity to ensure subsequent viable development. We therefore 25 focused our attention on understanding how reduced TORC1 signaling in the fat body contributes to 26 hypoxia tolerance.
28
Suppression of TORC1 signalling in the fat body leads to increases in lipid droplet size 29 and lipid storage. 30 We next examined how reduction of TORC1 signaling in the larval fat body contributes to normal 31 organismal development and survival in hypoxia. The role of the fat body as a coordinator of overall 32 body physiology and development has been best studied in the context of altered dietary nutrients. In 33 particular, when larvae are starved of nutrients the fat body mobilizes stored sugars and lipids in order 34 to maintain circulating levels of these nutrients and support tissue homeostasis 9,10 . Upon starvation, fat 35 body cells also rapidly engage autophagy to promote organismal survival 17 . We therefore examined 36 whether these changes are associated with exposure to low oxygen. We first examined autophagy 37 since this is a well-studied conserved process known to be induced by TORC1 inhibition. We subjected 38 early third instar larvae to hypoxia for six hours and then stained fat bodies with LysoTracker Red to 39 visualize lysosomes and late stage autophagosomes as an indicator of autophagy. We also stained fat 40 bodies from larvae maintained in normoxia and from larvae subjected to six hours of nutrient starvation, 41 a condition known to induce autophagy. We found that fat bodies from normoxic animals showed little 42 staining with LysoTracker Red, while starved fat bodies showed a marked increase in LysoTracker Red 43 punctae, consistent with induction of autophagy ( Fig 5). In contrast, we saw little or no LysoTracker Red 44 punctae in fat bodies from larvae exposed to hypoxia for six hours (Fig 5). Even longer hypoxia 45 exposure (24 hours) also did not induced autophagy.
47
We then explored effects on lipid metabolism. In the fat body, triacylglycerol (TAGs) are stored within 48 large lipid droplets. These lipid stores then can be mobilized under starvation conditions to supply a 49 source of free fatty acid for beta-oxidation and other metabolic processes required for homeostasis 35 . 50 We observed that when larvae were raised in hypoxia they showed a noticeable change in fat body 51 morphology, which became less opaque in appearance as has been reported previously 36 . When we 52 examined the fat bodies under light microscopy we saw an increase in cytoplasmic lipid droplet size 53 (Fig 6a). We examined this phenotype in more detail by using Nile Red to stain the neutral lipids that 54 compose these cytoplasmic lipid droplets. When we transferred second instar larvae to hypoxia for two 1 days we observed a significant increase lipid droplet diameter compared to larvae maintained in 2 normoxia for the same period (Fig 6b, c). This effect on lipid droplets was opposite to that seen in larvae 3 that were starved of all nutrients for two days (PBS only), which exhibited a marked decrease in lipid 4 droplet size (Fig 6b). Instead, the hypoxia phenotype was similar to animals that were transferred to a 5 sugar-only diet for two days. These results indicate that the effects of hypoxia on lipid droplet size are 6 opposite to those seen in nutrient-deprivation and suggest that under hypoxia larvae may increase TAG 7 levels through increase synthesis from dietary sugars. To measure TAG levels more quantitatively, we 8 raised larvae from hatching in either normoxia or hypoxia and then measured whole-body TAG levels 9 using a colorimetric assay. We found that hypoxic animals exhibited approximately a two-fold increase 10 on total TAG levels when corrected for total larval weight (Fig 6d). We additionally used a previously 11 described sucrose solution buoyancy assay to estimate larval lipid content 37,38 . In this assay groups of 12 isolated wandering third instar larvae are mixed with increasing concentrations of a sucrose solution 13 and the percentage of larvae floating at each concentration is measured. Using this approach, we found 14 that hypoxic larvae were more buoyant than larvae growth in normoxia, consistent with an increase in 15 lipids as a proportion of total body mass (Fig 6e). Altogether, these results indicate that hypoxia induces 16 a remodelling of lipid droplet and an increase in total lipid storage.
18
We next examined whether these changes in lipid metabolism occurred as a consequence of reduced 19 TORC1 activity. To test this, we generated GFP-marked fat body tsc1 mutant cell clones. As we 20 previously described, loss of TSC1 completely reversed the hypoxia-mediated suppression of TORC1 21 signaling. Hence, we examined these tsc1 mutant fat body cells to see if they still showed the hypoxia-22 mediated changes in lipid droplets. We induced clones during mitosis in the embryo and then when the 23 animals hatched we transferred them to hypoxia for their entire larval development. When we dissected 24 and examined the fat bodies from third instar larvae using DIC microscopy, we observed the hypoxia 25 increase in lipid droplet size in all non-GFP cells (Fig 7). However, the tsc1 mutant cells showed no 26 increase in lipid droplet size. Instead they maintained the small lipid droplet morphology typical of 27 normoxic animals at the same stage even though the animals had been grown in hypoxia for several 28 days (Fig 7). These data indicate suppression of TORC1 signalling is required of the hypoxia-mediate 29 remodelling of lipid storage.
31
Reorganization of lipid metabolism is required for hypoxia tolerance. 32 We next examined whether the changes in lipid storage caused by the hypoxia-mediated suppression 33 of fat body TORC1 signaling was important for development and survival. To do this we used genetic 34 knockdown of Lsd2, a Drosophila perilipin homolog 39-41 . Lsd2 is a protein associated with the surface of 35 lipid droplets that is necessary for normal lipid droplet formation. We used expression of an inverted 36 repeat (IR) to Lsd2 (UAS-lsd2 IR) to specifically knockdown Lsd2 in the fat body using the r4-Gal4 37 driver. When we did this and then transferred animals to hypoxia for two days, we found that the large 38 lipid droplet phenotype seen in control (r4>+) animals was blocked when Lsd2 levels were reduced 39 (r4>lsd2 IR; Fig 8a). We then explored how this inhibition of lipid droplet size affected tolerance to 40 hypoxia. We maintained r4>+ and r4>lsd2 IR larvae in hypoxia from larval hatching to pupation, and 41 then switched them back to normoxia and monitored viability to adult stage. We found that the r4>lsd2 42 IR showed a significant reduction in survival compared to r4>+ control animals (Fig 8b). To confirm this 43 effect, we also examined a previously reported lsd2 mutant allele (lsd2 KG00149 ). These lsd2 mutants are 44 viable and show normal development when grown on normal laboratory food in normoxia. However, 45 when we maintained these lsd2 mutants in hypoxia throughout their larval period, they showed a 46 marked reduction in survival to adult stage compared to control (w 1118 ) animals (Fig 8c). These results 47 indicate that the increase in lipid droplet size caused by reduced TORC1 is required for organismal 48 adaptation to hypoxia. In this paper, we explored how Drosophila are able to tolerate hypoxia. A central finding of our work is 53 that when larvae are exposed to low oxygen, the fat body serves as a key hypoxia sensor that mediates 54 changes in physiology to ensure viable organismal development. This hypoxia sensor role is mediated 1 through inhibition of TORC1 signaling and reorganization of lipid storage. This function of the fat body 2 as a hypoxia sensor is reminiscent of the role of the fat body is coordinating whole body physiology 3 responses to changes in dietary nutrients 9,10,17,42,43 . As we find in hypoxia, these nutrient effects are 4 also dependent on modulation of TORC1 activity and they can exert both metabolic and endocrine 5 effects to control growth and development. These studies and our findings in hypoxia, emphasize how 6 the fat body functions a sentinel tissue to detect changes in environmental conditions and to buffer the 7 internal milieu from these changes. Moreover, while most work on hypoxia has focused on studying 8 cells in culture 27,44 , our findings emphasize the importance of non-cell autonomous mechanisms in 9 controlling how animals adapt to low oxygen. 10 11 Inhibition of TORC1 in larvae exposed to hypoxia occurred rapidly and, interestingly, only in response to 12 a specific range of low oxygen (~2-6%). At <2% oxygen and lower, the response to hypoxia is very 13 different compared to exposure to 5% oxygen that was used in this study -larvae crawl away from the 14 food and eventually undergo complete movement arrest, which can be reversed within minutes of return 15 to normoxia. Larvae can only tolerate this level of low oxygen (<2%) for a few hours before dying. Since 16 this low oxygen hypoxic response is different to the behaviour of larvae at 5% oxygen (which maintain 17 their feeding and growth) it may also rely on qualitatively different changes in hypoxia sensing and 18 signaling that do not involve suppression of TORC1. The hypoxia-mediated inhibition of TORC1 that we 19 found required TSC1/2 but was independent of two main mechanisms defined in mammalian cell 20 culture experiments -induction of REDD1 by the well-studied HIF-1 alpha transcription factor or by 21 activation of AMPK. Although the Drosophila homolog of REDD1, Scylla, was previously shown to be 22 sufficient to inhibit TORC1 32 , we found that it was not necessary. Indeed, analysis of the REDD1 23 mutant mouse also showed that in certain tissues, hypoxia-mediated repression of TORC1 was also 24 REDD1-independent 34 . A previous report in cell culture showed that upon different stresses including 25 hypoxia, TSC2 could translocate to the lysosome and inhibit Rheb activation of TORC1 45 . Therefore, 26 upon hypoxia exposure in larvae, the TSC1/2 complex may rapidly re-localize to inhibit TORC1 27 function. The mechanism that could drive this (or any other potential mechanism of TORC1 inhibition) 28 must be triggered rapidly in response to hypoxia in larvae. Given the importance of oxygen as an 29 electron acceptor in the electron transport chain in the mitochondria, it is plausible that the rapid One result we found interesting was that the suppression of fat body TORC1 and altered larval lipid 53 storage was not necessary for viable larval development under hypoxia, but was required for 54 subsequent development in the pupal stage to produce viable adults. During the pupal stage, tissues 1 undergo metamorphosis to establish the adult body. Since this is also a non-feeding stage of the life 2 cycle, the energy required to fuel these extensive tissue rearrangements in pupae must therefore come 3 from stored nutrients. It has been calculated that the lipid stores provide 90% of this energy 52 . Our 4 findings suggest that pupae may be more dependent on these lipid stores after a period of prior larval 5 hypoxia. Hence failure to maintain these stores, either by preventing TORC1 inhibition (Rheb 6 overexpression) or genetic disruption of lipid droplet formation (Lsd2 knockdown), lead to reduced 7 viability in hypoxia, while having no effect on normal development in normoxia. It is also possible that 8 the requirement for altered lipid stores may reflect a role for lipid droplets beyond simply providing a 9 usable energy source 53 . A pertinent example is a report describing how increases in glial lipid droplets 10 in larvae were important for maintaining neuroblast cell proliferation in larvae exposed to hypoxia or 11 oxidative stress 54 . In this case, the lipid droplets were required to play an antioxidant role to buffer 12 neurons from ROS-induced damage. Mammalian cancer cells in culture have also been shown to 13 accumulate lipid droplets in low oxygen, an effect that is important to promote their survival and 14 tumorigenic phenotypes in mouse models 55 . Cancer cells with high levels of TORC1 activity have also 15 been shown be dependent on exogenous fatty acids for their survival in hypoxic conditions 56,57 . Hence, 16 the lipid droplet phenotypes we observed may be important for ensuring cell and tissue viability in pupal 17 stages independent of any role in energy production.
19
In conclusion, our studies presented here pinpoint the Drosophila fat body as a key hypoxia sensing 20 tissue that ensures viable animal development in low oxygen. We suggest that, given the importance of looking at TORC1 activity, either total eIF2 alpha, actin or total S6K levels were used as loading 10 controls because the levels of these proteins were unaffected by hypoxia.
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Domain: Biology
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Complementary anatomy of Actinocyclus verrucosus ( Nudibranchia , Doridoidea , Actinocyclidae ) from Indo-Pacific
The last review of the genus Actinocyclus consider only two valid species for the genus: Actinocyclus verrucosus Ehrenberg, 1831 (type species of the genus) and Actinocyclus papillatus (Bergh, 1878), both with a geographical distribution in the Indo-Pacific. The anatomy of these species is still unknown, except for some scanty anatomical information. A detailed anatomical study of Actinocyclus verrucosus is performed, including inedited structures such as digestive system, odontophore muscles and circulatory system, beyond complementary information on the commonly studied structures, in order to clarify the taxonomy and distribution.
The current systematics of this family is mainly based on Gosliner and Johnson (1994) who studied the phylogenetic relationship of the Hallaxa, and hypothesized Actinocyclus as its sister taxa, considered only these two genera as members of Actinocyclidae, and this family as sister taxon of Chromodorididae (Valdés 2002). The last review of Actinocyclus was based on some traditional characters, such as the external morphology, the reproductive system and radular data (Valdés 2002).
As have been seen in recent papers (e.g., DaCosta et al. 2007, Simone 2011, Lima and Simone 2015), the scenario of the morphological characters is an effective tool to better understanding the relationship among species and has been useful to clarify and refine their taxonomy.
In this paper the morphological data of Actinocyclus verrucosus are described in more details, including previously unexplored structures, such as digestive tubes, odontophore muscles and circulatory system, and builds a conceptual scenario for comparative characters in future analysis.
Material and methods
The studied material belongs to museum collections, consisting of specimens preserved in 70% ethanol; a complete information follows description. Dissections were performed under a stereomicroscope by standard techniques (Simone 2004(Simone , 2011)). The initial steps of the anatomical investigation were done through a longitudinal cut on the integument covering the dorsal visceral mass. Digestive, circulatory, excretory, reproductive and central nervous systems were investigated in detail. The terminology used for odontophore muscles was based on Ponder et al. (2008), Simone (2011) and Lima and Simone (2015). Drawings were done with the aid of a camera lucida. Scanning electron microscopy (SEM) was used to examine details of the radula at the Laboratório de Microscopia Eletrônica of Museu de Zoologia da Universidade de São Paulo (MZSP).
Haemocoel organs : pericardium and posterior half of visceral mass occupying ~40 % of haemocoel volume. Buccal mass located anteriorly, occupying ~20 % of haemocoel volume. Nervous system dorsal to buccal mass, covered by blood gland, occupying ~10 % of haemocoel volume. Genital system on right side; occupying ~20 % of haemocoel volume. Stomach internal to digestive gland, intestine with small curve at anterior portion, both occupying ~10 % of haemocoel volume.
Circulatory and excretory systems (Figs 8-11): pericardial cavity dorsal and posterior to digestive gland, anterior to gill circle. Afferent and efferent veins located at edge of each branchial leaves (Fig. 9). Gill retractor muscle divided in two fibers originating from base of gill circle, running lateral to haemocoel longitudinally up to half of foot level, inserting into dorsal surface of foot (Fig. 8). Auricle funnel-like (wider anteriorly) with thin walls (Fig. 10). Ventricle slightly taller than wide, with thick muscular walls (Fig. 10). Aortic trunk anterior to pericardium, connected to anterior ventricular region ; posterior artery branched into anterior artery irrigating reproductive system, buccal mass, odontophore and nervous system inserting on blood gland; anterior artery irrigating stomach, caecum and digestive gland (Fig. 11). Auricular vessels connecting lateral cavities of integument to auricle (Fig. 10). Medial sinus connected to afferent branchial ring, irrigating entire digestive gland. Renal vesicle located on right dorsal side of pericardium, near base of auricle, connected to inner surface of pericardium; renal chamber elliptical, with longitudinal folds, ~1/4 of size of ventricle (Fig. 10). Renal chamber extending from dorsal to medial sinus, previously connected to renal vesicle, extending posteriorly to center of gill circle and opening in nephrostome, next to anus pore (Fig. 8). Blood gland undivided (Fig. 11).
Digestive system (Figs 8,(13)(14)(15)(16)(17)(18)(19)(20)(21)(28)(29)(30)(31): Oral tube composed of outer lip, with pleats lengthwise; inner lip with transverse fold; mt, two pairs of retractor muscles of buccal mass, originating on oral tube, running dorsally and ventrally to oral tube, inserting on body side, about three times as wide and twice as long as m10 (Fig. 15). Odontophore oval, connected to oral tube by several fine longitudinal dorsal and ventrolateral protractors muscles of buccal sphincter, originating in anterior region of odontophore, inserting in posterior region of integument, close to oral tube (m10) (Fig. 14); Buccal sphincter surrounding chitinous part of oral tube. Odontophore muscles: m2, pair of strong retractor muscles of buccal mass, six times longer than wide, originating on anterior dorsal odontophore, running laterally to m4 and inserting ventrally on dorsal portion of foot; m3, two times wider than long, transverse fibers between esophagus and odontophore (Figs 15-16); m4, pair of dorsal tensor muscles of radula, strong and broad, 1/2 winder than long, covering externally 2/3 of cartilage, inserting on ventral portion of subradular membrane; m5, pair of dorsal auxiliary tensor muscles of radula, twice as long as wide, originating on most posterior region of odontophore cartilages, covering ~1/3 of posterior cavity of odontophore, as long as, but with ~1/3 of m4 width, inserting on ventral side of subradular membrane, around radular sac; m6, unpaired horizontal muscle, with transverse fibers connecting anterior surfaces of left and right odontophore cartilages, as long as wide, about same length and half as wide as m4 (Fig. 20); m7, pair of thin muscles originating each into an odontophore cartilages and inserting on m7a passing ventrally by m5, and on radular sac (Fig. 19); m7a, originating on posterior region of odontophore cartilage and inserting on radular sac, m7' auxiliary (Fig. 19). Pair of odontophore cartilages slender, elliptical. Subradular membrane thin, strong, translucent (Fig. 18). Radular sac ~1/6 of odontophore (Fig. 16). Jaw elements not analyzed. Radular teeth (Figs 28-31): rachidian teeth absent; formula 50 x 17.0.17(preserved specimen, ~15 mm-long, AUS C333868001). Innermost lateral teeth broad and thick, with large and rounded cusp and about six to eight cusps along inner edge (Fig. 31). Mid-lateral teeth narrow basally and elongated, with apical cusp larger than other, twenty-one lateral cusps (Fig. 30). Outermost teeth shorter than middles laterals, about sixteen to eighteen cusps (Fig. 29). Pair of salivary glands long, tubular, about same length as esophagus; duct inserting in anterior region of esophagus, extending posteriorly to anterior region of digestive gland (Fig. 14). Esophagus simple, originating dorsally to odontophore, inserting directly in anterior region of stomach, internal longitudinal folds with same diameter along entire length . Stomach internal to digestive gland, oval, close to anterior region of intestine, with distinct digestive ducts (Fig. 21). Intestine with longitudinal folds along its entire length, diameter same as esophagus diameter. Caecum as an elongated sac, located ventrally to stomach, opening on anterior portion of stomach (Fig. 21), close to esophageal insertion, ~½ length and ~1/2 of width of stomach. Digestive gland dark brown, internal to hermaphrodite gland, cone-shaped; inner face of gland sponge-like, bearing three ducts (Fig. 21). Anus opening into pore at center of gill circle (Fig. 8); anal papilla absent.
Genital system (Figs 2,(11)(12)(22)(23)(24): located between buccal mass and digestive gland, mainly on right and dorsal sides. Hermaphrodite gland around digestive gland, dark beige in color . Hermaphrodite duct thin, long located posterior end of ampulla . Ampulla located on female gland, elongated and tubular, about same length as oviduct, inserting distally at junction of oviduct and prostate (Fig. 23). Prostate glandular, connected to female gland, ~1/2 of ampulla's length. Vas deferens and penis muscular, cylindrical, elongated, ~1/2 of prostate's width (Fig. 22). Female gland well-developed, rounded, occupying ~40% of reproductive system volume, about same length and twice width as oviduct . Oviduct occupying ~1/3 of female gland volume . Uterine duct located at base of bursa cop- ulatrix and seminal receptacle, inserted in female gland near oviduct, relatively short, ~1/10 of vagina's length and same diameter as vagina (Figs 22,24). Seminal receptacle pyriform, as large as bursa copulatrix, connected to vagina near uterine duct through short stalk (Figs 22,24). Bursa copulatrix rounded, connected to vagina after seminal receptacle, length ~1/6 of vagina's length, also through stalk three times longer than uterine duct (Figs 22,24). Vagina cylindrical, elongated, same width and four times longer than penis, followed ventrally by prostate and located parallel to penis on gonopore (Fig. 24). Gonopore on right side, located in anterior fifth of length of animal from head, between foot and notum (Fig. 2).
Discussion
The presence of a short pair of digitiform tentacles around the mouth (Fig. 4) is noteworthy, they were also reported by Gosliner and Johnson (1994). However, these digitiform tentacles have been reported as absent by Kay and Young (1969) and Valdés (2002). The most external differences of A. verrucosus, when compared to other Doridoidea, for example Hallaxa apefae, Chromodoris magnifica and Doris verrucosa (Tab.1), is the presence of an anterior border of foot concave, not convex and not grooved, nor notched (Figs 2, 4).
The rhinophores have 17 lamellae instead of 20 described by Valdés (2002), and the number of branchial leaves ranges from 16 to 19, instead of only 16. Regarding the color of the body, no alive specimens have been analyzed.
In the circulatory system, interesting features were found in the position in relation to gill circle, the afferent and efferent vessels, the gill retractor muscle, medial sinus, renal chamber and nephrostome. Despite some of these features have already used in phylogenetics analyzes (Lima 2016), because of lack of further information, a deeper analysis is still difficult.
The oral tube is composed of a pair of retractor muscles, which attaches to the body wall (mt), in A. verrucosus there are two pairs of mt, while in Hallaxa apefae, and the most species of Doridoidea, present three pairs (Lima 2016). A buccal sphincter and the m3 (transverse muscle) involve the odontophore (Figs 15-16) that have a pair of long retractor muscles (m2) . A group of muscles are described for the first time (m4, m5, m6, m7) (Figs 16-20), with similar functions of their counterparts in other heterobranchs (Simone 2011). The odontophore cartilage is well-developed (Fig. 18) like in other nudibranchs as, e.g., Doris verrucosa Linnaeus, 1758 (Lima and Simone 2015). However, some differences are visible between A. verrucosus and D. verrucosa as following: m5 pair originates on the middle region of the odontophore cartilages in A. verrucosus instead on the posterior region in D. verrucosa (Lima and Simone 2015, fig.8B); m6 located more anteriorly in A. verrucosus (Fig. 20), whereas in D. verrucosa the m6 connects the both odontophore cartilages anteriorly and posteriorly (Lima and Simone 2015, Figs 8A-B). However, the most significant difference of odontophore muscles of A. verrucosus and others Doridoidea species appears to be the presence of the pair m7a.
The reproductive system seems to be similar to those described by Valdés (2002), but it has some different features from the interpretation by Kay and Young (1969) that described the prostate without the glandular portion, which was not observed in the present studied samples .
In the central nervous system, the abdominal ganglion described by Valdés (2002) was not observed, but a pleural commissure , that is not mentioned by him, was found. This last feature was uncovered as autapomorphy in a recent phylogenetic study (Lima 2016) as well as the presence of m7a -originating on posterior region of odontophore cartilage and inserting on radular sac, probably m7's auxiliary.
In the same recent phylogenetic study (Lima 2016) Hallaxa apefae appears more related to Chromodorididae (Tab. 1) clade and could be considered as sister group based on the posterior projection of the foot beyond the notum and the absence of integumentary spicules. In the same analysis, A. verrucosus resulted as sister group of a clade that united Dorididae and Discodorididae with two characters: radula with many lateral teeth and buccal commissure readily visible.
The present complementary anatomical investigation improved the species delimitation of A. verrucosus. In addition, allowed to evaluate the characters usually used in taxonomy and phylogenetic studies, as well as the discovery of new characters with phylogenetic signal and provided more bases for the synonymies. The evaluation of new morphological characters will improve the knowledge of the Actinocyclus evolutionary history, or even Doridoidea. This paper also shows the importance in investigating systems and organs beyond the traditional external features, radula and genital structures, which sometimes bear clearer data for comparative analysis as, e.g., the odontophore muscles.
Table 1 .
Comparative table of some features between Actinocyclus verrucosus, Hallaxa apefae and Chromodoris magnifica (all these features of the three species was analyzed in Lima 2016).\===
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BinSPreader: Refine binning results for fuller MAG reconstruction
Summary Despite the recent advances in high-throughput sequencing, metagenome analysis of microbial populations still remains a challenge. In particular, the metagenome-assembled genomes (MAGs) are often fragmented due to interspecies repeats, uneven coverage, and varying strain abundance. MAGs are constructed via a binning process that uses features of input data in order to cluster long contigs presumably belonging to the same species. In this work, we present BinSPreader—a binning refiner tool that exploits the assembly graph topology and other connectivity information to refine binning, correct binning errors, and propagate binning to shorter contigs. We show that BinSPreader could increase the completeness of the bins without sacrificing the purity and could predict contigs belonging to several MAGs. BinSPreader is effective in binning shorter contigs that often contain important conservative sequences that might be of great interest to researchers.
INTRODUCTION
The amount of microbial organisms that can be easily cultivated is relatively small in proportion to the Earth's total diversity (Rappé and Giovannoni, 2003); therefore, most of the Earth's microbiota proves difficult for analysis. Whole metagenomic shotgun sequencing, which allows for a comprehensive analysis of microbial DNA from a sample, provides an alternative method for understanding the functional potential and genetic composition of different microorganisms that have not been previously cultured. Metagenomic sequencing libraries are then assembled using metagenomic assemblers, such as metaSPAdes (Nurk et al., 2017) or MEGAHIT (Li et al., 2015) for short read libraries or metaFlye (Kolmogorov et al., 2020) for long read libraries.
To extract useful information from complex metagenomic assemblies, a process called binning is used. State-of-the-art binners use all different kinds of information including nucleotide content, observed contig abundance, paired-end read connectivity, and other connectivity (e.g. from Hi-C links (DeMaere and Darling, 2019)) to cluster contigs that might belong to the same species. However, this kind of information could only be considered reliable for long contigs, and therefore, the majority of binners discard contigs that are shorter than several kilobases. Given this, the set of contigs could not be considered the ultimate result of a metagenomic assembly. Indeed, the complete information about the assembly is provided via the assembly graph. Usually, the edges of an assembly graph are the maximal nonbranching genomic sequences (unitigs), and the resulting contigs are paths in this assembly graph obtained after the repeat resolution process. The recent development of such assembly graph-aware alignment tools such as SPAligner (Dvorkina et al., 2020), PathRacer (Shlemov and Korobeynikov, 2019), and GraphAligner (Rautiainen and Marschall, 2020) among the others shows that the proper utilization of the assembly graph could significantly improve the obtained results.
To date, it seems that the connectivity information between the contigs in the assembly graph is ignored by the majority of the common binning tools such as MetaBAT2 (Kang et al., 2019), MetaWrap (Uritskiy et al., 2018), and VAMB (Nissen et al., 2021), potentially reducing the overall precision of the results. Recently developed graph-aware binning refining tools such as METAMVGL (Zhang and Zhang, 2021), MetaCoAG (Mallawaarachchi and Lin, 2022), and Binnacle (Muralidharan et al., 2021) also do not utilize the assembly graph in the usual sense of the term. Instead, they are relying on the so-called scaffold graph that only preserves the connectivity information between different scaffolds. However, the original assembly graph contains more information including the multiplicity of edges and the set of edges that comprise a contig. To utilize this greater amount of information, we suggest using the original assembly graph instead of the scaffold graph; this brings to us many opportunities such as multiple binning of individual edges, binning correction, and more precise bin label propagation (from edge to edge and not from scaffold to scaffold).
Standard MAG quality assessment tools such as AMBER (Meyer et al., 2018) and CheckM (Parks et al., 2015) do not assess MAGs for the presence of important sequences, such as mobile genetic elements (MGEs), antibiotic resistance genes (AMR), and CRISPR arrays, that have very high agricultural or clinical importance. As such, MAGs with over 80% completeness as reported by AMBER or CheckM may contain less than 45% of genomic islands and less than 30% of plasmid sequences (Maguire et al., 2020). Mobile genetic elements are commonly flanked by direct repeats (Schmidt and Hensel, 2004) and are therefore located on short repetitive edges of the assembly graph and associated with multiple organisms.
Besides MGEs, MAGs often miss contigs containing rRNA genes. Bacterial genomes contain multiple copies of ribosomal genes forming tangled repeat structures that are often poorly assembled. In a metagenome the situation is further complicated by the presence of conservative parts of rRNA genes shared between different species. Such sequences form intra-and interspecies repeats, and therefore, the overall recovery of decent-length rRNA genes sequences from a metagenome assembly is quite low (Meyer et al., 2022). Finally, the contigs containing rRNA genes have different abundance (due to high copy number) and nucleotide content effectively preventing the majority of binning attempts. Therefore, the inclusion of short edges of the assembly graph into MAGs is crucial for detecting MGE and rRNA sequences.
In this work, we show that assembly graph representation provides more accurate binning of short edges in comparison with scaffold graph representation. We present a new software tool, BINSPREADER, which can produce refined MAGs from initial binning by combining metagenomic assembly graph and sequencing data. We show that BINSPREADER improves upon state-of-the-art binning refining tools with respect to completeness/purity metrics of MAGs and MGE and rRNA recovery and can accurately predict contigs belonging to multiple bins. BINSPREADER is available from cab.spbu.ru/software/binspreader.
Datasets
We used several mock metagenomic datasets, simulated metagenomes as well as real metagenomes for the refining evaluation. These metagenomes are derived from different communities exhibiting different microbial compositions, abundance profiles, genome characteristics, and similarity intended to provide a broader scope of binning data features. (Singer et al., 2016) is composed of 23 bacterial and 3 archaeal strains isolated from heterogeneous soil, aquatic environments as well as human, bovine, and frog microbiota. For 25 of those strains, reference genomes are known. The genomes of these species span a wide range of genome sizes (1.8-6.5 Mbp), GC-contents (28.4%-72.7%), and repeat contents (0%-18.3%).
MBARC26
BMock12 (Sevim et al., 2019) includes DNA from 12 bacterial strains belonging to actinobacterial, flavobacterial, and proteobacterial taxa that also display a large spread of genome properties. For 11 of those strains, reference genomes are known. Apart from this, it includes three bacteria with genomes of high %GC and high average nucleotide identity (ANI), which complicates the assembly and binning.
ZymoBIOMICS Microbial Community Standard (Nicholls et al., 2019) (referred to as Zymo) is a mock community consisting of eight bacterial and two fungal strains. These organisms are lysed in varying degrees and significantly differ in terms of the completeness of sample DNA extraction, which is a determining factor for sequencing and downstream analysis.
The benchmarking dataset from Maguire et al. (2020) (referred to as magsim-MGE) contains paired-end Illumina sequencing data of 30 bacteria with randomly assigned relative abundance. It is designed to display a high diversity of genetic features, such as plasmids and genomic islands.
We assembled each of these datasets from Illumina shotgun sequencing data using metaSPAdes 3.15.3 and used reference genomes of included bacteria, archaea, and yeasts to construct ground truth binning standards for benchmark studies. iScience Article simHC+ simulated dataset (Wu et al., 2014) was derived out of genome assemblies of 100 bacterial species that mimic high-complexity communities lacking dominant strains. As no original reads for this dataset were available, we used metagenomic assembly, abundance profiles, and ground truth binning standard as provided in MetaCoAG paper (Mallawaarachchi and Lin, 2022).
IC9 is a real clinical gut metagenome of a chronically ill patient collected in a critical care unit. The dataset contains both paired-end and Hi-C data that were crucial for better resolution of MAGs (Ivanova et al., 2022). The metagenome is harboring many antibiotic-resistant strains with elevated levels of horizontal gene transfer. The dataset was assembled as described in Ivanova et al. (2022).
Sharon dataset (Sharon et al., 2012) contains the metagenomic sequencing data of preborn infant fecal samples collected across 18 time points. All these sequencing libraries were co-assembled together using metaSPAdes 3.15.3 before binning and refining.
MetaCoAG, Binnacle, and BINSPREADER require assembly graph in GFA format as an input. METAMVGL utilizes assembly graphs in obsolete FASTG format, which makes it difficult to use on assembly graphs produced by, e.g. metaFlye. METAMVGL, Binnacle, DAS_TOOL, and BINSPREADER require initial binning to refine, whereas MetaCoAG produces initial binning internally using provided coverage profiles. Pairedend read library is required for both METAMVGL and Binnacle as a source of connectivity information between scaffolds and for BINSPREADER input paired-end library may be provided optionally to supplement assembly graph links.
Binning refining certainly depends on the quality of the initial binning, as no refining procedure could introduce new bins. In order to reduce the variation of the results that might depend on the initial binning, we used three state-of-the-art binners, MetaBAT2 (Kang et al., 2019), MetaWrap (Uritskiy et al., 2018) (which internally bins using MetaBAT2, CONCOCT, and MaxBin2 (Wu et al., 2014) and produces some sort of consensus binning), and VAMB (Nissen et al., 2021) to produce three initial binnings for METAMVGL and BINSPREADER. Because Binnacle is compatible with a limited number of binners, we used it with MetaBAT2 only. Unless stated otherwise, an input metagenomic assembly graph was constructed using metaSPAdes 3.15.3 (Nurk et al., 2017). The comparison of running times of used binners are presented in Table 1.
The resulting binnings of mock and simulated samples were analyzed with AMBER (Meyer et al., 2018). AMBER assessment of bin quality is based on the annotation of metagenomic contigs using the reference iScience Article genomes provided as a ''gold standard binning.'' Contig alignment to reference genomes was performed using metaQUAST (Mikheenko et al., 2015). Evaluations of real metagenomes without references were done via CheckM (Parks et al., 2015). AMR genes were searched using RGI 5.2.1 with CARD database 3.1.4 (McArthur et al., 2013). CRISPRs were detected using MinCED 0.4.2 (Bland et al., 2007). rRNA were annotated with Barrnap 0.9 (Seeman, 2013).
Completeness, contamination, and F1
To benchmark BINSPREADER, we analyzed the average (mean) purity, completeness, and F1-score of the binning results calculated by AMBER (at the nucleotide level) for five synthetic datasets. To complement these metrics, we also took into account the number of recovered high-quality genomes with > 90% completeness and < 5% contamination as reported by AMBER. Benchmark metrics on real IC9 and Sharon datasets included mean purity, completeness, and F1-score metrics, which were assessed using CheckM (Parks et al., 2015), as well as total number of bins and the number of high-quality bins with > 90% completeness and < 5% contamination as reported by CheckM.
Mean F1-scores for initial and refined binnings, and the number of recovered high-quality genomes, across all seven datasets are summarized in Figures 1 and 2, respectively. Individual F1-scores for refined bins for IC9 and Sharon can be found in Figures S10 and S11, respectively. Individual F1-scores for refined bins across all datasets can be found in Figures S1-S4.
On magsim-MGE dataset, MetaBAT2, VAMB, and MetaWRAP recovered very pure bins with average purity taking values from at least 97.2% for MetaBAT2 to 99.9% for VAMB and MetaWRAP (refer to Table S1 for all AMBER metrics of this dataset). Yet these binnings had very low average completeness with a maximum value of 69.2% for MetaBAT2 and a minimum of 43.5% for VAMB. This poor trade-off between purity and completeness is indicated by the moderate values of the mean F1 score. Best-performing binning tool, MetaBAT2, resulted in an F1 score of 80.8% and recovered 12 high-quality out of 30 total genomes; the worst-performing tool was VAMB with an F1 score of only 60.6% and 8 recovered genomes.
Although refining of initial bins with METAMVGL and BINSPREADER led to a minor decrease in average bin purity (no more than 3% for METAMVGL and 1% for BINSPREADER across all bins), it significantly reduced the number of unbinned contigs and increased average bin completeness. Bins refined with METAMVGL and BINSPREADER had average completeness ranging from 50% for VAMB and MetaWRAP to 72% for MetaBAT2. Refining MetaBAT2 bins using Binnacle did not affect bin purity compared with running MetaBAT2 alone, iScience Article but reduced average completeness. MetaCoAG produced bins with an average purity of 97.5%, average completeness of 47.3%, F1 score of 63.7%, and 10 high-quality MAGs yielding results somewhat worse than several standalone binners.
Of all binning and refining approaches MetaBAT2 bins refined using BINSPREADER with paired-end reads showed the best average F1 score of 85.0%, although metaWRAP bins refined using BINSPREADER contained more high-quality MAGs (14 for MetaWRAP + BINSPREADER vs 12 for MetaBAT2 + BINSPREADER).
Available data of simHC+ dataset allowed benchmarking of the BINSPREADER performance against MetaCoAG only (refer to Table S2 for all AMBER metrics), as no original paired-end reads were available in the MetaCoAG paper and therefore one cannot run METAMVGL or Binnacle using only assembly graph and provided abundance profiles. For initial binnings, we used VAMB bins as well as precomputed bins of MaxBin2 and MetaBAT2. The initial bins had the average F1 scores of 23.0%, 84.5%, and 91.7% for MetaBAT2, MaxBin2, and VAMB, respectively. The poor value of the F1 score for MetaBAT2 binning is a result of 13.0% average bin completeness, which is the lowest among all binners. Refining of MetaBAT2 with BINSPREADER overall increased bin completeness to 88.4% and F1 score to 76.3% but caused a major drop in average purity of bins. VAMB showed the best balance between precision and sensitivity, although many of the contigs remained unlabeled by VAMB. Refined with BINSPREADER VAMB bins showed an increase of the F1 score value to 94.1% and the number of high-quality MAGs increased from 56 to 61. MetaCoAG showed somewhat lower F1 score of 86.7% and captured only 43 high-quality genomes; therefore, BINSPREADER + VAMB is the best-performing pair for the simHC+ dataset.
Binning assessment of Zymo mock metagenome showed 100% average purity of MetaBAT2, VAMB, and MetaWRAP bins (refer to Summarizing the results on all datasets, graph-aware refiners, METAMVGL and Binnacle, either yield no noticeable effect (magsim-MGE) or impaired the characteristics of the original binning (MBARC26, BMock12, Zymo). MetaCoAG showed a decent ratio of precision to sensitivity but left large portions of contigs unbinned. Exploiting the assembly graph to the fullest extent allowed BINSPREADER to augment the bins with unbinned contigs and improve their F1 score with the best trade-off between completeness and contamination. Moreover, it also increased the number of complete MAGs represented with minimal contamination.
We need to outline that the performance of any binning refining tool including BINSPREADER depends on the quality of the input bins, as the refiner cannot ''invent,'' e.g. a missed bin. This pitfall is demonstrated on BINSPREADER refining of the simHC+ binning by MetaBAT2. Because of the extremely low completeness of the initial binning, BINSPREADER failed to accurately perform contig labeling, causing additional contamination of the bins.
As reported in Tables S7 and S8, MetaWRAP showed the best average F1-score among the initial binners for both IC9 and Sharon datasets (96.5% for IC9 dataset, 98.3% for Sharon). None of the graph-based refiners, namely BINSPREADER, METAMVGL, and Binnacle, showed any significant improvement upon initial binnings for both real datasets, with the exceptions of BINSPREADER complemented with Hi-C reads for MetaBAT2 on IC9 dataset (64.7% average F1 score for MetaBAT2 against 69.6% average F1 for BINSPREADER) and Binnacle-refined MetaBAT2 binning for Sharon dataset (81.3% for Binnacle against 76.6% for MetaBAT2). DAS_TOOL refining demonstrated the best increase in average F1-score for all initial binnings. This, however, could be explained by a consistent decrease in the number of bins after DAS_TOOL refining due to filtering out bins with poor CheckM metrics. As a result, DAS_TOOL recovered less high-quality genomes than BINSPREADER (7 instead of 8). Specifically, MetaBAT reported 50 bins for IC9 dataset, whereas DAS_TOOL reported only 23 refined MetaBAT2 bins.
Negligible increase of CheckM purity and completeness metrics after graph-based refining for real datasets could be explained by limitations in CheckM single-copy gene-based purity and completeness estimation (they are essentially located on long contigs that are likely properly binned and no shorter contigs iScience Article contribute to these metrics) and by segmentation of metagenomic assembly graphs constructed for these datasets. Indeed, for Sharon and IC9 datasets, the mean number of links outgoing from an assembly graph node (single unitig) are 1.62 and 0.51, respectively, whereas for mock Zymo dataset the mean number of outgoing links is 2.71. Also, the bins seem not to cover the whole assembly (30%-60% depending on the binner).
Still, even sparse assembly graphs provide BINSPREADER with sufficient information to reconstruct different functional genes more efficiently compared with initial binning as we show below.
Conservative genes recovery
Efficient binning of rRNA still remains one of the greatest challenges in metagenomics, as rRNA gene clusters are hard to assemble due to a high number of intra-and interspecies repeats. Consequently, contigs containing rRNA genes are usually small and belong to multiple genomes. Most of the binners do not support the assignment of one contig to multiple bins making it nearly impossible to recover a sufficiently complete set of rRNA genes for more than one genome, even if rRNA genes were lucky to be assembled completely. We show how BINSPREADER's ability to propagate bin labels to small contigs and repeat regions as well as multiple bin assignment could help in rRNA recovery. Beyond that, this approach could also help in genomic islands (GI) recovery that contain regions that are important for clinical applications such as CRISPRs and antimicrobial resistance (AMR) genes.
CRISPRs (Table S9) are not very well assembled in MBARC26 and magsim-MGE datasets, as 18% and 28% of them, respectively, are missing from the assemblies. Nevertheless, BINSPREADER shows the best performance recovering all repeat clusters for mock datasets regardless of refining mode. All standalone binners recover nearly equal amounts of CRISPRs, but MetaCoAG manages to greatly surpass them on MBARC26 (42 recovered CRISPRs against 33 for the best initial binner, MetaWRAP).
However, the most interesting dataset in terms of GI recovery is magsim-MGE, as it was specifically designed to showcase this problem (Maguire et al., 2020). Refining with BINSPREADER using assembly graph alone does not significantly increase the amount of recovered CRISPRs, but the usage of supplementary paired-end connectivity information gives one of the best results among all binners and BINSPREADER runs particularly well (17 recovered CRISPRs out of 23 total assembled versus 13 without paired-end reads). On this dataset, METAMVGL manages to recover the similar number of CRISPRs as BINSPREADER.
The results of AMR genes recovery (Tables S10 and S11) are pretty much consistent with CRISPRs recovery. BINSPREADER and MetaCoAG still show the best performance, recovering every single assembled AMR gene on mock datasets. In contrast with CRISPRs results, running BINSPREADER with paired-end information on magsim-MGE dataset yields the best result with MetaBAT2 as initial binner (138 recovered CRISPRs out of 145 assembled), whereas the number of recovered AMR genes after refining with METAMVGL was lower compared with initial MetaBAT2 binning (108 recovered genes after refining vs 115 original AMR genes).
The influence of supplementary connectivity information on the binning refining productivity can be seen on IC9 dataset, where Hi-C data are available in addition to paired-end reads (Table S11). BINSPREADER provided with Hi-C links recovered the maximum amount of AMR genes among all binners and refiners (191 recovered AMR gene out of 300 assembled); this result could be explained by the presence of Hi-C links between chromosomes and plasmids harboring AMR genes, allowing BINSPREADER to propagate bin labels to plasmidic contigs more accurately.
Although the amount of recovered GI and functional elements appears to be an informative benchmark for metagenomic studies, the final goal of most research is to get as many high-quality MAGs containing all these elements as possible. In order to make a high-level assessment of MAG recovery, we applied MAG reporting standards developed by the Genomic Standards Consortium (Bowers et al., 2017). MIMAG standard uses different levels of genome completeness and contamination as well as rRNA gene presence. Depending on these metrics MAGs are divided into several groups including a medium-quality draft (R 50% completeness, <10% contamination) and a high-quality draft (>90% completeness, <5% contamination, full set of rRNA genes and, at least 18 tRNA). Because rRNA recovery is primarily limited by its assembly completeness, we constructed perfect binning from input assemblies that comprises MAGs with 100% purity and 100% completeness to use it as reference. We also added the second type of high-quality MAGs iScience Article somewhat lowering the standard: we require a complete set of 16S or 18S rRNAs, as these particular rRNA genes are of most importance for further taxonomic annotation.
Results obtained for Zymo and BMock12 datasets (Figures S12 and S13) emphasize that the assembly quality plays a crucial role in rRNA recovery. Only one high-quality MAG could be obtained from BMock12 assembly due to the fragmentation of rRNA gene contigs and only two high-quality MAGs (including only 16S rRNA) could be recovered from Zymo (Tables S12 and S14) in general. Still, BINSPREADER was able to recover these MAGs from VAMB bins with the help of supplementary paired-end connectivity information. Also, BINSPREADER refining enriches MetaBAT2-produced bins with medium-quality MAGs ( Figure S12) for Zymo dataset.
On MBARC26 and magsim-MGE datasets ( Figures S14 and S15), we can observe a great improvement in high-quality MAG recovery after the refinement with BINSPREADER in multiple binning mode. In comparison with initial bins, BINSPREADER refining clearly led to saturation of MAGs with rRNA genes and other small contigs, rather than increasing the number of medium-quality MAGs. The usage of multiple binning approaches increases the number of high-quality MAGs almost down to the assembly level.
Particularly, refining of VAMB binning of MBARC26 dataset resulted in the recovery of all four possible high-quality MAGs. Different variations of BINSPREADER modes yield one high-quality MAG with the full set of rRNA in the worst case, which is still unattainable for the most binners; moreover, all BINSPREADER runs increased the number of high-quality MAGs containing only 16S rRNA dramatically, especially when multiple bin assignment mode was used. Even greater improvements could be observed in the refining of binning results obtained on magsim-MGE dataset. BINSPREADER manages to recover all highquality MAGs using metaWRAP and VAMB bins without losing any medium-quality MAGs. In addition, BIN-SPREADER recovers 16S rRNA for almost every MAG in VAMB and MetaWRAP-produced bins. Refining MetaBAT2-produced bins using paired-end connectivity information leads to the recovery of five new medium-quality MAGs.
On the real IC9 metagenome, BINSPREADER retrieved all 16S and 23S rRNA genes present in the assembly regardless of initial binning and genome fraction (GF), as shown in Table S16, whereas the second-best refiner-binner combination, bin3C + DAS_TOOL, reconstructed only four 23S rRNA out of six and two 16S rRNA out of three (for rRNA genes assembled at 90% GF). Overall, BINSPREADER recovered 71 rRNA genes out of 73 (against 36 for the next best refiner, MetaCoAG). On the Sharon dataset, BINSPREADER supplemented with paired-end reads retrieved 20 out of 29 of all rRNA genes assembled with at least 50% GF, whereas second-best refiner, MetaCoAG, recovered only six rRNA genes (see Table S17).
Binning refining supplemented with paired-end and Hi-C linkage
To assess the effectiveness of paired-end reads information for binning refining, we used paired-end read libraries available for Zymo, MBARC26, Bmock12, and magsim-MGE datasets. We compared MetaBAT2, VAMB, and MetaWRAP bins refined with BINSPREADER supplemented with paired-end reads (BSP-PE mode) and bins refined with BINSPREADER provided with assembly graph only (BSP mode). We also assessed Binnacle and METAMVGL refiners that utilize paired-end reads as well. We evaluated binning results using AMBER (Meyer et al., 2018) and reported an F1-score for the initial and refined bins.
For magsim-MGE dataset, Table S1 shows that BSP-PE results in higher F1-scores than BSP for all three initial binners. For Zymo dataset, Table S5. For MBARC26 dataset, BSP-PE resulted in lower F1-scores than BSP for all three initial binners (Table S4). The possible reason for this is contamination in paired-end library for MBARC26, as applying METAMVGL and Binnacle to all three initial binnings resulted in lower F1-score (Table S4). For all samples and all initial binners, BSP-PE resulted in higher F1-scores than METAMVGL and Binnacle. F1-scores for separate bins are reported in Figures S1-S4.
The potential of Hi-C technology as a means to cluster metagenomic contigs into bins has been demonstrated on both synthetic and real microbial communities ( Ivanova et al., 2022). We followed two approaches to analyze possible integration of Hi-C technology and binning refining methods for MAG recovery.
First, we obtained initial binning for Zymo Hi-C library using dedicated Hi-C bin3C (DeMaere and Darling, 2019) binning tool and refined bin3C binning using BINSPREADER (in both BSP and BSP-PE modes). As shown in Table S6, F1-scores reported by AMBER were higher for bin3C bins refined by BINSPREADER (0.927 for BSP and BSP-PE against 0.865 for unrefined bin3C bins).
Although BSP-HiC did not show any improvement upon BSP-PE in terms of standard contamination and completeness metrics for Zymo dataset, AMR gene detection results for the plasmid-rich IC9 dataset described earlier (see Conservative genes recovery) show that BSP-HiC can be used to reconstruct additional functional elements located on the unbinned contigs that were not connected to the main genome on the assembly graph.
MAG distance estimation using prob Jaccard index
Sometimes binners produce very pure but incomplete bins (results of Completeness, contamination, and F1 show that this usually applies to MetaBAT2 and MetaWRAP bins). After refining, such bins tend to overlap on an assembly graph, and therefore, the size of such overlap could potentially be used to decide whether one needs to merge certain bins. Also, overlapped labeling of the edges of the assembly graph could measure possible contamination or otherwise shared genome content. Figure 3 shows the hierarchical clustering of bin distance information calculated from Zymo MetaBAT2 bins. One could easily see the bins of different genomes clustered together as well as an overlap of E. coli and S. enterica bins. Figure 4 shows the hierarchical clustering of bin distance information calculated from BMock12 MetaBAT2 bins. Again one could see several bins of the same species located together on the graph as well as significant bin overlap between two Micromonospora strains as well as contamination of Marinobacter bins.
DISCUSSION
Although metagenome-assembled genome binning methods based on TNF distance, coverage profiles, and single-copy marker genes are useful for untangling complex bacterial communities as a whole, they face challenges with the reconstruction of functional elements located in conservative genomic regions, such as rRNAs, CRISPRs, and AMR genes; this is unfortunate, given the phylogenetic and clinical relevance of these functional elements. Conservative genomic regions are usually associated with short repetitive edges of a metagenomic assembly graph. Therefore, there is a clear need for metagenomic binners or refiners that enrich MAGs with short and possible repetitive contigs.
BINSPREADER is a binning refining tool that effectively utilizes assembly graph connectivity information and predicts contigs belonging to several MAGs. We show that existing binning refining tools, which utilize scaffold graphs instead of assembly graphs, are less effective than BINSPREADER in terms of functional element recovery (Tables S9-S11) and in terms of rRNA genes recovery for artificial (Tables S12-S15) and real (Tables S16 and S17) metagenomes. Although BINSPREADER does not show significant increase in 16S/18S rRNA genes reconstruction compared with initial binning for BMock12 and Zymo datasets, we show that for these datasets ability for rRNA recovery is limited mostly by assembly quality (Tables S12 and S14). Experimental results on synthetic and simulated datasets show that BINSPREADER also outperforms existing refiners in terms of standard contamination and completeness metrics ( Figures S1-S4).
In addition to MAG recovery, BINSPREADER provides two additional features: first, the read splitting feature, which takes into account possible overlap between MAGs and thus enables fuller MAG reconstruction after reassembly. We also introduced a bin distance measure that provides an overlap-based estimation of evolutionary distance between MAGs, thus potentially providing a novel source of information for taxonomic classification as well as detecting possible bin contamination.
OPEN ACCESS
iScience 25, 104770, August 19, 2022 9 iScience Article Limitations of the study BINSPREADER heavily relies on the quality of the input binning. In particular, it cannot clean the contaminated bins occurred when several MAGs are joined together by a binner. The second input to BINSPREADER is an assembly graph where the graph connectivity is in the heart of BINSPREADER algorithm. If the assembly graph is disconnected or otherwise fragmented, then BINSPREADER naturally cannot propagate the binning in the absence of additional connectivity information (e.g. from scaffolds, paired-end links or HiC data).
STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:
ACKNOWLEDGMENTS
The research was carried out in part by computational resources provided by the Resource Center ''Computer Center of SPbU.'' The authors are grateful to Saint Petersburg State University for the overall support of this work. IT, SO, and AK were supported by the Russian Science Foundation (grant 19-14-00172).
AUTHOR CONTRIBUTIONS
AK and IT developed the BINSPREADER concept. AK, YK, and IT implemented and maintained BINSPREADER. SO and RK benchmarked BINSPREADER and analyzed results. SO analyzed the datasets with respect to the standard performance metrics. RK performed analysis of conservative sequences recovery. All authors read and contributed to the final manuscript.
DECLARATION OF INTERESTS
The authors declare no competing interests.
METHOD DETAILS
From scaffold binning to edge binning Most binners output their results in a form of scaffold binning, i.e., a map B from a set of scaffolds P to a set of bins C. This representation is not entirely accurate, since long scaffolds in a metagenomic assembly may contain repetitive regions, which can belong to multiple species in a sample, and therefore in multiple bins.
To alleviate this, BINSPREADER transforms the initial scaffold binning to the edge binning using an assembly graph. Let G be an assembly graph in GFA format consisting of a set of edges EðGÞ, links LðGÞ between them, and scaffolds PðGÞ with their corresponding paths in the assembly graph. Here each row Y i represents a soft binning of edge e i , which can be interpreted as the containment probability distribution over the set of bins. Edge binning represents a more fine-grained representation of initial binning than scaffold binning, as repetitive edges may contain multiple bins if they are traversed by several paths.
Link graph
While edges of the assembly graph G are used to store the initial binning and the end results, vertices of the assembly graph provide minimal required connectivity information for BINSPREADER. Connectivity information is stored in a form of a weighted link graph H, where V ðHÞ = EðGÞ, EðHÞ = V ðGÞ and the edge weight L ij represents the weight of a link between assembly graph edges e i and e j . The higher L ij is, the more likely is that e i and e j belong to the same bin. Initially BINSPREADER uses adjacency matrix of an assembly graph G for weights with L ij = 1 if the edges e i and e j are adjacent in G and zero otherwise.
Besides the adjacency weights, BINSPREADER also by default considers the set of scaffold links: if two edges are joined in a scaffold, but not adjacent in the graph we add the link in H (add edge and set L ij = 1) between them. Usually, such scaffold joins are made by an assembler to jump over coverage gaps or long unresolved repeats. In both cases adding these links increases the contiguity of the link graph and could help the binning propagation across assembly gaps.
In addition to the assembly graph itself, BINSPREADER is able to construct links from paired-end and Hi-C (Lieberman-Aiden et al., 2009) libraries which can be provided optionally. Reads from paired-end libraries and Hi-C libraries are aligned using k-mer alignment similar to (Cheng et al., 2021). First, we index unique k-mers in the assembly graph. Then we align a Hi-C read pair if it contains two or more non-overlapping k-mers. We use k = 31 by default as most 31-mers in the metagenomic assembly graph are unique, but that value can be adjusted depending on the size of the sample. We then increase the link weight L ij by the logarithm of the total number of read-pairs aligned to e i and e j from all input libraries.
Binning refinement
Informally speaking, we say that an edge binning is smooth if soft bins associated with a pair of edges joined by a link with high weight are similar. As such, binning refining problem can be defined as finding smooth edge binning F which is close in some sense to the initial edge binning Y. iScience Article smoothness (Chung, 1997;Nie et al., 2010Nie et al., , 2016. Let D be a degree matrix of H, and L be an adjacency matrix of H. Then we define edge binning smoothness as SðH; FÞ = tr F T D À 1=2 ðD À LÞD À 1=2 F : We define binning refinement problem as where the second term penalizes the distance between resulting binning F and original binning Y according to regularization parameters defined separately for every edge. We use iterative algorithm for optimizing cost function (2), which is similar to one from Nie et al. (2009). LetL be the normalized weight matrix D À 1=2 ðD À LÞD À 1=2 , where D is a degree matrix of H. Then let P = I aD À 1L , whereD is a diagonal ofL, I is an identity matrix of size jV ðHÞj 3 jV ðHÞj, and I a is a diagonal matrix being I ii = 1=m i . Initially, we set Fð0Þ = Y . At each iteration, for every assembly edge e i the soft labels from neighboring links ðe i ; e j Þ with weight H ij are added to the soft label of e i with coefficient H ij . At iteration k + 1 we set Fðk + 1Þ = PFðkÞ + ðI À I a ÞY
(Equation 3)
As shown in Nie et al. (2009), the obtained sequence FðkÞ will eventually converge to solutionF, which is produced as the resulting edge binning.
We need to explicitly note that while all the matrices involved are quite large, they are extremely sparse and there is no need to store and calculate them explicitly. The soft binning for each edge at iteration k (the rows of FðkÞ) depends only on soft binnings of adjacent edges (which in ordinary de Bruijn graph case is not more than 8) as well as normalized link weights. This enables computational and memory-efficient way to perform the iterations by using sparse and succinct data structures.
Choosing regularization parameters
The choice of per-edge regularization parameters a i = 1=m i is different for different modes of BINSPREADER. Firstly, we always set a i = 1 for all repetitive edges (i.e. the edge that belongs to multiple scaffolds). As it could be easily seen from Equation (3), the original binning for such edges will be ignored and soft binning for such edge is determined entirely via binning propagation. However, the binning from binned repetitive edges will be propagated down to their neighbors. This ensures proper and fair binning in case of e.g. partially unresolved repeats.
Setting a i = 0 for edge e i would force use of original binning. This is done for all non-repetitive binned edges in propagation mode of BINSPREADER. In this case, the original binning is essentially preserved and only propagated further on to unbinned edges.
Setting 0 < a i < 1 for edge e i allows one to balance between preserving the initial binning and propagating the binning from adjacent edges. In correction mode of BINSPREADER a i is set to 0.6 by default for all binned edges longer than 1000 bp, for shorter edges the value of a i is gradually increasing up to a i = 1 for edges of length 1. The motivation for this is as follows: while short edges might be unique and belong only to the single scaffold, they are likely repetitive and belong to unresolved repeats. The shorter the edge is, the higher its likelihood of being repetitive and we equally treat all edges longer than 1000 bp. Certainly, the latter still might be repetitive and this is what the default value of a i = 0:6 tries to accommodate.
Sparse binning & propagation
Binnings of real metagenomic datasets are typically sparse, since large datasets contain strains with high enough coverage to contribute to metagenomic assembly, but not high enough to be binned using the abundance and nucleotide profiles.
BINSPREADER uses a special working mode of the binning refining algorithm for sparse binnings, where the total length of initially binned contigs is significantly lower than the total assembly length. iScience Article why the standard mode of BINSPREADER produces highly contaminated bins when refining sparse binnings and describe the sparse mode of BINSPREADER designed to alleviate that problem.
Given assembly graph G with the set of regularization parameters a i , and initial edge binning Y, we say that edge e i is refinable, if a i s0. If an initially unlabeled edge e is connected to an initially labeled edge by a path of refinable edges, it eventually will be labeled after applying binning refinement algorithm to graph G and binning Y. Therefore, in the standard correction mode of BINSPREADER with a i > 0 every unlabeled edge residing in the same connected component with labeled edges will become labeled after the refining. As such, refining of initially sparse (incomplete) binnings that cover only a small part of G with n bins via the standard correction mode of BINSPREADER will result in assigning the majority of contigs in the refined binning to one or several of these same n initial bins potentially inflating and contaminating them.
To reduce the number of refinable edges while still allowing binning propagation, we adjust regularization parameters a i for initially unlabeled edges with distance coefficients b i , reflecting assembly graph distance to the closest initially labeled edge. Given assembly graph G and initial binning Y, let Distðe; Y Þ be the length of the shortest path in assembly graph G from edge e to the closest edge which is labeled in Y. We say that edge e is distant, if Distðe; Y Þ > D, where D is distance threshold with default value 10,000. To ensure that distance coefficients b i change smoothly from 1 for labeled edges to 0 for distant edges we utilize the same binning refining algorithm.
We introduce two bins, one for all labeled edges in G and another one for all distant edges. Then we run the binning refining algorithm as in the standard correction mode of BINSPREADER and set b i to the obtained weight of the first (''labeled'') bin. This makes the values of b i gradually decrease from being 1 in the case of initially binned edge e i down to 0 when moving out of binning edges on the graph.
For sparse propagation the regularization parameters are then set as a i 0 = a i b i , where a i are regularization parameter values for the standard correction mode of BINSPREADER. This allows us to keep the initial binning intact for the edges located ''far away'' from the binned ones.
In addition to adjusted regularization parameters, the sparse mode of BINSPREADER also adds a dedicated bin for initially unbinned edges. However, while we allow the binning to propagate from binned edges down to unbinned ones we need to prevent the propagation of this special ''unbinned'' label. In order to do so, we modify the iteration procedure in sparse mode adjusting the weight matrix P accordingly.
Binning strategies: From edges back to scaffolds
After inferring refined edge binningF, BINSPREADER uses it to produce the scaffold binning F 0 . BINSPREADER can output results either in single assignment or multiple assignment mode, and utilizes either majority length or maximum likelihood strategy (default).
Given a scaffold s containing edges e 1 ;.;e m , and bin c j the binning strategy defines a score function Scoreðs;c j Þ.
For majority length strategy we define cðe i Þ = arg max jFij and use Scoreðs; c j Þ = P ei :cðei Þ = j lengthðe i Þ. For maximum likelihood strategy Scoreðs;c j Þ = P ei˛s lengthðe i Þ 3F ij .
In single assignment mode BINSPREADER outputs a single bin label arg max cj Scoreðs; c j Þ for every scaffold s. In a multiple assignment mode, BINSPREADER outputs a set of labels fc j g with maximal Score 0 s, which cumulatively explain at least 95% of the total Score. Note that raw Scoreðs; c j Þ values are reported by BINSPREADER as well, so one could use them for their own binning assignment procedures.
Measuring MAG distance using prob Jaccard index
The typical measure to estimate the overlap of two sets is Jaccard index (Jaccard, 1912). However, in the case of BINSPREADER the sets (bins) are fuzzy as the result of binning refining is a set of weights that represent the bin labeling probability distribution. LetF be a refined multiple edge binning. In order to estimate a possible overlap of bins on the assembly graph from the soft binning, we assign an edge probability distribution fp ðjÞ i g to every bin c j by normalizing its edge weight vectorF Ã;j . We than calculate the prob-Jaccard index J p from (Moulton and Jiang, 2018)
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Domain: Biology
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Biological activity of Spondias pinnata: a review
Open a cess: [URL]/ and [URL]/ Biological activity of Spondias pinnata:
INTRODUCTION
Herbal products are a promising alternative strategy to overcome infection in the era of antibiotic resistance that began to occur. Natural products from plants that are applied in medicine are increasingly and are often used because of the minimal side effects, less toxicity. WHO reports 65-80% of the world's population entrusts efforts to fight disease and maintain health by using ingredients prepared from plants. Plant species range from 250,000 that exist today, only 1% of those species which ability and effect of therapy and pharmacy have been studied. The treatment of drugs from synthetic materials over a long period gives rise to resistance. 1
HISTORY OF THE PLANT
The origin of S. pinnata itself is still unclear and confusing to ascertain due to extensive cultivation and natural tendencies. This plant species is commonly found in Malaysia, India, and Indonesia. The origins of S. pinnata are still being debated to this day. The full DNA analysis is not yet available. Thus it is still considered "naturally present" in Indonesia, Malaysia, including in various places in Bali. Characteristics and plant content of the same species are different depend on ecology and geographic condition of land where the plant is cultivated. 2 S. pinnata in Bali was first called 'catsjemtsjem' (pronounced: 'kecemcem') in the meaning of the Balinese condondum. The first scientific report on the existence of the species in Bali showed that it was a native plant of Bali or at least Bali was included in the natural distribution area. It was likely brought to Bali from Java by Javanese Hindus Majapahit who fled in Bali. 2 "Loloh" is a traditional drink from Bali, one of which is quite well known is "loloh cemcem" which is often found mainly in the tourist village of Penglipuran Bangli. "Loloh cemcem" is made from "cemcem" leaves or "kedondong" leaves originating from other languages S. pinnata.
CHARACTERISTICS
S. pinnata are decidua trees, can be found in primary and secondary forests. People generally name S. pinnata by the name of "kedondong hutan". 2 Plants of the genus Spondias, consisting of 18 species, have been used as a traditional medicine to treat various diseases. 1-5 The characteristics are ornamental trees whose height can reach 12 to 18 m with erect growth. The surface of the tree bark is smooth, with irregular cracks, grey to pale reddishbrown, with clear, sticky and turpentine sap. The leaves are fragrant, arranged in a spiral. The leaves have a sour taste and can be used for flavouring.
REVIEW
and anti-bacterial. 7 The Sabara tribes of Orissa state use the decoction of fresh barks for treating helminthiasis in children. 3 Plant parts of the genus Spondias, as from the skin, roots, fruit, and leaves, have a variety of benefits and have been used as a traditional medicine in various countries. In terms of ethnomedicine, S. pinnata has anti-bacterial activity and inhibits lime impact. 8 In traditional medicine, the skin of S. pinnata is used as dysentery-diarrhoea, vomiting, roots are used to regulate menstruation, plants as antituberculosis, leaves as a drug for dysentery, raw fruit as aphrodisiac, while ripe fruit as constipation and anti-scorbutic drug. 7 Spondias pinnata is also used as a remedy for bronchitis and skin diseases. 1 Traditionally, it is also used to treat leprosy, diabetes, diuretics, inflammation of the eye, antithirst, antioxidants, antimicrobials, thrombolytic agents, laxatives. 5 Its benefits such as the root functions to regulate menstruation, fruits for the treatment of rheumatism and laryngitis, skin for the treatment of dysentery and to prevent vomiting, the plant is useful as an anti-tuberculosis, the leaves are helpful as aromatic, astringent, acidic, anti-emetic, diarrhoea, and dysentery. 4 The flowers are used for obesity, hemorrhagic disease, anti-vomiting, dyspepsia, gonorrhea. 9 A description of the traditional uses of parts from various countries which have been published can be seen in Table 1. The leaves of the genus Spondias in several countries are used for multiple purposes including in Mexico for stomach aches and bloating, a decoction of fresh leaves is used to treat anaemia, diarrhoea, dysentery, skin infections, leaf decoction used for treating diarrhoea and dysentery in Belize, while in Nigeria, Benin and Togo are used to improve memory, whereas in southwestern Nigeria, leaves are used for diabetic patients. 5 Ambonese people shower using boiled water with S. pinnata leaves when they are sick or healthy. 2 Other writings mention its leaves have benefits as a cure for various diseases including stomach ache, urolithiasis, and diabetes. 2 The anti-diabetic effect of 'cemcem' or S. pinnata leaves as a herbal drink can reduce high sugar levels. "Cemcem" leaf herbal drank in the form of juice, used traditionally by Balinese for a thousand years, as written in the usada of Bali, namely since the 11th century. The earliest writing about S. pinnata treatment was found in an ancient Sanskrit book known as Ayurveda, while the first scientific report on the use of S. pinnata was in 'Hortus Malabaricus' . In the description, it was stated that S. pinnata could cure menstruation, dysentery, and uncontrolled gonorrhoea. 2
ETHNOMEDICINE
In Ayur Vedas, it is said that S. pinnata destroys Vata, enriches the blood and heals rheumatism. Leaf buds taste sour with a fruit-like odour. 5 In India, S. pinnata is traditionally used as an anthelmintic, anti-inflammatory, anti-pyretic, anti-tumour
REVIEW
Chloroform and methanol extracts of S. pinnata peel produce significant diuretic and laxative activity. Stem heart extract and S. pinnata bark show anthelmintic activity against Pheretima posthuma earthworms. 4 Bark decoction is used to treat anaemia, diarrhoea, dysentery, and skin infections. In India, its bark is used as an ointment on joint pain, diarrhoea, and dysentery to prevent vomiting. The decoction of the root bark is used to regulate menstruation and treat gonorrhoea. 5 Its bark is also used to treat dysentery, has antioxidant effects, freeradical scavenging, anti-mucolytic property. 10
PHYTOCHEMICAL SCREENING
S. pinnata is an energy source of 348 kcal / 100 grams, containing phenol components, natural antioxidants and minerals. Also a source of ascorbic acid, malic acid, calcium, phosphorus. Its phytochemical screening contains alkaloids, saponins and tannins. It also contains gallic acid, salicylic acid, chlorogenic acid, ellagic acid, p-coumaric acid, 6-hydroxy-2,5,7,8tetramethylchroman-2-carboxylic acid, quercetin, catechin, myricetin, routine, vitamin E, furfural, phytosterol, campesterol and fatty acids. 11 Its leaves contain flavonoids, tannins, gums, alkaloids, saponins and terpenoids. High total phenolic and flavonoid content is equivalent to gallic acid and quercetin. 1 Phytochemical screening results revealed that S. pinnata ethanol extract containing alkaloids, carbohydrates, flavonoids, triterpenoids, steroids, tannins, resins, saponins. Essential oils from pulp contain carboxyl acid, esters, alcohol, aromatic hydrocarbons. 12 Other research mentioned its content includes tannins, flavonoids, sterols, triterpenoids, saponins, essential oils, amino acids, polysaccharides. 4 Phytochemical analysis from different research found 24-methylene cycloartanone, lignoceric acid, sitosterol and D-glucoside has been isolated from S. pinnata. 13 The pharmacological activity of S. pinnata varies according to phytoconstituents that exist in these plants. The content includes sterols, flavonoids, polysaccharides, gums, β-amyrin, oleanolic acid, amino acids include glycine, cysteine, serine, alanine, leucine, daucosterol, cycloartanone-24 methylene, lignoceric acid, ellagitosinoic acid, lellagitosinsin gallon, lellocitgerinsin gallon, lellocitosinsin gallon, lellocitosino β carotene. 12 Secondary metabolites of plant products include tannins, terpenoids, alkaloids, flavonoids. Many plants are studied to get anti-bacterial and antioxidant effects. Natural ingredients can minimize free radicals induced by biomolecules such as fat, protein and amino acids. Natural antioxidants can fight oxidative stress related to cancer, atherosclerosis, inflammation, diabetes, hair loss, ischemic heart disease, neurodegenerative disorder including Alzheimer's and Parkinson's diseases. 1 The bark content includes β-Amyrin and oleanolic acid, glycine, cysteine, serine, alanine and leucine in fruits, lignoceric acid, β-sitosterol. 7 Spondias pinnata is rich in phenol and flavonoid components. 9 The essential oil consists of carboxylic esters, alcohols, aromatic hydrocarbons. Its extract as much as 100 mg contains 91.47 mg/ml gallic acid equivalent phenolic content and 350.5 mg/ml quercetin equivalent flavonoid content. 5 Almost 70% methanol extract of S. pinnata stem bark has cytotoxic activity against cancer cells of human lung adenocarcinoma with IC50 147.84 µg/ ml whereas for human breast adenocarcinoma of 149.34 µg/ml. The bark within the extract also has a hypoglycemic effect equivalent to glibenclamide, while ethanol extract one has an analgesic effect equivalent to acetylsalicylic acid. Methanol and water extracts of S. mangifera also have antibacterial activity against V.cholera, S. typhimurium and E. coli. Other 70% methanol extract bark can also reduce liver toxicity caused by iron. 5 Fruits contain polysaccharides namely L arabinose, D-galactose and galacturonic acid, B-amyrin and oleanolic acid, glycine, cysteine, serine, alanine and leucine. Ethanol extract of S. pinnata fruit has antibacterial activity against P.aeruginosa and S. epidermidis at a dose of 500 µg / disc with a disc diffusion method, potent cytotoxicity at IC50 2.12 µg / ml. 5
PHARMACOLOGICAL EFFECT
The pharmacological effect of S. pinnata in this section is a summary of various research that has been published in journals, obtained through Researchgate, Semantic Scholar, Google Scholar, NCBI. Studies show that several phytochemical bioactive components have been isolated from the genus Spondias.
Pharmacological effects of this genus include cytotoxic, anti-oxidant, protective ulcer, hepatoprotective, anti-inflammatory, anti-arthritis, anti-dementia, antipyretic, analgesic, thrombolytic, hypoglycemia, antifertility, anti-hypertension, antimicrobial, and anthelmintic. 5 Bark extract of S. pinnata has an anthelmintic effect on Pheritima posthuma, chloroform extract is better than other extracts. 3 Various S.pinnata extracts have antibacterial, hypoglycemic, anti-oxidant and free radical scavenging activities. In mice, that were made into diabetes with streptozotocin, S.pinnata bark extract was reported to have anti-diabetic activity with an optimum therapeutic dose of 1 g/ REVIEW kg BW. 13 Ethanol extract of S. pinnata bark has an antipyretic effect at doses of 200 and 400 mg/kg orally. 7 Leaves extract of S. pinnata has anti-viral and anti-microbial abilities. 5 The methanol extract of S. pinnata leaves contains a large number of phenolic compounds which are mainly responsible for free radical scavenging. Other studies have shown that S. pinnata methanol extract is useful for relieving heartburn, urolithiasis, diabetes, heartburn. 2 A crude extract of S.pinnata has antimicrobial, anti-inflammatory, antioxidant effects. 12 Leaf extract has antibacterial activity against grampositive bacteria including Bacillus cereus, Bacillus subtilis, Staphylococcus aureus, Sarcina lutea, and gram-negative Salmonella paratyphi, Vibrio parahimolyticus, Escherichia coli, Pseudomonas aeruginosa. 5 Other studies have shown that it has a mild and robust antibacterial and cytotoxic effect on gram-positive and gram-negative bacteria at a concentration of 500 µg / disc, which is significant activity against P. aeruginosa and S. epidermidis, intermediate sensitivity of S. aureus and S. typhii, mild sensitivity to V. cholera, S. flexneri, and S. pyogenes. 14 The benefits of S. pinnata that have been studied include ethanol extract and methanol extract of S. pinnata leaves affect gastrointestinal motility. 6 Other studies also show S.pinnata has anti-TB activity. 5 Ethanol and acetone extracts of S. pinnata stem bark as a hepatoprotector in liver damage caused by alcohol in mice. 9 The combination of whey and S. pinnata has a mucoprotective effect on mice receiving etoposide chemotherapy which causes mucositis as side effects. 10
DOSAGE AND INHIBITORY CONCENTRATION (IC 50) S. pinnata
The administration of S. pinnata leaf extract on Balb/ c mice with doses less than or equal to 15 g / kg body weight, is relatively safe for the liver while for the kidneys is relatively safe at doses less or equal to 15 g / kg BW. Based on acute toxicity studies, ethanol extract of S. pinnata is safe at doses of 2000 mg/kg BW. Side effects and deaths due to ethanol extract were observed at doses of 100, 200,400 mg / Kg BW for six weeks, showing a range of safe treatments for S. pinnata is very wide. 15 Inhibitory concentration ( IC) 50 for S. pinnata as an antioxidant 49.97 µg / ml, while IC 50 as a scavenging free radical for hydroxyl, superoxide and nitric oxide 112. 18
TOXICITY
Acute and subchronic toxicity of S.pinnata's bark observed in Wistar rats found acute toxicity of skin extracts observed in the form of changes in skin, hair, eyes, mucous membranes, respiration, circulation, autonomic nerves, central nervous system, somatomotor activity, and behaviour patterns. Acute toxicity conditions that receive more attention include tremor, convulsions, salivation and diarrhoea, lethargy, sleep, and coma. At doses of 0.25, 0, 50, 0.75, 1.00, 1, 25 and 2.00 g/ kg weight, after giving the dose once during the first 30 minutes, periodically for the early 24 hours, and every day for a total of 14 days, acute toxicity was not found. 13 Administration of S. pinnata's bark at doses of 0.25, 0, 50, 0.75, 1.00, 1, 25 and 2, 00 g / kg weight did not find acute toxicity, mortality or morbidity. All test animals well tolerate extracts with selected doses, thus it is safe for remote administration of the extract, and the bark extract also did not inhibit mouse growth. 13 The administration of S. pinnata for a 28-day repeated dose does not affect the liver, lungs, small intestine, liver, pancreas and kidney. Its extract administration does not stimulate or suppress the appetite of mice. Research showed water extracts from the bark of S. pinnata were safe up to 2 g / kg weight on Wistar rats. Administration for 28 days did not cause changes in general conditions, appetite, body weight, growth, biochemical parameters, haematological values and histopathological abnormalities in body tissue. 13
MOLECULAR MECHANISM
Spondias sp leaf extract contains quercetin, rutin, ellagic acid, and acts as an antioxidant and antimicrobial (Table 3). Spondias pinnata leaf extract has antituberculosis activity against M. tuberculosis MDR bacteria. Other studies found that the water fraction of S. pinnata leaves causes protein damage and changes in the morphology of B. cereus cells. 17 Antimicrobial activity of S. pinnata resin has been investigated. Its resin, can eliminate the insects, fungi and inhibit excessive metabolism. Ethanol extract of S. pinnata's pulp has antibacterial ability against Staphylococcus aureus, E. coli, Pseudomonas aeruginosa and antifungal activity against Candida albicans and Aspergillus flavus. Resins secreted REVIEW by S. pinnata contain flavonoids, some literature states that flavonoids act as antibacterial. 18 Resins and flavonoids were found to play a role in the antibacterial activity of S. pinnata. However, S. pinnata resins are not effective against Gram (-) bacteria, S. cerevisiae, and fungi. Only B. subtilis bacteria are most susceptible to resins from S. pinnata. 19 The bark of S. pinnata extract has antioxidant effects while crude extract of S. pinnata has antibacterial activity. 10,18,20 In S. pinnata bark extract, the component which plays a role in inhibiting NO synthesis is 4-O-β glucoside. Beta-sitosterol is the bark of S. pinnata plays a role in the mechanism of protection by GSH through reducing cytokine levels. Administration of synthetic glutathione (GSH) maintains GSH levels in the kidneys and liver. 10 Free radicals cause oxidative stress that causes damage to proteins and DNA accompanied by fat peroxidation, thus can stimulate the occurrence of cancer, atherosclerosis, cardiovascular diseases, aging. Free radicals will become stable if bound to electrons from other macromolecules such as protein, fat and DNA in healthy human cells. All cells in a healthy human body will protect themselves from free radical damage through enzyme mechanisms such as superoxide dismutase (SOD) and catalase, or through the mechanism of antioxidant components such as ascorbic 20 The antioxidant activity of S. pinnata has been investigated using the ascorbic acid-induced lipid peroxidation inhibition method. S. pinnata leaf and bark extracts have antioxidant activity due to their high polyphenol content and flavonoids. Mechanism of action of flavonoids as antioxidants by acting as a superoxide anions scavenger. 20 By the time, its extract is added to the temporary solution.
The radical hydroxy will be removed from the sugar and prevent further oxidative stress reactions. This process shows the extract of S. pinnata is a better hydroxyl radical scavenger than mannitol as a control. 20 Nitric oxide plays a role in the inflammatory process. The continuous production of NO causes toxicity to tissues and causes vascular collapse that associated with septic shock, while the long-term NO expression contributes to cancer and inflammatory conditions including juvenile diabetes, multiple sclerosis, arthritis and ulcerative colitis. NO toxicity increases when interacting with superoxide radicals thus will stimulate peroxynitrite anion (ONOO-) formation which is very reactive. NO results from the side reaction of sodium nitrous with oxygen to form nitrite. S. pinnata extract inhibits the nitrite formation by competing directly against oxygen in reaction with NO. 20
Antioxidant
activities through various mechanisms include prevention of chain initiation, decomposition of peroxides, reducing capacity and radical scavenging. 21 The mechanism of flavonoids as antioxidants through the process of scavenging or chelation. Phenols within the bark of S. pinnata have scavenging ability because of their hydroxyl groups. That bark containing flavonoids and phenols has antioxidant activity, and free radical scavenging also chelates iron and has reduced power. 21 Extract of S. pinnata induces apoptosis in both malignant cell lines and induces DNA fragmentation in A549 and MCF-7 cells. The methanol extract of S. pinnata' s bark induces apoptosis through an intrinsic pathway by producing an increase in the Bax / Bcl-2 ratio which will cause activation of the cascade caspase which stimulates the breakdown of Poly adeno ribose polymerase. 21 S. pinnata have hepatoprotective, thrombolytic, protective ulcer, anti-diarrhoea, anti-hypertension, hypoglycemic, antioxidant, anthelmintic, antibacterial effects. 19 It has been widely studied, the benefits, content in the health sector, but research on the mechanism of action, especially molecular and cellular mechanisms seem still limited.
CONCLUSION
Every part of S. pinnata has numerous benefit. Phytochemistry screening has found it contains alkaloids, saponins and tannins, flavonoids, terpenoids, phenolic and flavonoid. It affects as antioxidant, antibacterial, anti-TBC, antifungus, anti-cancer, hepatoprotector, thrombolytic, ulcer protective, anti-diarrhoea, anti-hypertension, antidiabetic. However, the mechanism of action of those effects remains unclear. Further investigation regarding the mechanism of action of S. pinnata is necessary.\===
Domain: Biology. The above document has 2 sentences that start with '7 Spondias pinnata is',
2 sentences that start with 'pinnata stem bark',
2 sentences that start with 'Ethanol extract of',
3 sentences that end with 'extracts of S',
3 sentences that end with 'activity of S',
2 sentences that end with 'methanol extract of S',
3 sentences that end with 'bark of S'. It has approximately 2969 words, 222 sentences, and 30 paragraph(s).
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Annotation of glycolysis, gluconeogenesis, and trehaloneogenesis pathways provide insight into carbohydrate metabolism in the Asian citrus psyllid
Citrus greening disease is caused by the pathogen Candidatus Liberibacter asiaticus and transmitted by the Asian citrus psyllid, Diaphorina citri. No curative treatment or significant prevention mechanism exists for this disease, which causes economic losses from reduced citrus production. A high-quality genome of D. citri is being manually annotated to provide accurate gene models to identify novel control targets and increase understanding of this pest. Here, we annotated 25 D. citri genes involved in glycolysis and gluconeogenesis, and seven in trehaloneogenesis. Comparative analysis showed that glycolysis genes in D. citri are highly conserved but copy numbers vary. Analysis of expression levels revealed upregulation of several enzymes in the glycolysis pathway in the thorax, consistent with the primary use of glucose by thoracic flight muscles. Manually annotating these core metabolic pathways provides accurate genomic foundation for developing gene-targeting therapeutics to control D. citri.
HLB causes development of small, bitter fruits, loss of tree vigor, fruit drop, and ultimately tree decline and death [1][2][3][4]. This bacterium is transmitted by the psyllid vector, Diaphorina citri (NCBI:txid121845), when feeding on citrus [5,6]. Pesticide application to eliminate D. citri has been unsuccessful and no cure for HLB exists [7,8]. To develop new psyllid control strategies, the International Psyllid Genome Consortium was established in 2009 [9] to provide the genome, transcriptome resources, and an official gene set of D. citri [10,11].
Context
A community-driven annotation strategy was used to identify and characterize the genes encoding enzymes involved in glycolysis, gluconeogenesis, and trehaloneogenesis ( Figure 1).
Glycolysis is vital metabolic pathway in core energy processing reactions, and provides a source of metabolites for other biochemical processes. Insects utilize much glucose in flight muscles in the thorax [28]. Accordingly, the activities of glycolytic enzymes are increased in insect flight muscle compared with vertebrate muscle tissue [29]. Gluconeogenesis is essential in insects to maintain sugar homeostasis and serves as the initial step towards generating glucose disaccharide, also known as trehalose. Trehalose is the main circulating sugar in the insect hemolymph [30][31][32]. In trehaloneogenesis, glucose-6-phosphate is converted into trehalose by trehalose-6-phosphate synthase (TPS). Trehalase enzymes then degrade trehalose into two glucose molecules [33]. Genes involved in psyllid glycolysis, gluconeogenesis, and trehaloneogenesis have been targeted by several RNAi studies (Table 1) as a promising avenue for psyllid population suppression. In particular, one proof of concept experiment targeting trehalase led to the release of the first RNAi patent to control psyllid populations [49]. RNAi, as a biopesticide, and strategies for delivery and applications to target insect pests and viral pathogens have been thoroughly reviewed [50][51][52][53][54].
METHODS
The D. citri genome was manually annotated through a collaborative community-driven strategy [11] with an undergraduate focus that allows specific students to focus on main gene sets [55]. Orthologous protein sequences for the glycolysis, gluconeogenesis, and trehaloneogenesis pathways were obtained from the National Center for Biotechnology Information (NCBI) protein database [56] and were used to BLAST the D. citri MCOT (Maker (RRID:SCR_005309), Cufflinks (RRID:SCR_014597), Oases (RRID:SCR_011896), and Trinity (RRID:SCR_013048)) protein database to find predicted protein models [25]. MCOT predicted protein models were used to search the D. citri genomes (versions 2.0 and 3.0) [55]. Regions of high sequence identity were manually curated in Apollo v2.1.0 (RRID:SCR_001936) using Overview of the glycolysis, gluconeogenesis, and trehaloneogenesis pathways. The pathway image shows the enzymes that produce and utilize glucose and trehalose in insects [25]. The glycolysis pathway comprises 10 enzymes that convert glucose into pyruvate as the final product. These are divided into the energy investment phase (light green) and the energy production phase (dark green). The gluconeogenesis pathway comprises eight enzymes (blue), with three being unique to the pathway that bypasses the irreversible reactions in glycolysis to convert non-carbohydrate molecules into glucose. The trehaloneogenesis pathway comprises three enzymes: trehalose-6-phosphate synthase (TPS), trehalose-6-phosphate phosphatase (TPP), and trehalase (TREH), as well as trehalose transporters (TRET) and glucose transporters (GLUT1). Image adapted from a diagram in [26] and created with BioRender.com [27].
de novo transcriptome, MCOT gene predictions, RNA-seq, Iso-seq, and ortholog data to support and evaluate gene structure ( Table 2). The curated gene models were compared with other orthologous sequences, such as hemipterans, available through NCBI for accuracy. A more detailed description of the annotation workflow is available ( Figure 2) [58].
Neighbor-joining phylogenetic trees of the annotated hexokinase gene models in D. citri and orthologous sequences were created with MEGA v7 (RRID:SCR_000667) using the MUSCLE (RRID:SCR_011812) multiple sequence alignment with p-distance for determining branch length and 1,000 bootstrap replicates [59]. Tribolium castaneum HexA1 role in glucose metabolism is essential during oogenesis and embryogenesis [34] Aldolase UAS-Aldolase-RNAi Drosophila melanogaster Knockdown in Drosophila neurons and glia resulted in reduced lifespan; essential in glia for neuronal maintenance [35] Enolase -enolase Nilaparvata lugens Knockdown reduced egg production, offspring and hatching rate; mortality of adults was unaffected [36] Pyruvate kinase (PYK) NlPYK Nilaparvata lugens RNAi treatment including triazophos and dsNlPYK led to reduced ovarian protein content, ovarian and fat body soluble sugar contents, and fecundity [37] Phosphoenolpyruvate carboxykinase (PEPCK) Drosophila melanogaster Knockdown of two PEPCK mutant isoforms led to reduced circulating glycerol levels and reduced triglyceride levels in pepck1 mutant flies [38] Trehalose-6-phosphate synthase (TPS) Diaphorina citri Knockdown of the Trehalose-6-phosphate synthase gene using RNA interference inhibits synthesis of trehalose and increases lethality rate in Asian citrus psyllid [39] Trehalose phosphate synthase (TPS) NlTPS Nilaparvata lugens Feeding N. lugens larvae with NlTPS dsRNA led to disrupted expression and lethality [40] Trehalose-6-phosphate synthases Nilaparvata lugens Silencing of two TPS genes can lead to increased molting deformities and mortality rates leading to misregulation of chitin metabolism genes [41] chitin List of annotated genes in glycolysis (HK, aldolase, enolase, PYK), gluconeogenesis (PEPCK), and trehaloneogenesis (TPS and TREH), with their corresponding RNAi studies and references. ‡ indicates that additional genes were added, but not annotated in D. citri, such as muscle protein 20 and sucrose hydrolase. *indicates that the chitin synthase gene in the chitin synthesis pathway was also annotated in D. citri [48].
DATA VALIDATION AND QUALITY CONTROL
There are four phases of the carbohydrate metabolism pathways in D. citri: the energy investment phase of glycolysis, the energy production phase of glycolysis, gluconeogenesis, Each manually annotated gene in glycolysis, gluconeogenesis, and trehaloneogenesis associated with a D. citri identifier shows supporting evidence used in the curation of the gene model [57]. Evidence tracks are as follows: RNA-seq, long-read Iso-seq, MCOT, de novo assembled transcripts and orthologous proteins. A gene marked with an "x" within the table indicates that the gene model is supported by the evidence track. A gene followed by "*" indicates that it is involved in both glycolysis and gluconeogenesis.
Energy investment phase of glycolysis
HK catalyzes the first step in glycolysis, utilizing adenosine triphosphate (ATP) to phosphorylate glucose, creating glucose-6-phosphate. Most insects have multiple HK genes and three copies of HK are present in the D. citri genome (Figure 3, Tables 2 and 4). In insect flight muscles, HK activity is inhibited by its product, glucose-6-phosphate, to initiate flight muscle activity [69]. Drosophila melanogaster has four duplicated HK genes, with Hex-A being the most conserved and essential flight muscle HK isozyme among Drosophila species [70,71]. For Diasporina citri, one of the copies of HK type 2-2 (Dcitr03g19430.1.1) showed moderate expression in the male and female thorax. In contrast, another copy HK PFK, which catalyzes the phosphorylation of fructose-6-phosphate using ATP to generate fructose-1,6-bisphosphate and adenosine diphosphate (ADP), is the key regulatory enzyme controlling glycolysis in insects, as it catalyzes a rate-determining reaction [76,77]. One copy of PFK (Dcitr01g16570.1.1) was found and annotated in D. citri (Table 4). Aldolase catalyzes the fourth step, the reversible aldol cleavage of fructose-1,6-bisphosphate to form two trioses, glyceraldehyde-3-phosphate (GAP) and dihydroxyacetone phosphate (DHAP).
Although most insects have a single copy of this gene, two well supported copies were found in D. citri (Table 4). One of the aldolase annotated copies, fructose-bisphosphate aldolase 1, (Dcitr04g02510.1.1) appears to have moderate expression in the male abdomen and terminal abdomen, and highest expression in the adult whole body (Figure 4). TPI catalyzes the fifth step, the reversible interconversion of DHAP and GAP. TPI is also important to sustain DHAP to maintain insect flight muscle activity [78]. D. citri contains a single copy of this gene (Dcitr10g08030.1.1), which is consistent with other insects (Table 4). Hexokinase amino acid sequence of D. citri compared with sequences from other insects. MUSCLE multiple sequence alignments of HK in D. citri and orthologs were performed using MEGA7 and neighbor-joining phylogenetic trees were constructed with p-distance for determining evolutionary distance and 1000 bootstrapping replicates [59]. Accession numbers for the orthologous sequences used in phylogenetic analysis are in Table 3.
Energy production phase of glycolysis
GAPDH catalyzes the reversible conversion of GAP to 1,3-bisphosphoglycerate during glycolysis. Two GAPDH genes were annotated in D. citri and the expression data for the two paralogs show that GAPDH-like 1 (Dcitr10g11030.1.1) has higher expression in the male terminal abdomen and whole body and GAPDH-like 2 (Dcitr01g03200.1.1) has higher expression values overall with a considerable increase in male thorax, female thorax and whole body (NCBI BioProjects PRJNA609978 and PRJNA448935) (Figure 4 and Table 4 in GigaDB [79]). The number of genes identified in glycolysis, gluconeogenesis and trehaloneogenesis in D. citri and related organisms. †indicates that there are possibly more PYK genes in D. melanogaster and potentially six in A. mellifera. * indicates that there is phosphoglucomutase 2a and 2b in D. melanogaster. Copy numbers for the orthologs were obtained from NCBI [56], OrthoDB [67], and Flybase [68]. (Table 4). PGAM is an enzyme that converts 3-phosphoglycerate to 2-phosphoglycerate.
Members of the PGAM family share a common PGAM domain, and function as either phosphotransferases or phosphohydrolases [80]. Two copies of PGAM were annotated in the D. citri genome (Table 4). PGAM 1 (Dcitr03g11640.1.1) has high expression evident in the midgut and the other paralog, PGAM 2 (Dcitr03g17850.1.1) is highly expressed in the whole body ( Figure 4).
Enolase catalyzes the conversion of 2-phosphoglycerate to phosphoenolpyruvate in the ninth step of the glycolytic pathway and a single copy was annotated in the D. citri genome (Table 4). RNAi knockdown of the -enolase in Nilaparvata lugens reduced egg production, offspring, and hatching rate; however, mortality of adults was unaffected [80]. Pairwise (Table 4). In D. citri, two PYK genes were characterized and annotated ( Table 2). One of the PYK genes (Dcitr07g06140.1.1) is highly expressed in male and female thorax and the other PYK gene (Dcitr01g11190.1.1) has relatively low overall expression with the highest expression in the male terminal abdomen (Figure 4). Expression analysis of the enzymes from this phase of glycolysis in thoracic tissue shows that the highest expression is observed for GAPDH-like 2 and PYK-like 1 and the lowest occurs for both GAPDH-like 1 and PYK-like 2 ( Figure 5). In addition, PGK (Dcitr00g01740.1.1) and enolase (Dcitr02g07600.1.1) also have high expression in the male and female thorax and PGAM 2 (Dcitr03g17850.1.1) has high expression in whole body besides the male and female thorax (NCBI BioProject PRJNA609978, NCBI BioProject PRJNA448935) (Figure 4 and Table 4 in GigaDB [79]).
Enzymes of gluconeogenesis
Gluconeogenesis is the metabolic process to re-generate glucose from non-carbohydrate substrates. It uses four specific enzymes. PC catalyzes the ATP-dependent carboxylation of pyruvate to oxaloacetate. The curated PC model (Dcitr08g01610.1.1) in D. citri shows highest overall expression in the male and female thorax, male and female head, and male and female antenna (Figures 6, 7 and Table 5 in GigaDB [79]). PEPCK controls cataplerotic flux and converts oxaloacetate from the tricarboxylic acid cycle to form phosphoenolpyruvate (PEP). Two PEPCK genes were annotated and [25]. Data in the heatmap show transcripts per million scaled by gene. RNA-seq data are available from NCBI Bioprojects PRJNA609978 and PRJNA448935 and published datasets [72]. characterized in the D. citri genome ( Table 2). The first PEPCK copy (Dcitr05g10240.1.1) has higher expression in most tissues than all of the other gluconeogenesis genes as is evident in the male and female antenna, male and female thorax, and the male and female head. copy of this gene was annotated in D. citri, similar to other insects, although two copies are present in the pea aphid, A. pisum, and the honeybee, A. mellifera (Table 2). FBPase (Dcitr11g08070.1.1) shows highest expression in the egg ( Figure 5). Glucose-6-phosphatase (G6Pase or G6P), which is specific to gluconeogenesis, catalyzes the conversion of glucose-6-phosphate to glucose [31]. However, this enzyme is not present in most insect species, including D. citri. Though present in N. lugens, RNAi studies showed that knockdown of G6Pase in N. lugens had no effect on the genes involved in trehalose metabolism [82].
Enzymes of trehaloneogenesis
Trehalose is a non-reducing disaccharide present in many organisms, including yeast, fungi, bacteria, plants and invertebrates. As the main hemolymph sugar in insects, it is found in high concentrations [32,83]. Trehalose is synthesized from glucose by trehalose-6-phosphate (Tre-6-P), where the mobilization of trehalose to glucose is considered critical for metabolic homeostasis in insect physiology [30]. Synthesis of trehalose occurs in the fat body, when stimulated by neuropeptides from the brain [32]. These peptides decrease the concentration of fructose 2,6-bisphosphate, which strongly activates the glycolytic enzyme PFK and inhibits the gluconeogenic enzyme fructose 1,6-bisphosphatase. Fructose 2,6-bisphosphatase is thus a key metabolic signal in regulating trehalose synthesis in insects. After synthesis, trehalose is transported through the hemolymph and enters cells through trehalose transporters, where it is converted into glucose by trehalase.
However, many insects appear to lack this gene, including D. citri as it was not found in the v3 genome. Most insects with multiple TPS genes encode proteins with TPS and TPP domains [85,86]. TPS in Drosophila appears to have the functions of both TPS and TPP [87].
Trehalase (TREH) catalyzes stored trehalose by cleaving it to two glucose molecules. There are two trehalase genes: TREH-1, which encodes a soluble enzyme found in hemolymph, goblet cell cavity and egg homogenates, and TREH-2, which encodes a membrane-bound enzyme found in flight muscle, ovary, spermatophore, midgut, brain and thoracic ganglia [84]. The two curated TREH genes in D. citri show different expression in the psyllid.
TREH is the only enzyme known for the irreversible splitting of trehalose in all insects [84] and D. citri and T. castaneum are the only insects with the second copy, TREH-2 (Table 2).
The two main trehalose transporters are trehalose transporter 1 (TRET1) and trehalose transporter 2 (TRET2), which both transport trehalose to and from cells with TREH. One gene copy for each of these trehalose transporters was annotated in D. citri (Table 2).
CONCLUSION
Manual annotation of the central metabolic pathways of glycolysis, gluconeogenesis, and trehaloneogenesis provides the accurate gene models required for development of molecular therapeutics to target D. citri. RNAi studies targeting genes involved in trehalose metabolism produced significant mortality in D. citri, [39,88] [79]). Annotation of the carbohydrate metabolism genes advances the understanding of the basic biology of D. citri and will aid in the development of RNAi-based applications.
REUSE POTENTIAL
The manually curated gene models were annotated through a collaborative community project [11] to further understand psyllid biology and with a goal to annotate gene families species-specific gene targets to control psyllid populations (potentially through RNAi) and reduce the effects of pathogens such as CLas.
DATA AVAILABILITY
The datasets supporting this article are available in the GigaScience GigaDB repository [79]. The gene models are part of an updated official gene set (OGS) for D. citri submitted to NCBI under Bioproject PRJNA29447. The OGS (v3) is also publicly available for download, BLAST analysis and expression profiling on Citrusgreening.org and the Citrus Greening Expression Network [25]. The D. citri genome assembly (v3), OGS (v3) and transcriptomes are accessible on the Citrusgreening.org portal [12]. Accession numbers for genes used in multiple alignments or phylogenetic trees are provided in Table 1.
EDITOR'S NOTE
This article is one of a series of Data Releases crediting the outputs of a student-focused and community-driven manual annotation project curating gene models and, if required, correcting assembly anomalies, for the Diaphorina citri genome project [95].
ETHICAL APPROVAL
Not applicable.
CONSENT FOR PUBLICATION
Not applicable.
==
Domain: Biology
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Nrf1 is not a direct target gene of SREBP1, albeit both are integrated into the rapamycin-responsive regulatory network in human hepatoma cells
The essential role of protein degradation by ubiquitin-proteasome system is exerted primarily for maintaining cellular protein homeostasis. The transcriptional activation of proteasomal genes by mTORC1 signaling depends on Nrf1, but whether this process is directly via SREBP1 remains elusive. In this study, our experiment evidence revealed that Nrf1 is not a direct target of SREBP1, although both are involved in the rapamycin-responsive regulatory networks. Closely scrutinizing two distinct transcriptomic datasets unraveled no significant changes in transcriptional expression of Nrf1 and almost all proteasomal subunits in either siSREBP2-silencing cells or SREBP1–∕–MEFs, when compared to equivalent controls. However, distinct upstream signaling to Nrf1 dislocation by p97 and its processing by DDI1/2, along with downstream proteasomal expression, may be monitored by mTOR signaling, to various certain extents, depending on distinct experimental settings in different types of cells. Our further evidence has been obtained from DDI1–∕–(DDI2insC) cells, demonstrating that putative effects of mTOR on the rapamycin-responsive signaling to Nrf1 and proteasomes may also be executed partially through a DDI1/2-independent mechanism, albeit the detailed regulatory events remain to be determined.
Introduction
The normal homeostasis must have to be maintained in all healthy life forms, due to homeostasis robustness, plasticity and resilience ensuring that their structural organization, physiological function and biological behavior are being properly performed and perpetuated at a stable, robust steady-state [1]. Conversely, defects in the maintenance of cell fitness and homeostasis have emerged as an underlying feature of a vast variety of pathologies, such as cancer, senescence and aging-related diseases [2][3][4]. Of note, protein homeostasis (i.e.proteostasis) is preserved or not, depending on a steady balance between protein synthesis and turnover [5,6]. Interestingly, as reported by Manning's group [7], such a finely-programmed balance between protein synthesis and degradation was coordinately regulated by the mechanistic target of rapamycin complex 1 (mTORC1), a central kinase that is generally activated by cell growth-and proliferation signaling to trigger protein translation [8]. Besides, they had also shown that mTORC1 signaling activates transcriptional expression of nuclear factor erythroid 2-related factor 1 (Nrf1 with multiple isoforms, encoded by NFE2L1) directly by sterol regulatory element-binding protein 1 (SREBP1) [7,9], although SREBP1 has been commonly accepted as a key control of lipid synthesis and membrane homeostasis, even in response to the rather multifaceted mTOR signaling [10][11][12].
Intriguingly, in the present study, our evidence has been presented revealing that Nrf1 is not a direct target of SREBP1, albeit they are involved in the rapamycin-responsive regulatory networks. Further experiments are designed to determine whether p97, DDI1 (also called VSM1 (v-SNARE binding protein-1)), which is a highly conserved aspartyl protease among all eukaryotes from yeast to human [13][14][15] and DDI2 (only present in vertebrates [16]) exert the putative rapamycin-responsive effects on Nrf1 processing and activity to regulate the proteasomal expression. Moreover, the human HepG2-derived DDI1 -∕-(DDI2 insC ) cell line and tumor xenograft model have also been established herein, to further elucidate the rapamycin-responsive effects on Nrf1 and cognate proteasomes.
Cell culture and treatments
HepG2 and HL7702 cell lines were grown in DMEM supplemented with 5 mM glutamine, 10% (v/v) fetal bovine serum (FBS), 100 units/ml of either penicillin or streptomycin, in a 37˚C incubator with 5% CO 2 . Additional HepG2-derived cell lines with the knockout of DDI1 -∕-were herein established by CRISPR-editing of DDI1 with specific gRNA (S1 Table in S2 File). The authenticity of DDI1 -∕-cells had been confirmed by its authentication analysis. Thereafter, experimental cells were transfected with a Lipofectamine 3000 mixture with indicated plasmids or siSREBP1 (with a pair of sequences, S1 Table in S2 File) for 8 h, and allowed for recovery from transfection in a fresh medium for 24 h before being experimented. Additional cells were treated with the mTOR inhibitor rapamycin (RAPA, 20 to 200 nM) or proteasomal inhibitor MG132 (1 to 10 μM) for different time periods (4, 16 or 24h).
Expression constructs
An expression construct for human SREBP1 was made by cloning its full-length cDNA sequence into the pcDNA3 vector, with a pair of its forward and reverse primers (S1 Table in S2 File), which were synthesized by Sangon Biotech Co. (Shanghai, China). Another expression plasmid of Nrf1 was reported previously [17]. The fidelity of all these constructs was confirmed to be true by sequencing.
Luciferase reporter assay
After experimental cells (1.0 × 10 5 ) were allowed for growth in each well of the 12-well plates to reach 80% confluence, they were co-transfected with a Lipofectamine 3000 mixture with pNrf1-luc or pNrf2-luc established by Qiu et al. [18], plus other expression plasmids. In this dual reporter assay, the Renilla expression by pRL-TK served as an internal control for transfection efficiency. The resulting data were normalized from at least three independent experiments, each of which was performed in triplicate, and thus shown as a fold change (mean ± S. D) relative to the control values.
Quantitative real-time PCR
About 500 ng of total RNAs from experimental cells were subjected to reverse-transcriptase reaction to generate the first strand of cDNA. The newly-synthesized cDNA was used as the template for quantitative PCR in the Master Mix, before being deactivated at 95˚C for 10 min, and amplified by 40 reaction cycles of annealing at 95˚C for 15 s and then extending at 60˚C for 30 s. The final melting curve was validated to examine the amplification quality. While βactin mRNA level was employed as an optimal internal standard control, target gene expression levels were determined by quantitative real-time PCR, as described previously [19], with each pair of the indicated primers (S1 Table in S2 File). The resulting data were shown a fold change (mean ± S. D) relative to the control values.
Western blotting with distinct antibodies
Total cell lysates in a lysis buffer (0.5% SDS, 0.04 mol/L DTT, pH 7.5) with protease and phosphatase inhibitors (each of cOmplete and PhosSTOP EASYpack tablets in 10 ml buffer), were denatured immediately at 100˚C for 10 min, sonicated sufficiently, and diluted in 3× loading buffer (187.5 mmol/L Tris-HCl, pH 6.8, 6% SDS, 30% Glycerol, 150 mmol/L DTT, 0.3% Bromphenol Blue) at 100˚C for 5 min. Subsequently, equal amounts of protein extracts were subjected to separation by SDS-PAGE containing 4-15% polyacrylamide, and then visualization by Western blotting with distinct antibodies as indicated (S1 Table in S2 File). Some of the blotted membranes were stripped for 30 min and re-probed with additional primary antibodies. Therein, β-actin or GAPDH served as an internal control to verify equal loading of proteins.
Subcutaneous tumor xenograft model
Mouse xenograft models were made by subcutaneously heterotransplanting human HepG2 or derived DDI1 -∕-cells. Briefly, equal amounts of cells (1 × 10 7 ) growing in the exponential phase was suspended in 0.1 ml of serum-free medium and then inoculated subcutaneously at a single site in the right upper back region of male nude mice (BALB/C nu/nu, 4-6 weeks, 18 g). The procedure of injection into all the mice was completed within 30 min. Thereafter, the formation of murine subcutaneous tumor xenografts was successively observed until they were sacrificed. These transplanted tumors were excised immediately after being executed, and also calculated in size by a standard formulate (V = ab 2 /2). All mice were maintained under standard animal housing conditions with a 12-h dark cycle and also allowed access ad libitum to sterilized water and diet, according to the institutional guidelines for care and use of laboratory animals with a license SCXK (JING) 2019-0010. All experimental procedures were approved by the Ethics Committee of Chongqing Medical University.
Pathohistology with H&E staining
The xenograft tumor tissues were immersed in 4% paraformaldehyde overnight and then transferred to 70% ethanol. In processing cassettes, tumor tissues were dehydrated by a serial alcohol gradient and then embedded in paraffin wax blocks, before being sectioned into a series of 5-μm-thick slides. Subsequently, the tissue sections were de-waxed in xylene, rehydrated through decreasing concentrations of ethanol and washed in PBS, before being stained by routine hematoxylin and eosin (H&E) and visualized by microscopy. The resulting images were photographed herein.
Statistical analysis
Significant differences were statistically determined using the Student's t-test and Multiple Analysis of Variations (MANOVA), except for somewhere indicated. The data are here shown as a fold change (mean ± S. D.), each of which represents at least three independent experiments that were each performed in triplicate.
Transcriptional expression of Nrf1 is unaffected by SREBP1
Based on the data obtained from chromatin immunoprecipitation (ChIP) [7] and microarray [20,21], Manning's group considered that aberrant activation of mTOR in TSC2 -∕-MEFs led to upregulation of Nrf1 by SREBP1. However, we showed that transcriptional expression of Nrf1 was unaffected by SREBP1 knockdown or overexpression in both HepG2 and 7702 cell lines (Fig 1A to 1D). Almost no changes in transactivation of Nrf1 or Nrf2 promoter-driven luciferase reporters (pNrf1-luc and pNrf2-luc, established by Qiu et al. [18]) were determined in the cellular response to rapamycin (RAPA), but they were induced by tert-butylhydroquinone (tBHQ, a pro-oxidative stressor) rather than the antioxidant N-Acetyl-L-cysteine (NAC) (Fig 1E to 1H). Such discrepancy may result from a strange disparity in the cytosolic to the nuclear distribution of Nrf1 examined by Manning's lab (S1A Fig in S2 File, cropped from their extended data [7]). This is due to the authors showing abnormal accumulation of a peculiar nuclear Nrf1 isoform with a higher molecular weight than its cytosolic isoform, which is never recovered by all relevant subcellular fractionation experiments (cropped in S1B to S1D Fig in S2 File, as reported by our and other groups) [22][23][24][25]. Such strange nuclear Nrf1 accumulation revealed by Manning's group seems to challenge against the well-established spatiotemporal character of this ER-localized transcription factor [26,27].
Intriguingly, further examination showed that abundances of all Nrf1 isoforms were marginally enhanced by silencing of SREBP1 (Fig 2A, cf.a2 vs a1). Accordantly increased mRNA levels of Nrf1-target PSMB6, PSMB7 and PSMB5 (encode the core proteasomal ß1, ß2 and ß5 subunits, respectively [28]) were accompanied by enhanced protein abundances of PSMB6, PSMB7 and slightly PSMB5, concomitantly with SREBP1 knockdown (Fig 2B and 2D). The difference between protein and mRNA levels of PSMB5 (as a key housekeeper in the ubiquitin-proteasomal system) suggests that its protein stability may be finely tuned by the proteasomal feedback regulatory loop, facilitating proteostasis maintenance. Conversely, phosphorylated S6 kinase 1 (pS6K1, required for protein synthesis) protein and mRNA expression levels were significantly down-regulated by siSREBP1 (Fig 2B and 2D). Thereby, SREBP1 may be directionally responsible for regulating protein synthesis and degradation.
Next, an examination of siSREBP1's effects on the upstream regulators of Nrf1 revealed that evident abundances of DDI1 and DDI2 were increased (Fig 2C). This was accompanied by modest decreases in p97/VCP protein and mRNA levels, while the ER-resident E3 ligase Hrd1 was almost unaffected by silencing of SREBP1 (Fig 2C and 2D). Further scrutinizing two distinct transcriptomic datasets (at [URL]= GSE93980 and = GSE90571) unraveled that, though putative SREBP1-binding sites exist in both the promoter region of Nrf1 and its first exon (S2 Fig in S2 .(E to H) HepG2 cells, that had been transfected with pNrf1-luc (E, F) or pNrf2-luc siSREBP2-silencing PANC-1 cells or SREBP1 -∕-MEFs, when compared to their wild-type controls. Collectively, these lines of evidence together demonstrate that Nrf1 is not a direct target of SREBP1, albeit its indirect effects on upstream signaling to Nrf1 cannot be ruled out.
Discrete effects of rapamycin on the signaling to Nrf1 and proteasome
It was, to my surprise, found that stimulation of HepG2 cells by feeding 10% FBS after 10-h free-serum starvation caused significant decreases in mRNA expression levels of Nrf1 and SREBP1, but their protein abundances were strikingly increased to varying extents (Fig 3A vs 3B and 3C), and markedly diminished or abolished by rapamycin (20 nM, Fig 3B and 3C). Similar results were obtained for S6K (Fig 3C and 3D). Of note, mRNA expression of S6K, but not Nrf1 or SREBP1, was reversed and increased by rapamycin (Fig 3D). Together, these demonstrate that mTOR is likely involved in at least two different mechanisms for regulating Nrf1 and SREBP1 at mRNA and protein expression levels, which are distinctive from controlling its downstream S6K1 by potential 'bounce-back' response to mTOR inhibitor.
Further experimental evidence showed that Nrf1-target PSMB5, PSMB6 and PSMB7 (Fig 3B ), as well as the upstream signaling DDI1, DDI2, p97 and Hrd1 (Fig 3E ) were significantly upregulated by feeding FBS, of which all those except Hrd1 were also inhibited by rapamycin. However, their mRNA expression levels were down-regulated or unaffected by FBS, but also partially reversed or event enhanced by rapamycin (Fig 3F , cf. left vs right panels). Of note, mRNA expression of PSMB6 and DDI2 was unaltered or down-regulated by FBS, respectively, but both were also significantly augmented by rapamycin. Overall, these results further indicate that key upstream and downstream signaling molecules of Nrf1 were, to some certain extent, influenced by mTOR involved in distinct hierarchical mechanisms.
Alteration in the putative processing of Nrf1 in DDI1/2-deficient cells
Since Nrf1 and C. elegans SKN-1A are activated by DDI1 in the proteasomal 'bounce-back' response [14,17,19] Assessment of subcutaneous tumor xenograft mice unraveled that no differences in in vivo tumorigenesis and tumor growth of DDI1 -∕-(DDI2 insC ) cells were observed when compared to those of its parent wild-type cells (Fig 4D and 4E). Also, no obvious changes in their tumor pathohistological sections were shown (in Fig 4F). Such DDI1/2-deficient cells were then treated with distinct concentrations of MG132 and subjected to determination of (G, H) reporters, along with pRL-TK (an internal control) and then treated for 24 h with rapamycin (RAPA, at 0, 100 or 200 nM) (E, G), NAC (10 mM) or tBHQ (50 μM) (F, H), were subjected to an assay of dual-luciferase activity (n = 3×3) with significant increases ($, p<0.01) or no significances (NS). All the results representing at least three independent experiments, each of which was performed in triplicates, were determined as fold changes (mean ± S. D.) relative to equivalent controls. [URL] effects of DDI1 -∕-(DDI2 insC ) on the processing of Nrf1. As anticipated, the results revealed that processed Nrf1 isoforms-C/D were significantly reduced, but its full-length glycoprotein-A and deglycoprotein-B were almost unchanged following treatment of DDI1 -∕-(DDI2 insC ) with a lower dose (1 μM) of MG132 when compared to those measured from wild-type cells (Fig 4G, middle two lanes). By sharp contrast, a higher dose (10 μM) of MG132 treatment of DDI1 -∕-(DDI2 insC ) caused Nrf1 isoforms-C/D to be further diminished or abolished, but its full-length proteins-A/B were not augmented, when compared to their wild-type controls (Fig 4G, right two lanes). These demonstrate a requirement of 26S proteasome for DDI1/2-directed proteolytic processing of Nrf1 because the stability of both proteases per se is also controlled by ubiquitin-proteasome pathways [30,31]. However, endogenous Nrf1 isoforms-A/B was marginally reduced in untreated DDI1 -∕-(DDI2 insC ) cells, where Nrf1 isoforms-C/D were rather faint to be distinguishable from wild-type controls (Fig 4G, left two lanes). This implies that Nrf1α-derived isoforms may be much unstable to be rapidly destructed, but shorter isoforms Nrf1 ΔN , Nrf1ß and Nrf1γ were unaffected, in DDI1/2-deficient cells (Fig 4G, left two lanes). Such seemingly-contradictory data, showing no increased full-length Nrf1 isoforms-A/B in DDI1 -∕-(DDI2 insC ) cells, suggest that ER membrane-associated protein degradation and/or autophagy [32] may also be triggered in possibly 'bounce-back' response to DDI1/2 deficiency.
Intriguingly, wild-type DDI1 and its short isoform in DDI1 -∕-(DDI2 insC ) cells were not enhanced, but slightly reduced by treatment of 1 μM or 10 μM MG132 for 4 h (Fig 4H , h1), and the reduced abundances were further decreased as treatment time was extended to 24 h (Fig 4H , h4) when compared with their untreated controls. By contrast, wild-type DDI2 and its remaining protein in DDI1 -∕-(DDI2 insC ) cells were largely unaffected by 24-h treatment of 1 μM or 10 μM MG132 (Fig 4H , h5), but after 4-h treatment of cells, they became marginally reduced by 1 μM MG132, and also rather augmented by 10 μM MG132 (Fig 4H , h2). Such distinct effects of this proteasomal inhibitor on DDI1 and DDI2 demonstrate that both protease stability may be governed through different mechanisms, albeit these details remain to be elucidated.
Fig 3. Distinct effects of rapamycin on FBS-altered expression of SREBP1, Nrf1 and relevant signaling molecules. (A) HepG2 cells that had
been starved in a serum-free medium for 10 h and then stimulated for 12 or 24 h by feeding 10% FBS, were subjected to real-time qPCR analysis of Nrf1 and SREBP1 at mRNA expression levels. The results were shown as fold changes (mean ± S. D. n = 3×3) with significant decreases (*, p<0.01; **, p<0.001) relative to the negative controls (NC, with no FBS treatment).(B to F) The free-serum starved HepG2 cells were treated with 10% FBS alone or plus 20 nM RAPA for 0, 12, 24 h, before being subjected to Western blotting with indicated antibodies (B, C, E), in which the intensity of (Fig 5D , d1). By contrast, FBS-stimulated protein expression of Nrf2 was partially suppressed by rapamycin (Fig 5D , d2). However, mRNA expression levels of Nrf1 and Nrf2 were not induced, but rather repressed by FBS, and such repression was partially or completely reversed by rapamycin, as a result of enhanced mRNA expression of Nrf2 by this mTOR inhibitor (Fig 5F and 5G). These data indicate there exists a feedback negative regulatory circuit between mRNA and protein expression of Nrf1 and Nrf2, during stimulation or inhibition of mTOR.
Further examinations of DDI1 -∕-(DDI2 insC ) cells uncovered that, upon the absence of DDI1, the remnant DDI2 were still partially enhanced by FBS, and also partially inhibited by rapamycin (Fig 5E). Thereof, FBS-repressed mRNA expression of DDI2 was fully reversed to a slight increase by rapamycin (Fig 5F and 5G). Similarly, modest stimulation of PSMB5, PSMB6 and PSMB7, abundances by FBS was also partially inhibited by rapamycin (Fig 5E , e3 to e5), but their FBS-repressed mRNA levels were not reversed by rapamycin (Fig 5F and 5G). Such opposite (stimulatory or inhibitory) effects of mTOR on rapamycin-responsive signaling to Nrf1 and downstream proteasome may partially occur in DDI1/2-deficient cells, implying that a DDI1/2-independent mechanism also accounts for this process.
Discussion
The ubiquitin proteasome system is crucial for protein degradation and homeostasis, whilst Nrf1 is a key regulatory factor for governing the transcriptional expression of all proteasome subunits. Of note, the transcriptional activation of proteasomes by mTORC1 may be in an Nrf1-dependent manner, and the mTORC1 signaling to upregulation of Nrf1-targeted proteasomal expression profiles was also considered to occur directly by SREBP1, as reported by Manning's group in 2014's Nature [7]. However, a core point from Manning's work on the control of proteasomal proteolysis by mTOR [7] was first challenged by Goldberg's group, arguing that their methodology to measure the rates of protein degradation (labelled by 35 S-Met/Cys rather than by 3 H-Phe) and the former labelled-protein pulse-chase experimental data appear questionable [34], because the resulting data were considered to be rather inconsistent with those well-established discoveries [35,36]. Amongst the best-studied actions of mTORC1 are enhancing protein synthesis, and also inhibiting protein degradation by autophagy and proteasomes [35,37]. While mTORC1 is inactivated during starvation, an increase in proteolysis and autophagy provides a recycling of amino acids for next protein synthesis and energy production. As such, Manning's group showed that inhibition of mTORC1 activity for 16 h or more resulted in a delayed reduction in overall proteolysis by down-regulating transcriptional expression of proteasomal subunits [7]. Similar results were also obtained from 3 H-Phe-labelled pulse-chase experiments in their reply to doubt by Goldberg's group [38]. Such discrepant results presented by Manning's and Goldberg's groups are not attributable to nuances in the assays but are inferable due to differences in their chosen culture conditions [38]. A higher dose of rapamycin (300 nM, at least 100-fold more than the IC 50 for inhibiting mTORC1) was employed [34] to enable two mTOR kinase complexes (i.e., mTORC1 and mTORC2) to be completely blocked in mouse embryonic fibroblasts (MEFs) with genetic loss of tuberous sclerosis complex 2 (TSC2 -∕-, immunoblots was calculated and shown on the bottom, or real-time qPCR analysis of indicated genes at mRNA levels (D, F). The results were shown as fold changes (mean ± S. D. n = 3×3) with significant decreases (*, p<0.01; **, p<0.001), significant increases ($, p<0.01; $$, p<0.001), or no significant differences (NS), relative to their equivalent controls. These results are representative of at least three independent experiments, each of which was performed in triplicates. [URL] is accompanied by aberrant activation of mTOR). By contrast, a much lower dose (20 nM) of rapamycin for treatment of TSC2 -∕-cells grown in the low-serum conditions [7,38] enabled for specific separation of effects of mTORC1 from mTORC2, as described by [39]. Just under this status, it was found that TSC2 -∕--leading activation of mTORC1, rather than mTORC2, stimulates a transcriptional programme involving SREBP1 and Nrf1, leading to an evident enhancement of proteasome-mediated proteolysis exclusively by Nrf1, but not Nrf2 [7,9]. Since Manning's work was fantastically well done [7,38] and has gained nearly 200 citations (from the Web of Science at [URL]), it is worth interrogating why no more further experimental evidence confirming their findings has been provided to date by any other groups, so far as we know. Of particular concern is a key issue arising from Manning's work, which merits reexamination of whether mTORC1 signaling to upregulation of Nrf1-targeted proteasomal expression profiles occurs directly by SREBP1 because this controversial mechanism remains obscure. Here, we found that transcriptional expression of Nrf1 and all proteasomal subunits is almost unaffected by SREBP1 (or SREBP2), but conversely, Nrf1 contributes to negative regulation of SREBP1 involved in lipid metabolism ( [40] and this study). Besides, a Yin-Yang relationship between Nrf1 and SREBP2 for maintaining cholesterol homeostasis was elaborately unraveled in Hotamisligil's laboratory [22]. Recently, a mechanistic study by Xu's group [41] has shown that activity of SREBPs is inhibited by promoting degradation of SREBP-cleavage activating protein (SCAP, a central sensor for cholesterol) through the protein-ubiquitin E3 ligase RNF5-dependent proteasomal pathway, in this process whereby this ligase is recruited to the endoplasmic reticulum (ER)-localized transmembrane protein 33 (TMEM33), a direct target of Nrf1. Therein TMEM33 is also a downstream effector of pyruvate kinase isoform 2 (PKM2), which coordinates together with p97/ VCP to control the processing of Nrf1 and SREBPs, as well as their bidirectional regulatory ability to dictate lipid metabolism and homeostasis [41].
Because Nrf1 and SREBP1 manifest distinct topobiological behavior around membranes [42], they are endowed with their respective discrepant spatiotemporal partitioning and unique biological functionality to be though exerted, only after being dislocated from the ER into the nucleus so as to regulate distinct sets of target genes. In this study, we have discovered that Nrf1 is not a direct target of SREBP1, albeit both are involved in rapamycin-responsive signaling networks (Fig 6). Of note, the upstream signaling to Nrf1 dislocation by p97 and its processing by DDI1/2, along with downstream proteasomal expression, should be indirectly monitored by potent mTOR signaling networks, to various certain extents, depending on distinct experimental settings in distinct cell types. Therefore, the potential indirect effects of SREBP1 on DDI1/2 and proteasomes cannot be ruled out. Further experimental evidence from DDI1 -∕-(DDI2 insC ) cells demonstrates that putative effects of mTOR on the rapamycin-responsive signaling to Nrf1 and proteasome may also be executed partially through a DDI1/2-independent mechanism, albeit the detailed regulatory events remain to be elucidated. DDI1 -∕-DDI2 insC ) cells were treated with MG132 at 0, 1 or 10 μM for 24 h (G, H) or 4 h (H), and then subjected to Western blotting with distinct antibodies against Nrf1, DDI1 or DDI2. In addition, a long-term exposed image was cropped from part of the corresponding gel (G). These results are representative of at least three independent experiments, each of which was performed in triplicates. [URL]004 File), no significant changes in transcriptional expression of Nrf1 and other homologous factors (Fig 2E and 2F), as well as almost all proteasomal subunits (S3A and S3B Fig in S2 File), were determined in either
Fig 1 .
Fig 1. Transcriptional expression of Nrf1 and its reporter is unaffected by SREBP1 or rapamycin.(A to D) Two cell lines of HepG2 (A, B) and L7702 (C, D), that had been transfected with: (A, C) siNC (a negative control) or siSREBP1; (B, D) a pSREBP1 expression construct or empty plasmid, were subjected to real-time qPCR analysis of mRNA expression levels of SREBP1 and Nrf1 (n = 3×3; with significant decreases (*, p<0.01), significant increases ($, p<0.01), or no significances (NS)).(E to H) HepG2 cells, that had been transfected with pNrf1-luc (E, F) or pNrf2-luc , we established a DDI1 -∕-cell line by CRISPR-editing with specific gRNA (S4A Fig in S2 File). Further examination of DDI1 -∕-cells by its DNA sequencing, real-time qPCR and Western blotting revealed that two overlapping nucleotide segments of DDI1 were deleted from its two alleles (Fig 4A and S4B Fig in S2 File), but the remnant mRNA levels were expressed (Fig 4B and 4C). This implies there may exist alternative mRNA-splicing and inframe translation start sites to yield two isoforms with distinct molecular weights, as described in yeast DDI1 [29]. In addition to DDI1 -∕-, an extra-cytosine base was inserted in the open reading of DDI2 (S4C Fig in S2 File), thus recalled DDI1 -∕-(DDI2 insC ) collectively. This should be a result of DDI1-recognized gRNA targeting the highly conserved sequence of DDI2 (S4A Fig in S2 File).
Fig 2 .
Fig 2. The upstream signaling to Nrf1 and proteasome are to no or fewer degrees, affected in SREBP1-deficient cells.(A to C) HepG2 cells were transfected with siNC or siSREBP1 for 24 h and then subjected to Western blotting with those indicated antibodies. The intensity of immunoblots representing each protein was quantified by the Quantity-One software and shown on the bottom.(D) The mRNA levels of those examined genes were determined by real-time qPCR and shown as fold changes (mean ± S. D. n = 3×3) with significant decreases (*, p<0.01) or significant increases ($, p<0.01) relative to equivalent controls. These results are representative of at least three independent experiments, each of which was performed in triplicates.(E, F)
Fig 4 .
Fig 4. Changed processing of Nrf1 in DDI1/2-deficient cells, but with no different xenograft models.(A) HepG2-derived DDI1 -∕-cells were initially identified by their genomic DNA-sequencing. The results were shown graphically, along with the alignment of two mutant alleles and wild-type (WT).(B, C) In contrast with WT cells, DDI1 -∕-cells were further determined by real-time qPCR (B, shown by mean ± S. D. n = 3×3; *, p<0.01) and Western blotting (C), respectively.(D) No different phenotypes of xenograft tumors in nude mice were observed after murine subcutaneous inoculation of WT and DDI1 -∕-(DDI2 insC ) hepatoma cells.(D) No differences in both tumorigenesis and in vivo growth between WT and DDI1/2-deficient and xenograft tumors were measured in size every two days, before being sacrificed. The results are shown as mean ± S. D. (n = 5).(F) The pathohistological images were obtained by routine HE staining of the aforementioned xenograft tumor tissues.(G, H) Both lines of WT and KO (i.e.
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Domain: Biology
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Mesenchymal stem cell-derived exosome as a nano weapon to target the COVID-19 pandemic
In these times of despair when a nano-sized organism, the SARS-CoV-2, has rendered the human race helpless, made the global health status decline, and drowned the world economy, a ray of hope comes from another nano-sized particle, the exosome The potential of mesenchymal stem cells has already been established in COVID-19;however, cell-based therapy has its risks We thereby propose cell-free therapy using stem cells-derived exosomes to fight against COVID-19, as they can be a game-changer owing to their immunomodulatory nature, which combats the cytokine storm characterizing this disease, and their practical efficiency, which will realistically aid large access to therapy worldwide
Introduction
COVID-19 is a deadly disease that has caused worldwide havoc and shaken the core of the economy, even for the most developed countries. Moreover, the face of survival against COVID-19 has been divergent for people that belong to different socio-economic classes as the availability of the best healthcare services is significantly diminished to the impoverished part of the society, including developing countries and nations. Conditions are specifically severe in such regions due to factors like overpopulation, limited resources, logistic gaps, improper infrastructure, and poverty. Although the governments are taking several measures to ensure the safety of the citizens, the feasibility of medicine-for-all and medicine-to-all seems far-fetched considering the current scenario of therapy.
A therapeutic agent suitable to combat such a pathologically and socially complex situation needs to be physiologically efficient, mechanically flexible, and practical to use for feasibility in mass manufacturing. Considering all these properties, we can state that the therapeutic agent that displays these attributes is the exosome. These are small vesicles in the size range of 30-150 nm, which are synthesized in the cell inside multivesicular bodies (MVBs) as intraluminal vesicles (ILVs) by double invagination of the plasma membrane. These are secreted upon the fusion of MVBs with the plasma membrane. Given their versatile properties and small size, exosomes offer novel opportunities for cell-free therapeutics and are state-of-art in regenerative medicine (Jabbari et al., 2020;Jing et al., 2018). The exosomes have proven potential as drug delivery carriers, and their immunomodulatory properties are unmatched, so employing them as a therapeutic agent against COVID-19 is an ideal approach (Akbari and Rezaie, 2020;Popowski et al., 2020). Among all the therapies tried, a major focus for the treatment of this disease has been shifted towards Mesenchymal Stem Cells (MSCs) and their secretome (Akbari and Rezaie, 2020). MSCs, apart from transdifferentiation, also actively exhibit their functions via the mechanism of paracrine signaling aided by the secreted extracellular vesicles, especially exosomes. Several clinical trials have employed MSCs and the exosomes secreted by them against the pathophysiology of COVID-19 and shown outstanding outcomes (Sengupta et al., 2020). Exosomes carry many bioactive molecules involved intricately in cell signaling and communication, and so we could infer that exosomes are reflected as a shadow of their parent cells per se and the flag holders of MSCs mechanism of action (Magdy Beshbishy et al., 2020).
Even though the regenerative potential of exosomes derived from MSCs has been explored widely, there are yet several roadblocks that hinder the commercialization of these nano-sized vesicles as a cell-free therapeutic agent. The primary concern lies in source selection. We would like to highlight that even though MSCs from all sources manifest similar properties, some minuscule differences are observed in their functioning depending upon their source, which therefore also confer differences to exosomes shed by them. In our view, the common sources of MSCs isolation like bone marrow, adipose tissue, dental pulp, etc., are immunologically unbenefited and hold a higher risk of packaging coronavirus or any other pre-existing infection into their exosomes, whereas Wharton's jelly derived MSCs (WJ-MSCs) are considered to be immunologically privileged due to the sheltered nature of their source, and because it is known to consists of exceedingly naive cells that are least exposed to environmental stresses (Marino et al., 2019;Bullard et al., 2019;Kamal and Kassem, 2020). WJ-MSCs are known to exhibit high immunosuppression, a property direly needed to fight COVID-19. This suggests that exosomes derived from WJ-MSC represent a better ability to manage this disease, given that SARS-CoV-2 bind to the ACE-2 receptors present on the ciliated secretory cells in the nasal epithelium with high affinity, forth which they replicate and propagate towards lungs where they enter using the ACE-2 receptors on type II pulmonary alveolar epithelial cells (Hassanpour et al., 2020;Parasher, 2020). This small virus then works by inducing a tempest of cytokines in the lungs by inciting some major proinflammatory factors like IL-2, IL-6, IL-7, and TNFα and activating inflammatory pathways including NF-AT, IRF-3, IRF-7, NF-κB, ATF-2/jun, and jun/fos (AP-1) (Emameh et al., 2020;Tay et al., 2020). This cascade attracts immune cells like monocytes, macrophages, and T cells, thereby leading to the creation of a cycle that increasingly intensifies inflammation (Shetty, 2020). Apart from many other probable causes, this loop of altered immunomodulation instigates some of the signature symptoms characterizing SARS-CoV-2, thereby causing lethal damages to the lung ultrastructure and multiple organs upon the propagation of this cytokine storm. Breaking into an immunologically complex milieu like this, while specifically targeting the virus, is an exasperating task. In such a conflicting situation, the instinctive immunosuppressive abilities of WJ-MSC-exosomes make them a trustable candidate. MSC-derived exosomes are able to act against the pathophysiology of SARS-Cov-2 by virtue of their cargo. These nano-vesicles act as bullets encapsulating bioactive molecules including mRNAs, proteins, miRNAs, etc., which target the infected cells, thereby initiating an antiinflammatory and antiviral response, thus providing a reparative effect (Wang et al., 2019). miRNAs have been found to be the primal component to be transferred from exosomes to the 'cells-in-need', which could form the basis of another mechanism that could potentially be responsible for suppressing the proliferation of the virus via silencing action and epigenetic alterations (Gupta et al., 2020). MSCderived exosomes are immunomodulatory in nature as they down-regulate the pro-inflammatory cytokines including TNF-α and IFN-γ which further subdues T-cell maturity while elevating the levels of anti-inflammatory cytokines like nitric oxide, TGF-β, and IL-10 (Xie et al., 2020). They have also been seen to modulate the function of B-cells by differentially regulating the expression of relevant gene encoding mRNAs (Khare et al., 2018).
Cell-free therapy using exosomes is considered safer than cell-based therapy using MSCs as exosomes cannot replicate, and therefore negate the risk of teratoma; even though multiple studies have proven otherwise, it remains a major concern pertaining stem cell transplantation (Jing et al., 2018;Abraham and Krasnodembskaya, 2020). Also, MSCs are large in size and are unable to cross physiological barriers, so they also hold a risk of blocking circulation and getting caught in lung vasculature, whereas exosomes have the ability to cross physiological barriers, are stable in circulation, and are non-toxic (Jing et al., 2018;Bang and Kim, 2019). They even display a lower risk of immune rejection when administered from an allogenic source (Chase and Gallicchio, 2019). The research to assess the capability of exosomes derived from MSCs to act as a drugdelivery vehicle by exosome engineering and manipulation of their surface and cargo is still in its infancy, but there have been several studies that prove its targeting ability and specificity in therapeutics (Choi et al., 2020;Guo et al., 2019). We would also like to add here that the viability of exosomes is unquestionable as 'live or dead' status cannot be assigned to them; however, we cannot assure that all their parent cells remain targeted and viable in vivo upon transplantation, hence making these nanoparticles a better therapeutic candidate than their parent cells itself.
Apart from their immensely elaborate therapeutic cascade, the undemanding operational protocols of exosomes highlight them as a practical candidate for offthe-shelf remedial approach against COVID-19, especially in developing countries, as exosomes can be easily procured, lyophilized, manufactured, and transported in different formulations like freeze-dried, sprays, ointment or injectables. These diverse features of the exosomes make them an appropriate nano-platform for future commercialization and biomedical applications. Certain startups like Exomedx, Codiak Biosciences, Exocyte Therapeutics, etc., have explored the commercialization of exosomes for regenerative medicine, e.g., Kimera labs have created an exosome-based product called XoGlo™ for skin rejuvenation (Yousef and Abdelnaser, 2019). This will decrease the cost of therapy drastically, resolving the question of affordability and providing medicine-for-all, while the easy handling procedures will aid in logistics hence providing medical-to-all (Fig. 1). Few minor glitches that remain to be resolved like standardization in protocols, batch variations, and improving technological scalability, if circumvented, will highlight these nanovesicles as the key-players in the pharmaceutical industry (Jing et al., 2018;Bari et al., 2019).
Thus, we recapitulate from our article that using exosomes derived from MSCs as a cell-free therapeutic agent is a quintessential approach to combat COVID-19. Exosomes derived from WJ-MSCs especially possess astonishing remedial capabilities, and their viability in vivo is explicitly assured. These natural nanoparticles can be maneuvered into different kinds of formulations as complex as vaccines or in commonly benchside therapeutics like nasal-sprays. Hence, the diverse scope and possibility of manipulations offered by MSC-derived exosomes make them an ultimate curative cell-free bioagent to defeat COVID-19. Acknowledgement: The image was created using BioRender.com.
Author Contribution: The authors confirm contribution to the paper as follows: YS contributed to conceptualization, writing and graphics. SG contributed to critical evaluation of the manuscript. SM contributed to conceptualization, critical evaluation, and coordinated throughout the manuscript writing. All authors approved the final version of the manuscript.
Funding Statement: The author(s) received no specific funding for this study.
Conflicts of Interest: All authors have read the journal's policy on disclosure of potential conflicts of interest and have none to declare.\===
Domain: Biology. The above document has 2 sentences that end with 'the plasma membrane',
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Molecular Characterization of Trichoderma spp. Isolates by Internal Transcribed Spacer (ITS) Region Sequencing Technique and its Use as a Biocontrol Agent
All of the ITS1 nucleotide sequences obtained in this study matched 97% 100% with the published sequence of Trichoderma spp. The results confirmed the strains as T. asperellum and T. viride with gene bank accession no. (ZTa); MK937669 and (ZTv); MK503705, respectively. When phylogenetic analysis was done for the isolates, the optimal tree with the sum of branch length = 0.69585023 and 0.10077756 for T. asperellum andT. viride, respectively, was observed. There were a total of 678 and 767 for T. asperellum and T. viride positions in the final dataset, respectively. Antagonistic activity was done for the isolated strains of Trichoderma spp. against A. niger, and it was found that T. asperellum showed maximum antagonistic activity (79.33±7.09%).
INTRODUCTION
Trichoderma spp. is a kind of fungal species that assists as an impending substitute to restrain chemicals and pathogen as resistance to crop cultivars. This aggressive and competitive property of the fungus is due to their metabolic competence [1]. Specific and precise description of Fungus is necessary for accurate diagnosis of disease and their remedy of accompanying fungal contagions. Trichoderma species are maintained during the course of evolution, as they are involved in the interaction of multifarious intermolecules and intramolecules to sustain their mechanism of protein synthesis [3 -5]. Due to the antagonistic activities of Trichoderma spp., they are operative in contributing biocontrol of pathogens that are soil borne as they are simple inhabitants in the soil [6]. The chief characteristic of efficacious biological resistant strategies comprises of the manufacture, preparation, and the distribution of bioagents. It has utmost been characteristically suitable for systematic molecular investigation at the level of species, and also even within the species [7,8]. The first attempt for phylogenetic exploration of the complete genus of Trichoderma was done by means of sequence investigation of rDNAs ITS-1 region [9]. In rDNA, an individual cluster comprises of 18S rDNA, 5.8S rDNA, 28S rDNA, External Transcribed Spacer (ETS) and Internal Transcribed Spacer 1 and 2 (ITS1 and ITS 2) [5]. For the determination of phylogenetic relationships, the spacer regions and the genes, distinctly or in grouping, are used as molecular markers [10]. However, 28S rDNA and 18S rDNA (length-conservative regions) are generally applied in the determination of phylogenetic relationships amongst groups of upper levels [10 -12], although when considered at species level, regions of length variability like internal transcribed spacer 1 and 2 (ITS1 and ITS 2) are often used [13 -15]. Thus, in the present study, the identification of Trichoderma spp. at species level was determined with consideration to the regions of length variability i.e Internal Transcribed Spacer (ITS).
As a biocontrol agent, Trichoderma spp works extensively virtuous (mycoparasitism mechanisms), because of its antagonistic characteristic [16,17]. The progression ostensibly consists of Trichoderma chemotropic growth, mycoparasitic identification of the host, extra-cellular enzymes secretion, hyphae permeations, and finally, host lysis [18]. Mycoparasitism encompasses outbreak of one fungal species, directly on the other [19]. This complicated progression comprises of chronological proceedings, which comprises of identification of other fungal strain by Trichoderma spp, followed by an outbreak of cellular machinery of the host, thereafter its permeations inside the fungal host and lastly carnage the host [20]. Trichoderma spp. grows to the concerning fungal host by identifying them. Such offensive remote sensing property of Trichoderma spp. Is, to some extent, due to the consecutive formation of pathogenesis linked proteins typically chitinase and glucanase proteases [21]. Continuous secretion of exo-chitinases by Trichoderma spp. damages cell-walls of the fungal host, thereby discharging oligomers, which play a crucial part in the inhibition of fungal host [17]. Trichoderma spp. is associated with the host pathogen, loops around it and forms appressoria, thereby discharging its content [22]. Therefore it results in the formation of peptides associated with pathogenesis that services in both the entrance of Trichoderma spp. hyphae as well as cell wall content digestion [23]. The degradation of the cell of host fungus due to the formation of these biologically synthesized chemicals outcomes as parasitism. Thus, the identification of such a valuable non-pathogenic fungus would help us in commercialization and ultimately developing a superior tactic to eliminate pathogens that are soil-borne.
Culture Preparation
The soil sample was collected from sugarcane cultivation field (from rhizospheric plane) located at IIM Road Lucknow, Uttar Pradesh, for isolation of Trichoderma spp. (isolation and purification processes was done by serial dilution and subculturing technique). Morphologically, microscopically, and biochemically identified strain was cultured and maintained on potato dextrose broth (PDB; Hi Media) and potato dextrose agar (PDA; Hi Media) plate [24]. The isolates were assigned as ZTa and ZTv for T. asperellum and T. viride, respectively. Freshly cultured pure colony was further used for DNA isolation.
Primers
The primers obtained from Bio kart India Pvt. Ltd. India, were used for Trichoderma spp. viz. ZTa and ZTv for PCR amplification were ITS 1 (´TCCGTAGGTGAACCTTGCGG) and ITS 4 (´TCCTCCGCTTATTGATATGC) with an annealing temperature of 61°C and 53°C, respectively. These primers bind to conserved regions, with corresponding positions. The PCR product was amplified that encompasses a portion of the Internal Transcribed Spacer (ITS) region. The size of the PCR product generated was varying and according to the organism tested. The same primers were used for ZTa and ZTv for direct sequencing with the same annealing temperature of 61°C and 53°C, respectively.
DNA Isolation and Sequencing
The genomic DNA of primarily identified fungal isolates was extracted and amplified using PCR, followed by the sequencing analysis, using its ITS1 region of the rRNA gene. Primarily, for genomic DNA extractions, approximately 100 mg of mycelial powder was used; mycelial powder was obtained by grinding a small portion of the fungal culture in liquid nitrogen using a mortar and pestle. Fungal genomic DNA was isolated using a modified Phenol: Chloroform method [25]. Amplification of ITS genes was carried out with the ITS 1 (TCCGTAGGTGAACCTTGCGG) and ITS 4 (TCCTCCGCTTATTGATATGC) universal primer pair, which produced ~500 bp amplicon products. PCR Master Mix (Promega ™) was used to amplify the ~500 bps region of the ITS region. A negative control (PCR mix without template DNA) was also performed in all PCR experiments. The PCR reaction conditions were set for 95°C for 2 min (1 cycle), followed by 35 cycles of denaturation at 95°C for 30s, annealing at 52°C for 30 min and extension at 72°C for 2 min, before a final extension at 72°C for 15 min (1 cycle). PCR products were subjected to purification using Omega™ PCR Purification Kit, by following the protocol of Liu et al. [26]. Purified PCR products were then subjected to an ethidium bromide-stained 1% agarose gel (Fisher Scientific) along with a 1 kb DNA ladder (Promega) to estimate the size of the amplified band. The purified PCR products were subjected to sequencing [27].
Gene Bank and BLAST
The ITS rRNA gene sequences were subjected to the BLASTn search program (National Center for Biotechnology Information) to find a similarity index between sequences. Sequences are edited using MEGA (Molecular Evolutionary Genetics Analysis) software [28]. Each sequence was subjected to an individual BLAST search to be verified in Gene Bank. The newly obtained sequences were aligned with highly similar, homologous sequences from Gene Bank using the multiple sequence alignment program MUSCLE, with default parameters. The BLASTn similarity search program was used to find homologous sequences against the NCBI nucleotide database that confirmed the species level similarity with the query sequence of the isolates.
The percentage of replicate trees, in which the associated taxa clustered together in the bootstrap test (500 replicates), were shown next to the branches [29]. This analysis involved 12 nucleotide sequences. All ambiguous positions were removed for each sequence pair (pairwise deletion option). The phylogenetic tree was drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the maximum composite likelihood method [30,31] and were in the units of the number of base substitutions per site. Evolutionary analyses were conducted using MEGA X [28].
Biocontrol Activity
In vitro antagonastic activity was done according to the protocol of Dennis and Webster [32]. As per the protocol, for seven days, the fungal isolates were cultured on PDA media in petri plates. The 5mm diameter disks of growing colonies of Trichoderma mycelium and Aspergillus niger were cut and transferred on other PDA media petri plates, 7 cm apart from each other. The plates were incubated for five days at 28± 1°C. A standard strain of Trichoderma spp. (MTCC167), obtained from Department of Biosciences, Integral University, was also taken and similar treatment was done. The percent inhibition of growth was determined by the method of Watanabe [33]. The experiment was done in triplicate (n=3).
Statistical Analysis
All the experiments were performed in triplicate and calculated as mean ± SD. Two-way ANOVA was performed using MS Excel (2016).
Identification of Fungal Strains Using its Sequencing Technique
Morphologically, microscopically and biochemically identified strain, as reported by Haque et al. [24], were used for further identification and confirmation, using ITS sequencing molecular technique. The isolated fungal strains were identified using sequencing analysis in the present study. However, primarily, the strains were identified on the basis of morphological, microscopic, and biochemical characterization. The isolates were visually characterized on the basis of phenotypic characters like colony color (dark green and cottony whitish green colonies), growth pattern, shape and size of conidiophore, phialides and conidia were observed microscopically. The morphologically and microscopically analyzed isolates were assigned as T. asperellum (ZTa) and T. viride (ZTv), respectively. Therefore, the present sequence analysis was performed for fungal identification using its Internal Transcribed Spacer (ITS) region. The Internal Transcribed Spacer (ITS) was referred to the spacer DNA situated between the small-subunit ribosomal RNA (rRNA) and large-subunit rRNA genes in the chromosome or the corresponding transcribed region in the polycistronic rRNA precursor transcript. Using the ITS1 amplified products for all isolated fungi, a bi-directional DNA sequencing was completed with high quality (HQ) bases (>98% HQ-100% HQ). Analysis of the generated ITS1 nucleotide sequences confirmed speciesidentification of the fungal isolates. All of the ITS1 nucleotide sequences, obtained in this study, matched 97% -100% with the published sequence of Trichoderma spp. with gene bank accession number of T. asperellum (ZTa), MK937669 and that of T.viride (ZTv), MK503705. No intra-specific genetic variation was noticed among the fungi. The results confirmed the Trichoderma spp. strains as T. asperellum and T. viride.
When phylogenetic analysis was done for the isolates of T. asperellum and T.viride, the following results were obtained using the Neighbor-Joining method [34]. The optimal tree with the sum of branch length = 0.69585023 and 0.10077756 for T. asperellum and T. viride, respectively, was observed. There was a total of 678 and 767 positions for T. asperellum and T. viride, respectively, in the final dataset. The phylogenetic analysis and the sequence obtained is given in Fig. (1A and B) for T. asperellum and T. viride, respectively.
Biocontrol Activity
When the antagonistic activity was tested for the isolated strains of Trichoderma spp. against A. niger, it was found that T. asperellum showed maximum antagonistic activity (79.33±7.09%) while minimum antagonistic activity was shown by the standard strain of Trichoderma spp. MTCC167 (54.33±9.50%). However, when Two-way ANOVA was applied between the three Trichoderma spp. strains, significant variation was observed with p value < 0.05 (0.024) and the effect of days on the growth were also found to be significant (p value < 0.05) (4.75E-06). The results are shown in Fig. (2).
DISCUSSION
The specie concept of micro-organisms (prokaryotic and eukaryotic) is well defined by Rossello´-Mora and Kampfer [35], where it was described that micro-organisms might be capable of extensive diversity range amongst the various other living organisms and can be determined by the phylogenetic analysis which shows ancestry with a common pattern. Moreover, there are various techniques for the identification of pathogenic micro-organisms for the recognition of pathogens in food crops, water, etc [36]. However, in a study by Bajinka and Secka;, it was mentioned that molecular techniques were found to be the best for identification of micro-organisms, which confers precision and accuracy. The molecular methods like gene sequencing using 16S ribosomal RNA for fungal ITS sequence can be applied for the identification of organisms [40 -42]. As per the previously reported findings, in the present study, we have applied the ITS sequencing technique to identify Trichoderma spp. isolates. The identification of fungus at species level by the use of ITS region (as a genetic marker) of the gene was well established [43 -45]. In the past few decades, the ITS1 region sequencing technique has been extensively applied for genotyping of fungal strains, which are pathogenic to humans causing various health ailments like cutaneous infection, meningitis, allergies, and respiratory illness, etc [46]. The ITS1 region sequencing technique recognized the pathogenic human yeast and 40 species out of 106 strains have been tested [47]. It was also investigated that 44 human-pathogenic mold species were identified in 201 strains using the ITS region sequencing technique [48].
It was further reported that ITS1 region sequencing technique was used to identify and distinguish five species of Rhizopus, which can cause meningitis in human beings [49]. Infection due to Rhizopus causing intestinal mucormycosis was investigated efficaciously using the ITS region sequencing technique [50]. Since the ITS region sequencing technique is promptly being envisioned as a barcodes, suggestions have been put forward to construct a distinct set of public reference data for ITS sequences of various fungal species [51]. Efforts have been made to form a guiding principle for the genuineness and consistency of newly produced sequences of fungal ITS [52]. The query sequence of isolate T. asperellum (MK937669) showed 97.16% identity with T. asperellum (MH013956), and isolate T. viride (MK503705) showed 99.82% with T. viride of (JF304319). The findings clearly indicate that the isolated strains were confirmed as Trichoderma spp. Fig. 1 contd..... Fig. (1A and B). Sequence and phylogenetic tree of (A)T. asperellum; ZTa and (B)T. viride; ZTv.
Region Sequencing Technique and its Use as a Biocontrol Agent
The Open Biotechnology Journal, 2020, Volume 14 75 Fig. (2). Antagonistic activity of Trichoderma spp. against A. niger (n=3).
The antagonistic activity of Trichoderma spp. showed ZTa (MK937669), the better strain against A. niger. The results are in good agreement with the report of Gajera and Vakharia [53] where twelve isolates of Trichoderma spp. showed antagonistic activity against A. niger. In another study by Kurt et al. [54], Trichoderma spp. strain SJ3-4, which expresses the A. niger glucose oxidase-encoding gene, goxA, under a homologous chitinase (Nag1) promoter, increased the competence as a biocontrol agent. The mechanism of Trichoderma spp. as a biocontrol agent is well established. The synthesis of siderophores by Trichoderma spp. makes it an effective biocontrol fungus. It was elucidated that due to the synthesis of iron-chelating siderophores by Trichoderma spp., to survive against micronutrient insufficiency, makes itas an effective biocontrol agent. It has been further reported that the development of iron-chelating siderophores by Trichoderma spp. is due to the presence of other pathogenic fungi [55,56]. Struggle for space by the pathogen from Trichoderma spp. as a biocontrol agent causes hindered root colonization, resulting in feeble establishment and poor disease cause. Thus, the observations of the present study showed that the isolated Trichoderma spp. strains are of virtuous biocontrol agents and method (ITS sequencing) [57] used for its genetic identification confirmed its occurrence.
CONCLUSION
Since fungi are very diverse at the specie level, there are several molecular techniques that have been developed for the identification of fungal species. DNA Barcoding technique is applied to a small and standardized DNA region with unique pattern and is considered as one of the most accurate and rapid method to categorize unidentified fungal species. In DNA barcoding, Internal Transcribed Spacer (ITS) region is predominantly used as it is the most sequenced region for fungal identification at species and within the species level. The ITS region is extremely polymorphic and non-coding with adequate taxonomic parts which enables to isolate sequences at species level. Molecular based techniques have endorsed more in-depth experimentation on Trichoderm, howeverthe outcomes are limited due to the lack of information of genome sequence. With this consideration, the identification of genome ZTa (MK937669) and ZTv (MK503705) is a contribution to understand the mode of action as a biocontrol agent by the use of genetic markers, which are activity-specific. The findings prolong the genome availability for relative investigations pointing to compare phenotypic variances with Trichoderma genetic diversity. It can be concluded that this investigation delivers the base for future studies for better knowledge of complicated connections of Trichoderma with multiple objectives for the improvement of effective Trichoderma strains as biocontrol agent.
ETHICS APPROVAL AND CONSENT TO PARTI-CIPATE
Not applicable.
HUMAN AND ANIMAL RIGHTS
No animals/humans were used for studies that are the basis of this research.
CONSENT FOR PUBLICATION
Not applicable.
AVAILABILITY OF DATA AND MATERIALS
The data supporting the findings of the article is available within the article.
CONFLICT OF INTEREST
The authors declare no conflict of interest, financial or otherwise.
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Domain: Biology
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Prevalence and Antimicrobial Resistance of Campylobacter Species Isolated from Backyard Chickens in Grenada, West Indies
RS designed the study, the research wrote the of the Author HH supervised the laboratory work reviewed the manuscript. Authors KT, VMB, VAA and NW performed specimen collection, isolation of cultures, antimicrobial drug testing and data analysis. Authors SMG and SK performed PCR on cultures and review of final draft of the manuscript. ABSTRACT Aim: This study was carried out to assess the prevalence of Campylobacter spp. in free range chickens in Grenada, West Indies and to determine the antimicrobial susceptibility of isolates. Study Design: Cloacal swabs were collected from 315 free range chickens from randomly selected households from all six parishes of Grenada between June and July 2014. Cloacal swabs were cultured for Campylobacter in the Pathobiology Diagnostic Laboratory, School of Veterinary St. University Grenada. Isolates were further tested through PCR for speciation. BMRJ, Methodology: Standard culture methods for isolation of Campylobacter spp were used. Isolates were characterized by conventional phenotypic tests and confirmed by PCR using species specific primers. The 16s rRNA gene for Campylobacter spp.; the map A gene for C. jejuni and the ceuE gene for C. coli were selected for PCR. Isolates were tested through E-test for their antimicrobial susceptibility using Ampicillin, Ciprofloxacin, Chloramphenicol, Erythromycin, Gentamicin, Metronidazole and Tetracycline. Results: A total of 158 isolates (50.2%) were obtained by culture. PCR on 93 isolates identified 33 isolates as C. jejuni, 6 as C. coli and 54 as mixed infection with C. jejuni and C. coli. None of the isolates was resistant to chloramphenicol and erythromycin; susceptibility to other antimicrobials varied among isolates. Multidrug resistance was high in C. coli (33.3%), followed by mixed infection isolates (22.2%) and C. jejuni (12.0%). Conclusion: Results of the study show that approximately 50% of backyard chickens in Grenada harbor Campylobacter spp. These backyard chickens pose a great risk for humans as hazard analysis critical control point (HACCP) is not observed during the slaughter and processing of these chickens.
INTRODUCTION
Campylobacter is recognized as the most important zoonotic pathogen in both developed and developing nations of the world [1,2]. Campylobacter colonizes intestine tracts of animals and birds and is excreted in feces. Meat of animals gets contaminated by intestinal contents of infected animals during careless slaughter process. Humans get infected by handling animals and birds colonized by Campylobacter in their intestines, consumption of Campylobacter contaminated meat, or ingestion of contaminated food and water [3].
In developing countries, people rear backyard chickens for food and income [4]. Grenada is a small island in the southeastern Caribbean with 344 km 2 land size [5]. Approximately 30% of households in Grenada rear approximately 26,000 backyard chickens on the island (Dr. Bowen Louison, Chief Veterinary Officer, Ministry of Agriculture, Lands, Forestry, Fisheries and Environment, Grenada: Personal communication). The flock size in each household ranges from 5 to 40.
Information on Campylobacter infection in animals and birds in Caribbean islands is scanty. The isolation of Campylobacter from food animals, dogs and chickens has been reported from Trinidad [6] and Barbados [7], both neighboring islands of Grenada. Exhaustive research conducted in Grenada revealed the presence of Campylobacter in healthy pigs [8], sheep and goats [9], and commercial broilers and layers [10,11]. To authors' knowledge there is no published report on the isolation of Campylobacter from backyard chickens in Grenada or other Caribbean islands. In the present study, we investigated the prevalence in Grenada of Campylobacter in backyard chickens followed by antimicrobial susceptibility of the isolates.
Ethical Approval
All authors hereby declare that "Principles of laboratory animal care" (NIH publication No. 85-23, revised 1985) were followed. All aspects of the project were examined and approved by the Institutional Animal Care and Use Committee (IACUC) of the St. George's University (Approval number-IACUC-12005-R).
Birds and Sampling
Three hundred fifteen backyard chickens selected randomly from all six parishes were included in the study after obtaining verbal permission from bird owners. Cloacal swabs were collected from these birds using sterile swabs and Cary Blair transport medium (BBL Beckson and Co. Cockeysville, Maryland, USA). The cloacal swabs were transported within 4 h. on ice to Pathobiology Research laboratory, School of Veterinary Medicine, St George's University.
Culture and Identification of Campylobacter
Method described by Hariharan et al. [10] was followed for bacterial culture. The cloacal swabs were plated on Campylobacter blood free selective agar (CBF) containing charcoal, cefoperazone and amphotericin B supplement (Oxoid Ltd, Basingstoke, Hampshire, England). The plates were incubated at 42°C for 48 h. in anaerobic jars under microaerophilic conditions (5% oxygen, 10% carbon dioxide and 85% nitrogen) using campy gas generating pack (BBL Becton Dickson and Co. Cockeyville, Maryland USA). The grayish non translucent colonies grown on plates were stained with Gram's stain and examined under a microscope at 1000X magnification for typical Gram negative gullshaped morphology of Campylobacter. Positive colonies were sub-cultured on CBF for purification. Campylobacter isolates were stored in 10% skim milk at -80°C for further research [12]. Methods for culture identification and speciation were those described by Nachamkin [13]. Briefly, fresh cultures were tested for catalase and oxidase reactions (BBL Becton, Dickinson and Co. Sparks, MD, USA) and hippurate tests (Remel, Lennexa, KS, USA). Cultures were also tested for their susceptibility to nalidixic acid (30 µg disc) and cephalothin (30 µg disc) on Mueller-Hinton agar with 5% sheep blood. Hippurate positive isolates were identified as C. jejuni and hippurate negative and nalidixic acid susceptible isolates as C. coli. C. jejuni (ATCC33291) was used as control.
DNA Extraction and PCR Based Identification of Campylobacter
Genomic DNA was extracted from enriched broths of the isolates using DNeasy blood and tissue kit (Qiagen, USA) following the manufacturer's instructions. Two separate PCRs were conducted for screening of 93 isolates of Campylobacter following the methods described by Denis et al. [14]. A Multiplex PCR was performed by targeting 16S rRNA gene for Campylobacter genus and mapA gene for C. jejuni. Another individual PCR was performed by targeting ceuE gene for C. coli. The primers used in PCR for Campylobacter species and the size of amplified fragments are presented in Table 1.
PCR amplifications (both multiplex and individual) were carried out using a 25 µl reaction mixture containing 12.5 µl of the master mix, 1 µl of each primer (10 µM), and 2 µl of purified DNA (containing approx. 20 ng of DNA) and the final volume was made up to 25 µl by adding dH 2 O. The conditions for both multiplex PCR and the individual PCR were as follows: initial denaturation at 95°C for 15 min followed by 35 cycles of 1 min denaturation at 95°C, 1 min of annealing at 48°C, and 1 min of extension at 72°C, and a final 10 min extension at 72°C after the last cycle. PCR products at 10 µl were subjected to electrophoresis with 1.5% agarose gel, stained with ethidium bromide, and photographed under gel documentation system (LabNet International Inc.).
Antimicrobial Susceptibility Testing
Campylobacter isolates were tested for antimicrobial susceptibility using the Epsilometer test (E-Test) strips (AB Biodisk, Solna, Sweden) following manufacturer's instructions. Fresh bacterial cultures adjusted to MacFarland No. 1 turbidity standard using sterile distilled water were plated on Mueller-Hinton agar with 5% sheep blood. After applying E-test strip, the cultured plates were incubated for 24 h. at 42°C in microaerophilic condition using a Campy pack (Oxoid). The MIC of the drug was read directly from the scale printed on the E-Test strip at the point of intersection between the bacterial growth zone and the strip, according to manufacturer's instructions. The interpretation of MIC was based on the description of Luber et al. [15]. C. jejuni (ATCC33291) susceptible to all tested antimicrobials and given reproducible MICs was used as control. The MIC values to classify a strain as resistant were: ampicillin and chloramphenicol ≥32 µg/ml; ciprofloxacin ≥4 µg/ml; erythromycin ≥8 µg/ml; gentamicin and tetracycline ≥16 µg/ml; and breakpoint for metronidazole was set at ≥16 µg/ml as per Lorian [16].
RESULTS
A total of 315 backyard chickens were examined for Campylobacter spp. Of these, 158 (50.2%) were positive for Campylobacter. Of the 158 isolates, only 93 were obtained in a viable form. These isolates were further tested by PCR for speciation. The results are presented in Table 2.
Antimicrobial resistance determined by E-Test against seven drugs on 93 isolates of Campylobacter revealed 0% resistance to chloramphenicol (CL) and erythromycin (EM).
Results of antimicrobial resistance are presented in Table 3.
Using phenotypic criteria (hippurate test and nalidixic acid resistance), we found 70 C. jejuni, 21 C. coli and 2 non-typable isolates. When tested by gel-based PCR using genus-specific and C. coli and C. jejuni specific primers, 33 isolates were C. jejuni, 6 were C. coli and 54 were mixed infection with C. coli and C. jejuni. Although Nachamkin [14] advocates hippurate test as the most important phenotypic test to differentiate C. jejuni from C. coli, Ronner and Lindmark [30] and Ronner et al. [31] contradict the specificity of hippurate test. In the present study, hippurate test and nalidixic acid resistance tests failed to identify the specific species of the isolates.
Superiority of genotyping by PCR for species identification of Campylobacter has been proved and advocated by many authors [29,31,32]. Upon genotyping of isolates in the present study a majority (58.1%) was contaminated with mixed infection (C. jejuni and C. coli) whereas 35.5% and 6.5% cultures were pure C. jejuni and C. coli, respectively. Similar to our results of mixed infection, Rivoal et al. [18] reported a high percentage of mixed C. jejuni and C. coli infection in free range broiler farms in France. In a separate study in Belgium, Sabrina et al. [33] found overall mixed infection of 40.6% while C. jejuni was 46.9% and C. coli was 12.5% in free range broiler chickens. Reason for mixed infection especially in free range birds may be because of exposure to multiple sources of contamination [19,33]. As far as authors are aware, report on isolation of Campylobacters in free range chickens are scanty.
The microbial susceptibility test showed zero resistance for erythromycin and chloramphenicol. Erythromycin is the drug of choice for treatment of human campylobacteriosis, but our results show zero or minimum resistance to this drug as has been observed by others [10,34,35,36]. Another drug of choice for treatment of human campylobacteriosis is ciprofloxacin. Moderate resistance to ciprofloxacin was observed in our study as has been reported by previous researchers [10,34,35]. The higher rate of resistance to ciprofloxacin has been correlated with use of fluoroquinolones in other countries. In our study lower resistance was observed for tetracycline and ampicillin, which can be used for Campylobacter infections [35]. The resistance rate for tetracycline varied in different studies; Hariharan et al. [10] found 33.3% resistance for tetracycline against C. jejuni in commercial broilers. In a study in Canada, Guevremont et al. [37] reported 66% resistance for tetracycline in C. jejuni isolates from broilers. Absence of resistance for tetracycline in C. jejuni isolates in our study was similar to the observation of Frediani and Stephan [38].
Compared to other studies, in the present study, higher percentage of isolates showed resistance to metronidazole. Previous studies have reported variable resistance pattern for metronidazole. For example, Hariharan et al. [10] found 9.5% of the isolates resistant to metronidazole in Grenada while in a separate study in the same country, Rohini et al. [11] reported 34% resistance for metronidazole. Further, these authors [10,11] could not correlate the farm use of this drug to high percentage of resistance. As far as we are aware, there is paucity of literature on antimicrobial resistance pattern of Campylobacter isolates from backyard chickens and also on mixed infection with Campylobacter species. Although sensitivity tests should be done on pure cultures, our results on mixed culture are also presented to ensure a complete picture on what we found. A majority of reports show a correlation between microbial resistance in Campylobacter species and the use of drugs on commercial chicken flocks. Luangtongkum et al. [39] reported multiple drug resistance on chicken and turkey farms where antimicrobials were routinely used. Since the backyard chickens hardly get any medication, exact comparison of the susceptibility pattern with isolates of commercial chickens is not appropriate. A wider study on the antimicrobial pattern of the Campylobacter isolates from free range chickens is warranted.
Infection of humans with Campylobactercontaminated meat and meat products has been well documented. Poultry is the major reservoir of thermophilic campylobacters. If the slaughter, handling and packing of meat from commercial poultry are done at modern slaughter facilities, hazard analysis critical control point (HACCP) can be well applied; HACCP has been found effective in reducing the meat contamination of poultry meat. The backyard chickens are usually slaughtered at home without implementing HACCP. This poses a risk to humans mainly through handling of contaminated meat of backyard chickens.
In conclusion, 50% of backyard chickens in Grenada were found positive for Campylobacter species. Persons keeping backyard chickens need be educated to observe HACCP while slaughtering and cooking of the meat from their flocks.
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Domain: Biology
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Biofortification of Common Bean for Higher Iron Concentration
Common bean (Phaseolus vulgaris L.) is a staple food of smallholder farmers and poor urban consumers in Latin America and eastern-southern Africa among whom iron deficiency is frequent. Bean was domesticated in Mexico and the southern Andes, creating two distinct gene pools. Evaluation of a core collection of 1,441 entries revealed average concentrations of 55 mg kg−1 iron. A breeding target was set at 44 mg kg−1 iron above the level in a local check variety, while 50% of goal or a 22 mg kg−1 advantage was accepted as “biofortified.” In a bioefficacy trial among college-age women in Rwanda, high iron beans improved iron status and enhanced cognitive ability, brain function, and work efficiency. However, breeding progress has been slow, likely due in part to homeostatic mechanisms whereby organisms moderate iron and zinc uptake. This phenomenon may represent resistance to increasing concentration of these elements. Crosses between gene pools may “jumble” genes for homeostasis and permit high levels. A second breeding strategy is the use of sister species that evolved in iron-poor environments and that could be more receptive to iron uptake. Future breeding may also increase attention on improving bioavailability through mechanisms such as non-or-slow darkening grain or low phytate mutants. Changing dietary patterns in developed countries could increase iron deficiency and create demand for iron biofortified beans.
INTRODUCTION
Common bean (Phaseolus vulgaris L.) was domesticated in Mexico and in the southern Andes resulting in two major gene pools, the Mesoamerican and the Andean (Kwak and Gepts, 2009), and with possible incipient domestication in the northern Andes (Islam et al., 2001). Production and consumption of bean are greatest in Latin America, the Caribbean and in East and southern Africa, with specific preferred grain types that condition acceptance in each country or region. Many farms are <10 hectares, and some holdings in Africa are less than a hectare. In such situations climbing beans are an important alternative, producing as much as three times the yield per area compared to bush growth habits (Sperling and Muyaneza, 1995). Where land shortages are less acute, most farmers prefer bush beans which require less labor inputs. The smallholder mode of production is often associated with low productivity and acute levels of poverty with accompanying problems of both macronutrient and micronutrient deficiencies.
Focused on micronutrients, HarvestPlus adopted genetic enhancement of iron, zinc, and vitamin A as priorities. Beans emerged as an option for enhancing iron and zinc concentrations. In 1994 the common bean participated in an exploratory initiative to determine the feasibility of breeding for higher micronutrients. After two short term phases to explore genetic variability, determine the stability of the trait over environments, and confirm the possibility of transferring the trait through breeding, HarvestPlus (a.k.a. the Biofortification Challenge Program) was launched, later to be incorporated into the Agriculture for Nutrition and Health (A4NH) CGIAR Research Program, with high priority for beans in Africa Mulambu et al., 2017). After a quarter of a century of experience in biofortification of beans, it is timely to reflect on progress, obstacles, learnings, and the way forward.
BIOFORTIFICATION AND PUBLIC HEALTH
Biofortification was originally conceived as addressing populations with limited access to health services or industrially fortified foods in rural areas, but it is now evident that urban populations also need improved diets. An estimated 1.5-2 billion individuals suffer from iron deficiency (Lynch, 2011). The priority target populations for improved iron nutrition are children, and women in the fertile age. Children in particular cannot consume sufficient volumes of staples to satisfy their requirements, and need nutrient dense foods. While poverty is an important determinant of anemia (Balarajan et al., 2011), data from the World Bank (2020) suggest that economic development does not necessarily assure low levels of anemia. Costa Rica has very low poverty, but its level of anemia in children (28.7%) is comparable to that of Honduras (31.4%), one of the poorest countries in the hemisphere. Rwanda has almost three times as much poverty as South Africa (55 vs. 18.9%) but the two countries have almost identical prevalence of anemia in children (36.2 vs. 36.8%). Even in the United States anemia has risen over a 10-year period from 4 to 7%, with levels three times higher among ethnic minorities (Hong Le, 2016). These observations suggest that anemia is not readily addressed by economic development alone. Quality of diet must be addressed consciously to assure adequate intake of bioavailable nutrients.
BIOEFFICACY OF BIOFORTIFIED BEANS
Bioefficacy trials evaluate the value of foods in supplying nutrients to living beings. Trials with biofortified beans in rats (Welch et al., 2000), pigs (Tako et al., 2009), and chicks (Tako et al., 2014) gave positive results, leading to the establishment of trials with humans. A study with high iron beans (HIB) and normal beans was carried out with young women in Rwanda, most of whom were iron deficient or anemic. After four and a half months the high iron bean group showed a statistically greater increase in hemoglobin (3.8 g/L), log serum ferritin (0.1 log µgr/L) and total body iron (0.5 mg kg −1 ) (Haas et al., 2016), superior cognitive ability (Murray-Kolb et al., 2017), increased neuron activity (Wenger et al., 2019), and superior work capacity (Luna et al., 2020). In a trial involving Mexican school children age 6-10, the effect of high iron beans on transferrin receptor was narrowly not significant (p = 0.054) (Finkelstein et al., 2019). However, among the 25% of children that were most deficient, high iron beans resulted in lower transferrin receptor and a significant reduction in deficiency (Haas et al., 2011). Additional studies among other populations with different dietary patterns and combinations of foods would be preferable.
BREEDING FOR HIGHER IRON
Although increased concentrations of both iron and zinc are breeding objectives, most effort has been devoted to iron which has responded more rapidly to selection. As the first step in developing a breeding program, a core collection was evaluated to assess the genetic diversity of iron and zinc concentrations (Tohme et al., 1995). Among 1,441 entries evaluated by Inductively Coupled Plasma Spectrometry (ICP), samples presented an average of 55 mg kg −1 iron, and 28 mg kg −1 zinc, with extremes of 102 mg kg −1 and 54 mg kg −1 , respectively, while G10022 was identified within a core collection of wild Phaseolus vulgaris . In the same period a core collection with 150 accessions of sister species P. coccineus and P. dumosus was created. One P. dumosus accession, G35575 presented iron levels well above 100 mg kg −1 , and was incorporated into the breeding program.
Consultations with nutritionists established a breeding goal level of 44 mg kg −1 iron above the value of a standard local variety (assumed to be ∼50 mg kg −1 ) to achieve 30% of average daily iron requirement, assuming 7% bioavailability, 90% retention after cooking, and a high level of consumption of 200 grams per day for adults and 100 grams per day for children . Since breeding goals are not met in the short term, an intermediate goal of 50% or 22 mg kg −1 over a local check variety was accepted as "biofortified." In a QTL study of mineral concentration, Blair et al. (2009) found four linkage groups associated with iron and four with zinc, with a QTL cluster on B11 (now Pv11). Later, a meta-QTL analysis summarized results of seven different populations, identifying 12 meta-QTL on eight chromosomes, of which eight meta-QTL were associated with both iron and zinc (Izquierdo et al., 2018). A diallel study of six parental materials revealed both additive and non-additive inheritance, with narrow sense heritabilities of 71% for iron, 83% for zinc, and a correlation of r = 0.75 between the two elements (Mukamuhirwa et al., 2015).
To be adopted by farmers, HIB must perform agronomically as well as standard varieties, but combining multiple quantitative traits of high iron and acceptable yield in grain types of the desired color, size, and shape was a challenge. High iron was associated with poor yield potential, and with a reduced seed sink (fewer pods, fewer seeds per pod, or poorly filled seed), leading to concentration of iron in a smaller seed mass. Once these limitations were recognized, selection focused on lines with well filled pods and high iron. While iron levels may vary over environments, ranking of genotypes is normally very similar.
High iron varieties have been released in at least 10 countries in Africa and Latin America ( Table 1). High iron was first attained in climbing beans that were released as varieties in Rwanda, Uganda and the Democratic Republic of Congo, and later Colombia. These are represented by lines such as NUV 119 and BIO 102. In the Mesoamerican gene pool, breeding objectives have sought to combine high iron with tolerance to drought
BIOLOGY OF IRON IN BEANS
Reflections on the biology of iron in plants may reveal characteristics of mechanisms for higher levels. Iron is a heavy metal and can be toxic in high concentrations. Therefore, its uptake is regulated by mechanisms of homeostasis that maintain its concentration within biologically acceptable limits (Morrissey and Guerinot, 2009). Iron homeostasis has been studied intensively in plants (Connorton et al., 2017) and comparable mechanisms exist for zinc (Sinclair and Krämer, 2012). Homeostasis may have important implications for biofortification if efforts to increase concentration in grains confront resistance to substantial changes from homeostasis. A possible case of homeostasis limiting iron uptake was observed in the bush forage legume Cratylia argentea that evolved in an acid, iron-rich soil in Brazil (Argel and Lascano, 1998), and that appears to protect itself against excess iron. When cultivated in an alkaline, iron-poor soil in CIAT, Colombia it suffers iron chlorosis, supposedly due to self-imposed restriction of iron uptake acquired through evolution. On the other hand, disruption of homeostasis in a mutant of garden pea (Pisum sativum) led to toxicity through excess iron absorption (Kneen et al., 1990). These examples illustrate homeostasis, both in its tight regulation in Cratylia, or its disruption in Pisum. To successfully increase concentration of minerals in grain, it will be necessary to modify homeostatic mechanisms (though much more modestly than in the example in Pisum).
Such disruption of homeostasis may explain the results with several high iron gene bank accessions in the core collection cited above . DNA analysis demonstrated that accessions G21242, G23818, and G23823 that were employed as sources in the breeding program were the result of intergene pool hybridizations (Islam et al., 2004). Furthermore, QTL analysis of a Mesoamerican-Andean inter-gene pool cross revealed different alleles for seed iron concentration and for iron reductase which is a key mechanism for iron acquisition (Blair et al., 2010). If the two gene pools have evolved some distinct genes for homeostatic mechanisms, hybridization and segregation could have rearranged these genes and disrupted homeostasis, creating genetic variability and the potential for genetic improvement.
On the other hand, the experience cited with Cratylia suggests that the species evolution is a factor in determining receptiveness to iron uptake. Just as evolution in an iron-rich acid soil may lead to reduced receptivity to iron, evolution in an iron-poor alkaline soil might lead to homeostatic mechanisms with greater receptivity to iron. For example, P. acutifolius and its sister species P. parvifolius evolved in alkaline soils (Freytag and Debouck, 2002) and were crossed to common bean (Barrera et al., 2018). Interspecific progeny and checks were evaluated over seasons in CIAT's experiment station in Cali, Colombia (average annual temperature 26 • C; mollisol soil; pH 7.8) with standard agronomic management of pest and disease controls. Mineral concentrations were evaluated with X-ray Flourescence (XRF) technology (Guild et al., 2017). Lines presented more than 15 mg kg −1 iron above the high mineral check, and 10 mg kg −1 zinc higher than the check ( Table 2). This illustrates how an appreciation of the evolution of a species over millennia can reveal its potential to contribute useful alleles. Examining the pedigrees of biofortified bean varieties reveals the diversity of sources employed for high iron genes ( Table 1). Common bean landraces combining Andean and Mesoamerican genes have been the backbone of the program, while wild bean G10022 and P. dumosus accession G35575 also appear in many pedigrees. "ICTA Superchiva" from Guatemala deserves special attention, since its only source of high iron is P. parvifolius. This tends to validate the hypothesis that species that evolved in dry alkaline soil environments can contribute high iron genes. Furthermore, the P. parvifolius accession in "Superchiva" is not the same accession that is in the pedigree of the high iron lines in Table 2, again suggesting that high iron is a trait associated with the species.
FRONTIERS IN BIOAVAILABILITY
Enhancement of iron bioavailability could broaden the scope for impact from biofortified beans to regions of intermediate levels of consumption, but to date no varieties have been created with improved bioavailability per se. Breeders need simple and rapid phenotyping methods to adapt selection to broad based varietal development programs. Recent findings offer hope for such selection tools for bioavailability based on traits of multiple utility.
One such promising trait is the slow darkening (SD) characteristic of the seed coat, and its related trait, seed nondarkening (ND). A comparison of SD and normal pinto beans suggested as much as a 4-fold increase in iron bioavailability with slow darkening . SD also improves market value because consumers associate darkened grain with grain aging, seed hardness and slow cooking. SD is controlled by a single recessive gene sd, while ND results from the recessive j gene (Elsadr et al., 2011). An SSR marker is closely associated with the SD allele (Felicetti et al., 2012). Seed darkening can be phenotyped by observation over time, or by exposing grain to sunlight or to ultra-violet light for a few hours. It can also be selected through marker assisted selection (Felicetti et al., 2012). Thus, simple systems exist for selection of the SD or ND traits that will simultaneously improve bioavailability. Some yellow beans also present high iron bioavailability in the Caco2 test (Wiesinger et al., 2018), and in an in vivo chick model . Yellow beans accumulate kaempferol 3-glucoside in their seed coats which was identified as an uptake promoter.
Shorter cooking time is highly desirable and has also been associated with superior iron bioavailability. A study of 12 cultivars revealed a negative correlation between longer cooking time and iron bioavailability in the Caco2 test (r = −0.537) (Wiesinger et al., 2016). Another approach to improve bioavailability is to remove phytate and so to eliminate one of the major anti-nutrients in the grain. Campion et al. (2009) reported a promising mutant of common bean that presented a 90% reduction in phytate in grain. The low phytate method was approached with caution and is still under evaluation as a breeding strategy. In other crops low phytate has carried a yield penalty but in beans this has not been detected in environments where it has been tested (unpublished data).
BIOFORTIFICATION FOR ZINC IN BEANS
Although zinc was adopted as a breeding objective for beans, and Donangelo et al. (2003) demonstrated a positive effect of high zinc beans in a human trial using zinc isotopes, response to selection has been slower than that for iron. The goal level for zinc biofortification is 17 mg kg −1 above local materials, such that 50% of goal or 8.5 mg kg −1 could be considered as biofortified. Data presented in Table 2 are representative of experience until recently. High mineral check SMC 33 presents 32 mg kg −1 , while standard black seeded variety DOR 500 presents 24 mg kg −1 , such that the best materials can just scarcely be considered biofortified.
Two recent developments may raise attention to biofortification for zinc in beans. First, nutritionists recently highlighted zinc as even more important than iron in East Africa (IFNA, 2018). Secondly, recent data mentioned above and cited in Table 2 with interspecific crosses with Phaseolus parvifolius offer promise of a more significant gain in zinc concentration. These results suggest that wider attention to zinc is merited.
DISCUSSION
While bioefficacy results are positive, they also present a conundrum. Bioefficacy trials have been executed in populations with high levels of consumption, but many other populations consume beans at a lower level and are also iron deficient. Novel breeding strategies are needed to create bean varieties that impact significantly on iron nutrition with more moderate levels of consumption.
In retrospect, more attention to the biology of iron in bean could have accelerated genetic gain in iron levels, through an appreciation of homeostatic mechanisms and their evolution, and earlier understanding of the factors affecting iron concentration. On the other hand, knowledge of bioavailability factors bodes well for future progress. Several traits offer options to address bioavailability more systematically in breeding programs, either through phenotyping or through marker assisted selection. The emerging nutritional benefits of these traits present a win-win scenario. As broader understanding leads to higher levels and/or better absorption of bean iron, more consumers with intermediate levels of consumption may benefit from biofortified beans.
Accumulated knowledge on the genetics of high iron can also speed progress. Meta-QTL analysis showing oligogenic control could be exploited through conventional marker assisted selection, or genomic selection might be employed. Gene editing could block the production of antinutrient fractions. Genome sequencing would reveal reliable molecular markers for known genes such as the recessive j gene. A more speculative approach could be to activate genes for leghemoglobin in the seed. Heme iron is highly bioavailable and legumes express genes for hemoglobin in nodules. Is leghemoglobin iron bioavailable? If so, can leghemoglobin genes be expressed in seed through genetic engineering? Such options highlight a need for a better knowledge of basic genetics.
The original focus of HarvestPlus has been poor populations in Africa and in Latin America with limited access to health services and industrially fortified foods. In the future other tendencies may create new demand for biofortified crops.
Currently the ecological movement highlights the need for reduced meat consumption. Will a move toward plant protein and reduced meat consumption create a dietary iron gap and make HIB more relevant? Another tendency is toward low carbohydrate diets and less processed foods. Will low carb, more "natural" diets reduce consumption of industrially fortified wheat flour and increase iron deficiency? Addressing such possible needs could require informing the public about the advantages of HIB.
Crop plant breeders work on a time horizon of 8-10 years from the making of crosses, to the point of seeing populations consuming their products widely. On this time scale, one can still expect biofortified beans to have a significant role for the rural and urban poor in the 2030's. On the medium to long term, bean breeders will want to consider if other populations will be an audience for their products, and what form those products may take.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.
AUTHOR CONTRIBUTIONS
The author confirms being the sole contributor of this work and has approved it for publication.
FUNDING
Financial support from DANIDA, the Bill and Melinda Gates Foundation and the Department for International Development (UK) through HarvestPlus, the United States Agency for International Development, and the Canadian International Development Agency is gratefully acknowledged.\===
Domain: Biology. The above document has 2 sentences that start with 'High iron was', 2 sentences that start with 'On the other hand', 2 sentences that end with 'to iron uptake', 2 sentences that end with 'et al., 2017)', 2 sentences that end with 'et al., 2011)', 2 sentences that end with 'sister species P', 2 sentences that end with 'et al., 2018)', 2 sentences that end with '( Table 1)', 2 sentences that end with '(Felicetti et al., 2012)', 2 paragraphs that start with 'Common bean (Phaseolus vulgaris L.)'. It has approximately 3226 words, 151 sentences, and 38 paragraph(s).
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A Predator ’ s Perspective of the Accuracy of AntMimicry in Spiders
Among spiders, resemblance of ants (myrmecomorphy) usually involves the Batesian mimicry, in which the spider coopts the morphological and behavioural characteristics of ants to deceive ant-averse predators. Nevertheless, the degree of resemblance between mimics and ants varies considerably. I used Portia fimbriata, a jumping spider (Salticidae) with exceptional eyesight that specialises on preying on salticids, to test predator perception of the accuracy of ant mimicry. Portia fimbriata’s response to ants (Oecophylla smaragdina), accurate ant-like salticids (Synageles occidentalis), and inaccurate ant-like salticids (females of Myrmarachne bakeri and sexually dimorphic males of M. bakeri, which have enlarged chelicerae) was assessed. Portia fimbriata exhibited graded aversion in accordance with the accuracy of resemblance to ants (O. smaragdina > S. occidentalis > female M. bakeri > male M. bakeri). These results support the hypothesis that ant resemblance confers protection from visual predators, but to varying degrees depending on signal accuracy.
Introduction
Predator avoidance of dangerous prey is often exploited by deceptive prey species; the Batesian mimics are those that deceitfully advertise to potential predators that they also can induce the negative repercussions associated with this prey [1,2], which often use warning (aposematic) signals to indicate their defences to would-be predators. The Batesian mimicry works solely to the advantage of the sender of the counterfeit signal, as both the receiver and the model are exploited. The receiver is cheated out of a source of food, and the model is less likely to benefit from its cues. The negative effect on models is due to frequency-dependent selection: if mimics exist in large numbers, the predators may take longer to learn an aversion or the potential for evolving innate fear of dangerous prey is lessened. Although studies of the Batesian mimicry have usually emphasised learning as a mechanism for the evolution of mimicry (e.g., [3]), both innate and learned fear of dangerous or distasteful prey can favour the evolution of the Batesian mimicry, as is clear from studies using naïve jumping spiders (Salticidae) as potential predators (e.g., [4]).
While we traditionally think of dangerous prey as one using bright, contrasting colours as aposematic signals, as in the case of poison dart frogs [3], not all dangerous species that are mimicked use aposematic signals. Correspondingly, deceitful use of aposematic signals appears to be an evolutionary strategy used by some Batesian mimics, but not others. Many spiders are the Batesian mimics of ants [5], animals which do not intuitively fit into the category of aposematic. Having a slender body, narrow waist, and an erratic style of locomotion, ants have a distinctive appearance, but this is unlikely to have evolved as an antipredator defence signal. Ants are, nevertheless, potentially harmful to predators through their ability to bite, sting, or spray formic acid. Being social, ants are all the more dangerous because they can mount communal attacks on potential predators [6]. Predators often respond to ant-like appearance as a cue for avoidance [4], and to disqualify ant mimicry as examples of the Batesian mimicry on the basis of hypotheses about the evolutionary origin of the ant's appearance places undue emphasis on a distinction that is irrelevant to the predator. In fact, ants appear to be particularly suitable as models for mimicry, especially among spiders. Illustrating how Psyche predation plays an important role in evolutionary diversification, ant mimicry (myrmecomorphy) has evolved in at least 43 spider genera within 13 families [5].
The 300 or so species of described myrmecomorphic spiders are typically characterised by a thin, elongated body, the creation of an antennal "illusion" by waving the forelegs, and an erratic style of locomotion [5,[7][8][9]. The vast majority of these species are Batesian mimics that are avoided by antaverse arthropod predators [9][10][11][12][13][14], although the response of vertebrates is largely unknown. A few rough numbers may best express the efficacy of this deceptive signal. With over 5,300 described species, the Salticidae is the largest family of spiders [15]. The most speciose genus within the Salticidae, Myrmarachne, has over 200 described species-all of them ant mimics.
Theoretically the Batesian mimics are under selective pressure to closely resemble their models while the models are under pressure to distance themselves from the deceitful signalling of the mimics, so there should be an arms race in which mimics are expected to converge upon their models (e.g., [16]). Yet polymorphism can also be maintained in populations of the Batesian mimics [17], particularly when more than one model species is available [3]. It is especially noticeable that several species of ant mimics are polymorphic [18,19]. As judged by humans, there is also considerable range in the accuracy of ant mimicry, with some being imprecise mimics, while others are remarkably similar in appearance to their model. Additionally, species in the large salticid genus Myrmarachne are sexually dimorphic as adults [20], with males seeming to be rather poor mimics due to their greatly enlarged chelicerae. Nevertheless, previous findings have suggested that males actually resemble ants carrying something in their mandibles [21]. In other words, they appear to be the Batesian mimics of a compound model (an ant plus the object it is carrying).
The exceptionally acute visual ability of salticids [22] enables them to identify motionless lures made from dead prey [23] and also enables them to escape some interactions with predators [11], such as ants. Although Myrmarachne can distinguish conspecifics and other mimics from ants [24][25][26], current evidence suggests that non-ant-like salticids are unable to make this distinction [4,21]. The question of interest in this study is whether accuracy of ant mimicry, as judged by humans, is reflected in predator behaviour. The answer is of significance because most salticids will readily prey on each other [27], yet most salticids also appear to avoid ants [4], encounters with which are often lethal to salticids, including Myrmarachne [28,29]. Clearly, it is also pertinent to determine how nonhuman animals classify objects and to determine the differences (or not) that may be found according to very different visual systems.
Here I tested Portia fimbriata, an Australian spider-eating (araneophagic) salticid that specialises on capturing other salticids as prey [30], with Asian weaver ants (Oecophylla smaragdina). I then compared whether their response toward ant-like salticids was similar to that elicited by O. smaragdina by testing P. fimbriata with males and females of Myrmarachne bakeri from the Philippines. This species is an imprecise ant mimic [19], and males are expected to be less precise than females due to their enlarged chelicerae. Finally, I tested P. fimbriata with an unrelated, but accurate, ant-like salticid from North America, Synageles occidentalis. In this study I address two specific questions: (1) does the non-antlike salticid P. fimbriata avoid ants? (2) does P. fimbriata avoid or stalk ant-like salticids, and does this predators' behaviour differ depending on the accuracy of the mimic? smaragdina [4], there were no longer any individuals of this species in the laboratory in New Zealand when this study was done. As we were unable to procure any more, tests were carried out using another excellent mimic, S. occidentalis, instead. No test spiders had any previous experience with ants or with ant mimics.
Materials and Methods
Spiders were maintained in individual plastic cages, cleaned weekly, with a cotton roll through the bottom that dangled in a small cup of water to provide humidity. Spiders were fed twice a week with house flies (Musca domestica). Testing was done between 0800 h and 1700 h (laboratory photoperiod 12L : 12D, lights on at 0800 h). A 200 W incandescent lamp, positioned ca. 600 mm overhead, lit the apparatus; fluorescent lamps provided additional, ambient lighting. Using standard protocol for experiments on predatory behaviour, spiders were fasted between 4 to 7 days prior to testing. No individual spider was tested more than once with a given type of lure.
The testing apparatus was a wooden ramp (see Figure 1 for dimensions) raised at a 20 • angle, which was supported by a wooden pole, glued to a wooden base. The entire apparatus was painted with two coats of polyurethane and was wiped with 80% ethanol and allowed to dry for 30 min between each test to eliminate possible chemical traces from salticids in previous tests. The ramp was marked in a 5 mm grid to allow accurate distance measurements to be obtained. A thin piece of wood glued to the top end of the ramp served as a background against which the salticid saw the lure. The lure was placed 40 mm from the top end of the ramp, equidistant from both edges, and placed such that it was faced 45 • away from the pit, enabling test spiders to view cues from both the body and the head or cephalothorax of the lure. Lures were made by immobilizing an arthropod with CO 2 and placing it in 80% ethanol. One day later, I mounted the arthropod in a life-like posture on the centre Before each test, P. fimbriata was placed in a 32 mm diameter "starting pit" drilled halfway through the thickness of the ramp 200 mm from the lure. The salticid was left in the pit to acclimate for 60 s before a piece of cardboard, which was placed over the pit, was removed, allowing the salticid to exit from the pit. A white paper screen running along three sides surrounded the apparatus, leaving one side open for observations. The ramp was positioned so that the salticid moved away from the observer during tests. Tests began when P. fimbriata walked out of the pit and on to the ramp and ended when P. fimbriata either attacked the lure or walked off the top end of the ramp. If the salticid jumped off the ramp at a point below the lure or if it stayed in the pit for more than 30 min (no spiders walked under the ramp), tests were aborted. After testing for normality (D'Agostino and Pearson omnibus test), data were analysed using ANOVA in Prism v.5.
Results
There was a significant overall effect of lure type on the distance to which P. fimbriata approached the lure (F 3 = 2.794, P < 0.05), although in general P. fimbriata showed an aversion to both ants and ant mimics. P. fimbriata avoided contact with lures by circling around the lure and then continuing up the ramp. Tukey's post hoc comparisons revealed no differences between responses to O. smaragdina and S. occidentalis or female M. bakeri, but male M. bakeri were approached significantly closer than O. smaragdina (P < 0.05). Overall P. fimbriata was kept furthest away from the ant (O. smaragdina), followed by S. occidentalis, then female M. bakeri, and lastly male M. bakeri (Figure 2). There were three instances of attacks towards lures, and all of these were aimed at lures of male M. bakeri.
Discussion
Portia fimbriata was unable to correctly classify the mimics as its preferred prey, salticids [30], and instead generally responded toward the mimics as it did toward ants. These results provide additional evidence that ant mimicry in spiders functions as Batesian mimicry, even with naïve predators. However, it appears that the degree of resemblance to ants may have repercussions when faced with predators with acute eyesight, such as salticids. Synageles occidentalis is thought to mimic Lasius alienus or Myrmica americana, with which it is associated [10]. The salticids we had in the laboratory bore an extremely accurate resemblance to the former ant species. Although Myrmarachne bakeri resemble ants, they do not have a specific model to which they render a faithful portrait [19]. Portia fimbriata apparently also classified the potential prey with which it was faced in a similar manner to the way in which humans classify these animals, which is by no means a given. Males of M. bakeri were significantly less effective at deterring P. fimbriata than ants and slightly less aversive than M. bakeri females and S. occidentalis. Nevertheless, it should be noted that in these experiments prey behaviour was not taken into account. It is known, for example, that some myrmecomorphs will actively display to ant-eating salticid predators, deterring potential attack through mistaken identity [31]. While there is currently no evidence supporting the idea that accurate ant-like spiders behave more like ants than poor mimics, it is conceivable that this might have exacerbated the results of the current study.
The only striking visible difference between the male and the other stimulus animals was the male's large chelicerae. The chelicerae of sexually mature Myrmarachne males, which can increase their body size by 30-50% [27], is believed to have evolved as a sexually selected trait [32]. To our eyes, Myrmarachne males resemble ants considerably less convincingly than Myrmarachne females and juveniles, suggesting Psyche that, along with impaired feeding mechanics [32], impaired predator deterrence through inaccurate mimicry has been a cost of sexual dimorphism for male Myrmarachne. Contrary to the other potential prey, lures of male M. bakeri were occasionally attacked. Nevertheless, P. fimbriata generally avoided lures of male M. bakeri, suggesting that mimicry among males, despite possessing some cost in terms of diminished efficacy of mimicry due to their enlarged chelicerae, is still effective at deterring visually based predators. This supports the idea that the shape of the chelicerae of male Myrmarachne is in keeping with its mimicry because it looks like an ant worker carrying something in its mandibles [21], as is commonly observed in worker ants [6].
In a study using hoverfly mimics of wasps as prey and pigeons as predators, Dittrich et al. [33] found that despite some species being poor mimics, they were still protected by their mimicry, perhaps due to some constraint in the birds' visual or learning systems. Here it is apparent that imprecise mimics, although not avoided to the same degree as accurate mimics, were nevertheless aversive to naïve predators, suggesting that learning is not essential for the same effects to be seen. A mutually compatible alternative explanation is simply that very numerous and very dangerous models may produce a wider "cone of protection," thus allowing for imprecise mimicry [34] because the payoff to a predator for attacking prey with a given resemblance to a numerous and highly noxious model is limited [35]. Furthermore, polymorphic mimics that do not resemble any particular ant species especially closely may gain other advantages. For example, imprecise ant mimics may not be restricted to the geographical area or microhabitat (e.g., arboreal ants) in which a specific model species is found. Ants are notorious for both their abundance and their formidable defences [6], and it may not be surprising to find that among ant mimics there is considerable variation in form, ranging from accurate to imprecise mimicry. What is unusual is that here we have an example of a mimic resembling one of its own predators [28,29].\===
Domain: Biology. The above document has
* 2 sentences that start with 'The ramp was',
* 3 sentences that end with 'lures of male M'. It has approximately 2458 words, 131 sentences, and 18 paragraph(s).
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Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics
Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting their clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs an annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 14.1%–119.1% relative gain in predictive R2 compared to state-of-the-art methods based on GWAS summary statistics. Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic risk prediction.
for 31 traits in East Asians. X-axis represents 4 folds and Y-axis is 10 folds repeated learning. The Pvalue of two-sided Wilcoxon signed-rank test is !"#$%&%' = 0.14. Suppose the standardized traits Y 1 and Y 2 in two populations follow the linear models with random effects: where X k is a N k × M standardized genotype matrix; β k is a M -dimensional vector of genetic effect sizes; ϵ k are non-genetic effects. We assume cross-population genetic covariance is localized in small regions R 1 , . . . , R r , i.e. the joint genetic effect sizes follow the multivariate normal distribution: where h 2 1 and h 2 2 denote heritability in two populations; ρ g is the cross-population genetic covariance;Ĩ is a diagonal matrix whereĨ[i, i] = 1 if and only if i ∈ ∪ r j=1 R j ; K = r j=1 |R j |, which is the number of SNPs with correlated genetic effect. We assume non-genetic effects ϵ k are independent across two populations.
Our goal is to use scan statistic to identify small regions enriched for local genetic correlation between two populations. Therefore, we design the numerator in the scan statistic as i∈R z 1i z 2i , the inner product of z-scores in a region across two populations, which quantifies the concordant association pattern of SNP effect sizes. To normalize the effects of LD in two populations, we use Sd i∈R z 1i z 2i under the null hypothesis as the denominator of scan statistic. Under the null hypothesis that cross-population genetic correlation is zero, the joint z-scores for two populations follow the multivariate normal distribution as Since individual genotype data is hardly accessible due to privacy issues in practice, we use LD matrices (denoted by V k ) estimated from reference panels (e.g. the European and East Asian individuals from 1000 Genome Project) to approximate the sample LD matrices can be unbiasedly estimated as is the sample size of the reference panel for population k. Using this approximation, we have We use XPASS 1 to estimate heritability for two populations h 2 1 and h 2 2 . Let Σ k = Denote z kR as the sub-vector of z k indexed by R, and Σ k,RR as the sub-matrix of Σ k whose rows and columns are both indexed by R. Then for a given region R, the joint z-scores in R follows the multivariate normal distribution Following bilinear form theory, we can show that Var i∈R z 1i z 2i = Tr [Σ 1,RR Σ 2,RR ], where Tr[A] denotes the trace of matrix A. However, computing value of Tr [Σ 1,RR Σ 2,RR ] for all possible regions R is computationally expensive. Therefore, we use i∈R Σ 1,ii * Σ 2,ii to replace Tr [Σ 1,RR Σ 2,RR ]. We add tuning parameter θ to the power term of i∈R Σ 1,ii * Σ 2,ii to accommodate the approximation bias and control the penalty strength of LD effects.
We select the best tuning parameter θ from a candidate set {0.5, 0.55, 0.6, 0.65, 0.7, 0.75} such that the identified regions for that given θ achieve the highest proportion of genetic covariance. The cross-population genetic covariance for aggregated regions can be estimated using XPASS 1 .
We compute the cross-population LD matrix by taking the maximum of population-specific LD matrix in an element-wise fashion, as previously suggested 2 . We use ldetect 3 to divide the genome into 185 LD blocks (average size of 15MB) that are approximately independent in both populations based on the cross-population LD matrix. We first apply LOGODetect to identify regions with family-wise error rate cutoff of 0.05 in each LD block separately. Then we collect all the candidate regions identified across different LD blocks and control FDR level of 0.05 using the Benjamini-Hochberg procedure.
Model
Consider an additive genetic model: where β k is a M -dimensional vector of SNP effect sizes in population k, ϵ k is a vector of error terms with variance σ 2 k , to which we assign a non-informative Jeffreys prior. MVN denotes multivariate normal distribution, and I k is an identity matrix.
Consider an annotation with A category, we assign an annotation-dependent horseshoe prior to β jk : Here, β jk denotes the effect of SNP j in population k, ϕ is the global shrinkage parameter shared across all M SNPs, ψ j represents the local shrinkage parameters for SNP j, λ f (j),k denotes the annotation-dependent shrinkage parameter for SNP j in population k, f : j → a ∈ {1, . . . A} is a function that maps the j-th SNP to its corresponding category a in the annotation.
To perform the full Bayesian model fitting, we assign the half-Cauchy priors to the global, local, and annotation-dependent shrinkage parameters as follows: Using the half-Cauchy decomposition, we have where IG denotes the inverse-gamma distribution.
Gibbs sampler
Next, we derive the full conditional distribution of all parameters in the above model.
For notation purpose, we rewrite the prior in matrix form: The Gibbs sampler involves the following steps in each Markov Chain Monte Carlo (MCMC) iteration: where D k is the LD-matrix for population k,β k is the marginal least squares estimates obtained from GWAS summary statistics. To avoid the numerical issue caused by colinearity between SNPs, we restrict where M k is number of SNPs in population k.
where k j = 1 if SNP j exists only in one population and r if it exists in r populations included.
where s a is the number of predictors in category a, l(a, k) = {j ∈ {1, . . . , M } : λ f (j),k = λ a,k } is the set of SNP predictors that belongs to category a.
Example of annotation-dependent shrinkage based on local genetic correlation annotation
Here, we provide an example of the annotation-dependent shrinkage λ f (j),k based on local genetic correlation annotation. WLOG, we assume that we have K = 3 populations in total and population 1 is the target population.
Given the local genetic correlation annotation Ω 2 and Ω 3 , the λ f (j),k in the Full Bayesian model fitting process is specified as: Here, k-th row represents the specification of the annotation-dependent shrinkage parameter λ f (j),k ′ for k ′ = 1, 2, 3 when obtaining the posterior effects for k-th population.
Model-tuning strategy
Instead of assigning a prior for ϕ, we select the global shrinkage parameter ϕ from a grid of value {10 −6 , 10 −4 , 10 −2 , 1} with respect to the largest R 2 in the validation set. The detailed algorithm is listed below: Algorithm 1: Model-tuning X-Wing Input: GWAS summary statistics and population-matached LD reference panel from population 1 to K, target sample genotype.
Output: X-Wing PRS. 1 We perform local genetic correlation analysis between population 1 and population k (k = 2, . . . K) to identify top s regions with positive local genetic correlation. We denote the set of regions as Ω k .
2 For each ϕ ∈ {10 −6 , 10 −4 , 10 −2 , 1}, we fit our PRS model with annotation-dependent shrinkage specified below: when estimating the posterior SNP effects for the non-target population k that When estimating the posterior SNP effects for target population, we used λ f (j),k = 1 for all j = 1, 2, . . . M, k = 1, . . . K. 3 For each ϕ ∈ {10 −6 , 10 −4 , 10 −2 , 1}, based on the posterior mean effects of population k obtained in step2, we calculate population-specific score P RS k,ϕ . A common practice to combine these population-specific scores is to fit a regression model using the same phenotype Y (v) 1 and K population-specific PRS in an independent validation dataset from the target population: Instead of fitting a regression in independent samples, we employ a novel strategy to obtain the least squares estimates of regression weights (i.e. w 1,φ , . . . w K,φ ) using GWAS summary statistics. We introduce this approach in the section 3.3. 4 The final X-Wing PRS is then calculated as:
Incorporating multiple annotations 2.2.1 Model
We generalized our model to incorporate T annotations with the annotation-dependent shrinkage prior to β jk : Here, β jk denotes the effect of SNP j in population k, ϕ k is the global shrinkage parameter shared across all SNPs for population k, ψ j represents the local shrinkage parameters for SNP j and are shared across population, λ f (j,t),k is the annotation-dependent shrinkage parameters for SNP j in population k for t-th annoration, f : (j, t) → a t ∈ {1, . . . , . . . A t } is a function that maps the j-th SNP to its corresponding category a t in the t-th annotation.
To perform the full Bayesian model fitting, we assign the half-Cauchy priors to the global, local, and annotation-dependent shrinkage parameters as follows: Using the half-Cauchy decomposition, we have where IG denotes the inverse-gamma distribution.
Gibbs sampler
Next, we derive the full conditional distribution of all parameters in the above model.
The Gibbs sampler then involves the following steps in each MCMC iteration: where D k is the LD-matrix for population k,β k is the marginal least squares estimates obtained from GWAS summary statistics. To avoid the numerical issue caused by colinearity between SNPs, we restrict where k j = 1 if SNP j exists only in one population and r if it exists in r populations included.
Sufficient statistics for least squares estimator of linear combination weights
Consider the linear combination problem for K centered population-specific PRS using the individual-level validation data X Here, superscript v highlights the fact that phenotypes and PRS in this regression exercise need to be obtained from a validation dataset that is different from any data used for GWAS and PRS training. Y T is a K-dimensional linear combination weights vector. For simplicity, is standardized, and b quantifies standardized SNP effects.
Next, we showed the The least squares estimator for w is This indicates that b, X are sufficient statistics for w, where b is obtained from the PRS training procedure, X is from in-sample LD matrix, and X can be obtained from the summary statistics of the validation sample. When the in-sample LD information is not available, we use LD matrix from the reference panel as replacement. Then we have where N (ref ) and P RS (ref ) denote the sample size and PRS matrix in the reference panel. Taken together, this shows that in order to obtain w, we only need the LD reference and summary statistics from a validation sample.
Derivation for subsampling GWAS summary statistics from training and validation sets
Consider the phenotype-genotype model: where Y i is the standardized phenotype with mean 0 and variance 1 for individual i, X i is a 1×M standardized genotype matrix, and ϵ i is the error term, β is a p-dimensional effect sizes vector. Note that the subscript i in section 3.2 denotes the individual rather than the population.
Here, we consider X i and Y i as random and i.i.d. distributed (i.e., . We denote Y = (Y 1 , . . . , Y N ) T as a N -dimensional phenotype vector and X = (X T 1 , . . . , X T N ) T as a N × M standardized genotype matrix.
The standard approach the process for model validation technique involves first randomly sampling a subset of N −N (v) individuals from full sample (X, Y ) as the training data (X (tr) , Y (tr) ), and use the remaining The GWAS sample size is large and hence by the central limit theorem, we have approximately The covariance between X T Y and X (tr)T Y (tr) is Here, we use the formula for the conditional distribution of two multivariate normal random vectors: , and Cov(A, B) = Σ AB , we have the distribution of A|B following a multivariate normal distribution with mean and covariance matrix.
Thus, we have For the conditional expectation, we plug in the estimator x T y for N E[X T 1 Y 1 ], the estimator for the conditional expectation is For notation purpose, we define the conditional variance as The diagonal term Σ jj of Σ is The off-diagonal term Σ jk of Σ, j ̸ = k is It turns out that the Σ is exactly the LD matrix constructed using the standardized genotypes. Thus, we obtain the estimator for the conditional variance as is obtained from the reference panel, In conclusion, we have Thus, we subsample the summary statistics for training set given full summary statistics X T Y by where g is a N (ref ) -dimensional vector with elements drawn from a standard normal distribution.
Dealing with tuning parameters in the PRS model
If there are tuning parameters in the PRS model, we use the correlation R between phenotype and linearly combined PRS in the validation set to select the optimal tuning parameter, as well as to estimate the linear combination weights.
Followed the notation above, suppose there are tuning parameters γ in the PRS model, consider the linear combination problem for K centered PRS using the individual-level validation data: where P RS k,γ , . . . , P RS × K centered PRS matrix with respect to to the tuning parameter γ in the PRS model, w γ is a K-dimensional linear combination weights vector.
The least squares estimator for w γ iŝ where P RS The correlation R γ between the linearly combined PRS and phenotype in the validation set with respect to to the estimated weightsŵ γ is : Then we select the optimal tuning parameterγ aŝ and use the linear combination weightsŵγ with respect to the optimal tuning parameterγ to linearly combine the PRS.
Grid search to handle negative least squares estimates for mixing weights
In practice, the least squares estimates for linear combination weights of a particular PRS can be negative.
It may decrease the prediction accuracy of the linearly combined PRS. Thus, we provide a grid search strategy to mimic the non-negative least squares.
We pre-specify a grid of positive value for the linear combination weights Then, we use the formula in the above section to calculate the correlation between the linearly combined PRS and phenotype in the validation set. The linear combination weights with respect to to largest correlation, w grid = argmax w∈W R w , will be used to linearly combine the PRS.
Summary statistics-based ridge regression to combine multiple PRS
When linearly combined many PRS with multicollinearity problems, the least squares of the linear combination weights may be sub-optimal. A remedy for multicollinearity is ridge regression.
We first describe a individual-level data-based ridge regression. Consider the linear combination problem for K centered population-specific PRS using the individual-level validation data X Here, Y T is a K-dimensional linear combination weights vector. For simplicity, we assume Y is standardized, and b quantifies standardized SNP effects.
The ridge regression estimator for w is where λ is the shrinkage parameter. It has a closed-form solution: (39) common practice to obtain the ridge regression estimator is to use two disjoint validation set, one to select the optimal tuning parameter λ and the other to estimate w with the selected tuning parameter.
Next, we proposed a summary statistics-based ridge regression for combining multiple PRS. Given the GWAS summary statistics with sample size N 1 , we subsample GWAS summary statistics for the training set 1 , and for the validation set X to subsample GWAS for two disjoint validation sets: X We first apply the PRS method using X (tr)T 1 Y (tr) as training data to obatin SNP effects b. Next, we obtain grid of the ridge regression estimate w ridge,λ using X The λ with respect to the largest correlation between phenotype and linearly combined PRS in validation set 1 will be used:λ where P RS (ref ) = X To avoid overfitting, we recommend using distinct reference panel in summary statistics sampling, PRS model training, ridge regression hyperparameter λ selection, and linear combination weights estimation.
Transforming allele count scale SNP effects into standardized scale
For simplicity, we assume that the genotype matrix is standardized, thus the SNP effects should be on standardized allele scale. Since many PRS method outputs the allele count SNP effects , we use allele frequency from the target population to transform the allele count SNP effects to the standardized effects. The k-th where b (allele) k is the allele count SNP effects for M SNPs in k-th population, f 1 is the M -dimensional target population allele frequency vector. When the in-sample allele frequency is not available, we estimated it from the target population reference panel.
GWAS summary statistics-based cross-validation
Suppose we divide the full GWAS sample (X 1 , Y 1 ) into a training set X individuals, and a validation set X individuals. Given the association z-scores from GWAS summary statistics and genotype data from the reference panel, association summary statistics based on training and validation sets can be sampled as: To perform P -folds cross-validation, we first uses the above formula to sample X T 1,p Y 1,p , p = 1, . . . P − 1 from P independent subset with sample size N1 P and obtain the GWAS summary statistics from training and validation sets in fold p as: and estimate the linear combination weights in each fold.
Regarding the marginal linear assumptions
X-Wing assumes a marginal linear regression between the phenotype and SNP. In practice, many of the GWAS are performed using the mixed model. Mathematically, the GWAS association results using the linear mixed model is equivalent to the results using the marginal linear model on phenotypic residual after adjusting for best linear unbiased prediction (BLUP). This phenotypic residual can be considered the phenotype after adjusting for the sample relatedness (genetic relationship matrix). In our analysis, the BBJ GWAS are performed using BOLT-LMM. BOLT-LMM utilized a two-step approach to conduct the linear mixed model GWAS: "Our algorithm fits a Gaussian mixture model of SNP using a fast variational approximation to compute approximate phenotypic residuals and tests the residuals for association with candidate markers via a retrospective score statistic". Therefore, the mixed model GWAS from BOLT-LMM is from the marginal linear regression between the phenotypic residuals and the SNP. To summarize, most current GWAS (such as BBJ used in this study) essentially comes from a marginal linear model with phenotypic residuals as outcome and SNP as predictor. Therefore, our marginal effects assumption is valid, and we don't expect it will influence the PRS model performance. And our repeated learning approach can be considered as splitting on this phenotypic residual instead of the raw phenotype that is correlated among correlated individuals. Therefore, applying the summary statistics-based splitting using summary statistics that have already been accounted for sample relatedness should not cause problems.
Implementation of other methods
XPASS XPASS 1 is an empirical Bayes-based PRS framework that leverages genetic correlation for crosspopulation polygenic prediction. In our paper, XPASS is used to compute heritability and cross-population genetic covariance (correlation), and estimate the SNP posterior effects used to calculate PRS. We used populationmatched 1000 Genomes Project data as the reference panel. Five principal components of genotypes in reference panel were used as covariate files as suggested by the software. We estimated the global genetic correlation using genome-wide SNPs. We also created two SNP sets: SNPs inside and outside significant genome regions identified by X-Wing, and computed cross-population genetic correlation using GWAS summary statistics restricted to the two SNP sets separately. Standard errors of genetic parameters (heritability, genetic covariance, and genetic correlation) were estimated using block-wise jackknife method. For PRS construction, we obtained the posterior effects for each population to generate population-specific PRS. Although XPASS did not propose to linearly combine PRS, we applied the linear combination to XPASS-derived PRS for a fair comparison.
PESCA As suggested by PESCA paper 2 , we pruned SNPs such that correlation between SNPs does not exceed 0.95 in the population-matched 1000 Genomes Project data. We used ldetect 3 SNPs identified by PESCA into regions with equal size, such that the aggregated size was the same as that of X-Wing.
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Domain: Biology
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A study of expression performed for the construction of Staphylococcus aureus adhesins FnBPB / ClfA and immunogenicity of the expressed product
In this this study, a cloning plasmid named pMD19-FnBPB-ClfA was constructed, by PCR specific amplifications of genes from region D, fibronectin-binding protein B (FnBPB) and region A in clumping factor A (ClfA), all from Staphylococcus aureus. Splicing by overlap extension using PCR tandem gene FnBPB-ClfA was performed and then gene segments of pMD19-FnBPB-ClfA were inserted into a prokaryotic expression vector named pET-32a (+). They were ultimately transferred into the host strain BL21 (DE3), resulting in the expression plasmid pET-FnBPB-ClfA. SDS-PAGE demonstrated an extrinsic protein belt consistent with the desired protein at 51 kDa, when the expression constructed was induced with 1 mmol/L isopropyl β-D-1-Thiogalactopyranoside (IPTG). Western-blot identification demonstrated consensus between the expressed protein and the endogenous protein. After purification and emulsification with Freund's adjuvant, the expressed protein was used to immunize mice. After three subsequent immunizations in the same mice, we gained a highly effective antiserum. Through ELISA, tube agglutination, phagocytosis of opsonized experiment, and antibodies against S. aureus adhesion ability test, it was demonstrated that the fusion gene was successfully expressed in prokaryotic cells, the expressed protein was adherence active, the prepared immune antiserum was capable of preventing S. aureus from adhering to bovine fibrinogens and the antiserum had functions of phagocytosis and opsonization.
INTRODUCTION
Staphylococcus aureus is one of the most common and important pathogenic bacteria that cause mastitis infection, responsible for significant economic loss in cow breeding and dairy industry (Olde Riekerink et al., 2006). It has been demonstrated in multiple studies, that surface-associated adhesins, caspsular polysaccharide (CP), toxins and enzymes are all important factors influencing the pathogenicity of S. aureus (Brouillette et al., 2003). Among them, adhesins, also named microbial surface components recognizing adhesive matrix molecules (MSCRAMMs), consist of specific proteins expressed on the surface of S. aureus, and are able to specifically recognize and attach to components of the extracellular matrix (ECM) of an organism (Patti et al., 1994). In the early phrase of S. aureus infection, they are the most important pathogenic factors (Barkema et al., 2006). Therefore, blocking bacteria from adhering to and colonizing the mucosal surface in this phrase might *Corresponding author. E-mail+8613604717115. Fax: +8604714309177.
Primer
Primer sequence (5'-3') FnBPB up CGC GAA TTC GGC CAA AAT AGC GGT AAT CAG TC FnBPB down aga acc gcc tcc tcc ATG ACC ACT TAC TTG TGG ClfA up gga gga ggc ggt tct GTA GCT GCA GAT GCA CC ClfA down CCG CTC GAG TTA CTC ATC AGG TTG TTC AGG The underlines represent the restrict enzyme sites and the lower letters represent the introduction of four glycine and serine sequence.
be one of the most effective strategies for preventing infection (Zhou et al., 2006).
It is known that S. aureus can express a series of cell wall-associated proteins that are capable of facilitating their clinging to host cells, extracellular matrixes and soluble plasma proteins (Nour El-Din, et al., 2006). These cell wall-associated proteins include clumping factors A and B (ClfA and ClfB), fibronectin-binding proteins A and B (FnBPA and FnBPB), collagen-binding protein (CnBP) and protein A (Nour El-Din et al., 2006). A number of studies have confirmed that ClfA and FnBPs are the most crucial virulence factors for the S. aureus-caused mastitis infection in cows (Hettinqa et al., 2008). The antigenic epitope in FnBPA and FnBPB, which has almost the same ligand combination area in its primary gene structures lies in region D (Joh et al., 1994), while that of ClfA lies in region A (Hettinqa et al., 2008).
Studies have shown that S. aureus expresses different adhesins responsible for mastitis infection in cows from different sources, sometimes FnBPs only or at other times ClfA only. Antibodies against FnBPs and ClfA can only inhibit adherence to mammary tissues of the corresponding adhesins (Jiang and Hao 2009;Shi et al., 2010). In this context, the aim of this study is to clone genes from region D of FnBPB and region A of ClfA, by constructing the expression plasmid through splicing by overlap extension using PCR tandem gene FnBPB-ClfA and immunogenically analyzing the expressed product. This study provided theoretical basis and experimental evidence for developing genetically-engineered subunit vaccine against cow mastitis infection caused by S. aureus.
Experimental materials
S. aureus, Escherichia coli DH-5α, BL21 (DE3), pET-32a (+)and antisera against FnBPB and ClfA were obtained from the Microbiology Laboratory, Veterinary Science Faculty, Agricultural University of the Inner Mongol. The cloning vector pMD19-T Simple, pfu mix DNA polymerase, dNTP Mix, and restriction enzyme were purchased from TaKaRa Company in Dalian, Liaoning Province. Bacterial genome extraction kits were purchased from Tiangen Biotech (BEIJING, China) CO. LTD. Gel extraction and plasmid kits were purchased from Axygen Company (San Jose, USA). Primers were synthesized by Shanghai Bioengineering Company (Shanghai, China). Goat anti mice IgG marked with alkaline phosphatase was purchased from Promega Beijing Biotech Co. Ltd (Beijing, China).
DNA extraction of S. aureus genome
After collection of bacteria, DNA extraction of S. aureus genome was done with cetyltrimethylammonium bromide (CTAB)-DNA precipitation method (Corinaldesi C, Danovaro R .2005).
Connection of region D in FnBPB and region A in ClfA
By using Linker and PCR overlapping extension, region D in FnBPB and region A in ClfA were connected in the order of FnBPB-Linker-ClfA (Table 1), where lowercase letters represent Linker sequences and underlined sequence signifies restriction enzyme cutting sites.
PCR
PCR amplification of region A in ClfA was carried out, using the extracted S. aureus genomic DNA as a template and ClfA up and ClfA down as primers. The reaction condition (Jiang and Hao 2009) is as follows: Cycling conditions were one cycle of 94°C for 2 min, followed by 30 cycles of 94°C for 1 min, annealing by 70°C for 30 s and extension by 72°C for 30 s, followed by a final extension of 72°C for 8 min. The PCR product was identified by 1% agarose gel electrophoresis (AGE), and the product was extracted using gel extraction kits from Axygen Company (San Jose, USA).
PCR amplification of region D in FnBPB was performed using the extracted S. aureus genomic DNA as a template and FnBPB up and FnBPB down as primers. The reaction conditions are as follows (Shi et al., 2010) : Cycling conditions were one cycle of 94°C for min, followed by 35 cycles of 94°C for 1 min, annealing by 68°C for 30 s and extension by 72°C for 30 s, followed by a final extension of 72°C for 8 min. The PCR product was identified by 1% AGE, and the product was extracted using gel extraction kits from Axygen Company (San Jose, USA).
Splicing by overlap extension PCR (SOE-PCR)
PCR amplification of the FnBPB-ClfA tandem gene was done using the gel extracted products of ClfA and FnBPB as templates and FnBPB up and ClfA down as primers (Anthony et al., 1997). Conditions were: Cycling conditions were one cycle of 94°C for min, followed by 30 cycles of 94°C for 1 min, annealing by 65°C for 45 s, and extension by 72°C for 2 min plus 30 s, followed by a final extension of 72°C for 8 min. The PCR product was identified by 1% AGE, and the product was extracted using gel extraction kits from Axygen Company (San Jose, USA).
Construction of the recombinant expression vector
The recombinant plasmid was confirmed by sequencing and digested by endonucleases EcoR I and Xho I; and then the target fragments were respectively recovered and ligated by T4 DNA ligase. The ligated products were then transferred into BL21 (DE3) competent cells, and the transformants were identified by double digestion of EcoR I and Xho I. The ones proved to be correct were the prokaryotic expression vector, pET32a-FnBPB-ClfA. After being verified by further double-enzyme cleavage of EcoR I and Xho I, it was delivered to Shanghai Biosynthesis Company (Shanghai, China) for sequencing and comparison of the sequencing results.
Induced expression of the fusion gene
The positive strain BL21 (DE3) was inoculated in 4 ml LB containing 100 µg/ml ampicillian (Amp) medium, the bacterium of pET-32a (+) empty vector not inducted by IPTG as a negative control. After overnight culturing at 37°C and 200 rpm shaking, 100 μl was removed to inoculate a fresh 10 ml LB containing 100 µg/ml Amp culture medium. Culturing at 37°C and 200 rpm shaking were done again until the culture reached an OD600 of 0.6~0.8. IPTG was then added to the final concentration of 1mmol/L. After a further growth, under the same conditions listed above, samples were removed at 3, 4, 5 and 6 h after being inducted by IPTG, and the induction time was confirmed by the brightness of the band through the electrophoretic results. Culture time points with positive expression induced protein, in contrast to control, where the bacterium of pET-32a (+) empty vector was inducted by IPTG, and the molecular weight of expression protein was approximately 51 kDa bands as the proposed protein bands. The supernatants containing soluble proteins and retained for further purification were analyzed by SDS-PAGE.
Western blot analyses
Purified recombinant protein was electrophoresed, and the best electrophoresis was chosen for transmembrane. The expressed products were electro-transferred to a nitrocellulose filter (NC) membrane using the BIO-RAD system. The membranes were sealed blocked with 5% (W/V ) skim milk powder, washed with PBST for 20 min and incubated with the antiserum against FnBPB (SHI DY,et al.2010) and antiserum against ClfA (Jiang and Hao 2009) overnight at 4°C, respectively.
Membranes were washed with PBST (six times for 5 min) and goat anti rabbit IgG labeled with alkaline phosphatase was utilized for secondary antibody. Finally, results were visualized by electrochemiluminescence liquid (ECL) method and pictures were taken.
Grouping
Female BALB/c mice, aged 6 to 8 weeks, were purchased from Experimental Animal Center of Inner Mongolia University, and evenly divided into five groups of ten mice each (Table 2).
Immunization procedure
For the first immunization, the fusion protein was blended and emulsified at 1:1 in Freund's complete adjuvant. A protein concentration of 1 μg/μl was used to immunize the mice by intramuscular injection in their hind limbs. For the booster immunizations given at a 10 days interval, the fusion protein was blended and emulsified at 1:1 in Freund' s incomplete adjuvant; and also, a protein concentration of 1 μg/μl was used to immunize the mice by intramuscular injection in their hind limbs (Table 3).
Tests on expressed products
ELISA 100 μl protein with a final concentration of 5 μg/ml was used to package the 96-well microtiter plates, and the plates were incubated at 4°C overnight; the next day PBST (PBS with hightemperature and pressure sterilized with 0.05% Tween) was used to wash the plates three times (each well by adding 200 µl), and every wash lasted for 5 min. Then they were then dried. After the last wash, the plates were made to dry with absorbent paper. Every hole of the 96-well microtiter plates was blocked with 200 µl, 5% non-fat dry milk at 37°Cfor 3 h; after washing, the untested sera were added. Sera were added to every hole after titra ratio dilution, they were mixed well and incubated at 37°C for 1.5 h. After washing the secondary antibody was added. To every hole, 100 µl 1:3000 dilution of sheep anti-mouse antibody labeled horseradish peroxidase (HRP) was added and incubated at 37°C for 2 h. Again, after washing, to every hole, 100 μl TMB solution (Tiangen Biotech Co, Beijing, China) was added, a plate reader was placed for 10 min and bright light was avoided. Then 50 μl H2SO4 (2M sulfuric acid) was added to every hole to terminate the reaction. The absorbance values were 450 nm
Serum agglutination test (SAT)
S. aureus was regulated at the exponential growth phase until it reached OD600 of 1. The prepared serum was diluted into eight small tubes numbered from 1 to 8 (Table 4). After the dilution, each of the tubes was supplemented with 0.5 ml of the exponential growth phase of S. aureus, and after blending, they were deposited
Group Protein vaccine
Injection and/or blood collection Day 0 Day 10 Day 14 Day 21 Day 28 show immunizations and circles (○) show serum collections.
Table 4. Tube agglutination test of the antibody.
into 37°C constant incubators for 10 h cultivation. The cultures where then placed at room temperature for 14 h for observation (Table 4).
Antibody cytophagic test
S. aureus, at exponential growth phase, was monitored until an OD600 of 1 was achieved, and then a 1:10,000 dilution was made by PBS. Blood was collected from eyeballs of mouse under aseptic conditions.20 µl diluted S. aureus solution was combined with 200 µl of newly-collected anticoagulant blood from eye of mouse containing sodium citrate, with 200 rpm shaking and incubated at 37°C for 3 h. The solution was evenly coated onto sterile plain agar culture plates with sterile sticks. At the same time, 20 µl diluted S. aureus solution was coated onto another group of plain agar culture plates for a blank control. After inverted cultivation at 37°C for 18 to 24 h, bacterial colonies were counted and a sterilizing rate was calculated according to the formula: sterilizing rate = [1-Sa.thenumber of blood-incubated bacterial colonies/the number of bacterial colonies in control]×100%. The experiment was to be repeated three times.
T cell proliferation assay
Preparation of T-cells from mouse spleen: Three immunized mice were killed by cervical dislocation and steeped in 75% alcohol for 5 min. Spleens were removed from the bodies under aseptic conditions and put into Petri dishes containing 2 ml PRMI 1640 solution. They were grinded with sterilized syringes and made into cell suspension, which were then filtered into 10 ml centrifuge tubes with 200-eyed sterile copper nets. After centrifugation at 2000 rpm for 10 min, the upper layer of the culture solution was removed and the remaining solution was combined with 2 ml Red Cell Lysis Buffer (RCLB) for 2 min. Following the lysis, PRMI 1640 solution (v:v) was added to stop the reaction. After a centrifugation at 2000 rpm for 10 min, the upper layer of the culture solution was again removed and RPMI 1640 suspension containing 2 ml 10% fetal calf serum (FCS) was added. Cells were then counted under a microscope. Cell density was adjusted to 106/ml for cultivation on a 96-well culture plate.
PCR amplification and sequencing
Through the two PCR amplifications, a specific product of about 1300 bp was retrieved. This was consistent with what had been expected from the expressed product (Figure 1).
Construction and identification of the recombinant expression plasmid
By double-enzyme cleavage, with EcoRI and Xho I, of the recombinant expression plasmid pET-32a-FnBPB-ClfA, we got pET-32a (+) linear segments of approximately 5900 bp and insert segments of approximately 1300 bp. PCR tests also showed the successful construction of the prokaryotic expression plasmid pET-32a-FnBPB-ClfA (Figure 2).
SDS-PAGE analysis
Via IPTG inducement, pET-32a-FnBPB-ClfA was successfully expressed in E. coli BL21 (DE3). The results suggest that the aimed protein was expressed at high level in soluble form. It was confirmed that the highest induction was achieved at 5 h, and that the molecular weight of the expressed protein was 51 kDa (Figure 3).
Western blot analyses
Western blot analyses demonstrated that the expressed protein could react idiosyncratically to antisera directed against S. aureus FnBPB and ClfA (Figure 4). The recombined gene was successfully expressed and the gene product had fine reactionogenicity.
Results of the ELISA test
In this experiment, we detected the specific antibody IgG in serum with the method of indirect ELISA (enzymelinked immunosorbent assay). Table 5 shows that the antibody was detected in all sera of the experimental rabbits immunized with the protein. In contrast to group E (the control group), antibody levels of all experimental groups demonstrated an increasing trend. Among them, group A(the pClfA group) was higher than group B (pFnBPB) (p<0.01), which could be likely attributed to the fact that the pClfA protein has a large number of epitopes. Group C (the mixed immunization group) was extremely and significantly higher than groups A and B (p<0.01). This might be related to the intake efficiency of the antigen-presenting cells (APC). Group D (the expressed infusion group) also showed significant higher antibody levels as compared to the PBS groups (p<0.01).
Results of agglutination test
In contrast to the control, agglutination valence of serum from immunized mice increased remarkably. The increase of the antibody (anti S. aureus) level indicated that the expressed recombinant protein contains one of the components of S. aureus. The highest agglutination valence was found in the expression fusion immunity group, suggesting it contained more antibodies than the other groups (Table 6). Agglutination of S. aureus in the control serum was considered to be caused by protein A of S. aureus.
Results of cytophagic test
Counting of bacterial colonies was performed on the cultured product after the 18h cultivation at 37°C. Colonies above 300 were excluded (Table 7). As shown in Table 7, the number of bacterial colonies in the experimental groups was smaller than that in the control, which may be explained by the fact that the whole blood of the experimental mice was able to kill S. aureus. Colony numbers of the fusion protein immunized group and protein mixture immunized group and co-expression group were distinct significantly lower than the rest groups of the separate and combined Immunohistochemistry. This demonstrated that the combined fusion of two adhesins may be the most successful effective immunization against S. aureus.
Results of T lymphocyte proliferation test
T-cell suspension, prepared from spleens of the three immunized mice, was stimulated with the expressed protein and concanavalin A (ConA), so as to test the stimulation index of specificity and non-specificity of the T cells (Table 8). Antigenic specific lymphocyte proliferation is one of the common methods for externally assessing immunity of animal cells (Jin et al., 1998). After ConA stimulation, the T lymphocyte stimulation indexes in mice of the groupsthe single adhesin group, fusion group, mixture group and control group were all around 1.0, with no evident difference between the groups. However, things were much different in the case of antigen stimulationstimulation with the expressed protein. Differences were found, not only between the experimental groups and the control group, but also among the experimental groups. Extremely significant differences were found in the T cell proliferation capabilities of the pFnBPB-ClfA fusion group and the pFnBPB+pClfA mixture group as compared to the control (P<0.01). Significant differences were also seen between the single adhesin groups and the control group (P<0.05), as well as the mixture group and the control group (P<0.05).
Table 1 .
Primers sequences, restriction enzymes and respective digest sites and PCR amplification conditions.
Table 2 .
Immunization groups of laboratory animals.
Table 3 .
Strategies of mice immunization and blood collection.
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Domain: Biology
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Utilizing proteomics and phosphoproteomics to predict ex vivo drug sensitivity across genetically diverse AML patients
Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemia. This genetic heterogeneity is difficult to treat using conventional therapies that are generally based on the detection of a single driving mutation. Thus, the use of molecular signatures, consisting of multiple functionally related transcripts or proteins, in making treatment decisions may overcome this hurdle and provide a more effective way to inform drug treatment protocols. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify signatures that could predict patient-specific drug responses. The Clinical Proteomic Tumor Analysis Consortium is in the process of extending this cohort to collect proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can robustly predict drug response in AML patients. We sought to examine this hypothesis on a sub-cohort of 38 patient samples from Beat AML with proteomic and drug response data and evaluate our ability to identify proteomic signatures that predict drug response with high accuracy. For this initial analysis we built predictive models of patient drug responses across 26 drugs of interest using the proteomics and phosphproteomics data. We found that proteomics-derived signatures provide an accurate and robust signature of drug response in the AML ex vivo samples, as well as related cell lines, with better performance than those signatures derived from mutations or mRNA expression. Furthermore, we found that in specific drug-resistant cell lines, the proteins in our prognostic signatures represented dysregulated signaling pathways compared to parental cell lines, confirming the role of the proteins in the signatures in drug resistance. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to predict drug sensitivity in AML.
Introduction
Acute myeloid leukemia (AML) is characterized by the incomplete maturation of myeloblasts and their expansion in blood and bone marrow which impacts healthy blood cell formation resulting in decreased numbers of granulocytes, platelets, and red blood cells [1]. Even though the number of FDA-approved treatments for AML has increased significantly over the past five years, prognosis remains poor with a 5-year survival rate of 25% for individuals over the age of 20 [2]. Targeted agents have shown promise in mutationally defined subsets of patients, but due to the genetic evolution of this highly heterogenous disease, drug response is often lost and patients relapse. Proper selection of personalized drugs and drug combinations over the course of a patient's disease will be required to provide more durable clinical responses, and require a comprehensive mechanistic evaluation.
Computational modeling and machine learning approaches are able to predict the response of cancer samples to drug perturbation using baseline genomics or transcriptomics [3,4]. This approach has been widely successful using data from the Cancer Cell Line Encyclopedia and subsequent dose response measurements carried out by the Broad and Sanger Institutes [5,6] that identify specific signatures that predict which drugs affect cell lines from basal genomic and transcriptomic data of those same cell lines. These datasets have been further supplemented by global proteomic analysis of the same cell line library [7] that can also be used to predict drug response. However, cell line-derived computational models have their flaws, as they sample a limited subset of patient genetics and have been shown to correlate poorly with patient-derived xenograft data of the same tumor type [8]. There are still ongoing innovations in the computational space that predict drug response from underlying genomic phenotype [9] including Bayesian approaches [10], variational autoencoders [11], and deep learning [12]. To date, however, most of these predictive models are based on cancer cell lines, which are limited in their ability to recapitulate the diversity of patient genetic backgrounds.
The Beat AML Dataset addresses the challenges of model systems by combining ex vivo small molecule inhibitor assays performed on freshly isolated patient leukemia cells with comprehensive genomic and transcriptomic data. In these studies, peripheral blood and bone marrow mononuclear cells (MNCs) from AML patients are isolated and exposed to a panel of approximately 145 drugs over a three-day period and cell viability is used as the primary readout for drug efficacy. Patient genomics and transcriptomics, as well as extensive clinical annotation, are also captured, enabling the stratification of patients by these measures [13]. The depth of sequencing performed on the samples allowed for the characterization of clonal architecture and enabled assessment of co-occurring mutational events, helping to identify drivers and co-actionable targets. This functional genomic dataset uncovered numerous novel genetic and microenvironmental drivers of AML pathogenesis and drug resistance, as well as corresponding validation of drug sensitivity profiles [14][15][16][17][18].
Through the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC), patientderived samples have been assayed using state-of-the-art mass spectrometry (MS) pipelines to produce proteomic and phosphoproteomic measurements of hundreds of tumors in breast, ovary, kidney, head and neck; endometrium, brain and other tissues [19][20][21][22][23][24]. In each study, these proteomic measurements reveal clinically relevant patterns that are not available at the genomic or transcriptomic level [25]; to date, however, these data have not been used to assess drug response in patient-derived tumors.
In this work, we combine the rigorous pre-clinical drug testing of the Beat AML dataset, together with patientderived proteomic and phosphoproteomic measurements, to ascertain the potential for protein-level data to produce robust molecular biomarkers of drug response. We describe a retrospective study that uses the proteomic measurements of 38 patients to identify molecular signatures that can predict drug efficacy across a panel of 26 drugs of interest measured in our ex vivo protocol. We compare these signatures with those derived from genomic and transcriptomic data on the same patients and find proteomics to be extremely robust to inherent genetic sample diversity and often changes in abundance or phosphorylation of proteins that are directly related to the drug's mechanism of action. We measured the ability of these signatures to predict drug response on numerous published hematopoietic cell lines and found them to provide high concordance (via rank correlation), if not perform better than, genomic or transcriptomic signatures. Lastly, we interrogated the expression of these signatures in two AML cell-line derived models of resistance to targeted therapeutic drugs, and identified dysregulated pathways that may play a potential role in mediating drug response.
Experimental Procedures
Experimental design and statistical rationale Our overall experimental design is depicted in Figure S1. It entails the collection of patient AML samples, exvivo drug screening of these samples, and the construction of predictive models of drug response for each type of data collected.
Sample collection
Samples were collected and processed as described in detail previously [13]. Briefly, all patients gave informed consent to participate in the Beat AML study, which had the approval and guidance of the Institutional Review Boards (IRB) from participating institutions. Mononuclear cells (MNCs) were isolated from fresh obtained bone marrow or peripheral blood samples from AML patients via Ficoll gradient centrifugation. Isolated MNCs were utilized for genomic (500x WES; RNA-seq) and ex vivo functional drug screens. WES and RNA-seq were performed using standard methods and data analysis was performed as previously described [13]. Clinical, prognostic, genetic, cytogenetic and pathologic laboratory values as well as treatment and outcome data were manually curated from the electronic medical records of the patient. Patients were assigned a specific diagnosis based on the prioritization of genetic and clinical factors as determined by WHO guidelines.
Ex vivo drug screening analysis
For drug sensitivity assays, 10,000 viable cells were dispensed into each well of a 384-well plate containing 7 point series of drugs from a library of small molecule inhibitors. Cells were incubated with the drugs in RPMI media containing 10% FBS without supplementary cytokines. After 3 days of culture at 37 °C in 5% CO 2 , MTS reagent (CellTiter96 AQueous One; Promega) was added, the optical density was measured at 490 nm, and raw absorbance values were adjusted to a reference blank value and then used to determine cell viability (normalized to untreated control wells). Ex vivo functional drug screen data processing was performed as described [13], resulting quality controlled, probit fit drug curves which were used to calculate normalized AUC and IC50 values used in this analysis.
Identifying drugs and samples for analysis
We selected 38 unique patients from our ongoing study that had complete proteomic and phosphoproteomic measurements. The list of available data for each patient is in Table S1. Although ~ 145 total compounds were tested in the drug panels, we filtered the drugs in this study to collect those that exhibited a range of responses across the 38 patients as determined by area under the curve (AUC) of the dose response. AUC represents the amount of drug required to reduce cell viability, so higher AUC mean the samples are less sensitive to the drug, and lower AUC indicates the samples are more resistance. Specifically, we selected drugs for which at least 10% or 2 (whichever was greater) samples exhibited an AUC less than 100 (determined to be sensitive to the drug in question); this definition produced a "balanced" distribution of AUC scores depicted in Figure 1A.
We also added Gilteritinib (ASP-2215) to the panel as it is currently being evaluated in the clinic. The full range of drug responses across patients is shown in Figure 1B. While some patient samples lacked data on all 26 drugs (indicated in red in Figure 1B), we were still able to use these samples to compare the efficacy of genomics, transcriptomics, proteomics and phosphoproteomics to model drug sensitivity based on the available data.
Protein digestion and tandem mass tag (TMT) labeling
Sample preparation for proteomics was based on the protocol developed under the CPTAC consortium with minimal modifications [26]. Patient cell pellets were lysed with 500 μL fresh lysis buffer, containing 8 M urea (Sigma-Aldrich), 50 mM Tris pH 8.0, 75 mM sodium chloride, 1 mM ethylenediamine tetra-acetic acid, 2 μg/mL Aprotinin (Sigma-Aldrich), 10 μg/mL Leupeptin (Roche), 1 mM PMSF in EtOH, 10 mM sodium fluoride, 1% of phosphatase inhibitor cocktail 2 and 3 (Sigma-Aldrich), 20 μM PUGNAc, and 0.01 U/ μ/μL Benzonase. The samples were then vortexed for 10 seconds and then placed in thermomixer for 15 minutes at 4°C and 800 RPM, vortexing was then repeated and the samples incubated again for 15 minutes utilizing the same settings. After incubation, the samples were centrifuged for 10 minutes at 4°C and 18000 rcf to remove cell debris. The supernatant was then transferred to a fresh tube. A BCA (ThermoFisher) assay was performed on the supernatant to determine protein yield.
Protein concentrations were then normalized to the same concentrationprior to beginning digestion. The sample was reduced with 5 mM dithiothreitol (DTT) (Sigma-Aldrich) for 1 hour at 37°C and 800 rpm. Reduced cystines were alkylated with 10 mM iodacetamide (IAA) (Sigma-Aldrich) for 45 minutes at 25°C and 800 rpm in the dark. The sample was diluted fourfold with 50 mM Tris HCL pH 8.0 and then Lys-C (Wako) is added at a 1:20 enzyme:substrate ratio, followed by incubation for 2 hours at 25°C, shaking at 800 rpm. Trypsin (Promega) was then added at a 1:20 enzyme:substrate ratio, followed by a 14-hour incubation at 25°C and 800 rpm. The sample was quenched by adding formic acid to 1% by volume, and centrifuged for 15 minutes at 1500 rcf to remove any remaining cell debris. Peptides samples were desalted using a C18 solid phase extraction (SPE) cartridge (Waters Sep-Pak).
After drying down SPE eluates, each sample was reconstituted with 50 mM HEPES, pH 8.5 to a concentration of 5 μg/ μ/μL. Each isobaric tag aliquot was dissolved in 250 μL anhydrous acetonitrile to a final concentration of 20 μg/ μ/μL. The tag was added to the sample at a 1:1 peptide:label ratio and incubated for 1 hour at 25°C and 400 rpm and then diluted to 2.5 mg/mL with 50 mM HEPES pH 8.5, 20% acetonitrile (ACN). Finally, the reaction was quenched with 5% hydroxylamine and incubated for 15 minutes at 25°C and 400 rpm. The samples were then combined per each plex set and concentrated in a speed-vac before a final C18 SPE cleanup. Each 11-plex experiment was fractionated into 96 fractions by high pH reversed phase separation, followed by concatenation into 12 global fractions for MS analysis.
LC-MS/MS analysis
Proteomic fractions were separated using a Waters nano-Aquity UPLC system (Waters) equipped with a homemade 75 um I. D. x 25 cm length C18 column packed with 1.9 um ReproSil-Pur 120 C18-AQ (Dr. Maisch GmbH). A 120-minute gradient of 95% mobile phase A (0.1% (v/v) formic acid in water) to 19% mobile phase B (0.1% (v/v) FA in acetonitrile) was applied to each fraction. The separation was coupled to either a Thermo Orbitrap™ Fusion Lumos™ (patient samples) or Q Exactive™ HF (cell lines) Hybrid Quadrupole-Orbitrap™ mass spectrometer for MS/MS analysis. MS Spectra were collected from 350 to 1800 m/z at a mass resolution setting of 60,000. A top speed method was used for the collection of MS2 spectra at a mass resolution of 50K. An isolation window of 0.7 m/z was used for higher energy collision dissociation (HCD), singly charged species were excluded, and the dynamic exclusion window was 45 seconds. For the Fusion Lumos™, a top speed method was used for the collection of MS2 spectra at a mass resolution of 50K.
For the Q Exactive™,™ HF, experiments a top 16 method was used for the collection of MS 2 spectra at a mass resolution of 30K.
TMT global proteomics data processing All Thermo RAW files were processed using mzRefinery to correct for mass calibration errors, and then spectra were searched with MS-GF+ v9881 [27][28][29] to match against the human reference protein sequence database downloaded in April of 2018 (71,599 proteins), combined with common contaminants (e.g., trypsin, keratin). A partially tryptic search was used with a ± 10 parts per million (ppm) parent ion mass tolerance. A reversed sequence decoy database approach was used for false discovery rate calculation. MS-GF+ considered static carbamidomethylation (+57.0215 Da) on Cys residues and TMT modification (+229.1629 Da) on the peptide N terminus and Lys residues, and dynamic oxidation (+15.9949 Da) on Met residues. The resulting peptide identifications were filtered to a 1% false discovery rate at the unique peptide level. A sequence coverage minimum of 6 per 1000 amino acids was used to maintain a 1% FDR at the protein level after assembly by parsimonious inference.
The intensities of TMT 11 reporter ions were extracted using MASIC software [30] . Extracted intensities were then linked to PSMs passing the confidence thresholds described above. Relative protein abundance was calculated as the ratio of sample abundance to reference channel abundance, using the summed reporter ion intensities from peptides that could be uniquely mapped to a gene. The relative abundances were log2 transformed and zero-centered for each gene to obtain final relative abundance values.
TMT phosphoproteomics data processing IMAC enriched fraction datasets were searched as described above with the addition of a dynamic phosphorylation (+79.9663 Da) modification on Ser, Thr, or Tyr residues. The phosphoproteomic data were further processed with the Ascore algorithm [31] for phosphorylation site localization, and the top-scoring assignments were reported. To account for sample loading biases in the phosphoproteome analysis, we applied the same correction factors derived from median-centering of the global proteomic dataset for normalization.
All proteomic data can be found on our synapse site. The cohort is spread across three tranches, as described below.
Patients
Proteomic File Proteomic Table Primary patient cohort syn22130778 syn22172602 Patients with Sorafenib treatment syn22313435 syn22314121 Patients with drug combination syn25672089 syn22156810 The phophoproteomic measurements are also divided, with the resources listed below. Patients Phosphoproteomic File Phosphoproteomic Table Primary patient cohort syn24610481 syn24227903 Patients with Sorafenib treatment syn24227680 syn24228075 Patients with drug combination syn24240156 syn24240355
Linear models of proteomics and drug response
We constructed linear models for each of the 26 different drugs across up to 38 patients (depending on how many patient samples were evaluated with that drug) by regressing the AUC values (which ranged between 0 and 300, as depicted in Figure 1A) against the molecular data as shown in Table S1. The input data for each model were each scaled slightly differently: the genetic mutations were represented as a binary matrix in which a 1 represented the presence of a somatic mutation and a 0 represented no mutation, the transcriptomics was represented by Counts per million (CPM) of gene expression values, while proteomics and phosphoproteomics were represented as the log ratio of gene/phosphosites compared to the reference sample described above.
For each data type/drug combination, we constructed a linear model Y~X where Y represents the vector of AUC values and X represents the molecular measurements for that patient. To reduce the number of features selected by the model we used the LASSO regression [32] as implemented by the `glmnet` package [33]. We employed leave one out cross-validation for each combination of data to select the alpha parameter that minimized cross-validation error. All of our analysis can be found in the `amlresistancenetworks` package we built at [URL] and implemented at [URL]. Those models that failed to select any molecular features were not included in our final analysis, depicted in Table S2. To evaluate the hierarchical clustering of the features selected by the models, we used the `pheatmap` R package based on the log ratio values for the proteomics and phosphoproteomics features selected. The results are depicted in Figure 2 and summarized in Table 1.
We also discreted the AUC by representing Y as a binary variable, where 1 represented an AUC greater than 100 (patient is resistant to drug) and 0 if the AUC is less than 100 (patient is sensitive to drug) and used this as input into a logistic regression using `glmnet`. Example results depicted in Figure 3 and summarized in Table 2. Direct comparisons of the LASSO and Logistic regression models are shown in Figure 4.
Signature interpretation using pathway annotations and statistical enrichment
To identify patterns in the features selected by the LASSO and Logistic models we employed three main approaches. For gene, transcript, and proteomic signatures, we first used the `clusterProfiler` package [34] to identify GO biological process tools that are enriched for the specific genes, transcripts, or proteins selected by the model. The results are listed in Table S2. In cases where there were no significant (corrected p<0.01) terms, the column is blank. For phosphoproteomic features, we used the `leapR` R package [35] to identify specific kinases that were over-represented among the selected substrates, though none were identified. We believed this is due to the sparsity of the signatures as well as the lack of more information about the kinasesubstrate interactions.
Cell line data comparison
To evaluate the patient-derived signatures on an external dataset we collected mRNA expression data from and mutational data from the Cancer Cell Line Encyclopedia (CCLE) [36] together with proteomics data from the same cell lines [7]. The data is merged into a single Synapse table at [URL]/. Drug sensitivity data was collected from both the Cancer Therapeutics Response Portal (CTRP) [5,37] and Sanger [6] datasets and stored at [URL] lines were filtered for those from hematopoietic origin, to include the 22 showed in Table S3.
Of the 25 drugs we modeled using the Beat AML patient cohort, we identified 17 which were also measured in cancer cell lines. We therefore took the LASSO and Logistic models we built for these 17 drugs and evaluated their performance on the cell line data. We applied a linear transformation to the AUC data from each dataset to align with the AUC values from our patient cohort. The performance of each predictive model is depicted in Table S4 and To establish resistant cultures, 10 million MOLM14 cells were treated with 10 nM of quizartinib (Selleck Chemicals, Houston, TX) in media alone (N = 4) or in media supplemented with 10 ng/mL of FGF2 (N = 4) or FLT3 ligand (N = 4, FL; PeproTech Inc., Rocky Hill, NJ) [38]. All cultures were maintained in 10 mL of media. Every 2 or 3 days, recombinant ligands and quizartinib were replaced and cell viability was evaluated using the Guava personal flow cytometer (Millipore Inc., Burlington, MA). Following ligand withdrawal, quizartinib and media were similarly replenished and viability was monitored every 2 to 3 days. All cell lines were tested for mycoplasma on a monthly schedule.
Building networks from proteomic signatures and network reduction strategies
To provide further context for the phosphoproteomic features selected by the models, we mapped selected phosphosites and proteins to published protein-protein [39] and kinase-substrate [40,41] interactions and then reduced this network to identify subnetworks using the `PCSF` R package [42,43]. Specifically, we used the STRING database [39] together with networkKin [40] and PhosphoSitePlus [41] predictions of kinase substrate interactions to build a graph that combined protein-protein interactions with kinase-substrate interactions. To do this we added each phosphosite as its own node in the underlying graph. We weighted each edge from the node representing the substrate gene to the phosphosite with a cost of m/4 where m represents the mean cost of all the edges in the graph. The weight of each edge between the phosphosite node and the kinase gene was weighted with a cost of 3m/2 where m represents the mean cost of all edges in the graph. We then ran the PCSF algorithm [42,43] over 100 randomizations using phosphosites together with proteins from a single drug model. The results for the Trametinib LASSO signatures are in Figure 6C, and the results for the Quizartinib LASSO signatures are in Figure 7C.
Using the proteins selected by the PCSF algorithm, which are a combination of those selected by the linear model as well as those selected by the PCSF algorithm, we used Cytoscape [44] and the BinGO [45] application to identify which GO biological process terms were enriched. The results are depicted in Tables S5 and S6.
Linear models identify robust proteomic signatures of drug response
We constructed linear models as described above for each combination of drug and data modality for total of 104 different combinations. Due to data loss (e.g., lack of drug response data) and general lack of signal, we were able to build LASSO models for 74 of these combinations, including 20 and 21 models using proteomic and phosphoproteomic data, respectively. To assess the quality of these models we employed both quantitative and qualitative metrics. Our quantitative signature metrics are summarized in the bottom two rows of Table 1. The ideal data modality would be able to model the most drugs (second column) with the lowest amount of mean squared error (third column) and the highest correlation with the actual value (R, fourth column). The proteomic and phosphoproteomic data can model most of the drugs with low mean error and high mean correlation.
DATA TYPE
To assess the signatures in a qualitative manner, we used two other metrics. First, we used hierarchical clustering to determine if the values of the model-selected features clustered the patient samples in a way that recapitulated drug response. An example of this approach on quizartinib is shown in Figure 2B and C. Here we were able to identify proteins (Figure 2A) and phosphosites ( Figure 2B) whose expression clustered patient samples by the AUC of Quizartinib in the ex vivo patient samples using the LASSO regression. Specifically, we demonstrate that the expression of FLT3, a direct target of quizartinib, is reduced in samples that are more sensitive to quizartinib (Figure 2A). As a second qualitative measure, we tested the features selected by the model to ask if specific GO biological processes or kinases were enriched in the proteins (Figure 2A) or phosphosites ( Figure 2B) selected by the models. Of the 20 LASSO proteomic regression models, 14 had at least one significantly enriched biological process. The results for these signatures (and all others) are depicted in Table S2.
Logistic regression predicts fewer drug responses with less accuracy
Given the sparsity of the data we sought to explore the ability to create a binary predictor of drug response using a logistic regression model [33]. Across the 104 drug/data combinations, we were able to build logistic regression models for 57, fewer than what was possible for the LASSO. Of those, only 17 and 15 drugs using proteomic and phosphoproteomic data.
We evaluated the logistic proteomic regression models using the same quantitative and qualitative metrics as for the linear regression. The summary statistics are depicted in Table 2 and the hierarchical clustering in Figure 3. The summary statistics are similar to those in Table 1, with the exception of the error metric, as classification models are measured via a misclassification error (fraction of samples misclassified) instead of mean squared error. Figure 3 illustrates the proteomic and phosphosite features selected to predict the quizartinib response, which exhibited tight clusters using proteins selected by the logistic model. Again these heatmaps enabled us to further interrogate the proteins identified. In Figure 3 we noticed INPP5D, which is identified in both the LASSO and logistic regression models and highly down-regulated in sensitive samples ( Figure 3A). This gene encodes the inositol 5-phophatase know as SHIP1 which acts as a negative regulator of the PI3K/AKT pathway. SHIP-1 affects cell proliferation in AML, due to mutational alteration of the nuclear localization signature or phosphorylation [46]. It has also been shown to act as an adaptor protein linking to wild type FLT3 signaling [47,48]. Lastly, we also evaluated GO enrichment, and found that fewer (7 of the 17) proteomic signatures selected by the logistic regression had at least one enriched biological process when compared to the LASSO.
We compared the overall performance of proteomic and phosphoproteomic derived models (described above) to those derived from mutational or transcriptomic profiles. Many drugs, such as quizartinib or trametinib, a Mek kinase inhibitor, are designed to target important signaling pathways found to be frequently hyperactivated by specific mutations and therefore it is expected that the presence/absence of mutations in the FLT3/ MAPK pathway (FLT3-ITD, FLT3-TKD, RAS, PTPN11, NF1) will impact the efficacy of the drug [13]. Furthermore, transcriptional signatures derived from regression models have also proven to be able to predict drug response [13]. As such, we wanted to determine if proteomic features, which are more removed from direct genetic mutations but more proximal to function, are as effective in predicting drug response. We compared the two regression approaches to each other using the correlation metric, depicted in Figure 4.
MEASUREMENT
In short, the LASSO regression is superior to the Logistic in terms of overall correlation across all four data modalities and was also able to model more drugs, as indicated by the number of dots compared to triangles. When comparing data types used as input to the model, the proteomic and phosphoproteomic models performed as well as or better than those derived from mRNA or genomic data. While genomic mutationderived models were generally more correlated with the training dataset (Figure 4), they were far less accurate in terms of mean squared error ( Figure S2A) and misclassification error ( Figure S2B).
Since this patient cohort was primarily focused on the assessment of proteomic and phosphoproteomic measurements, we recognize that our results could be biased toward protein-level data due to larger training sets (Table S1, Figure 1B). We therefore repeated our analysis for a "square" dataset using the 18 samples that had all four data types and built our models of drug response on these patients. While highly limited, these results, depicted in Figure S3, show that proteomics and phosphoproteomics were able to produce performant signatures using leave-one-out cross validation, with higher correlation values in models built with proteomic and phospho-proteomic data than those from genomic or transcriptomic data.
Comparison with hematopoietic cell line data suggests proteomic signatures are most robust
Given the limited data available for cross validation in the Beat AML cohort as well as the lack of ability to control for genetic heterogeneity, we sought existing cell line panels as an external validation set for our prognostic signatures. We used the signatures developed in the Beat AML patient cohort and evaluated their ability to predict drug response in two publicly available cell line drug sensitivity studies (measuring percent viable cells post drug treatment), using genetic mutations, gene expression values, and proteomic data.
Specifically we collected hematopoetic cell line data from the Cancer therapeutics response portal (CTRP) [5] and Sanger [6] studies and measured the predictions on 17 drugs for which we had valid patient signatures and also data in cell lines from at least one of the two studies, described in Table S5. The results, depicted in Figure 5, show that, while only a handful of signatures were successfully applied to the cell line data, they generally correlated at least as well if not better than models built from genomic or transcriptomic data, and also had lower error (Figure S3).
Proteins and phosphosites that predict trametinib response are dysregulated in resistant cell lines
Given that the proteins and phosphosites selected by our models were able to distinguish patient samples that responded to drugs from those that did not, we hypothesized that the proteins selected by the models play a distinct role in drug response. To test this, we sought to alter the drug response in vitro by culturing AML cell lines in the presence of low concentration of a drug we modeled and measuring how the proteome changed in resistance samples. We focused our study one trametinib, a drug that targets the MAPK signaling cascade, over 3-4 months and compared the expression of the model-selected proteins in response to trametinib treatment.
We clustered the expression values of the proteins selected by the patient-derived LASSO trametinib regression model in both the trametinib-resistant and parental cell lines (across multiple replicates) and found two distinct clusters, depicted in Figure 6A, that aligned with the drug-resistant phenotype. When we clustered the phosphosites selected by the same LASSO model, it also grouped many parental cell lines in the same cluster ( Figure 6B). While many drug signatures did not exhibit significant GO enrichment (Table S2), we found two biological processes to be enriched with proteins from the trametinib LASSO signature: respiratory chain complex III assembly and mitochondrial respiratory chain complex III assembly. The limited number of enriched GO terms is driven by the small size of the LASSO regression model.
Given the limited number of GO terms identified by the proteins selected in this model, we expanded the proteins and phosphosites selected by the model by building a network model that joined the selected proteins and phosphosites in a network using published protein-protein [39] and kinase-substrate [40,41] interactions (see Methods) via the Prize-Collecting Steiner Forest algorithm [42,43]. The result, depicted in Figure 6C, highlights the proteins from our statistical model in blue together with neighboring proteins in yellow. Using this enhanced network model we were able to identify more significantly over-represented GO terms than by the model-selected proteins along. This analysis, depicted in Table S5, shows that many apoptosis and cell deathrelated pathways are over-represented, primarily driven by the presence of known apoptotic genes DAPK3 and CASP2 in the proteomics signature in Figure 6A.
Proteins and phosphosites that predict quizartinib response are dysregulated in models of late, but not early, resistance
To further explore the extent to which the protein signatures affect drug response, we utilized a recentlypublished cell line model that represents patients that develop early vs late resistance to the FLT3 inhibitor quizartinib. Here, cells are cultured in the presence of a quizartinib plus an activating ligand -either the FLT3 ligand (FL) or FGF2 [49]. Using this model we can compare cell lines that show symptoms of early resistance (shortly after co-treatment) or late resistance, which is ligand independent and accompanied by the outgrowth of genetically altered resistant clones [49]. We hypothesized that the patient-derived signature would more closely resemble the long-term resistance phenotype.
To test this hypothesis, we plotted the proteins and phosphosites selected by the LASSO model in these cell lines, as depicted in Figures 7A and 7B respectively. We observed a similar split between sensitive and resistant cells as we did in Figure 6, as the proteins and phosphosites that predict drug response cluster the MOLM14 parental cells (beige) distinctly from the fully resistant cells (gold). However, in this case, these proteins separates those cells that represented 'early resistance' (dark blue) from those that represent 'late resistance' (light blue) in our previous work. This fits with our previous claim that the resistance to FLT3 inhibitors involves a two-step process, as cell lines exhibiting the early resistance phenotype cluster more closely to the parental cells than to the late resistance cells. Since no GO terms appeared to be enriched among the proteins in the LASSO signature (Table S2), we also examined the network linked by the proteins and phosphosites using the Prize-Collecting Steiner Forest [42,43] as described in our Experimental Procedures and depicted in Figure 7C. Because the proteins and phosphosites selected by the model were not enriched in any specific GO terms, the network enabled us to get a broader picture of how the proteins involved could participate in the same signaling pathways. Similar to the trametinib network, these proteins were also enriched in apoptotic related pathways (Table S6), but unlike the previous network we found enrichment in B cell activation, differentiation, and homeostasis, driven by network proteins INPP5D, FLT3, and CASP3.
Discussion
This study describes a new approach to predicting drug response in AML patient samples using global proteomic and phosphoproteomic data in combination with traditional genomic and transcriptomic approaches. Both linear regression (LASSO) and discrete (binarized) logistic regression approaches were used on a set of samples from 38 AML patients with corresponding ex vivo drug response data. Both LASSO and logistic regression approaches performed satisfactorily, with quantitative metrics of model coverage, error and correlation comparable between mutation-based, transcript-based, protein-based and phosphosite-based approaches. Neither LASSO nor logistic regression was clearly superior, with LASSO providing slightly better quantitative metrics while the logistic regression model was more robust when applied to cell line data. These positive results suggest that protein biomarkers could be used to better stratify patients to identify which treatment would be best for their disease.
Models generated on patient samples were tested for performance on both published cell line perturbation datasets (CTRP and Sanger), and additionally on two in-house models of drug resistance based on AML cell lines under prolonged inhibitor exposure. The predictive ability of the proteomic and phosphosite models was quite strong, generating tight clusters of sensitive and resistant cell lines, and effectively subdividing cell lines treated with stromal factors to model early and late drug resistance into their respective phases. Use of Prize-Collecting Steiner Forest algorithms to identify specific protein networks that were altered to gene set enrichment based on GO ontologies alone, highlighting apoptotic pathways that have been shown to be synergistic with both trametinib and quizartinib. Recent drug trials of venetoclax, a drug that targets the apoptotic pathway, has been shown to be successful in combination with trametinib [50] and quizartinib [51].
In summary, this study presents an effective workflow for the future analysis of integrated genomic, transcriptomic, proteomic and phosphoproteomic data in larger cohorts, such as the 210 patient Beat AML cohort currently under analysis. While the patient cohort used in this preliminary study is relatively limited in size, the robust verification in cell line studies provides confidence in the scalability of the method. Additionally, the comparable performance of protein-based models compared to mutation-based models opens up the possibility of developing antibody-based, CLIA-eligible assays for the rapid assessment of likely therapeutic targets at the time of biopsy or surgery, without the need for DNA sequencing. Lastly we believe that our network approaches could identify other potential drug synergies that have not yet been tried in the clinic.
We believe that studying the proteins and phosphosites directly can enable new biological insights into the mechanisms of drug response and resistance. This has been shown in both in vitro studies and studies with archived patient samples (i.e., CPTAC studies), as measuring proteins directly can identify the dysregulation in signaling beyond the genetic mutations or altered transcripts.
Data Availability
Data was uploaded to Synapse where it was used for subsequent analysis at [URL]. mRNA (counts per million) and genetic mutation measurements (variant allele frequency) can be found at [URL] data used for this project is stored on Synapse at [URL], where you can request access to the data specifically mentioned in this manuscript. All analysis and figures can be viewed at [URL] . Figure 4: Comparison of LASSO (circle) and Logistic (triangle) models across data types, measured by Spearman rank correlation. Figure 5: Comparison of LASSO (circle) and Logistic (triangle) models across data types, measured by Spearman rank correlation. Table 1: List of drugs and available samples Supplemental Table 3: Description of cell lines used Supplemental Table 4: Number of cell line measurements on our drug panel Supplemental Table 5: GO enrichment of Trametinib network Supplemental Table 6
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Domain: Biology
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Primordial germ cell DNA demethylation and development require DNA translesion synthesis
Mutations in DNA damage response (DDR) factors are associated with human infertility, which affects up to 15% of the population. The DDR is required during germ cell development and meiosis. One pathway implicated in human fertility is DNA translesion synthesis (TLS), which allows replication impediments to be bypassed. We find that TLS is essential for pre-meiotic germ cell development in the embryo. Loss of the central TLS component, REV1, significantly inhibits the induction of human PGC-like cells (hPGCLCs). This is recapitulated in mice, where deficiencies in TLS initiation (Rev1-/- or PcnaK164R/K164R) or extension (Rev7 -/-) result in a > 150-fold reduction in the number of primordial germ cells (PGCs) and complete sterility. In contrast, the absence of TLS does not impact the growth, function, or homeostasis of somatic tissues. Surprisingly, we find a complete failure in both activation of the germ cell transcriptional program and in DNA demethylation, a critical step in germline epigenetic reprogramming. Our findings show that for normal fertility, DNA repair is required not only for meiotic recombination but for progression through the earliest stages of germ cell development in mammals.
The development of germ cells and their differentiation into gametes is crucial for the faithful transmission of both genetic and epigenetic information to the next generation. Primordial germ cells (PGCs) are the first germ cells to emerge in the post-implantation embryo. In mice, as few as 3−5 founder PGCs are specified at E6.0-6.5 and these undergo extensive proliferation, increasing to around 20,000 by E12.5 [1][2][3] . To develop into functional gametes, PGCs must undergo a unique developmental program, repressing somatic genes and activating pluripotency and germ-cell-specific factors 4,5 . This entails extensive epigenetic reprogramming resulting in altered histone modifications and DNA demethylation which facilitates the erasure of genomic imprints, and reactivation of the inactive X-chromosome 6,7 .
DNA demethylation has been proposed to occur by multiple mechanisms 8 . A prevalent model describes a two-step process involving a passive demethylation phase in which DNA methylation is diluted by DNA replication ('reprogramming step 1') followed by active, enzymatic DNA demethylation that occurs upon colonization of the embryonic gonads ('reprogramming step 2') [9][10][11][12][13][14] . Thus, PGC development is highly dependent on DNA replication, both for lineage expansion and for epigenetic reprogramming.
The replication of DNA can be hindered by various obstacles, such as chemical damage to the DNA molecule or DNA secondary structures 15 . The failure to resolve these impediments can have catastrophic consequences for a cell. Incomplete or under-replication of the genome can block cell cycle progression, either directly or by activation of the DNA damage response (DDR) including cell cycle checkpoints 16 . If this persists the cell may ultimately die. To combat these challenges, eukaryotes have evolved both DNA repair as well DNA damage tolerance (DDT) mechanisms. One route of DDT is errorprone translesion synthesis (TLS). TLS allows DNA replication to continue past impediments and facilitates the filling of gaps which remain at the end of S-phase 17 . TLS utilizes specialized polymerases with active sites that can accommodate damaged or distorted DNA templates and which lack proofreading activity, hence increasing the risk of DNA mutagenesis 18,19 . In the germ cell compartment, ensuring that replication can proceed is of paramount importance to allow sufficient cellular expansion to guard against sterility. However, any increase in the mutagenicity of replication increases the risk of deleterious phenotypes and inherited disease in future generations.
Recent genome wide association studies (GWAS) have implicated multiple DDR pathways as determinants of infertility in humans, however little is known about the underlying mechanism or if this requirement extends to the TLS pathway [20][21][22] . In this study, we find that TLS plays a crucial role in the development of embryonic germ cells in both humans and mice. Our results show that factors involved in sequential stages of TLS are essential for PGC development. In the absence of TLS, PGCs are specified in normal numbers but fail to expand, resulting in a > 150-fold reduction. In contrast to the severe effect on the germline, somatic tissues of these mutants are unperturbed in their development with no discernible impact on homeostasis or survival. Consistent with a defect in TLS, mutant PGCs show reduced proliferation, accumulation at the G2/M phase of the cell cycle and increased markers of unresolved DNA damage. In addition, the loss of TLS prevents progression of the germ cell transcriptional program and results in failure of genome-wide DNA demethylation, an essential and conserved step in the development of mammalian embryonic germ cells. Our findings define a critical role of TLS specifically in germ cell development, safeguarding fertility and enabling successful PGC epigenetic reprogramming.
REV1 is required for PGC development in human and mouse
GWAS studies focused on infertility have revealed genes important for human germline development [20][21][22] . Notably, factors that deal with DNA damage are frequent hits. In agreement with these findings, the DDR has been implicated in germ cell production and maintenance. Loss of several DDR factors results in infertility due to failure during meiosis, likely as a result of the inability to resolve meiotic DNA double strand breaks (DSBs) and recombination intermediates [23][24][25][26][27][28][29] . However, it has been found that DDR pathways and factors including Fanconi Anemia crosslink repair, base excision repair and homologous recombination amongst others are also required for the embryonic development of germ cells 21,[30][31][32][33][34][35][36][37][38] . One of these factors, REV7, is involved in multiple DNA repair transactions including the repair of DNA DSBs, mitotic progression, and TLS [39][40][41][42][43][44][45][46] . Due to the requirement of various replication-coupled repair pathways in the embryonic germline, we set out to test if the TLS pathway is required for fertility. TLS is important for the completion of DNA replication, and we therefore hypothesized that it may play a role in ensuring successful genome duplication during highly proliferative stages of gametogenesis. As early embryonic germ cell development is particularly proliferative, we asked if TLS is required during primordial germ cell (PGC) development. We employed an in vitro model in which iPSCs are differentiated into human PGC-like cells (hPGCLCs) (Fig. 1a) 47 . To study the potential roles of TLS in human germ cell development we focused on REV1, a core component of the TLS pathway [48][49][50][51][52] . REV1 was disrupted in human BTAG iPSCs carrying both the BLIMP1-tdTomato and TFAP2C-EGFP PGCLC reporters (Supplementary Fig. 1a) 47 . Clones with successful disruption of REV1 were identified by PCR then validated by testing for hypersensitivity to mitomycin C (MMC) (Supplementary Fig. 1b, c). We first assessed if the loss of REV1 affected iPSC function. As REV1 plays a role in DNA replication we assessed proliferation of mutant iPSCs and found no difference between REV1 -/-and the parental line (Supplementary Fig. 1d). We also assessed the ability of REV1 -/-iPSCs to differentiate into different lineages by measuring embryoid body formation 53 . We found that REV1 lines were able to differentiate into all three germ layers similarly to the parental wildtype line (Supplementary Fig. 1e). In contrast, we found that three independent REV1 -/-lines had a significant reduction in the ability to induce hPGCLCs, consistent with a role in embryonic germ cell development (Fig. 1b, c and Supplementary Fig. 1f). While the frequency of hPGCLCs generated was significantly reduced in the absence of REV1, the few induced hPGCLCs expressed the canonical germ cell markers SOX17, TFAP2C and OCT4 (Fig. 1d). The magnified inset shows the nuclear localization of each factor (Fig. 1d).
Due to limitations of studying human gametogenesis in vivo, we asked if the requirement for TLS factors in PGC development was conserved to mice, thus facilitating mechanistic studies. REV1-deficient mice were crossed with wildtype mates to assess fertility. Consistent with our observations in human, we found that neither male nor female Rev1 -/-mice gave rise to offspring despite evidence of copulation (Fig. 1e) 52 . Upon analysis of the gonads, we observed a striking reduction in testis mass but the mass of the ovaries was unaffected (Fig. 1f, g). Histological analysis of the testes revealed a majority (96.8%) of Sertoli-cell-only (SCO) seminiferous tubules and an absence of promyelocytic leukemia zinc finger (PLZF) + cells, which marks undifferentiated spermatogonial stem cells (SSCs, Fig. 1h, i and Supplementary Fig. 1g) 54 . In females, we observed no follicles upon histological examination of Rev1 -/-ovaries, suggesting a complete failure of oogenesis (Fig. 1h, j). Together, these data reveal a lack of gametes in both Rev1 -/-male and female mice. The lack of visible PLZF + cells in male gonads and meiotic cells in either sex argues that the defect is premeiotic which is consistent with the hPGCLC data suggesting a failure in germ cell development during embryonic stages. Therefore, we studied the development of embryonic germ cells using the GOF18-GFP PGC reporter in which GFP expression is driven by a fragment of the Oct4 promoter (Oct4ΔPE) (Fig. 1k) 55,56 . We intercrossed Rev1 +/-mice carrying the GOF18-GFP PGC reporter and harvested the embryonic gonads at E12.5, prior to sexually divergent germline development. Imaging of the gonads revealed a dramatic reduction in GOF18-GFP + cells in Rev1 -/-embryos compared to wildtype controls, consistent with the hPGCLC data (Fig. 1l). Quantification of PGCs (double positive for the marker SSEA-1 (stage specific embryonic antigen-1) and GFP; SSEA1 + GOF18-GFP + ) by flow cytometry revealed a significant reduction in Rev1 -/-embryos (Fig. 1m, n). Together, these data show that the TLS factor REV1 plays a key role in embryonic germ cell development in both humans and mice.
The requirement for the C-terminal domain of REV1 and the Polζ subunit REV7 links TLS to PGC development REV1 can act enzymatically through its deoxycytidyl transferase activity which can drive mutagenesis and is important for immunoglobulin diversification 48,50,51 . Alternatively, it can act as a proteinscaffold through its C-terminal protein interaction domain which is able to recruit TLS factors to sites of lesion bypass 57 . This region of REV1 plays a crucial role in maintaining cellular resistance to DNA damage that impedes replication indicating that the C-terminus is the key region of REV1 for TLS transactions. To determine if the catalytic or recruitment function of REV1 is required for PGC development, we generated a catalytically dead allele of Rev1, in which the catalytic residues D568 and E569 were mutated to alanine (Rev1 AA ) (Fig. 2a and Supplementary Fig. 2a) 58 . We also generated mice with a mutant Rev1 allele in which the DNA encoding the final 100 amino acids of REV1 was deleted (Rev1 CT ) (Fig. 2a and Supplementary Fig. 2b, c) 57 . To confirm the stability of the protein product of the Rev1 CT allele, we expressed N-terminally FLAG-tagged full length or C-terminally truncated REV1 in cell lines and performed immunoblotting. This revealed comparable protein levels and a lack of degradation products (Supplementary Fig. 2d). Furthermore, immunofluorescence analysis of cell lines expressing GFP-tagged full length and C-terminally truncated REV1 showed localization of both to the nucleus (Supplementary Fig. 2e). To further validate these Rev1 mutant alleles, we derived cell lines from mice and measured mRNA expression and cellular sensitivity to DNA damaging agents. The mRNA expression of mutant alleles was comparable to that of Rev1 in wildtype cells (Supplementary Fig. 2f). Moreover, the C-terminus but not catalytic activity of REV1 was 0.10% 14.5% Rev1 -/- required to overcome replication blocking DNA damage, in line with previous reports (Supplementary Fig. 2g) 57,59 .
We next crossed the Rev1 AA or Rev1 CT alleles with Rev1 +/-mice carrying the GOF18-GFP PGC reporter. At E12.5, we found that Rev1 -/AA embryos had a reduction in the frequency of PGCs (median wildtype = 1383, median Rev1 -/AA = 884, Fig. 2b, c). However, this was moderate when compared to Rev1 -/CT or Rev1 -/-embryos. Furthermore, Rev1 -/AA PGCs generated mature gametes in the gonads of adult mice which were competent in giving rise to viable offspring (Fig. 2d−f and Supplementary Fig. 2h). In contrast, E12.5 Rev1 -/CT embryos showed a significant reduction in the number of PGCs, with similar numbers to those in Rev1 -/-embryos (median = 27 and 20, respectively, Fig. 2b, c). Together these data show that the catalytic activity of REV1 plays a moderate role in germ cell development whilst the C-terminus, which coordinates protein-protein interactions during TLS, is critical for PGC development.
To investigate the function of TLS in PGC development further, we investigated known REV1 interactors. First, we measured the expression of a subset of interactors (REV7, POLΚ and POLΘ) and found that like Rev1, each was more highly expressed in PGCs than in the surrounding somatic cells (Fig. 2g) (29, 37). To determine if these factors are also required for PGC development, we utilized previously generated and validated genetic models to generate E12.5 embryos deficient in each factor expressing the GOF18-GFP PGC reporter 45,60,61 . Whilst Polk -/-and Polq -/-embryos had comparable numbers of PGCs to wildtype, REV7deficient embryos had a significant reduction similar to that observed in Rev1 -/-embryos (Fig. 2h, i). Consistent with this, the profound reduction in the number of PGCs in Rev7 -/-embryos resulted in adult gonads devoid of germ cells (Fig. 2j−m and Supplementary Fig. 3a).
Together these data reveal that it is the C-terminal protein interaction domain of REV1 and its interactor REV7 that are required during PGC development. REV7/repro22 has previously been identified in an ENU screen as a factor needed for fertility and subsequent studies have shown REV7 to be important during PGC expansion [39][40][41] . REV7 is the non-catalytic subunit of Polζ, a B-family polymerase that extends the nascent strand following lesion bypass before handing back to replicative polymerases 62,63 . As only Polζ has such an activity it is considered essential for TLS transactions. However, REV7 also has non-TLS roles in DSB repair, cell cycle regulation and the shelterin complex [42][43][44][45][46] . For the polymerase activity of Polζ, REV7 must bind to REV3L which is not required for the other functions of REV7 64 . As REV3L is essential for embryonic development any potential role in PGC development cannot be tested [65][66][67] . However, we find that Rev3l and Rev7 have similar patterns of expression in PGCs at E10.5 which mimics that of Rev1 (Supplementary Fig. 3b). Crucially, Polζ is recruited to sites of lesion bypass for DNA synthesis through physical interactions between the REV7 subunit and REV1's C-terminal domain 46 . Hence, our discovery that both the C-terminus of REV1 and the REV7 subunit of Polζ are required in PGCs argues that the common function of both -in TLS -is needed during germ cell development.
The post-translational modification of PCNA preserves PGC development
If TLS is critical for the development of PGCs, we hypothesized that PCNA ought to play an important role. PCNA is an essential component of the replisome that links DNA repair and DDT responses to replication 68 . Upon stalling of replication forks, lysine 164 (K164) of PCNA can be sumoylated, mono-, or poly-ubiquitinated to engage DDT pathways [69][70][71] . Monoubiquitination of PCNA at K164 is required for TLS, facilitating the recruitment of specialized polymerases to sites of replication blocking lesions, enabling their bypass [72][73][74] . As our results found a requirement for the TLS factors REV1 and REV7, we wanted to study if there is also a role for PCNA K164 modification during PGC development. We used CRISPR/Cas9 to generate mice in which K164 of PCNA is mutated to arginine (Pcna K164R ) (Supplementary Fig. 4a). In this model, PCNA K164-modification dependent DDT, including TLS, is abolished 68 . We first set out to determine if our allele recapitulated features of previously described mice carrying the PCNA K164R mutation (Pcna tm1Jcbs MGI:3761720 and Tg (Pcna*K164)1Mdsc MGI:97503) 75,76 . As expected, Pcna K164R/K164R (herein referred to as Pcna R/R ) mouse embryonic fibroblasts (MEFs) derived from our allele were hypersensitive to ultraviolet irradiation (Supplementary Fig. 4b) 75 . However, despite PCNA K164 being important for the cellular response to replication blocking DNA damage, Pcna R/R mice were born at the expected Mendelian ratios with adult mice having similar lifespan to wildtype littermates (Supplementary Fig. 4c, d). Consistent with previously published data, we found that the gonads of homozygous adult mice were smaller than wildtype and that both male and female Pcna R/R mice were sterile (Fig. 3a−d) 75 . Histological analysis revealed that the majority (98.2%) of the testes contained SCO seminiferous tubules and that the ovaries were devoid of follicles (Fig. 3e−h). Similar to REV1deficiency, the failure in gametogenesis began during embryonic development (Fig. 3i, j). At E12.5, the numbers of PGCs observed in Pcna R/R embryos was similar to Rev1 -/-with a > 150-fold reduction when compared to wildtype (Fig. 3k). To study if there were sex-specific differences in the embryonic germ cell defect of TLS-deficient embryos, we compared E12.5 male and female Pcna R/R embryos. We found comparable PGC numbers in both sexes, confirming a common defect (Supplementary Fig. 4e).
These findings led us to investigate the timing of the PGC defect in Rev1 -/-, Rev7 -/-and Pcna R/R embryos more systematically. The PGCs in mouse embryos are specified between E6.0-6.5 then start to migrate and extensively proliferate from E8.5 1 . We first quantified the number of PGCs at E8.5 and found no significant difference in the number of PGCs between wildtype and the three TLS mutants (Fig. 3l). We confirmed this result using an alternative PGC-reporter, Stella-GFP (Supplementary Fig. 4f) 77 . Next, we quantified the number of PGCs by flow cytometry at each day of development between E9.5-12.5, replotting the E8.5 data on the same axis, and found that from E9.5 onwards Rev1 -/- and Pcna R/R embryos had a significantly contracted PGC pool when compared to wildtype (Fig. 3m and Supplementary Fig. 4g−i). These Fig. 1 | REV1-deficiency leads to defects in hPGCLC induction and infertility in mice.a Schematic of the hPGCLC differentiation protocol adapted from 47 .b Representative flow cytometry plots and (c) quantification of hPGCLC frequency at day 4 from three independent wildtype or REV1 -/-clones differentiated three times.(Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).d Representative images of wildtype and REV1 -/-aggregates immunostained for SOX17, TFAP2C and OCT4.e Offspring when male (left) and female (right) wildtype or Rev1 -/-mice were mated with wildtype mates of the opposite sex (n = 6 mice per genotype, 3 per sex. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).f Quantification of testis mass of 8−12-week-old wildtype and Rev1 -/-mice (n = 12, 2, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney Utest).g Quantification of ovary mass from 8−12-week-old wildtype and Rev1 -/-mice (n = 13, 2, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).h H&E-stained ovaries and testes seminiferous tubules from 8−12-week-old wildtype and Rev1 -/-mice.i Quantification of SCO tubules per section of testis of 8−12-week-old wildtype and Rev1 -/-mice (n = 10 and 4 animals, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).j Quantification of follicles per section of ovary from 8−12-week-old wildtype and Rev1 -/-mice (n = 8 and 3 animals, left to right). Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test.k Schematic for generation of Rev1 -/-embryos harboring the GOF18-GFP reporter.l GFP fluorescence images of gonads from wildtype and Rev1 -/- E12.5 embryos.m Representative flow cytometry plots and (n) quantification of PGCs by flow cytometry from wildtype and Rev1 -/-E12.5 data reveal that in addition to similar magnitudes, the timing of PGC defect is comparable across the TLS mutants.
Having observed an essential requirement for TLS factors in mammalian germ cell development, we wondered if this was unique to the germline. In agreement with a previous report, we found a 2.6-fold reduction in the frequency of hematopoietic stem and progenitor cells (HSPCs) in adult Pcna R/R mice (Supplementary Fig. 5a, b) 78 . We went on to assess genome stability in the hematopoietic compartment by measuring the frequency of micronucleated normochromic erythrocytes (Mn-NCE). We found a significant increase in the frequency of Mn-NCE in Pcna R/R mice when compared to wildtype (Supplementary Fig. 5c). However, there was no change in the frequency of peripheral red blood cells or hemoglobin concentration (Supplementary Fig. 5d, e). As adult HSCs are largely quiescent we hypothesized that the defect may begin during embryonic development when HSPCs are highly proliferative 79 . Similar to adult mice, we found that E12.5 Pcna R/R embryos had a 1.73-fold reduction in the frequency of HSPCs, revealing that the blood stem cell defect begins during embryonic development (Supplementary Fig. 5f). Despite a numerical HSPC defect and evidence of genomic instability, we found that blood compartment homeostasis was remarkably intact with adult Pcna R/R mice showing comparable bone marrow cellularity to littermates, normal blood cell maturation, and sustained peripheral blood homeostasis (Supplementary Fig. 5g−l).
We next examined a panel of vital somatic tissues and found no gross histological abnormalities consistent with the normal longevity of Pcna R/R mice (Supplementary Fig. 6a). As PCNA is an essential component of the replication machinery, and its modification at K164 directly couples DDT to the replisome, we assessed the proportion of actively dividing cells in highly mitotic tissues. We found comparable numbers of Ki67 + cells in the bone marrow, crypts of the ileum and hair follicle bulges of the skin of Pcna R/R mice compared to controls (Supplementary Fig. 6b−d). Furthermore, we also assessed the effect of PCNA K164R mutation on the function of the liver and kidney and found no difference compared to wildtype (Supplementary Fig. 6e−h). These data show that the K164R mutation of PCNA does not lead to a global reduction in proliferation nor to loss of tissue homeostasis or function. In contrast, histological analysis of the adult gonads revealed complete loss of homeostasis (Supplementary Fig. 7). Consistent with the histological analysis, we observe loss of tissue function in mutants leading to disruption of the hypothalamic-pituitary-gonadal axis. This led to systemic hormonal dysregulation with elevated levels of luteinising hormone (LH) and follicle stimulating hormone (FSH) driving reactive stromal hyperplasia and a significant increase in testicular interstitial cells and ovarian stroma. As animals age, persistent hypergonadotrophism leads to a substantial increase in ovarian mass with mutant ovaries of 12-month-old mice being 3 times larger than wildtype controls (Supplementary Fig. 7d). These features are consistent across TLS-deficient mice with similar defects seen in Rev1 -/-and Rev7 -/- adults (Supplementary Fig. 7).
Overall, whilst the PCNA K164R mutation results in increased sensitivity to exogenous DNA damage and a reduction in HSPCs, our findings reveal that the development and homeostasis of somatic tissues are largely unaffected. In contrast, the modification site of PCNA is essential for PGC development and fertility. Failure of embryonic germ cell development results in dysregulation of hypothalamicpituitary-gonadal hormone regulation with inappropriate stromal proliferation in adults. The temporality and magnitude of Rev1 -/-, Rev7 -/- and Pcna R/R germ cell defects and the molecular dissection of REV1 suggest a common origin of the defect -likely their shared role in TLS.
Loss of genome stability and reduced proliferation of Pcna R/R and Rev1 -/-PGCs Next, we investigated the mechanisms of PGC failure in the absence of TLS factors. As TLS mitigates replication blocks, we asked if Pcna R/R or Rev1 -/-PGCs accumulate unresolved DNA damage. We stained PGCs for the phosphorylation of histone variant H2A. X (ɣ-H2A. X), a marker of DNA DSBs 80 . A greater proportion of TLS-deficient PGCs had >10 ɣ-H2A. X foci compared to wildtype (Fig. 4a). Consistent with our previous data this finding was specific to PGCs as an increase was not observed in the surrounding somatic tissue (Supplementary Fig. 8a). To build on this, we stained for the presence of RPA foci, which binds to single stranded DNA generated during repair transactions or when DNA replication is perturbed and leads to under-replicated regions in the genome 81 . We stained for the RPA subunit RPA32 and quantified the frequency of PGCs harboring RPA foci as this is indicative of DNA damage 82,83 . Consistent with the results from the ɣ-H2A. X staining we observed a higher frequency of PGCs with nuclear RPA foci in Pcna R/R and Rev1 -/-embryos (Fig. 4b). We went on to assess if the DDR was activated by measuring threonine 68 (Thr68) phosphorylation of CHK2, the critical checkpoint kinase 84 . We found that an increased frequency of Pcna R/R and Rev1 -/-PGCs stained positive for pCHK2 when compared to wildtype (Fig. 4c). This shows that PGC development requires TLS and that in its absence cells accumulate damaged DNA.
Altered proliferation and apoptosis are frequent cellular responses to persistent DNA damage. The failure of the PGC pool to expand between E8.5-E12.5 in TLS-deficient embryos could be explained by either reduced PGC proliferation, increased PGC death, or a combination of both. We therefore first asked if TLS-deficiency leads to increased PGC death by staining E12.5 urogenital ridges for the apoptotic marker cleaved-caspase 3 (CC3) 85 . We observed significant 7.7fold and 5.1-fold increases in the proportion of apoptotic (CC3 + ) PGCs in Pcna R/R and Rev1 -/-embryos respectively (Fig. 4d). In contrast, there was no significant increase in the proportion of CC3 + somatic cells in either mutant (Supplementary Fig. 8b). We next assessed in vivo PGC proliferation by injecting pregnant dams with a single dose of ethynyl-2'-deoxyuridine (EdU), which is incorporated into the DNA of replicating cells allowing the quantification of the proportion of cells in S-phase during the EdU pulse (Supplementary Fig. 8c) 86 . We found a significant reduction in the frequency of EdU + PGCs in both Pcna R/R and Rev1 -/-embryos compared to wildtype (Fig. 4e). In the soma however, we observed a comparable number of EdU + somatic cells in mutant and wildtype embryos, suggesting that the reduced proliferation is restricted to the germ cell compartment (Supplementary Fig. 8d). To assess if reduced incorporation of EdU may be due to defects in cell cycle kinetics, we assessed the cell cycle properties of PGCs. First, we stained cells for Ser-10 phosphorylation of histone H3 (pH3) that occurs during cell division facilitating chromatin compaction, necessary for mitosis 87 . We found that a higher proportion of PGCs stained positive for pH3 in Pcna R/R and Rev1 -/-genital ridges (Fig. 4f). We also Fig. 2 | REV7 is required for PGC development in mice.a Schematic of wildtype (Rev1 + ), null (Rev1 -), catalytically inactive REV1 (Rev1 AA ) and C-terminally truncated REV1 (Rev1 CT ) alleles.b GFP fluorescence images of gonads from wildtype, Rev1 -/-, Rev1 -/AA and Rev1 -/CT E12.5 embryos.c Quantification of PGCs by flow cytometry from wildtype, Rev1 -/-, Rev1 -/AA and Rev1 -/CT embryos at E12.5 (n = 35, 12, 10 and 7, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).d H&E-stained ovaries and PLZF-stained testes and quantification (e) of follicles per section of ovary (Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).or (f) frequency of PLZF + cells per seminiferous tubule of 8−12-week-old wildtype, Rev1 -/-and Rev1 -/AA mice (the data shown represent the median and interquartile range; n = 150 tubules per genotype, 50 per genotype, P values were calculated by a two-tailed Mann-Whitney U-test).g Droplet digital PCR (ddPCR) gene expression analysis of Rev1, Rev7, Polk and Polq in FACS-purified PGCs and surrounding somatic cells (SSEA1 -GOF18-GFP − ) from E10.5 embryos (n = 3 independent embryos. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).h GFP fluorescence images of gonads from wildtype, Rev7 -/-, Polk -/-and Polq -/-E12.5 embryos.i Quantification of PGCs from wildtype, Rev7 -/-, Polk -/-and Polq -/-E12.5 embryos by flow cytometry (n = 35, 14, 8 and 9, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).j H&E-stained testis seminiferous tubules from 8−12-week-old wildtype and mutant mice.looked at the localization of cyclin B1 which is in the cytoplasm during G2, before becoming phosphorylated during mitosis, driving its relocalization to the nucleus 88,89 . At E12.5 15.7% of wildtype PGCs had nuclear cyclin B1 and were therefore in G2/M-phase (Supplementary Fig. 8e). This was significantly higher in the absence of TLS with a 2.6fold increase in G2/M-phase PGCs in Pcna R/R gonads (Supplementary Fig. 8e). These data reveal that Pcna R/R and Rev1 -/-PGCs accumulate markers of DNA damage, are less proliferative, accumulate at the G2/M phase of the cell cycle and are more likely to undergo programmed cell death. The combination of these cellular defects likely explains the profound reduction in the number of PGCs in the absence of TLS factors. Despite their scarcity, there are a few remaining PGCs in Pcna R/R and Rev1 -/-embryos at E12.5 (medians Pcna R/R = 7 and Rev1 -/-= 15). However, adult mice are completely sterile suggesting that the remaining PGCs are not able to generate functional gametes. Successful PGC development requires the activation of the germ cell transcriptional program coupled to global epigenetic changes [90][91][92] . We therefore set out to test if Pcna R/R and Rev1 -/-PGCs underwent these critical transcriptional and epigenetic processes. Initially, we performed gene expression analysis on E12.5 PGCs and found that both Pcna R/R and Rev1 -/-PGCs expressed the early markers of PGC development Nanos3 and Prdm1 at comparable levels to wildtype (Fig. 5a). Conversely, expression of the later-stage marker Mvh was dramatically reduced in both mutants (Fig. 5a). We extended the gene expression analysis to additional germ cell specific genes in Pcna R/R E12.5 PGCs. Consistent with the gene expression data presented above, genes normally expressed in the later stages of PGC development (Dazl, Mili, Mael, Sycp3, Mov10l1, Hormad1 and Brdt) had reduced expression compared to wildtype which was not true for early-stage genes (Stella and Fragilis) (Fig. 5b). Therefore, E12.5 TLS-deficient PGCs transcriptionally resemble earlier stages of development.
The expression of germ cell specific genes in PGCs is activated during epigenetic reprogramming, specifically through DNA demethylation of gene promoters 14,[90][91][92] . The process of DNA demethylation occurs across the whole genome and is unique to the PGC compartment in the embryo. Alongside gene expression regulation, DNA demethylation is critical for imprint erasure and X-chromosome reactivation, processes needed for germ cell function. Our gene expression data prompted us to assess DNA methylation of PGCs. We performed whole genome bisulfite sequencing (WGBS) on wildtype and Pcna R/R FACS-purified E12.5 PGCs and compared these to wildtype E6.5 epiblast cells, which are the origin of PGCs (Fig. 5c). Compared to epiblast cells, the genome of wildtype E12.5 PGCs had extremely low levels of DNA CpG methylation, reflecting demethylation during PGC development. In contrast, in Pcna R/R PGCs we found a near complete retention of DNA CpG methylation (Fig. 5c). We mapped the methylation levels across the genome and found the retention in Pcna R/R PGCs was genome-wide (Fig. 5d).
DNA methylation does not proceed uniformly, with different genomic features reaching the lowest level of methylation at different times 7,14,91,92 . We therefore asked if only a subset of genomic features retained DNA methylation in TLS mutants or if the defect was truly global. Analysis of gene bodies and promoters showed that these were hypermethylated in Pcna R/R PGCs compared to wildtype, suggesting a failure in the early, global phase of demethylation (Fig. 5e, f). We also found that repeat elements (LINE-1, SINE elements, and endogenous retroviruses) retained methylation in Pcna R/R PGCs (Fig. 5f and Supplementary Fig. 9a). We validated this result by locus-specific, targeted bisulfite DNA sequencing of LINE-1 as it makes up ~20% of the genome and again found increased methylation in both Pcna R/R and Rev1 -/-PGCs (Fig. 5g and Supplementary Fig. 10a−d). We went on to analyze features which undergo DNA demethylation in the later wave and for which demethylation is both characteristic of germ cell development but also required for function. We found that imprinted differentially methylated regions (DMRs) were hypermethylated in E12.5 Pcna R/R PGCs, including both maternally and paternally methylated DMRs (Supplementary Fig. 9b). Thus, this analysis reveals that the later wave of DNA demethylation also fails in the absence of TLS.
CGIs (CpG islands) in the promoters of genes associated with germ cell specific processes have been shown to retain DNA methylation until E11.5 and be demethylated in the later wave of PGC DNA demethylation 14 . These genes are involved in meiosis and gamete generation and are only expressed in germ cells but silenced and methylated in somatic cells. We found that these promoters were hypermethylated in mutant PGCs (Supplementary Fig. 9c). We next analyzed CpG methylation of genes required for germ cell production whose transcriptional activation is concurrent with promoter demethylation during the later stages of PGC development (germline reprogramming responsive genes -GRR genes) 92 . CpG methylation analysis of a panel of 45 GRRs showed that these were largely demethylated in E12.5 wildtype PGCs (Fig. 5h). In Pcna R/R E12.5 PGCs however, these were hypermethylated. We confirmed this by performing locusspecific BS-Seq of the promoters of two GRR genes Mili and Dazl (Supplementary Fig. 10e, f). It is striking that we observed both reduced expression of the GRR genes Mvh, Dazl, Mili, Mael, Sycp3, Mov10l1, Hormad1 and Brdt and also hypermethylation of these loci in Pcna R/R PGCs (Fig. 5b, i and Supplementary Fig. 9d). This retention of DNA methylation in TLS mutants may explain the reduced activation of GRRs We compared the levels of DNA CpG methylation in mutant and wildtype E12.5 PGCs to the E6.5 epiblast. The retention of methylation across a panel of genomic features revealed that E12.5 Pcna R/R PGCs were more akin to the epiblast than to wildtype PGCs (Fig. 5e−g). Together, these data show that loss of TLS prevents global DNA demethylation in PGCs with retention occurring at early and late demethylated regions. Furthermore, E12.5 mutant PGCs appear trapped at an earlier stage of development with a methylome similar to E6.5 epiblast cells.
Next, we focused on understanding the basis for methylation retention in the absence of TLS and its effect on expression of germline genes. First, we studied the machinery that normally adds methylation marks to DNA by either ensuring methylation patterns are inherited during cell division or by depositing new methylation marks. Following DNA replication, the newly synthesized DNA strand is unmethylated and DNMT1 is recruited to hemimethylated CpG dyads and transfers a methyl group to the unmethylated cytosine whilst DNMT3A and DNMT3B adds methylation marks to unmethylated DNA to deposit de novo methylation. We measured the expression of Dnmt1, Dnmt3a and Dnmt3b and found no difference between wildtype and Pcna R/R E12.5 PGCs suggesting that overexpression of the methyltransferases is not responsible for DNA hypermethylation in the absence of TLS (Fig. 5j and Supplementary Fig. 9e). The enzyme TET1 has been shown to play important DNA demethylation dependent and independent roles in regulating the expression of genes critical for gamete formation. Notably, TET1 has previously been shown necessary for the transcriptional activation and maintenance of low levels of methylation at GRRs 92 . We quantified the expression of Tet1 and its family member Tet2 in wildtype and Pcna R/R E12.5 PGCs and found no difference indicating that failure to activate the GRR genes is not mediated through Tet1 expression (Fig. 5j and Supplementary Fig. 9f). We further validated this through TET1 immunostaining in E12.5 urogenital ridges which revealed the presence of protein in wildtype and Pcna R/R E12.5 PGCs (Supplementary Fig. 9g).
Together, these data reveal that E12.5 TLS-deficient PGCs resemble earlier stages of germ cell development transcriptionally and in their methylome. As DNA methylation regulates gene expression it is plausible that the retention observed in TLS mutants may contribute to the observed reduction in expression of germ cell genes. As well as failure of GRR demethylation, mutant PGCs also fail to erase imprinted DMR methylation, processes essential for normal germ cell function.
Discussion
Declining birth rates and increasing infertility are drawing attention to environmental and genetic causes of human infertility. GWAS studies have identified the DDR as an important regulator of this, however mechanistic studies have for the most part been lacking. Our current work reveals a crucial role for DNA translesion synthesis during Alongside REV1, we show that REV7 and the modification site of PCNA, K164, are essential for embryonic germ cell development. Though all three of these factors can act in multiple different processes, our results assert that it is their common role in TLS that is required 50 . REV1 has both catalytic and non-catalytic functions and we have found that the C-terminal protein interaction domain, critical for TLS, is required in PGCs. Like REV1, the monoubiquitination of PCNA at K164 serves as a TLS scaffold. REV7 interacts with REV3L to form the TLS extender polymerase called Polζ which has a specialized function in catalyzing DNA synthesis after lesion bypass during TLS. Importantly, Polζ is recruited to TLS sites through direct interactions between its REV7 subunit and REV1's C-terminal domain 50,57 . Our finding that both the REV1 C-terminus and REV7 are essential in PGCs supports a role for both in TLS. Our results did not identify an inserter TLS polymerase which may be explained by the well-characterized redundancy between the inserter polymerases 18,19 . We found that the three core TLS factors REV1, REV7 and PCNA K164 are required during embryonic stages of germ cell development and have identical timing and magnitude of PGC defects. In addition, the remaining PGCs in Rev1 -/-and Pcna R/R embryos are phenotypically indistinguishable. This striking phenotypic overlap suggests that the cause of defect is the same in all three mutants with TLS being the only function common to all three factors. Whilst REV7 has described functions in DNA DSB repair and mitosis our data suggest that it is the role of REV7 in Polζ that is required for normal PGC development. Together, these data argue that TLS plays an essential role in fertility by preserving the development of PGCs.
S te ll a F r a g il is M a e l M il i S y c p 3 M o v 1 0 l1 D a z l H o r m a d 1 B r d t
DNA repair pathways such as Fanconi anemia (FA) DNA crosslink repair and DNA DSB repair, have previously been shown to be necessary for PGC development 30,32,[36][37][38] (23, 25, 70−72). It is interesting that both genetic and biochemical studies have shown that the TLS factors REV1 and Polζ (REV7-REV3L) act in a common pathway with the FA repair proteins to maintain cellular resistance to DNA interstrand crosslinking agents [93][94][95][96] . Indeed we found that Pcna R/R , Rev1 -/-and Rev7 -/- MEFs were mildly hypersensitive to DNA crosslinking agents, however this sensitivity was much less than in the absence of FANCA (Supplementary Fig. 11a). This does however leave the possibility that the loss of PGCs in FA-deficient and TLS-deficient mice shares a common cause. However, the magnitude and the temporality of the defects are different between the two classes of mutants with the loss of PGCs in TLSdeficient mice occurring 48 h before a loss of PGCs is observed in FAmutants 30 . The magnitude of PGC loss in E12.5 TLS-deficient embryos is also two orders of magnitude greater than in the absence of the FA pathway 30 . Furthermore, there are substantial phenotypic differences between TLS-deficient and FA-deficient PGCs -the loss of FA repair factors does not alter the expression of GRR genes nor block DNA demethylation unlike the loss of TLS components 30 . This shows that whilst multiple DNA repair pathways are required for normal PGC development, they have distinct roles with different phenotypic outcomes.
The phenotypic features of the few remaining PGCs in TLSdeficient embryos explains the basis of germ cell loss and sterility. We found that mutant PGCs have a higher burden of DNA damage, apoptosis and cell cycle abnormalities as well as a failure in DNA demethylation and activation of the germ cell transcriptional program. Together these phenotypic features are likely to explain the basis of the failure of PGC development and infertility. Our results reveal that TLS is essential for the developmental processes that are needed for the production of mature germ cells 4,92 .
The increase in DNA damage markers, whilst not previously shown in a physiological context, are consistent with known consequences of TLS deficiency upon genotoxin exposure in tissue culture systems. We observe an increase in the frequency of TLS-deficient PGCs with ɣ-H2A. X foci, RPA32 foci and CHK2 phosphorylation. Whilst none of these markers are entirely specific to the accumulation of DNA DSBs the combination of all three strongly suggests that in the absence of TLS PGCs accumulate DNA DSBs 84 . We also observed that an increased proportion of TLS-deficient PGCs are pH3-positive and have nuclear cyclin B1 localization, and therefore are at the G2/M phase of the cell cycle, with a reduced number staining positive for EdU following a 4 h pulse showing that fewer replicate their DNA and hence go through S-phase. These findings are consistent with a role for TLS either during S-phase (on-the-fly TLS) or in post-replicative repair. Whilst this data does not tell us when during the cell cycle DNA damage occurs it does suggest that it poses a block to mitosis following DNA synthesis unless it is resolved by TLS. It is interesting to note that embryonic stem cells have a less effective G1 checkpoint [97][98][99][100] . As this appears to also be the case in PGCs it would suggest that cells can enter into S-phase laden with DNA damage thus necessitating replication coupled repair such as TLS for resolution of damage and entry into mitosis 101 . Finally, we observed an increase in the proportion of TLSdeficient PGCs that undergo apoptosis which is a frequent cellular response to DNA damage. However, several studies have reported that loss of the DDR leads to a reduction in PGC number by altering the cell cycle rather than through apoptosis 102,103 . This difference may be due to the different repair pathways in these studies counteracting distinct classes of DNA damage that, when unrepaired, result in different cellular outcomes. However, apoptosis does play a role in the clearance of PGCs during unperturbed development or when epigenetic regulators, that normally suppress transposons, are lost [104][105][106] . Taken together, our data shows that in the absence of TLS PGCs accumulate markers of DNA damage, induce cell cycle arrest at the G2/M phase of the cell cycle and undergo apoptosis. It is likely that a combination of cell cycle arrest and apoptosis ultimately results in the numerical PGC defect. Whilst these findings are consistent with the described role of TLS Whilst wildtype PGCs have very low levels of CpG methylation by E12.5, we found that TLS-deficient PGCs have genome-wide retention of DNA methylation. Indeed, we found that the methylation of TLSdeficient PGCs at E12.5 was similar to that seen in E6.5 epiblast cells. These data argue that the PGC precursors in the epiblast have undergone de novo methylation during early embryogenesis before later undergoing demethylation in PGC development. The observed cell cycle defects could explain why demethylation is prevented in the absence of TLS. Cell cycle arrested PGCs may be unable to respond to time-dependent cell extrinsic cues, such as signaling molecules that instruct PGCs to proceed in their development. Alternatively, the cell cycle perturbations may block a cell-intrinsic process, preventing activation of the germ cell program and epigenetic reprogramming. Interestingly, the first stage of global DNA demethylation in PGCs relies upon replication to dilute methylation marks 14,92 . Therefore, the cell cycle arrest in TLS-deficient PGCs may result in the genome-wide retention of methylation marks that we observe. The normal expression of TET1 in mutant PGCs argues against dysregulation of either its enzymatic or non-enzymatic functions as a contributing factor to the phenotype observed.
Whilst early stage PGC genes are induced by extrinsic signals and maintained by PGC-specific and pluripotency-associated transcription factors, DNA demethylation is important for the transcription of genes expressed in the later stages of PGC development. Therefore, the failure to transcribe GRR genes and retention of DNA methylation may not be independent of each other. Indeed, we find reduced mRNA expression and DNA hypermethylation of each GRR gene that was analyzed. It is therefore likely that the block in cell cycle progression in the absence of TLS prevents DNA demethylation, transcription of germ cell factors and therefore blocks PGC development.
The specificity of the phenotype which only affects germ cell homeostasis is intriguing. Despite loss of genome stability in the erythroid lineage and a reduction in blood stem cell frequency that we show begins in utero, TLS-deficient mice sustain peripheral blood homeostasis throughout life. Moreover, in vivo assessment of rapidly dividing tissues in Pcna R/R mice revealed no defects or overt phenotypes in somatic tissue homeostasis and consistent with this, mutant mice show normal longevity (Supplementary Figs. 5 and 6). In contrast, there is a complete failure to generate functional gametes. This striking dichotomy raises the question of why TLS is only required to maintain homeostasis in the germ cell compartment. We have demonstrated that TLS is required for processes that occur only in the germ cell lineage: engagement of germline genes and genome-wide DNA demethylation. In addition, we find that the canonical phenotypes associated with TLS deficiency and the DNA damage response are also restricted to the germ cell compartment: unresolved DNA damage, apoptosis, and cell cycle perturbation (Fig. 4 and Supplementary Fig. 8). This raises the possibilities that (i) the DNA damage checkpoints that activate apoptosis or cell cycle arrest are different in PGCs than in somatic cells, (ii) there are replication impediments or other features unique to PGCs that confer a dependency on TLS activity or (iii) that PGCs are more reliant upon TLS to resolve replication impediments than somatic cell types.
Future work will be required to determine which of these possibilities explains the selective requirement for TLS during PGC development. Recent work has shown that PGCs frequently experience transcription-replication conflicts and it is plausible that TLS factors are required to overcome such sources of genome instability 107,108 . Alternatively, the lack of a G1 cell cycle checkpoint in PGCs may render these cells uniquely dependent upon TLS to bypass damage during S-phase that in other cells would be repaired prior to the initiation of replication. Finally, it is tempting to speculate that the dependency of PGC development on TLS may be directly connected to early epigenetic reprogramming events that are required for subsequent germline development.
Mitomycin C (MMC) treatment of BTAG hiPSC cells
Wildtype and Rev1 -/-cells were passaged as single cells then seeded at 1:10 density in Stemfit Basic04 and Y-27632.24 h after, medium was changed and Y-27632 was removed. The next day, 5 ng/ml MMC (Insight Biotechnology, sc-3514A) was added to the medium and 48−72 h later wells were assessed for surviving hiPSC colonies.
Embyroid body gene expression analysis
Total RNA was extracted from hiPSCs and their counterpart day 21 EBs using the RNeasy kit (Qiagen) according to the manufacturer's instructions. First-strand complementary DNA was synthesized using PrimeScript RT Reagent Kit (Takara) according to the manufacturer's instructions. PCR amplification was performed using the SensiMix SYBR No-ROX Kit (Bioline) on a CFX96 machine (Biorad). The primers used for this experiment are listed in Supplementary Table 3. Mean threshold values were determined using standard comparative C T methods. All expression levels were normalized to the hiPSC line used to generate the EBs.
Proliferation assays
hiPSCs were plated into flat-bottomed 96-well plates (ThermoFisher, 167008) at a density of 1000 cells per well in Stemfit supplemented with Y-27632 rock inhibitor. Every day for 5 days the absorbance of the different samples was assessed. Briefly 20 µl of MTS reagent (Promega, G3582) was added to each well and the plate was incubated at 37 °C for two hours. Following incubation, absorbance was measured at 490 nm using a plate reader.
Flow cytometry analysis of hPGCLC aggregates
Day 4 aggregates were collected and dissociated with 0.25% trypsin-EDTA (Life Technologies, 25200056) for 10 min at 37 °C under agitation. Cells were washed with PBS containing fetal bovine serum (FBS) and 0.1% bovine serum albumin (BSA) before being subjected to centrifugation. Dissociated cells were then resuspended in FACS buffer (PBS, 0.1% BSA), filtered by a cell strainer (BD Biosciences) and analysed or sorted by FACS (Aria III, BD Biosciences) based on the eGFP and tdTomato reporters. Analysis of hPGCLCs differentiation efficiency was performed on one wild type clone and three REV1 KO clones in at least three independent experiments. FlowJo version 10 was used for analysis.
Mice
All animal experiments performed in this study were approved by the Medical Research Council's Laboratory of Molecular Biology animal welfare and ethical review body and conform to the UK Home Office Animal (Scientific Procedures) Act 1986 (License no PP6752216). All mice were maintained under specific pathogen-free conditions in independently ventilated cages (GM500; Techniplast) on Lignocel FS-14 spruce bedding (IPS) and provided with environmental enrichment (fun tunnel, chew stick and Enviro-Dri nesting material (LBS)) at 19−23 °C. Mice fed Dietex CRM pellets (SpecialDietService) ad libitum with light from 7:00 a.m. to 7:00 p.m. No mice used in this study were wild and no field-collected samples were used. All mice were maintained on an isogenic C57BL/6J background. Sex-specific analysis was performed for analysis of adult reproductive tissues as detailed in the text. Primordial germ cell analyses were performed before sexually divergent development of the germline and hence data from both sexes was combined. The lack of a sex-specific phenotype was confirmed through disaggregation and separate analysis of E12.5 PGC numbers for Pcna R/R mutants. Embryos were examined at various developmental stages from E8.5 to E13.5 as indicated in the text. Female mice used in timed-mating experiments were aged between 6-25 weeks. The investigators were blinded to the genotypes of all mice throughout the study and data were acquired by relying entirely on identification numbers. The Stella-GFP (Tg(Dppa3/EGFP)6-25Masu) allele (MGI ID: 5519126) was a kind gift from Azim Surani 77 . B6;129P2-Polk tm1.1Rsky /J allele (MGI ID: 2445458) and the B6. Cg-Polq tm1Jcs /J allele (MGI ID: 2155399) have been described previously 60,61 . GOF18-GFP(Tg(Pou5f1-EGFP)2Mnn) (MGI ID: 3057158) JAX (stock ID: 004654) mice were purchased from The Jackson Laboratory 55,56 . We imported the previously described C57BL/6NTac-Mad2l2<tm1a(EUCOMM) Wtsi > /WtsiCnbc strain (EM: 05374), frozen sperm from The European Mouse Mutant Archive and used to derive live mice 45 . These mice were maintained on a C57BL/6J background.
Generation of Pcna K164R , Rev1 AA and Rev1 CT mutant mice Mice carrying the Pcna K164R and Rev1 CT alleles were generated by Alt-R CRISPR-Cas9 (IDT) mediated genome editing in zygotes on a C57BL/6J background.tracrRNA and crRNA were diluted to a final concentration of 1 μg/ul in injection buffer (10 mM Tris HCl pH 7.5, 0.1 mM EDTA). 5 μg of crRNA and 10 μg tracrRNA were mixed and annealed by heating to 95 °C for 5 min and then ramped to 25 °C at a rate of 5 °C per min. Alt-R SpCas9-3NLS was diluted to a concentration of 200 ng/μl in injection buffer. The RNP was assembled by diluting Alt-R SpCas9-3NLS and the annealed crRNA:tracrRNA at a final concentration of 20 ng/μl and incubated at room temperature for 15 min. The ssODN was then added to the RNP complex at a final concentration of 20 ng/μl and injected into zygotes. The zygotes were surgically transplanted into pseudopregnant CD1 females. Progeny were screened for correct gene targeting and the targeted allele sequenced. Rev1 AA mice were generated by gene targeting in mouse embryonic stem cells. Stella-GFP Bac9 ESCs were transfected with an ssODN and px451 plasmid containing a guide targeting exon 10 of Rev1. Clones were screened by PCR followed by AciI restriction digest. Two positive ESC clones were injected into C57BL6/J:TYR blastocysts. Mice with high levels of chimerism were back crossed and the progeny screened by PCR.
Isolation of mouse embryonic fibroblasts
Timed matings were performed between heterozygous mice. Pregnant females were culled at E12.5 to harvest embryos. Embryos were incubated in pre-warmed trypsin solution (2.5 μg.mL −1 trypsin (Gibco), 25 mM Tris, 120 mM NaCl, 25 mM KCl, 25 mM KH 2 PO 4 , 25 mM glucose, 25 mM EDTA, pH 7.6) for 10 min and disaggregated by gentle pipetting. Primary mouse embryonic fibroblast (MEF) cultures were established following standard methods and immortalized using the SV40 large T antigen as described previously. Briefly, Platinum-E retroviral packaging cells (Cell Biolabs) were transfected with pBABE-SV40-Puro and the culture media containing the virus was harvested 48 h later and passed through a 0.22 μm filter. The filtered retrovirus was mixed 1:1 with complete MEF media supplemented with 1 μg.mL −1 hexadimethrine bromide (Polybrene, Millipore). The infective medium was subsequently added to primary MEF cultures and transformed clones were selected for 14 days using 1 μg.mL −1 puromycin.to ultraviolet irradiation or mitomycin C (MMC). After 7 days of culture the MTS cell viability reagent (CellTiter 96® Aqueous One Solution Cell Proliferation Assay, Promega) was added and plates incubated for 4 h at 37 °C; absorbance was then measured at 492 nm.
Histological analysis
Tissues were fixed in 10% neutral-buffered formalin for 24−36 h then transferred to 70% ethanol. Fixed samples were dehydrated and embedded in in paraffin and 4 μm sections cut. Sections were deparaffinised, re-hydrated and stained with haematoxylin and eosin (H&E) following standard methods. Images were captured with an Eclipse Ti2-E (Nikon) microscope and tissue architecture was scored blindly.
Immunohistochemistry
Formalin-fixed, paraffin-embedded samples were sectioned at 4 μm, deparaffinised and rehydrated following standard methods. Slides were boiled in antigen retrieval buffer (10 mM sodium citrate, pH 6) for 10 min and allowed to cool to room temperature before being washed three times in water for 5 min and then once in TBS, 0.1% w/v Tween-20 for 5 min. A hydrophobic ring was drawn around the tissue sections and samples incubated with blocking buffer (TBS, 0.1% w/v Tween-20, 5% v/v goat serum) for 1 h at room temperature. Samples were incubated with the following primary antibodies diluted in blocking buffer at 4 °C overnight: anti-PLZF (1:200, sc-28319) and anti-Ki67 (1:200, ab16667). Slides were washed three times with TBS, 0.1% w/v Tween-20 for 5 min and incubated with the following secondary antibodies for 1 hour at room temperature: swine anti-rabbit (1:200, catalog no. P0339, Dako) or anti-mouse horseradish peroxidase (HRP)-conjugated immunoglobulins (1:200, catalog no. P0339, Dako). For HRP-based immunohistochemistry, slides were incubated for 3−10 min with Sig-nalStain diaminobenzidine substrate kit (catalogue no.8059 P; Cell Signaling and then washed once in water for 5 min. Slides were dehydrated in an ethanol gradient following standard methods and finally in xylene before being mounted with DPX neutral mounting medium (catalog no.317616, Sigma-Aldrich) and coverslips place on top of the slides. Images were captured with an Eclipse Ti2-E (Nikon) microscope. The frequency of PLZF + or Ki67 + cells were scored blindly.
Assessing fertility of mice
Mice were paired with wildtype C57BL/6J mice of the opposite sex. Female mice were monitored daily for the presence of copulation plugs and the number of offspring born over three successive months was recorded (only data from breeding pairs where at least 3 copulation plugs were observed was included in the analysis). Investigators performing the copulation plug checks were blinded to the genotypes of the mice.
Timed matings for embryo isolation
Timed matings were performed overnight and female mice were assessed for the presence of copulation plugs the following day and separated from males. Halfway through the light cycle on the day a copulation plug was observed was designated E0.5. Pregnant mice were culled at noon of the appropriate day during gestation (E8.5-13.5)and the embryos harvested. Samples were processed immediately for further analysis with a small tissue biopsy taken for genotyping.
Immunofluorescence on cultured cells
To analyze the localization and stability of C-terminally truncated REV1, the sequences for full length and truncated REV1 were cloned into pEGFP-C1. HEK293 cells were seeded on no.1.5 coverslips (catalog no.631-0150, VWR) and 24 h later transfected with either plasmid. Subsequently, cells were washed twice for 5 min with PBS then fixed with 4% paraformaldehyde (catalog no.43368, Alfa Aesar) for 20 min and washed with PBS twice for 5 min. Cells were then permeabilised for 10 min with PBS containing 0.1% Triton X-100 and washed with PBS twice for 5 min. Cells were then blocked with PBS-S/0.1% Tween 20 (PBS-S-T) containing 5% BSA for 30 min. Cells were then incubated overnight at 4 °C with the following primary antibody diluted in blocking buffer: anti-GFP (1:500, catalog no. GF090Rl; Nacalai). Cells were then washed with PBS and incubated for 1 h at 37 °C with the following secondary antibody diluted in blocking buffer: goat anti-rat Alexa Fluor 594 (1:1000, catalog no. A21209; Thermo Fisher Scientific). After washes with PBS, coverslips were incubated for 10 min with 2 μg/ ml DAPI. After washes in PBS, coverslips were mounted on glass slides using Prolong Gold Antifade Mountant (catalog no. P36934, Thermo-Fisher Scientific). Images were captured with a LSM780 confocal microscope (Zeiss).
Immunoblotting
To demonstrate the stability of C-terminally truncated REV1, the sequences for full length and truncated REV1 were cloned into pExpress with a 2x FLAG tag at the N-termini. HEK293 cells were transfected with either plasmid and were harvested for protein extraction 48 h later. Protein samples were supplemented with LDS buffer (catalog no. NP0007, Themo Fisher Scientific) and 5% β-mercaptoethanol final, boiled for 5 min at 95 °C and resolved by polyacrylamide gel electrophoresis on NuPAGE 4-12%, Bis-Tris, Mini Protein gels (catalog no. NP0321BOX, ThermoFisher Scientific) in MOPS-SDS buffer (50 mM MOPS, 50 mM Tris base, 3.47 mM SDS, 1 mM EDTA). Separated proteins were transferred onto 0.2 μm nitrocellulose membranes (catalog no 10600015, GE Healthcare) in Tris-glycine (25 mM Tris, 192 mM glycine, ph 8.3) buffer with 20% ethanol. Transfer was set at 35 V for 90 min in a Xcell II Blot module (catalog no. EI9051, ThermoFisher Scientific). After transfer, membranes were incubated for 1 h in blocking buffer (Tris-buffered saline, 0.1% Tween 20, 5% non-fat dry Proliferation Kit for Imaging, Alexa Fluor™ 594 kit (Catalog number: C10639) was used. Pregnant mice were given a single dose of EdU (50 mg.kg −1 ) by intraperitoneal injection (IP) at 10 ml.kg −1 . Females were subsequently culled 4 h post-IP at E12.5 and the embryos harvested; the fetal gonads were dissected and placed into ice-cold PBS. Fetal gonads were fixed in PBS, 4% w/v paraformaldehyde for 30 min at 4 °C. Fixed samples were washed once in PBS for 5 min and then three times in PBS, 1% w/v Triton X-100 for 15 min at room temperature. Samples were then pressed onto glass slides and a large hydrophobic ring drawn around the sample before being incubated in blocking buffer (PBS, 1% w/v Triton X-100, 1% w/v BSA) for 1 h at room temperature. Samples were then with anti-GFP (1:500, catalog no. GF090R; Nacalai) diluted in blocking buffer overnight at 4 °C. Subsequently, samples were washed three times in PBS, 1% w/v Triton X-100 for 5 min at room temperature and fixed in PBS, 2% w/v paraformaldehyde for 20 min at room temperature. Slides were washed three times in PBS and incubated in 200 μL of Click-iT® Plus reaction cocktail made per manufacturer's instructions. Samples were washed three times in PBS, 1% w/v Triton X-100 and incubated with goat antirat Alexa Fluor 488 (1:1000, catalog no. A11029, Thermo Fisher Scientific) secondary antibody. Slides were washed three times in PBS, 1% w/v Triton X-100 for 5 min and stained with DAPI (0.5 μg.mL −1 , PBS) before being mounted with ProLong Gold Antifade Mountant (catalog no. P36934; Thermo Fisher Scientific). Coverslips were placed on top of the slides and allowed to cure for 48 h before images were captured with an LSM780 confocal microscope (Zeiss) and the frequency of positive cells scored blindly.
Blood counts
A 50 μL total blood sample was taken from saphenous veins or via cardiac puncture and transferred into a K3EDTA MiniCollect tubes (Greiner bio-one) and analysed on a VetABC analyzer, using standard settings for mice (Horiba).
Serum hormone analysis
For the determination of serum luteinizing hormone, follicle stimulating hormone (FSH) and testosterone levels, blood samples were collected as described above and transferred into a Microvette collection tube (SARSTEDT) and centrifuged at 10,000 x g. for 10 min at room temperature. The supernatant (serum) was transferred to a 1.5 mL eppendorf tube and stored at −80 °C until further analysis. For serum LH and FSH concentrations were determined using the Milliplex Map Mouse Pituitary Magnetic Bead Panel (catalog no. MPTMAG-49K). Plates were loaded manually and washed using a Bio-Plex Pro wash station (BioRad) and data analysis performed using a Magpix Multiplexer reader (BioRad). Serum testosterone levels were determined using the Demeditec Diagnostics rat/mouse ELISA kit (catalog no. DEV9911). Samples were loaded manually and washes performed using a WellWash Versa platewasher (Thermo Scientific). Absorbance was measured at 450 nm using Perkin Elmer Multicalc software. Serum levels of urea, creatinine, aspartate aminotransferase and albumin were measured using a Siemens Dimension RxL analyser.
Haematopoiesis analysis
Flow cytometry was performed on bone marrow cells that were isolated from the femora and tibiae of mutant mice and appropriate controls by flushing cells and passing them through a 70-μm filter.
Micronucleus assay
The micronucleus assay 112,113 was performed by bleeding mice; 62 μl blood was mixed with 338 μl PBS supplemented with 1000 U ml −1 of heparin (Calbiochem).360 μl of blood suspension was then added to 3.6 ml of methanol at −80 °C and stored at −80 °C for at least 12 h. 1 ml of fixed blood cells was then washed with 6 ml of bicarbonate buffer (0.9% NaCl, 5.3 mM NaHCO3). The cells were resuspended in 150 μl of bicarbonate buffer and 20 μl of this suspension was used for subsequent staining.72 μl of bicarbonate buffer, 1 μl of FITC-conjugated CD71 antibody (GenTex, clone R17217.1.4)and 7 μl RNase A (Sigma) were premixed and added to 20 μl of each cell suspension. The cells were stained at 4 °C for 45 min, followed by addition of 1 ml bicarbonate buffer and centrifugation. Finally, cell pellets were resuspended in 500 μl bicarbonate buffer supplemented with 5 μg ml −1 propidium iodide (Sigma). The samples were analysed immediately on an LSRII FACS analyser (BD) and the data analysed with FlowJo v10.
Statistics and reproducibility
The number of independent biological samples and technical repeats
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
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t y p e R e v 7 -/ -P o l k -/ -P o l q -/ -
Fig. 3 |
Fig. 3 | Embryonic origin of sterility upon PCNA K164 mutation.a Representative images of testes and ovaries from wildtype and Pcna R/R mice.-Quantification of (b) testicular or (c) ovarian mass from 8-12-week-old wildtype and Pcna R/R mice (n = 12, 10, 13 and 8 left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).d Cumulative number of offspring when wildtype or Pcna R/R mice were mated with wildtype mates of the opposite sex (n = 6 mice per genotype, 3 per sex. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).e H&E-stained testis seminiferous tubules and (f) quantification of SCO tubules per section of testis of 8−12-week-old wildtype and Pcna R/R (n = 10 and 15, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).g H&Estained ovaries and (h) quantification of follicles per section of ovary from 8−12week-old wildtype and Pcna R/R mice (n = 8 and 11, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).i GFP fluorescence images of gonads from wildtype and Pcna R/R E12.5 embryos.j Representative flow cytometry plots and (k) quantification of PGCs from wildtype, Rev1 -/-and Pcna R/R gonads at E12.5 (n = 35, 12 and 21, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).l Quantification of PGCs by flow cytometry from wildtype, Rev1 -/-, Pcna R/R and Rev7 -/- embryos at E8.5 (n = 18, 5, 2 and 3, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).m Quantification of PGCs by flow cytometry from E8.5 to E12.5 in wildtype and mutant embryos (wildtype, n = 18, 12, 9, 3 and 17; Rev1 −/− , n = 5, 6, 5, 2 and 8; Pcna R/R , n = 2, 4, 4, 7 and 9; Rev7 −/− , I = 3, 0, 0, 0 and 7, independent embryos, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).
Fig. 4 |
Fig. 4 | Genome instability and cell cycle abnormalities in PcnaR/R and Rev1-/-PGCs.a Top: Representative images of ɣ-H2A. X foci in the nucleus of PGCs (GOF18-GFP + ) at E12.5. Bottom: Frequency of PGCs with >10 ɣ-H2A. X foci per nucleus (n = 6, 5 and 3, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).b Top: Representative images of RPA foci in the nucleus of PGCs at E12.5. Bottom: Frequency of PGCs with RPA foci (n = 5, 4 and 3, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).c Top: Representative images of E12.5 gonads stained for phosphorylation of CHK2 kinase at residue Thr68 (pCHK2). Bottom: Frequency of PGCs that stain positive for pCHK2 (n = 5, 3 and 3, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).d Top: Representative images of E12.5 gonads stained for cleaved-Caspase-3 (CC3) and GFP. Bottom: Frequency of PGCs that stain positive for CC3 (n = 8, 4 and 3, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).e Top: Representative images of E12.5 gonads stained for EdU and GFP. Bottom: Frequency of PGCs that stain positive for EdU (n = 3 per genotype. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).f Top: Representative images of E12.5 gonads stained for phosphorylated-histone-H3 (pH3) and GFP. Bottom: Frequency of PGCs that stain positive for pH3 (n = 11, 4 and 2, left to right. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).
Fig. 5 |
Fig.5| TLS preserves the PGC developmental programme.a RT-qPCR expression analysis of PGCs from E12.5 embryos (n = 3 embryos per genotype. Data represent mean and s.d. P values were calculated by a two-tailed Mann-Whitney U-test).b RT-qPCR expression analysis of PGCs from E12.5 embryos.(Wildtype n = 6; Pcna R/R n = 7. Except for Hormad1 and Brdt, wildtype n = 3; Pcna R/R n = 5).c Box plot of global DNA CpG methylation levels in E6.5 epiblast cells and E12.5 PGCs. The center displays the median, boxes the interquartile range and whiskers the minimum and maximum of CpG methylation distribution of the genome in 5Kbp genomic windows (n = 513027, 318836, 498368).d Circos-plot representation of DNA methylation levels in E6.5 epiblast and E12.5 wildtype PGCs and E12.5 Pcna R/R PGCs. CpG methylation was averaged in 5 Mbp genomic windows and the average DNA methylation is represented as a histogram track.e Heatmap showing methylation levels for E6.5 epiblast and E12.5 PGCs.f Unclustered methylation heatmap of CpG methylation in E6.5 epiblast and E12.5 PGCs.g Bisulfite sequencing and quantification of the Line-1 element from E12.5 wildtype, Pcna R/R and Rev1 -/-embryos (filled:methylated CpG, open:unmethylated CpG. Each point represents one embryo, data represent mean and s.d., P values were calculated by a two-tailed Mann-Whitney U-test).h Violin plots reflecting the DNA methylation levels of GRR gene bodies in E6.5 epiblast cells from wildtype embryos, E12.5 wildtype PGCs and E12.5 Pcna R/R PGCs. The center displays the median, boxes the interquartile range and whiskers the minimum and maximum of percentage methylation calculated over each gene body with each point representing an individual gene (n = 37, 37, 35 left to right).i CpG methylation across selected GRR genes in E6.5 epiblast and E12.5 PGCs. The plots represent the distribution of CpG methylation across genes segmented in 0.1 Kbp genomic windows.j RT-qPCR expression analysis of Tet1 and Dnmt1 in PGCs from E12.5 embryos.(For Tet1, n = 5 for both genotypes. Dnmt1, wildtype n = 6 and Pcna R/R n = 7. Data represent mean and s.d., P values were calculated by a two-tailed Mann-Whitney U-test).
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Domain: Biology
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Gene Expression Profiling of Skeletal Muscles
Next-generation sequencing provides an opportunity for an in-depth biocomputational analysis to identify gene expression patterns between soleus and tibialis anterior, two well-characterized skeletal muscles, and analyze their gene expression profiling. RNA read counts were analyzed for differential gene expression using the R package edgeR. Differentially expressed genes were filtered using a false discovery rate of less than 0.05 c, a fold-change value of more than twenty, and an association with overrepresented pathways based on the Reactome pathway over-representation analysis tool. Most of the differentially expressed genes associated with soleus are coded for components of lipid metabolism and unique contractile elements. Differentially expressed genes associated with tibialis anterior encoded mostly for glucose and glycogen metabolic pathway regulatory enzymes and calcium-sensitive contractile components. These gene expression distinctions partly explain the genetic basis for skeletal muscle specialization, and they may help to explain skeletal muscle susceptibility to disease and drugs and further refine tissue engineering approaches.
Introduction
Skeletal muscles (SM) provide voluntary body movement and locomotion, posture and body position, energy production, fatty acid (FA) β-oxidation, carbohydrate metabolism, and soft tissue support. Skeletal muscles are composed of myofibers, which are formed by fused myoblasts. Myofibers have defining metabolic and contractile properties.
SM contribute to glucose, lipid, and protein metabolism. SM of healthy and well-fed mammals rely on carbohydrate or glucose metabolism as their primary sources of energy from aerobic respiration, anaerobic respiration, or both. SM can also use FAs as a fuel source via β-oxidation when mammals consume a high-fat diet or are in a starving state. As lipid resources become depleted, SM proteins can be broken down for energy use. Oxidative myofibers utilize mostly aerobic respiration. Glycolytic myofibers use primarily anaerobic respiration. Myofibers that utilize both are called oxidative-glycolytic.
Based on contractile properties, myofibers are defined by which myosin heavy chain is present. Myosin heavy chains are a significant component of the thick filament in a sarcomere. Myosin heavy chains (MyHCs) differentiate myofibers into types I (MYH7), II(A) (MYH2), II(B) (MYH4), and II(D or X) (MYH1) [1]. The four contractile properties relate to the three metabolic distinctions. Type I myofibers are oxidative [2]. Type II(A) myofibers are oxidative-glycolytic [2]. Types II(B) and II(D) myofibers are glycolytic [2]. Type II(D) myofibers combine with type II(A) or (B) to create hybrids called II(AD) and II(DB).
The hindlimb SM Soleus (So) and Tibialis Anterior (Ta) were used to determine the gene expression profile of oxidative and glycolytic myofibers by RNA-Seq in one-month-All animal experiments were performed following the Institutional and National Health and Medical Research Council guidelines. The experimental protocol was approved by the Institutional Animal Care and Use Committee at Oregon State University. Biopsies from Pitx2 FL/Z mice were utilized as previously described for this study [5].
RNA-Sequencing (RNA-Seq) Data Analysis
Five So and two Ta myofiber biopsies were collected at P30. RNA was extracted, sequenced, and analyzed as previously described [5]. The raw RNA-Seq data files for these samples are publicly available through the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the reference numbers SRP127367 (So) and SRP145066 (Ta). The reads were aligned using the mouse genome (mm10) and TopHat2. The genomic feature counts were then obtained using HTSeq [5], resulting in a total of 24,421 elements identified. The read counts were normalized to reduce transcript length bias [6]. Longer transcripts appear more prevalent and overshadow smaller transcripts, if not corrected before performing differential expression analysis. Quantitative changes in gene expression levels between the So and Ta groups were discovered using the quasilikelihood F test from the R package edgeR [7], which resulted in 6123 differentially expressed (DE) genes (p < 0.05 c, False Discovery Rate (FDR)-corrected) between So and Ta. The "c" in 0.05 c refers to the p-value being FDR-corrected in all instances. A smaller set of DE genes based on a log-fold change of ±1.0 or greater was used, resulting in 2481 DE genes that passed our fold change cutoff.
RNA-Seq Data Quality Check
So and Ta were processed and subject to RNA-Seq as described above. Principal component analysis of standardized samples showed a clear separation of So and Ta based on RNA expression (Figure 1a). We use red font to denote increased expression in So relative to Ta and blue for decreased expression in So relative to Ta. A volcano plot (negative log of the gene's FDR-corrected p-value vs. log 2 fold change) of all 24,421 annotated genes showed that the subset of 2481 genes included many highly significant DE genes with increased expression differences in So and Ta (Figure 1b). A heatmap of sample-standardized RNA counts of the final subset of 2481 genes shows clear clustering of myofibers from So and Ta based on gene expression (Figure 1c), as we expect from the biplot in Figure 1a.
Molecular pathways enriched for DE genes from the So and Ta samples were identified using the Reactome database [8]. The 2481 DE genes were matched to molecular pathways, and those pathways were statistically analyzed for over-or under-representation using the Reactome database and its corresponding ReactomePA R package [8,9]. The number of genes analyzed by the Reactome R package decreased to 2198 after removing genes with no Entrez ID. Hypergeometric testing, provided by the ReactomePA R package, identified 19 Reactome pathways as overrepresented with an adjusted p-value of less than 0.05 c. Three pathways had fewer than ten associated genes and were removed from further analysis. The DE genes related to the 16 Reactome pathways were ranked from highest to lowest log 2 (FC) values ( Figure S1). The ranking revealed that the 16 Reactome pathways could be consolidated into broader categories, such as contraction, ion and amino acid transport, and glucose and glycogen metabolism ( Figure S1). We identified 119 DE genes with FC ≥ 20, of which 14 were on the Reactome-curated list (Figure 1d). Examination of known gene functions of the remaining 105 high FC genes revealed that 36 could be manually categorized into lipid metabolism, glycogen metabolism, glucose metabolism, or contraction. standardized RNA counts of the final subset of 2481 genes shows clear clustering of myofibers from So and Ta based on gene expression (Figure 1c), as we expect from the biplot in Figure 1a. Molecular pathways enriched for DE genes from the So and Ta samples were identified using the Reactome database [8]. The 2481 DE genes were matched to molecular pathways, and those pathways were statistically analyzed for over-or under-representation using the Reactome database and its corresponding ReactomePA R package [8,9]. The number of genes analyzed by the Reactome R package decreased to 2198 after removing genes with no Entrez ID. Hypergeometric testing, provided by the ReactomePA R package, identified 19 Reactome pathways as overrepresented with an adjusted p-value of less than 0.05 c. Three pathways had fewer than ten associated genes and were removed from further analysis. The DE genes related to the 16 Reactome pathways were ranked from highest to lowest log2(FC) values ( Figure S1). The ranking revealed that the 16 Reactome pathways could be consolidated into broader categories, such as contraction, ion and amino acid transport, and glucose and glycogen metabolism ( Figure S1). We identified 119 DE genes with FC ≥ 20, of which 14 were on the Reactome-curated list (Figure 1d). Examination of known gene functions of the remaining 105 high FC genes revealed that 36 could be manually categorized into lipid metabolism, glycogen metabolism, glucose metabolism, or contraction. Genes with an FDR-corrected p-value (p_c) less than 0.05 c are gray dots, and those with a p-value higher than 0.05 c are black. A select set of highly significant DE genes with large fold changes for So (log FC > 3, −log(p_c) > 50) are shown in red and for Ta (log FC < −3, −log(p_c) > 75) in blue. (c) Heatmap of z-scored log-transformed read counts. Positive z-scores are in orange, and negative z-scores are in blue. Each sample is a column, and each row represents a statistically significant DE gene Genes with an FDR-corrected p-value (p_c) less than 0.05 c are gray dots, and those with a p-value higher than 0.05 c are black. A select set of highly significant DE genes with large fold changes for So (log FC > 3, −log(p_c) > 50) are shown in red and for Ta (log FC < −3, −log(p_c) > 75) in blue. (c) Heatmap of z-scored log-transformed read counts. Positive z-scores are in orange, and negative z-scores are in blue. Each sample is a column, and each row represents a statistically significant DE gene from the 2481 set. (d) Workflow used to reduce the number of genes for further analysis. A subset of the 2481 DE genes with extremely high Genes 2021, 12, 1718 4 of 19 differential expression was selected; 119 DE genes had a log-fold expression greater than twenty (purple). Separately, 97 of the 2481 genes mapped to sixteen statistically overrepresented molecular pathways using the Reactome database R package (p < 0.05 c) and had a log-fold expression change >3 (orange).
Lipid Metabolism
SM primarily metabolize glucose. Oxidative muscles can also use FAs along with glucose and glycogen as fuel sources. Free FA molecules are transported from the liver via cholesterol transport, which shuttles cholesterol, triglycerides, and protein-bound FAs. Three cholesterol-related genes, Pon1, Tspo2, and Apoa2, were expressed in So by 68, 51, and 27-fold, respectively ( Figure 2, Table 1). Pon1 protects against lipid oxidation for highdensity lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol [10]. Tspo2 traffics free cholesterol in erythroid cells [11]. Apoa2 regulates steroid concentrations by modulating cholesterol transport [12], the precursor of steroid hormones.
Once free FA molecules pass through the plasma membrane, they enter the mitochondrial matrix to be broken down through β-oxidation. Specific protein channels handle different FA aliphatic tail lengths ranging from greater than 22 to fewer than 5 carbons. Acsm5 was expressed 30-fold higher in So ( Figure 2, Table 1). Acsm5 encodes for an enzyme that catalyzes the activation of FAs with aliphatic tails of 6 to 12 carbons by CoA to produce acyl-CoA, the first step in FA metabolism [13]. Dio1, expressed 69-fold higher in So ( Figure 2, Table 1), is involved in FA oxidation and oxidative phosphorylation uncoupling primarily in the liver, kidney, and thyroid [14], in addition to altering the thyroid hormone balance of triiodothyronine (T 3 ) and thyroxine (T 4 ) [15]. Thyroid hormone signaling regulates numerous genes involved in skeletal muscle homeostasis, function, and metabolism [16].
Several of the highest FC DE genes in So were involved in lipid, sphingolipid, and derived lipid biosynthesis. FA molecules can be elongated and modified into complex and derived lipid molecules. Tecrl, involved in FA elongation in polyunsaturated FA biosynthesis [17] and myoblast differentiation upon TNF activation [18], was expressed 55-fold higher in So ( Figure 2, Table 1). The increased expression of Tecrl could indicate that the So muscle is still developing in P30 mice.
One of the highest FC genes in this study was Cyp4f39 with a 269-fold higher expression in Ta, which encodes for a FA ω-hydroxylase involved in acyl-ceramide synthesis [19] ( Figure 2, Table 1). Cyp4f39 is involved in cell surface protection, cell recognition, signaling, membrane transportation, increased rigidity, hydrolyzation of very long-chain FAs, and steroid hormone synthesis [19]. Ceramides have been linked to insulin resistance in type II diabetes [20]. Positive or negative fold change represents increased gene expression in So or Ta, respectively.
Figure 2.
Lipid metabolism associated genes in So and Ta myofibers. Substrates and products of the enzymatic pathways for cholesterol transport, β-oxidation, FA elongation, and ceramide de-novo synthesis are shown based on their cellular location. The gene transcripts involved an enzymatic process and had increased expression in So or Ta, labeled in red or blue font, respectively. Font size correlates to relative FC.
Glycogen Metabolism
Glycogen metabolism combines two inverse processes: glycogenesis and glycogenolysis. During glycogenesis, glucose or glucose 6-phosphate molecules are converted into glycogen, using 1 adenosine triphosphate (ATP) and 1 ATP equivalent to add one glucose molecule to glycogen (Figure 3a). Only the glycolytic fibers store glycogen as a source of energy. Glycogen is tapped during times of nutritional insufficiency or rapid demand for Lipid metabolism associated genes in So and Ta myofibers. Substrates and products of the enzymatic pathways for cholesterol transport, β-oxidation, FA elongation, and ceramide de-novo synthesis are shown based on their cellular location. The gene transcripts involved an enzymatic process and had increased expression in So or Ta, labeled in red or blue font, respectively. Font size correlates to relative FC.
Glycogen Metabolism
Glycogen metabolism combines two inverse processes: glycogenesis and glycogenolysis. During glycogenesis, glucose or glucose 6-phosphate molecules are converted into glycogen, using 1 adenosine triphosphate (ATP) and 1 ATP equivalent to add one glucose molecule to glycogen (Figure 3a). Only the glycolytic fibers store glycogen as a source of energy. Glycogen is tapped during times of nutritional insufficiency or rapid demand for muscle activity. This study cannot determine the direction of glycogen metabolism alone, only inferring potentially emphasized steps in either So or Ta. The glycogenesis-involved enzymes phosphoglucomutase (Pgm2, Pgm2l1) and glycogen synthase (Gys2) had increased expression in Ta ( Figure 3a; Table 2). Phosphoglucomutase reversibly converts glucose 6-phosphate to glucose 1-phosphate. Increased expression of phosphoglucomutase could contribute to either glycogen metabolic processes.
Glycogen synthase is a critical enzyme in glycogenesis that converts glucose-1-phosphate into glycogen with the assistance of pyro-phosphorylase and a branching enzyme [25]. Increased expression of Gys2 in Ta may imply that glycogenesis is the priority for this muscle ( Figure 3a; Table 2). Gys2 activity is regulated by dephosphorylation and phosphorylation by protein phosphatase 1 (PP1) and cyclic-adenosine monophosphate (AMP)-dependent protein kinase. PP1 is an enzyme complex with regulatory and catalytic subunits, of which two regulatory subunits, Ppp1r1c and Ppp1r3g, were expressed higher in So ( Figure 3b; Table 2). Ppp1r3g regulates glycogen accumulation [26] and, along with the predicted inhibitory regulatory subunit Ppp1r1c, could be controlling glycogenesis in the So myofibers. The increased expression of the PP1 regulatory subunits in So could indicate a tighter control mechanism that prevents glycogen build-up. Epm2a, a factor that regulates PP1, was increased in Ta ( Figure 3b; Table 2). Epm2a encodes for laforin, which interacts with PP1 and regulates the dephosphorylation of glycogen to promote glycogen branching [27]. Laforin is also involved in preventing cytotoxicity by protein ubiquitination [28], and it may control glycogen synthesis by ubiquitinating PP1, which would prevent the activation of glycogen synthase.
Glycogenesis must be inhibited to trigger glycogenolysis or glycogen catabolism. Glycogenolysis neither requires nor produces energy when breaking glycogen into glucose molecules. The process is triggered by the accumulation of gluconeogenic precursors, such as lactate or alanine. Glycogen synthase is inactivated to inhibit glycogenesis. The inactivation triggers the activation of phosphorylase kinase, which phosphorylates glycogen phosphorylase. Phosphorylase kinase is an enzyme complex of four subunits [29], three of which (Phka1, Phkg1, Phkb) also had increased expression in Ta ( Figure 3c; Table 2). Phosphorylase kinase could be regulating the glycogenolysis in Ta without inhibiting glycogen synthase by ubiquitination. Glycogen phosphorylase pairs with a debranching enzyme to break off glucose 1-phosphate molecules from glycogen; a gene encoding for each (Pygm and Agl, respectively) was expressed higher in Ta (Figure 3a; Table 2). Phka1 phosphorylase kinase α 1 −8 activates glycogen phosphorylase [29] Phkb phosphorylase kinase β −7.5 activates glycogen phosphorylase [29] Positive or negative FC represents increased gene expression in So or Ta, respectively.
Glucose Metabolism
Glucose metabolism consolidates glycolysis and gluconeogenesis enzymatic processes. The former process breaks glucose into an intermediate pyruvate, and the latter is the reverse process. Glycolysis converts glucose into pyruvate in 10 steps, consuming one glucose, two NAD+, two Pi, four ADP, and two ATP molecules to generate two pyruvate, two NADH, two H+, four ADP, and four ATP molecules. The net products are two pyruvate, two ATP, two NADH, and two H+ molecules. Gluconeogenesis converts two pyruvate, four ATP, two GTP, two NADH, two H+, and two H2O molecules into fructose 6-phosphate or glucose 6-phosphate. Overall, gluconeogenesis consumes six ATP, and glycolysis generates a net two ATP. Therefore, the whole glycolysis/gluconeogenesis cycle costs four ATP.
Three critical enzymes regulate the complete glycolysis process, and they are hexokinase, phosphofructokinase-1 (PFK1), and pyruvate kinase. Genes encoding for PFK1 (Pfkm) and pyruvate kinase (Pkm) had increased expression in Ta (Figure 4a; blue font, Table 2). PFK1 converts fructose 6-phosphate to F1,6-BP and is activated by high AMP concentrations and fructose 2,6-bisphosphate (F2,6-BP) [31]. Pyruvate kinase converts phosphoenolpyruvate to pyruvate when activated by high F1,6-BP concentration and is deactivated by an elevated ATP concentration. Four other isoenzymes involved in glycolysis and gluconeogenesis, aldolase (Aldoa, Aldoc), triosephosphate isomerase (Tpi1), phosphoglycerate kinase (Pgk1), and enolase (Eno3 in Ta and Eno2 in So), were increased in Ta (Figure 4a, Table 2). Represented are the addition (+Ca 2+ ) and the removal of calcium ions (−Ca 2+ ). Substrates and products of the enzymatic steps in glycogen metabolism include the interconversion of inactive to active states of regulatory enzymes. Enzymes involved at specific stages are encircled and adjacent to the corresponding reaction arrow. Gene symbols located underneath enzyme names in red or blue font were increased in So or Ta, respectively. Font size correlates to relative FC.
Glucose Metabolism
Glucose metabolism consolidates glycolysis and gluconeogenesis enzymatic processes. The former process breaks glucose into an intermediate pyruvate, and the latter is the reverse process. Glycolysis converts glucose into pyruvate in 10 steps, consuming one glucose, two NAD+, two Pi, four ADP, and two ATP molecules to generate two pyruvate, two NADH, two H+, four ADP, and four ATP molecules. The net products are two pyruvate, two ATP, two NADH, and two H+ molecules. Gluconeogenesis converts two pyruvate, four ATP, two GTP, two NADH, two H+, and two H2O molecules into fructose 6phosphate or glucose 6-phosphate. Overall, gluconeogenesis consumes six ATP, and glycolysis generates a net two ATP. Therefore, the whole glycolysis/gluconeogenesis cycle costs four ATP.
Three critical enzymes regulate the complete glycolysis process, and they are hexokinase, phosphofructokinase-1 (PFK1), and pyruvate kinase. Genes encoding for PFK1 (Pfkm) and pyruvate kinase (Pkm) had increased expression in Ta (Figure 4a; blue font, Table 2). PFK1 converts fructose 6-phosphate to F1,6-BP and is activated by high AMP concentrations and fructose 2,6-bisphosphate (F2,6-BP) [31]. Pyruvate kinase converts phosphoenolpyruvate to pyruvate when activated by high F1,6-BP concentration and is deactivated by an elevated ATP concentration. Four other isoenzymes involved in glycolysis and gluconeogenesis, aldolase (Aldoa, Aldoc), triosephosphate isomerase (Tpi1), phosphoglycerate kinase (Pgk1), and enolase (Eno3 in Ta and Eno2 in So), were increased in Ta (Figure 4a, Table 2). Represented are the addition (+Ca 2+ ) and the removal of calcium ions (−Ca 2+ ). Substrates and products of the enzymatic steps in glycogen metabolism include the interconversion of inactive to active states of regulatory enzymes. Enzymes involved at specific stages are encircled and adjacent to the corresponding reaction arrow. Gene symbols located underneath enzyme names in red or blue font were increased in So or Ta, respectively. Font size correlates to relative FC.
When blood glucose concentrations are low, glucose can be generated through gluconeogenesis, an 11-step process for converting pyruvate to glucose. The enzymes pyruvate carboxylase, PEP carboxykinase, and fructose 1,6-bisphosphatase (FBPase1) are critical regulatory points in gluconeogenesis. FBPase1 (Fbp2) and PEP carboxykinase (Pck1) were increased in Ta and So, respectively (Figure 4a, Table 2). FBPase1 converts F1,6-BP to fructose 6-phosphate in the presence of a high concentration of AMP and F2,6-BP, while increased citrate levels deactivate it. PEP carboxykinase turns oxaloacetate into phosphoenolpyruvate and is enabled by a high ADP concentration.
All myofibers utilize glycolysis, whether or not they use aerobic and/or anaerobic respiration. Glycolysis occurs under both aerobic and anaerobic conditions because it does not require oxygen. Most of the DE genes associated with glucose metabolism had prominent expression in Ta compared to So. As a muscle that uses anaerobic metabolism to gain fuel, Ta may better utilize the anaerobic pathway due to the increased expression levels of glycolytic and regulatory enzymes.
(Pck1) were increased in Ta and So, respectively (Figure 4a, Table 2). FBPase1 converts F1,6-BP to fructose 6-phosphate in the presence of a high concentration of AMP and F2,6-BP, while increased citrate levels deactivate it. PEP carboxykinase turns oxaloacetate into phosphoenolpyruvate and is enabled by a high ADP concentration.
All myofibers utilize glycolysis, whether or not they use aerobic and/or anaerobic respiration. Glycolysis occurs under both aerobic and anaerobic conditions because it does not require oxygen. Most of the DE genes associated with glucose metabolism had prominent expression in Ta compared to So. As a muscle that uses anaerobic metabolism to gain fuel, Ta may better utilize the anaerobic pathway due to the increased expression levels of glycolytic and regulatory enzymes.
Contraction
The Reactome pathway overrepresentation analysis identified contraction as another significant difference between So and Ta. The sliding filament theory explains the molecular basis of muscle contraction, where myosin and actin filaments interact in a particular manner to produce contractile force. Most contraction-related DE genes exposed were associated with the thin filament, thick filament, and Z-line of the sarcomere (Figure 5a, Table 3).
The thin filament is composed of actin subunits. Actc1 had increased expression in Ta (Figure 5b, Table 3). Tropomyosin wraps outside the actin helix to stabilize the strand and rotate the thin filament to expose the binding sites. Two isoforms of tropomyosin were increased in So (Tpm3) [32] or Ta (Tpm1) [33] (Figure 5b, Table 3). The troponin complex is a regulatory element of the actin filament and is associated with calcium binding, inhibitory regulation, and tropomyosin binding. Tropomyosin-binding element encoding genes Tnnt1 [34] and Tnnt2 [35] were increased in So, while Ta had increased expression of Tnnt3 [36] (Figure 5b, Table 3). The calcium and inhibitory troponin units, Tnnc1, Tnni1, and Tnni3, had an increased expression in So (Figure 5b, Table 3). The actin filaments are capped by tropomodulin Tmod1, which was expressed higher in Ta (Figure 5b, Table 3).
The thick filament consists of several strands of heavy myosin chains, which are two myosin heavy chains wrapped around one another with myosin heads at the ends that attach and flex to move along the thin filament. Myosin heavy chain isoforms unique to type I (Myh7) [1] and type II(B) (Myh4) [37] myofibers were increased in So and Ta, respectively (Figure 5c, Table 3).
Myosin light chains and myosin-binding proteins regulate myosin head movement. Myosin light chains are classified into essential (ELCs) and regulatory (RLCs). ELCs stabilize the myosin head, while RLCs stiffen the myosin neck domain. ELCs encoded by Myl6b, Myl3, and Myl4 [2] and the RLCs encoded by Myl2, Myl10 [38] were increased in So (Figure 5c, Table 3) while only Mylpf [39] was increased in Ta. Mybpc2 [40] and Mybph that encode for myosin-binding proteins that assist in movement along the thin filament were increased in Ta (Figure 5c, Table 3).
The thin and thick filaments are held in an antiparallel configuration by a structure called the Z-line at the sarcomere ends (Figure 5d). The titin cap (Tcap) [41] is involved in mechano-electrical links between Z-lines and T-tubules and was expressed higher in So (Figure 5d, Table 3). Actinin (Actn3) [42] is involved in stabilizing the antiparallel formation and was increased in Ta (Figure 5d, Table 3). These structural differences could be altering the electrical activity and, in turn, the contractile properties of the different myofibers in the So and Ta muscles.
Discussion
Two broad-scale patterns emerged along with the three categories of overrepresented molecular pathways when examining the gene expression profiles of So and Ta muscles ( Figure S1). First, DE genes involved in lipid, glucose, and glycogen metabolism pathways associated more with one muscle than the other (Tables 1 and 2). Second, each muscle expressed unique genes encoding for similarly functioning isoenzymes, particularly for genes involved in the contraction and ion transport categories (Tables 3 and S1). We put these differences into context below, starting with the pathways that differentiate the muscle types. DE genes with the highest FC difference were not prevalent among genes associated with the overrepresented pathways. Therefore, DE genes with a greater than 20-fold expression difference were included for further investigation. Similarly seen in a previous study [54], we found that approximately 10% of DE transcripts were mainly related to fatty acid metabolism, structural components, and neuromuscular junction assembly.
Lipid Metabolism
FA lipids are an alternative energy source during fasting, starvation, and endurance exercise in oxidative muscles. Both So and Ta muscles contain oxidative myofibers, and both should express genes associated with FA metabolism (Figure 2). However, DE genes explicitly associated with FA transport and catabolism were increased in expression in So, which contains a higher percentage of oxidative fibers in its overall myofiber composition. In contrast, the 269-fold increased expression of Cyp4f39 in Ta suggests that lipid catabolism to generate ceramides is more active in Ta. Ceramide accumulation has been linked to insulin resistance in type II diabetes [55]. The increased Cyp4f39 expression may allow Ta to utilize ceramides to shift from being insulin-sensitive and from facilitating efficient glucose uptake [20] to becoming insulin-resistant. Alternatively, increased expression of Cyp4f39 and presumed increased ceramide levels in Ta might be associated with differential apoptosis, cell cycling, or autophagy.
Glycogen and Glycosaminoglycan Metabolism
Glycogen acts as an energy reserve for myofibers by storing excess glucose molecules that are then utilized when blood glucose levels are low. Glycolytic or oxidative-glycolytic myofibers retain glycogen because they rely heavily on glucose as energy, and Ta has a high percentage of those myofibers; therefore, the increased expressions of the glycogen metabolic enzyme genes Gys2, Pygm, Phka1, Phkg1, and Phkb in Ta support the known metabolic composition of the muscle relative to So (Figure 3a; Table 2).
Hydrolysis of muscle glycogen to glucose occurs in lysosomes that engulf glycogen granules. The lysosome-associated DE genes, Slc35d3, Atp6v0d2, and Galns, exhibited increased expression in So, suggesting that lysosomal-related organelles in So and Ta myofibers perform slightly different functions (Table S1). Slc35d3 is associated with the biosynthesis of platelet-dense granules [56]. Platelet-dense granules contain high concentrations of calcium, adenine nucleotides, pyrophosphate, and polyphosphate molecules that enhance autophagy in lysosome-related organelles. Atp6v0d2 is a part of vacuolar ATPases involved in proton translocation into vacuoles, lysosomes, or the Golgi apparatus to lower the pH [57]. The presence of Atp6v0d2 suggested that lysosome-related organelles in So need to reduce their pH levels routinely. If lysosome-related organelles in So myofibers have an enhanced autophagy, the pH levels within those organelles would fluctuate as the cell engulfs and metabolizes more molecules from the extracellular space and may require proton pumps.
Galns is involved in glycosaminoglycan biosynthesis. Glycosaminoglycans are cell surface proteins with branches of sugars projecting into the extracellular matrix to support cell identity, adhesion, and growth. Galns encodes for a protein that breaks keratan sulfate off glycosaminoglycans [58] in lysosomes. Stab2, B3gat1, and Cspg5 are also involved in glycosaminoglycan synthesis, indicating that So and Ta synthesize distinct glycosaminoglycans associated with cell adhesion and proliferation (Tables S1 and S2). Stab2 is involved in the endocytosis of metabolic waste products, including circulating hyaluronic acid (HA) that promotes cell proliferation once the plasma concentration decreases. B3gat1 is involved in generating HNK-1 carbohydrate (CD57) cell surface epitope associated with cell adhesion [59]. Cspg5 is involved in chondroitin sulfate synthesis, another molecule added to proteoglycans and connected to cell adhesion, growth, migration, and receptor binding in the central nervous system (Table S1). The high Cspg5 expression in So suggests that neurons associated with the So muscle are marked differently.
Glucose Metabolism
Genes associated with glucose metabolism were increased in Ta more than So despite both muscles utilizing glycolysis. Yet, Eno2 and Pck1 were increased in expression in So compared to the 11 isoenzymes in Ta, perhaps due to the higher percentage of type II(B) glycolytic myofibers in Ta ( Figure 4, Table 2). Glycolysis-related enzymes encoded by Pfkm, Pkm Pfkfb3, Pfkfb1, and Pfkfb4 were increased in Ta (Figure 4b, Table 2). In contrast, two gluconeogenesis regulatory enzymes that regulate the reversion of pyruvate to glucose in skeletal muscle encoded by Fbp2 and Pck1 were expressed higher in Ta and So, respectively. Gluconeogenesis in Ta might not be as tightly controlled across several steps as glycolysis seems to be based on the 3-to-1 regulatory enzyme ratio. Gluconeogenesis may be controlled by the PFK2/FBPase2 complex instead of relying on the energy-costing enzyme PEP carboxylase seemingly utilized by So. Pfkfb4 had a higher DE than Pfkfb3. Pfkfb4 opposes Pfkfb3 as it redirects glucose to the pentose phosphate pathway to promote the detoxification of reactive oxygen species and lipid and nucleotide biosynthesis. Fbp2, which was also increased in expression in Ta, converts F1,6-BP back into fructose 6-phosphate. These combined findings may indicate that Ta initiates gluconeogenesis at the PFK2/FBPase2 step of the glucose metabolism process to reduce energy loss.
Contraction
Several components that contribute to sarcomere formation were identified as DE between So and Ta, including MyHCs, myosin light chains, troponin subunits, and NMJassociated proteins (Figure 5a-e). The presence of type I (Myh7) and type II(B) (Myh4)associated MyHC isoforms among DE genes provided proof of the concept for this analysis. The fold change in differential expression of Myh7 and Myh4, 34.7-and 57.7-fold, respectively (Table 3), reflected the appropriate percentage of types I and II(B) myofibers in each muscle. So and Ta both have type II(A) and II(D) myofibers, so differential expression of their specific MyHCs was predictably absent from our analysis. Our data also support a previous analysis correlating Myh4 and Myh7 with other genes associated with myofiber types I and II(B) [54], respectively (Tables 3, S1 and S2). The myosin ELCs and RLCs mainly correlated to the appropriate muscle, except for Myl4 and Myl6 (Figure 5c, Table 3). The troponin complex subunits encoded by Tnnt1, Tnni1, and Tnnc1 were unique and highly expressed in So (Figure 5b, Table 3), suggesting that the troponin complex in So might be modified relative to Ta to keep the actin filament in an open position longer, facilitating longer durations of contraction.
Factors affecting NMJ specification and calcium handling have been theorized to be among the non-myofibrillar, muscle-specific systems that allow for increased maximal isometric stress after P28 [60]. Synaptic modeling affects the motility or twitching of the skeletal muscle. Muscle identity is determined before innervation, yet innervation maintains muscle identity. During regeneration, only innervated So muscles can upregulate slow isoform mRNAs [61]. Yet, muscles do not completely change into another phenotype when neural cues are altered [62]. Intrinsic signaling from nerves and other sources, and environmental cues, dictate adult muscle phenotype [61]. Genes are associated with neurotransmission and synaptic vesicles [43] at the presynaptic side of NMJs, in addition to clathrin-mediated endocytosis [47,49], and cell adhesion [48] of the postsynaptic side was highly expressed in So. The release and retrieval of the synaptic vesicles and their contents may play an important role in the slow-twitch mechanism. On the contrary, Ta had increased expression of genes associated with the presynaptic side of NMJs only, which modulate neuronal excitability [44], broaden the action potential [45], and slow down the degeneration of NMJs [46]. These genes may explain how Ta and its associated neurons handle the stimulation necessary for rapid contraction. The DE genes related to the ion transport pathways may be associated with the postsynaptic side of NMJs in Ta myofibers, such as the voltage-dependent calcium channel subunits (Table S1). However, all of this information and conjuncture is based on a small number of differentially expressed genes.
Conclusions
Two hindlimb muscles, So and Ta, which perform distinct locomotion functions, are characterized by unique metabolic and contractile properties. This focused investigation identified several genes uniquely tied to either So or Ta when these two muscle groups are compared. Approximately 10% of the mouse transcriptome was differentially expressed, including genes involved in lipid, glucose, and glycogen metabolism. Very highly differentially expressed genes such as Cyp4f39 highlight previously undescribed potential differences in fatty acids between the SM that may underlie susceptibility to pathologies. The high DE of Pfkm and Gys2 point to known differences in glucose and glycogen metabolism between the SM but add molecular details to the picture. Among contractionrelated genes, RLCs, ELCs, and troponin complex subunits were higher expressed in So. DE of genes involved in NMJs suggests that electrochemical signaling is different between the two muscle groups, supporting the contractile differences.
Overall, this detailed study into the gene expression differences between these two muscle groups adds to the abundance of data on molecular differences among SM. Some similar results were found in single nuclei studies, which also identified unique expressions among individual skeletal muscle groups [54,63]. This study also highlights the continuing need to maintain and update gene and pathway ontology databases, especially adding more information on gene expression in different cell types.
Supplementary Materials: The following are available online at [URL]/ 10.3390/genes12111718/s1, Figure S1: Evaluation of Reactome overrepresented pathway analysis, Table S1. Differentially Expressed Genes in So and Ta involved in Ion Transport and Glycosaminoglycan biosynthesis pathways, Table S2. Differentially Expressed Genes in So and Ta organized by Immune Response, Signaling, and Cellular function pathways.
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Domain: Biology
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Network Architecture Producing Swing to Stance Transitions in an Insect Walking System
The walking system of the stick insect is one of the most thoroughly described invertebrate systems. We know a lot about the role of sensory input in the control of stepping of a single leg. However, the neuronal organization and connectivity of the central neural networks underlying the rhythmic activation and coordination of leg muscles still remain elusive. It is assumed that these networks can couple in the absence of phasic sensory input due to the observation of spontaneous recurrent patterns (SRPs) of coordinated motor activity equivalent to fictive stepping-phase transitions. Here we sought to quantify the phase of motor activity within SRPs in the isolated and interconnected meso- and meta-thoracic ganglia. We show that SRPs occur not only in the meso-, but also in the metathoracic ganglia of the stick insect, discovering a qualitative difference between them. We construct a network based on neurophysiological data capable of reproducing the measured SRP phases to investigate this difference. By comparing network output to the biological measurements we confirm the plausibility of the architecture and provide a hypothesis to account for these qualitative differences. The neural architecture we present couples individual central pattern generators to reproduce the fictive stepping-phase transitions observed in deafferented stick insect preparations after pharmacological activation, providing insights into the neural architecture underlying coordinated locomotion.
INTRODUCTION
Walking is based on cyclic patterns of coordinated movement among legs. A step consists of the stance phase, during which the leg has ground contact, supports, and propels the animal, and the swing phase, when the leg is lifted and moves back to its initial position to complete the stepping cycle. We know a lot about the role of sensory organs in initiating or terminating the stepping phases in a single leg (1). However, the exact organization of the central neural networks underlying stepping-phase transitions in multi-segmented locomotor organs still remains elusive. So far there have been different approaches considered in modeling studies focusing on either vertebrates or invertebrates. According to one approach, stepping is based on motor neuron synergies controlled by central rhythm and pattern generating networks, which then interact with sensory feedback to generate stepping (2,3). However, in other studies sensory feedback is a larger focus, Ekeberg et al. (4) reconstruct stepping in a cat's hindlimb based on sensory signals and the mechanical coupling of the legs. Similarly, in models studying the stick insect walking system, sensory signals play a large role as the position of the leg is the main factor for ending a step phase and initiating the next one (5,6). Lastly, there are reduced models which define a single network for each leg and do not consider the individual networks controlling each leg segment (5,7).
For our modeling approach we have decided to use biological data from the stick insect because it is known that there are dedicated central pattern generators (CPGs) to move each of the main leg joints (8). Walking in the stick insect is based on intra-and intersegmental interaction among CPG networks and peripheral sensory input (9). The relative contribution of the central vs. peripheral neuronal mechanisms for walking has been often debated. Pharmacological activation of the central networks in deafferented insect preparations has contributed significantly to our understanding regarding the possible role of sensory input in adapting the default centrally-generated patterns, potentially giving rise to behaviorally-relevant coordination. Such experiments have not resulted in generation of motor patterns similar to the coordination patterns observed in vivo. Thus, there has been no indication of fictive walking in adult insect preparations (10)(11)(12). However, previous experiments in the deafferented stick insect mesothoracic ganglion, after application of the muscarinic agonist pilocarpine, have indeed revealed coordinated activity of the motor neuron pools within a hemisegment in the absence of phasic sensory input. This activity resembles stepping-phase transitions in vivo (8) and are therefore referred to as fictive stepping-phase transitions. There have been distinct patterns of coordinated motor activity described which spontaneously emerge in a non-cycle-to-cycle fashion throughout the recording, and are therefore called spontaneous recurrent patterns (SRPs). The SRP1 is characterized by a switch from protractor to retractor motor neuron activity during a depressor burst consisting of both the slow and fast depressor motor neurons. This switch resembles a transition from swing to stance stepping phase during forward walking. The SRP2 is characterized by the opposite switch from retractor to protractor activity during a combined slow and fast depressor burst, resembling a transition from swing to stance stepping phase during backward walking (8,13). These patterns have only been described for the mesothoracic ganglion of the stick insect so far, with the SRP1 showing a higher occurrence throughout the recording compared to SRP2. Although there have been neurons identified in the ventral nerve cord which influence the occurrence of SRPs, the underlying mechanisms resulting in SRP generation are still unknown (12,13).
SRP-like activity patterns have also been reported in neurophysiological recordings of motor activity in the fourth thoracic ganglion of the crayfish after bath application of cholinergic agonists (14). In their study, 90% of the recorded intervals showed coordinated activity consisting of the levator motor neurons bursting in phase with the retractor motor neurons. This pattern is indicative of fictive backward walking. In the stick insect mesothoracic ganglion, according to the only study focusing on intrasegmental coordination in insects, 78% of the patterns of coordinated activity belonged to the SRP1 type, representing a fictive transition from swing to stance phase during forward stepping (8). Here, we hypothesize that SRPs can also be found in other ganglia, such as the metathoracic ganglion, and that an underlying network architecture exists to generate these SRPs in the absence of sensory feedback. We have chosen to investigate potential architectures through simulation, following the sentiment by (15) that "... many interactive processes on the subcellular, cellular and network levels are dynamic and complex. Computational methods are therefore required to test whether tentative explanations derived by intuition can account for experimental findings." The majority of current engineering research develops coordination through feedback and subsequent rules based on the returned values. Popular controllers such as WalkNet (5) and neuroWalknet (16) use behavioral data from biology to create an artificial neural network capable of controlling 18 degrees of freedom. The individual leg controllers use coordination rules, network architecture, and sensory feedback to produce walking behaviors on a six-legged robot. WalkNet can perform forwards and backwards walking on smooth and uneven terrain as well as curved walking. neuroWalknet extends the walking ability to produce different footfall patterns including tripod, tetrapod, and pentapod patterns as well as other stable intermediate patterns observed in stick insects (17) and Drosophila (18). Ekeberg et al. (19) realize a neuronal control network sufficient for controlling stick insect legs in each segment-front, middle, and hind. They use a simulation to send feedback to the control network as the primary driver for joint coordination. von Twickel et al. (20) use a similar strategy by decentralizing the joint controllers and relying on feedback from a simulated leg to develop a single-leg controller. All of the mentioned studies have proposed different control models for joint (intralimb) and leg (interlimb) coordination to reproduce biological behaviors and confirm the importance of sensory feedback in coordinated walking. However, there have not been any studies investigating the underlying neural network architecture before sensory feedback is applied in order to reproduce the fictive stepping-phase transitions observed in deafferented stick insect preparations. We address this by using biologically-plausible spiking and non-spiking neuron models to create a single joint architecture similar to the one suggested by (21). The setup connects spiking rhythm-generating populations (RGPs) to non-spiking interneurons (NSIs) to inhibit the spiking motor neuron populations (MNPs). Coordination NSIs (cNSIs) are also used to connect the individual joints to create inter-joint coordination similar to the biological interneurons found by (13). The addition of NSIs is able to decouple the neuronal dynamics of the spiking populations, removing the issue of competing dynamics when communicating between spiking neurons. In this way, we find that the integration of NSI's into spiking neural networks reduces complexity when building the network while maintaining biological plausibility.
Our study shows that SRPs occur in both the meso-and metathoracic ganglia of the stick insect and highlight an interesting difference between the two thoracic ganglia. We propose a neural architecture that is able to reproduce the prominent SRP in each ganglia confirming that network architecture can account for the difference in SRP type.
Experimental Animals
The animals used in this study are adult female Indian stick insects of the species Carausius morosus bred in our colony at the Biocenter, University of Cologne. Animals are kept at 20-26 • C with 45-60% humidity, under a 12 h light/12 h dark cycle. The experimental procedures described below comply with the German National and State Regulations for Animal Welfare and Animal Experiments.
Preparation and Experimental Setup
The experimental procedure followed in this study has been previously established by (8). Some experiments are performed on the ipsilateral hemisegment of the isolated (disconnected from neighboring ganglia) meso-and meta-thoracic ganglia. Other experiments are performed on the interconnected meso-and meta-thoracic ganglia. In all studies, the ganglia are deafferented and isolated from the rest of the nerve cord. Additionally, the first abdominal ganglion is always left interconnected to the meta-thoracic ganglion. Rhythmic activity in motor neuron pools is induced by bath application of the muscarinic acetylcholine receptor agonist pilocarpine, and is assessed by recording extracellular activity by placing extracellular electrodes on the lateral nerves nl2, nl3, nl5, and C2. This captures the motor activity innervating the leg muscles. All lateral nerves in the ganglia of interest are either crushed or cut to block afferent input. The nerves nl2 and nl5 carry the axons that innervate the antagonistic protractor and retractor coxae muscles, while nl3 and C2 innervate the extensor tibiae and depressor trocanteris, respectively (22). The protractor and retractor move the leg forwards and backwards along the horizontal plane, the extensor muscle extends the tibia and the depressor muscle allows for downward movement of the leg along the vertical plane. Collectively, these muscles allow the movement about the three main leg joints: the Thorax-Coxa (ThC), the Coxa-Trochanter (CTr), and the Femur-Tibia (FTi) joint.
The signal recorded with the extracellular electrodes is preamplified by 100-fold using isolated low-noise preamplifiers (model PA101, Electronics workshop, Zoological Institute, Cologne). It is further amplified by ten-fold to reach an overall gain of 1,000 and filtered (low-cut: 200 Hz, high-cut: 3 kHz) using a standard four-channel amplifier/signal conditioner (model MA102, Electronics workshop, Zoological Institute, Cologne). Finally, the signal is digitized at a sampling rate of 12 kHz, using the Micro 1401-3 acquisition unit (CED, Cambridge, UK) and it is monitored using the Spike2 software (CED, Cambridge, UK).
Analysis of the Neurophysiological Data
The electrophysiological data are initially assessed by using tools provided by Spike2. Time series of the action potentials (spikes) are marked by manually setting a threshold. In cases where spikes of non-interesting neurons also cross the threshold, such as those of the common inhibitory motor neuron, the respective time-series are subtracted from the data. The depressor bursts are marked, noting the timing of the burst onset because the depressor cycles are used as a reference for the analysis. Figure 1 depicts how the phase difference between nerves is calculated. In this study, "Nerve 2" in Figure 1 represents bursts of motor activity in the depressor because it is the reference nerve. Spontaneous recurrent patterns of coordinated motor activity are manually marked throughout the recording to use for the phase calculation. The spike time-series are exported using a sampling rate of 1,000 Hz and the phase of each spike within the ongoing depressor cycle is calculated throughout the recording using MATLAB. Phase values are binned (bin size = 10 • ) and the mean bin value [± standard deviation (STD)] among animal preparations is plotted after normalizing data of each animal to the maximum number of spikes. Histograms are made by either considering all depressor cycles throughout the recording or selecting only those cycles during which an SRP1 or SRP2 occurred. Circular means and angular deviations, as well as the 90% confidence intervals (CI) are calculated using the Circular Statistics Toolbox in MATLAB (23).
Network Simulation
The neural network designed in this study is based on observations from biological research. Individual CPGs are known to control a single antagonistic muscle pair producing alternating rhythmic activity per joint (8,9). This means that the individual networks must be coordinated to produce the observed swing and stance phases of a single leg step cycle. Membrane potential oscillations of NSIs have been shown to correlate with SRPs (13) indicating that NSIs are most likely involved in inter-joint coordination. Our network uses this knowledge to couple three CPG networks with NSIs to produce coordinated firing.
Single Joint Architecture
Before coordination can be achieved, a reliable anti-phasic output must be generated corresponding to the antagonistic muscle pairs in the stick insect leg controlling each joint. Each individual joint is controlled by one CPG consisting of two RGPs mutually inhibiting each other in a half-center oscillator architecture (9). Figure 2A shows a detailed diagram of the neural network controlling a single joint.
The RGPs and MNPs consist of adaptive exponential integrate-and-fire (AdEx) neurons. The parameters for the RGP neurons are set to bursting whereas the parameters used for the MNP neurons produce tonic spiking. These parameters are set according to (24). The AdEx neuron dynamics are described by Equations (1) and (2).
when V m > 0mV then w → w + b Frontiers in Insect Science | www.frontiersin.org FIGURE 1 | Illustration of how the phase (φ x ) of each spike of a nerve (Nerve 1) is calculated in relation to the cycle of a reference nerve (Nerve 2). The time is recorded for all spikes reaching the set threshold within Nerve 1 (t x ). The phase is calculated for each spike and then averaged to find the average activity of a nerve as compared to the reference nerve. The cycle period is dynamic so the phase is calculated based on the corresponding cycle, the cycle period is represented by t 2 − t 1 where t 1 is the cycle onset and t 2 is the end of the cycle. "where C is the membrane capacitance, V m is the membrane potential, E L is the resting potential, g L is the leakage conductance, I e is the bias current plus Gaussian white noise, a is the sub-threshold adaptation conductance, b is the spiketriggered adaptation, T is the sharpness factor, τ w is the adaptation time constant, V th is the voltage threshold potential, V reset is the reset potential, and w is the spike adaptation current (24). Equation (1) defines the change in membrane potential per time step, whereas, Equation (2) outlines the current adaptation" (25).
All NSIs used within the network are modeled as leaky integrate-and-fire neurons with a high enough voltage threshold to avoid spiking. The neuronal dynamics are described by Equation (3).
"where τ m = RC is the membrane time constant, V m is membrane potential, I input is input bias current plus Gaussian white noise, and R is membrane resistance" (25). The RGPs are initialized with an excitatory current of 500 pA but this switches to a frequency regulating excitatory current. The current is designed to be low enough to induce bursting while producing the desired frequency. This current increases with frequency according to Equation (4).
Where I desc is the descending input current to the RGPs and V th is the voltage threshold potential. The parameter calculations defined within the network architecture depend on frequency. In previous work, Strohmer et al. (26) found a linear relationship between V th and frequency. Therefore, all linear relationship calculations use V th as a substitute to approximate frequency instead of calculating frequency at each time step. This substitution reduces computational load. The descending signals act as an "on/off " switch for the RGPs. Descending signals are also able to control the frequency of output oscillations from the RGPs by sending excitatory current to the velocity NSI (vNSI). This setup is based on biology, Berendes et al. (27) suggest that there could be multiple pathways for descending signals and that the signals controlling output frequency are connected to CPGs through sensory neurons. Validation that the vNSI is able to control output frequency by manipulating the V th of the RGP neurons was previously shown by (25). The stick insect steps at a frequency of ∼1-4 Hz on a slippery surface (28). These observed speeds are used to constrain the output frequency per joint during testing. Our work investigates the phase relationships observed in biological recordings of low-frequency motor activity in deafferented preparations to ultimately reproduce the phase relationships observed during live stepping. In this way, our aim is to match the phase relationship measured from deafferented samples and then visually confirm if this produces stepping-phase transitions in vivo. We can achieve this confirmation using a robot leg in simulation. We are able to directly compare network simulation and biological measurements by normalizing over degrees of a step cycle instead of timing, thereby, removing the problem of the difference in step cycle period.
In our network, the descending signals also send an excitatory current of 500 pA to the MNPs to allow the motor neurons to begin tonically spiking. The rhythm of the network's output is finally determined by phasic inhibition. From the RGPs, excitatory spikes are sent to the buffer NSIs. These NSIs act to inhibit the MNPs, mimicking the network of the stick insect (12,21). The synaptic weight from the RGP determines the extent of membrane potential fluctuation of the NSI. We limit the fluctuation to ∼15 mV to keep within a biologically-plausible range (29). The dependence of synaptic weight on frequency (V th ) is outlined in Equation (5).
Where w nsi is the synaptic weight from the RGPs to the NSIs and V th is the voltage threshold potential of the AdEx neurons in the RGP population. The inhibitory current from the NSIs to the MNPs is only dependent upon the momentary membrane potential of the NSI. This is described in Equation (6).
Where I mnp is the current from the NSI to the MNP and V m is the membrane potential of the NSI. The selection of 15 as the denominator ensures that the maximum inhibition is −1,000 pA. The maximum is determined through trial and error.
Interjoint Coordination
Figures 2B,C show a high level overview of the neural network for coordinating firing between joints. Each of these sub-figures represents the same neuronal populations, it is only split so that the relevant synaptic connections can be highlighted based on the desired SRP output. Each joint is represented by a single half center oscillator to increase the clarity of the figure even though the controller per joint is as shown in Figure 2A. Network coordination is achieved through the use of cNSIs which receive excitatory spikes from the depressor or levator RGP depending on walking direction. The synaptic weight from the RGP to the cNSI is determined by Equation (7).
Where w cnsi is the synaptic weight from the pre-synaptic RGPs to the cNSI and V th is the voltage threshold potential of the AdEx neurons in the RGPs. The weight is determined through trials and ensures the cNSI's membrane potential fluctuates by a maximum of 11 mV as is observed in biological NSIs found within networks generating SRPs (13). It should be noted that this change in membrane potential is smaller than the generic buffer NSIs used in the single joint architecture. The cNSI connects to the post-synaptic RGPs with a weight determined by Equation (8). During coordinated firing, the cNSIs are selected as active by the descending signals, allowing current to pass through them and coupling the joints.
Where w active is the synaptic weight from the cNSIs to the postsynaptic RGPs and V th is the voltage threshold potential of the AdEx neurons in the RGPs. In order to send current from a cNSI to a post-synaptic RGP, the weight is multiplied by the change in membrane potential of the cNSI at each time step as shown in Equation (9).
Where I rgp_act is the current from the cNSI to the post-synaptic RGP and V m is the membrane potential of the cNSI. During coordinated firing for the SRP1 swing to stance transition, the cNSIs are selected as active and Equation (9) determines the excitatory current sent to the Flx and Ret RGPs. During SRP2 coordination, the cNSIs send excitatory current to the Ext and Ret RGPs. The current injections from the cNSIs drive coordination between each of the joints but do not necessarily produce the same phase differences as recorded in deafferented samples. In order to tune the phase difference between joints, the synaptic delay from the CTr joint RGP to cNSI1 is modified (see synapse marked with "variable delay" in Figures 2B,C). The other synaptic delays are held constant and can be seen in Table 1.
Finally, Figures 2B,C show feedback current from cNSI2 to the driving CTr RGP. This feedback between the FTi and CTr joint has been observed in biological networks (30) and is added to our network to increase biological fidelity. The strength of the feedback current is set as 0.1% of the current from the cNSI (Equation 9). The strength of this current is determined through manual testing.
Protractor Burst Duration Modulation
Deafferented preparations of the stick insect indicate a decoupling of protraction burst duration from walking frequency during uncoordinated firing. (8)'s deafferented sample recordings show that retractor burst duration is correlated to cycle period but protractor burst duration is not. Therefore, this phenomenon must be accounted for when simulating uncoordinated firing to maintain biological plausiblity. We were able to reproduce the decoupling of protractor burst duration from cycle period using a burst duration NSI (bdNSI). The architecture is shown in Figures 2B,C. The presented solution is inspired by biological studies that find that blocking of calciumdependent ion channels is able to lengthen burst duration of a neuron (15). We replicate this by increasing current to the protractor RGP and reducing inhibition from the retractor RGP. The prolonged excitation of the protractor RGP inhibits the protractor MNP, reducing the burst duration to the muscle. Implemented in practice, this means the retractor RGP sends excitatory spikes to a bdNSI. When the retractor RGP is spiking, the protractor MNP is also spiking so these excitatory spikes serve to inform the system that the protractor MNP has started spiking. Once the bdNSI's membrane potential is depolarized by 2 mV, the system waits for a period of time based on the frequency of stepping before sending an excitatory current of 3,000 pA to the protractor RGP and reducing inhibition from −5,000 to 0 nS from the retractor to protractor RGP. The waiting time is defined by Equation (10).
Where t wait is the amount of time in milliseconds that the protractor RGP is inhibited by the retractor RGP thus allowing the protractor MNP to spike. V th is the voltage threshold potential of the AdEx neurons in the RGPs. As the cycle period increases due to the frequency slowing down, the amount of time that the protractor MNP is allowed to spike is reduced. The length of inhibition time to the protractor RGP is found through testing.
This mechanism is added to the network architecture and allowed to modulate the burst duration during uncoordinated firing. When the cNSIs are active to generate coordinated firing between joints, the bdNSI is inhibited so that burst duration is not modulated. The plot showing protractor burst duration remaining constant over increasing cycle period during uncoordinated firing is shown in Supplementary Figure 1.
Testing
The network architecture is confirmed by comparing simulation output to measurements from the deafferented stick insect. The phase data is extracted from each and equated by normalizing the timing into a 360 • step cycle. Figure 3 is a visual illustration of how the phase difference and burst duration are calculated from the simulation results. The calculation of biological phase difference is outlined in the Neurophysiology section (Figure 1).
The output from each MNP is rate-coded using a sliding time window of 5 ms. At each step, the number of spikes are counted within the time window and plotted as the y-value, the time window then moves by the time resolution of the simulation, 0.1 ms, and the spikes are counted again to plot the next point.
Each simulation is run for an equivalent of 8 s using the Neural Simulation Tool (NEST) (31) [source code available on GitLab; (32)]. The rate-coded spike data is saved and analyzed in MATLAB. Figure 3 shows how the calculations are made based on the simulation output. A threshold of 35 spikes is used because it removes noise from the data. When comparing = time_in_seconds) and the values are subtracted and normalized to find phase in degrees, as shown in Equation (11).
Where phase_diff is the phase difference in degrees between the MNPs bursting and t mnp1 , t mnp1 are the times in seconds when each MNP crosses the threshold. The cycle period is determined by averaging the period over a single simulation during coordinated firing. This is used to calculate the individual phase differences between MNP bursts. The single phase difference value presented in the results is the average of all these calculations (Equation 11) during coordinated firing of a single simulation. There exists biological variability between animals as high as 32% in the most extreme case when comparing the phase difference of the Ret-Dep motor activity between observed Animals 1 and 2 (Supplementary Table 1). Therefore, we have allowed a buffer of 20 • to either side of the circular mean ± angular deviation of a single animal (equivalent to 11%). This is increased to 40 • (equivalent to 22%) if the angular deviation was <10 • . This buffer allows a variability of 11-22%, keeping our simulation within the variation observed between animals. The burst duration is calculated by looking at a single MNP's rate-coded output and noting when it crosses the threshold as the slope is increasing and decreasing. This calculation is not normalized, it subtracts the time steps and divides by 10, 000 to formulate the burst duration time in seconds. When testing burst duration, uncoordinated firing is allowed for the complete simulation time of 8 s and the presented burst duration time is an average of all phase difference calculations from a single simulation.
Noise is added to the network through current noise to the RGP and MNP neurons. The noise is centered around 0 mV with increasing amounts of standard deviation. All tests are run at 5 noise levels, starting at 100 pA of current noise and increasing to 500 pA by increments of 100 pA. The standard FIGURE 4 | (A1) Extracellular recording of the ipsilateral protractor (Pro), retractor (Ret), extensor (Ext), and depressor (Dep) motor neuron activity of an isolated (disconnected from other ganglia and deafferented) mesothoracic ganglion after bath application of 5 mM pilocarpine. Motor activity becomes transiently coordinated when a switch from protractor to retractor activity occurs (blue dashed line) during an ongoing depressor burst and a pause in extensor activity (SRP1). Magenta dashed lines mark the depressor burst onsets during SRP1s. (A2) Same preparation as in (A1). There is a switch from retractor to protractor activity (blue dashed line) during an ongoing depressor burst and a pause in extensor activity (SRP2). The orange dashed line marks the depressor burst onset during an SRP2. (B) SRP1s occur more often than SRP2s in the recording in (A). (C) Spike-phase histograms relative to the depressor cycle throughout the recording (all bursts) or during cycles where only an SRP1 or SRP2 occurs. In each histogram the mean (±STD) of each bin value among animal preparations is plotted. The y-axis represents average normalized number of spikes. "N" corresponds to the number of animal preparations and "n" to the number of depressor cycles.
deviation of noise to all NSIs in the architecture is held constant at 25 pA.
Intrasegmental CPGs Can Be Coordinated in the Deafferented Preparation
Extracellular recordings of the ipsilateral motor activity in the isolated and deafferented mesothoracic ganglion after pharmacological activation with pilocarpine reveal patterns of coordinated activity similar to the SRPs previously reported by (8) (Figure 4A). SRP1 and SRP2 are denoted in Figure 4A1,A2 with a dashed magenta or orange line, respectively. The lines demarcate the onset of the respective depressor burst. All highlighted patterns on Figure 4A1 belong to the SRP1 type. Each time there is combined activity of both the fast and the slow depressor units, there is a pause in the activity of the extensor motor neurons. SRP1 is the most frequently observed pattern of coordinated motor activity in this recording ( Figure 4B). This substantiates previous results regarding the mesothoracic ganglion by (8). Note that not all depressor bursts are related to an SRP1, especially not those consisting of only the lowamplitude slow depressor units ( (8)). Out of a total number of 1,093 depressor bursts recorded from six mesothoracic preparations only 37.9% are accompanied by an SRP1 or SRP2. The phase analysis only displays the three preparations (n = 621) in which the extensor motor neuron activity is also The overall phase of the depressor and extensor activity does not change regardless of the type of analysis. Protractor and Retractor "SRP1"-values are similar to the "All bursts"-values and dissimilar to the "SRP2"-values.
recorded. However, the phase histograms corresponding to all six preparations can be seen in Supplementary Figure 2. The mean distribution of protractor, retractor, and extensor spikes within the depressor cycle show distinct peaks and troughs ( Figure 4C and Supplementary Figure 2). The mean angles of spiking activity relative to the depressor cycle with a 90% confidence interval are calculated for each animal preparation and the average values for all animals are given in Table 2. Some important numbers in the table are also highlighted in the text. Figure 4C (Figure 4C, SRP2). Taken together, these results show that hemisegmental activity in the deafferented nerve cord can indeed be coordinated. Further revealing a higher occurrence of SRP1 in the isolated mesothoarcic ganglion. This represents a fictive transition from swing to stance phase during forward stepping.
SRP2 Occurs More Often in the Isolated Meta-Than in the Meso-Thoracic Ganglion
Extracellular recordings of the ipsilateral motor activity after pharmacological activation with pilocarpine also reveal the occurrence of SRPs in the isolated and deafferented metathoracic ganglion (Figure 5A1,A2). SRP2s are denoted in Figure 5A1 with a dashed orange line and SRP1s with a dashed magenta line (Figure 5A2), always crossing through the onset of the respective depressor burst. Similar to the mesothoracic ganglion, from a total number of 890 depressor bursts recorded from six metathoracic preparations only 35.7% are accompanied by an SRP1 or SRP2. Extensor activity pauses each time there is combined activity of both the fast and slow depressor units (data not shown in Figure 5A). SRP2 occurs more often than SRP1 in this recording (Figure 5B). The distribution of protractor and retractor spikes throughout 752 depressor cycles show distinct peaks and troughs (Figure 5C, All bursts). Again, the mean angles of spiking activity relative to the depressor cycle with a 90% confidence interval are calculated for each animal preparation and the average values for all animals are given in Table 3. Figure 5C . This is similar to the histograms corresponding to "All bursts." The circular mean values point out the difference in the activity of the protractor and retractor between the deafferented preparations of the isolated meso-and meta-thoracic ganglia. Therefore, coordination of hemisegmental activity in the deafferented metathoracic ganglion is qualitatively different compared to the mesothoracic ganglion and there is a higher occurrence of SRP2s, representing a fictive transition from swing to stance phase during backward stepping. Figure 5D is based on data from different animal preparations. This plot reinforces the qualitative difference in ipsilateral coordination between meso-and meta-thoracic ganglia. The relative proportions of SRP1s and SRP2s are measured in the isolated meso-and meta-thoracic ganglia, and in the interconnected metathoracic ganglion [(n = 6) in each case]. The proportion of SRP1s is significantly higher than 50% in the isolated mesothoracic ganglion (p = 0.016, one-sample Wilcoxon signed rank test), whereas five out of six isolated metathoracic ganglion preparations show SRP1 proportions (C) Spike-phase histograms relative to the depressor cycle throughout the recording (All bursts) or during cycles where only an SRP1 or SRP2 occurs. In each histogram the mean (± STD) of each bin value among animal preparations is plotted. The y-axis represents average normalized number of spikes. "N" corresponds to the number of animal preparations and "n" to the number of depressor cycles. (D) Proportion of SRP1s calculated from the sum of SRP1 and SRP2 patterns observed in recordings of the isolated meso-and meta-thoracic, and interconnected metathoracic ganglia preparations. There is an overall higher occurrence of SRP1s in the isolated mesothoracic ganglion. below 50% (Figure 5D). SRP1 proportions in the isolated and interconnected metathoracic ganglion do not significantly differ from 50% (p = 0.219 and p = 0.437, respectively). However, four out of six interconnected metathoracic preparations show SRP1 proportions over 50%, implying a possible effect of intersegmental information on ipsilateral CPG coordination The overall phase of the depressor activity does not change regardless of the type of analysis. Protractor and Retractor "SRP2"-values are closer to the "All bursts"-values and dissimilar to the "SRP1"-values.
in the metathoracic ganglion (see Section 4). Taken together, our results indicate there is a higher frequency of SRP1s in the isolated meso-in contrast to a higher frequency of SRP2s in the isolated metathoracic ganglion.
Network Topology Promotes Coordinated Motor Activity
The main result of the simulation is the network architecture itself. The configuration of a single joint pictured in Figure 2A is designed and confirmed to produce anti-phasic output based on the suggested architecture by the biological study from (21). This study informed our use of NSIs between the RGPs and MNPs, revealing a critical layer in the single joint architecture to allow precise control of output amplitude. The successful regulation of amplitude across frequencies is displayed in Figure 6. Furthermore, we find NSIs to be an effective method to loosely couple joints because they separate network dynamics. The addition of NSIs for intra-leg communication is inspired by the observations of (13) indicating the role of NSIs during coordinated firing. In this way, our study provides corroborating evidence that NSIs may be important for coordination. Furthermore, our results indicate the insect may switch between different network architectures (communication pathways) based on walking direction. The FIGURE 6 | Plot of rate-coded output from the MNPs and the RGPs. The amplitude (amount of spikes) changes according to frequency from the RGPs but remains constant for the MNPs. This is achieved by using a constant bias current to achieve tonic spiking by the MNPs and using rhythmic inhibition from NSIs driven by the RGPs to create oscillations. SRP2 (bottom). The x-axis is the phase in degrees for all plots. The y-axis is normalized smoothed spiking activity for the biological output or number of spikes for the simulation output. The biological output for SRP1 is from the same animal as pictured in Figure 4A, also referred to as "Animal 1." The simulation output also pictures "Simulated Animal 1." The biological output for SRP2 is from the same animal as pictured in Figure 5A, also referred to as "Animal 4." The simulation output also pictures "Simulated Animal 4," which is the same as "Simulated Animal 3" because the two animals' phase difference measurements for SRP2 are similar. During the depressor burst there is a switch from protraction to retraction in the SRP1 plots. Conversely, there is a switch from retraction to protraction during the depressor burst in the SRP2 plots. These transitions are highlighted within the blue boxes. Data was not available from the extensor during biological SRP2 recordings so this plot is empty but still pictured to keep consistency. The recordings from Animal 1 are from the isolated mesothoracic ganglion and Animal 4's recordings are from the isolated metathoracic ganglion. architecture for the individual networks producing either forwards or backwards fictive step transitions is presented in Figures 2B,C.
Simulated Coordination Is Comparable to Biological Measurements
In order to confirm that the developed network architecture is biologically-plausible, its output is compared to biological measurements from a deafferented stick insect preparation. The comparison of network output to biological measurements starts with visual inspection to ensure the MNPs are bursting in the correct order to produce a swing to stance transition. Figure 7 shows the biological measurements on the left and the simulation results on the right for SRP1 and SRP2. As the measurements are at different timescales, the plots are normalized over the cycle period of each test, respectively.
The figure confirms that during SRP1 there is switch from protraction to retraction during the depressor burst. Similarly, during SRP2 retraction ends and protraction begins during the depressor burst. This pattern is seen in both the biological and simulated results.
All tests of the network architecture are completed by switching from uncoordinated to coordinated firing after 2.5 s, allowing the network to initialize before switching to coordination. Four different phase difference ranges are evaluated, mimicking measurements from four individual animals (Animals 1-4) producing either SRP1 or SRP2 fictive step transitions. Each phase range is achieved at four different frequencies from 1 to 4 Hz to ensure results are validated across observed walking speeds of the stick insect (28). The results confirm the developed architecture is able to produce a biological phase range when switching from uncoordinated to coordinated firing across the frequencies tested. Figure 8 plots the calculated phase differences from the network simulation on top of the measured biological phase differences in motor activity from a single animal using the angular deviation to create the minimum and maximum values. FIGURE 8 | Phase difference between motor activity measured within deafferented samples (calculation method described by Figure 1) and in simulation (calculation method described by Figure 3 and Equation 11). The filled circles and lines indicate the measured biological phase differences using the angular deviation from the mean to create a minimum and maximum. The empty circles and dashed lines indicate the buffer as described in Section 2.2.4. The "X" marker indicates the phase difference recorded from simulation. There are four X's per motor activity comparison reflecting each of the four frequency values tested. Each plot represents a single animal for a total of four different animals represented. The plots are labeled accordingly from Animal 1 to 4. Animal 1 is the same animal as pictured in Figure 4A, Animal 4 is the same animal presented with results in Figure 5A. The recordings from Animals 1 to 2 are from the isolated mesothoracic ganglion and from the isolated metathoracic ganglion for Animals 3 and 4. Testing reveals that the network architecture must be altered to produce SRP1 and SRP2. The depressor population drives coordination during SRP1 whereas the levator population is used during SRP2. This difference is a result of manually testing multiple architectures, finding that the topology in Figure 2C is the only one capable of meeting biological phase difference measurements. However, regardless of the driving population, tuning the single parameter of synaptic delay from the ThC joint RGP to cNSI1 (see Figures 2B,C in Section 2) is enough to match biologically measured phase differences in two different animals during both forward and backward walking. The exact synaptic delays from the RGP to cNSI1 used in this study can be found in Table 4.
The exact phase differences calculated from simulation output are also recorded in Supplementary Tables 2-4. As seen in Figure 8, the simulation is able to produce biologically-plausible phase ranges across the tested frequencies.
The system contains noise and investigation at different noise levels injected to the spiking populations indicates certain ideal levels depending on the individual animal's coordination and walking direction. A standard deviation of 400 pA is found to work for simulating Animal 1 to produce SRP1 while simulating Animal 2 needed 300 pA of current noise to produce the correct phase differences. Phase ranges exhibited by two different animals (Animals 3 and 4) producing SRP2s could be reached in simulation using 400 pA of current noise.
Applying the Simulated Network on a Robot Leg Shows Stepping Transitions
The output of the network is also tested on a physical robot simulator, CoppeliaSim (33), to visually confirm walking coordination on a robot leg. A link to the video can be found in the Supplementary Video 1. Figure 9 shows a sequence of stepping behavior obtained from the video during coordinated firing. The ability to control a single robot leg in simulation indicates the feasibility of eventually using this network with an additional interlimb coordination control mechanism (34) to control a physical legged robot.
Neurophysiology
Our study finds that SRPs occur not only in the meso-but also in the metathoracic ganglion. We substantiate the higher occurrence of SRP1s in the mesothoracic ganglion, as previously described in (8), and we report a higher occurrence of SRP2s in the metathoracic ganglion. As SRP2 represents a fictive transition from swing to stance phase during backward stepping, it is plausible to assume there is an inherent tendency for forward walking in isolated meso-and for backward walking in isolated metathoracic ganglia. This result is in line with findings by (35), reporting that the hind legs of the stick insect perform backward stepping when they do not receive input from anterior legs, indicating an inherent backward direction of movement for the metathorax. Interestingly, this segment specificity in relation to stepping direction can also be demonstrated after sensory stimulation. Load increase at the level of the mesothorax after pilocarpine application results in retractor activation, whereas at the level of the metathorax the protractor is activated, pointing toward activation of forward and backward stepping, respectively (36). Based on the mentioned studies and our results, we conclude that the meso-and meta-thoracic networks for stepping may rely on different architectures.
We also show the proportion of SRP1s is above 50% in four out of six preparations of the interconnected metathoracic gangion. Future studies should increase the sample size and verify our observation, which implies that intersegmental information descending from the meso-thoracic ganglion may affect coordination of motor activity at the metathoracic ganglion, ultimately changing the fictive backward stepping phase transition (SRP2) to forward (SRP1). A similar change in the activity is also observed in vivo, as the inherent backward FIGURE 9 | Stills from simulation of SRP1 using phase difference ranges measured from Animal 1 to create Simulated Animal 1. The stills reflect a full swing-stance step cycle. direction of the metathorax changes to forward when all other legs are stepping (35). Additionally, it has been found that intersegmental information from the middle legs is necessary to produce regular stepping in the hind legs in freely walking animals (37). Another notable phenomenon is that left-right coordination of motor activity is modified in the metathoracic ganglion when it is interconnected to the mesothoracic ganglion or the rest of the nerve cord in deafferented stick insect and locust preparations (10,11). Considering all of the above, we present evidence for segment specificity in the walking system of the stick insect. The difference in the underlying neural mechanisms among segments and the functional significance of our results will be the focus of our future experiments.
Network Simulation
Our study reveals that the addition of 2 cNSIs is enough to coordinate joints and produce fictive step transitions. However, the synaptic connections to these cNSIs must be altered depending on the direction of walking. SRP1 requires the depressor, retractor, and flexor to fire together, therefore, coordination is created through coupling their respective RGPs directly (see Figure 2B in Section 2). SRP2 needs the levator, retractor, and extensor to fire at the same time so the architecture switches to driving coordination from the levator RGP and exciting the retractor and extensor RGPs (see Figure 2C in Section 2). This indicates that there is a distinct difference in synaptic connectivity during forwards and backwards step transitions and could explain why the metathoracic ganglion produces mostly SRP2s when disconnected from the other ganglia. This finding is similar to the biological outcome from (13) showing that exciting certain NSIs increased the likelihood of one type of SRP vs. another. The replication of this phenomenon in simulation by means of altering network connectivity, points to a partial role of network architecture in stepping patterns. Figure 7 shows that the burst duration in simulation is much longer than in biological experiments. Therefore, there is more overlap when the MNPs are spiking. This does not affect the phase difference calculations since these only account for onset in spiking. Based on the visual observation of the robot leg in simulation, the transitions can be subjectively judged as acceptable even though there is significantly more spiking overlap. However, this must be further investigated when building upon the suggested network. It should also be noted that the phase difference calculation is from onset of spiking when evaluating the simulation data (Figure 3) as opposed to an average of all spikes in a burst when looking at biological data (Figure 1). We assert this is comparable because burst duration remains stable at any given frequency (8). Therefore, comparing the average vs. the onset will produce the same phase difference because the mean of the burst time remains equidistant to the onset of the burst time.
Our developed network architecture using both spiking and non-spiking neurons to regulate output from the MNPs should be considered for use in other research, expanding on the commonly used half-center oscillator (9, 38) architecture associated with CPGs. The addition of NSIs has several advantages including separating the dynamics of the system and increased control over input to spiking populations. As seen in (25), the output amplitude of the MNPs changes with frequency due to the use of a constant time window when rate-coding spikes. This variance is removed by adding buffer NSIs between the RGPs and MNPs (see Figure 6). The affect of the excitatory spikes from the RGPs is limited by the synaptic weight to the NSIs and neuronal dynamics of the NSIs themselves. The change in membrane potential of the NSI determines the inhibitory current sent to the MNPs acting as a way to smooth noisy spiking input. In addition to incoming spikes, current injection also affects an NSI's membrane potential. The amount of current and whether it is excitatory or inhibitory, regulates the membrane potential fluctuation of an NSI. The ability of NSI's to receive spikes or current and translate them into a predictable membrane potential fluctuation makes them useful for connecting neural network architectures or conveying sensory information to a neural network.
The rhythmicity of network output is driven by the RGP neurons. Coarse frequency modulation is achieved through changing the voltage threshold potential of the neurons in the RGPs. This relationship was found by (25) where a change of ∼1 Hz is achieved by manipulating the voltage threshold potential by 1 mV. This relationship only holds until a voltage threshold minimum of V th = −57 mV due to the other chosen neuronal characteristics. This V th still produces an output of approximately 2Hz when using the original study's suggested excitatory current injection of 500 pA to the RGPs (25). Therefore, further tuning is required to reduce the frequency to the desired 1 Hz minimum. During testing, fine frequency modulation is achieved by adjusting the level of current injection to the RGP. Reducing the excitatory current to 20 pA is able to reduce the frequency to ∼1.2 Hz which is accepted to be close enough to the desired minimum. The capacitance of the RGPs also needs to be increased to 400 pF to smooth the output at such low frequencies.
Phase difference is manipulated through synaptic delays from spiking populations to NSIs. The delay to cNSI1 (see Figures 2B,C) can be used to adjust the phase based on frequency. This tuning parameter is able to replicate the phase differences measured on animals displaying motor activity coordination as far as 114 • apart (Supplementary Table 1), exhibiting the sensitivity of the system to this variable. Supplementary Video 1 of the simulation confirms that the motor activity coordination between biological animals is significantly varied. Visual observation shows the Simulated Animal 1 has a more intuitive phase difference during swing to stance transition which more closely resembles forward walking in a live animal. As noted in the results, the optimal amount of noise to produce the correct motor activity coordination in simulation for forward and backward swing to stance transitions per animal differed. This is considered acceptable since noise is by definition not constant.
The network simulation phase differences presented in Figure 8 are an average over all calculations occurring after coordinated firing is initiated. However, SRPs are spontaneous and not cyclic so further testing must be done to see if the network can produce singular instances of coordinated firing. Evaluating individual phase differences between motor activity shows that it can take between 0.14 and 2.85 s before the compared phase of the motor activity is within the accepted range as compared to biological measurements (see Supplementary Tables 2-4). This difference in "initialization time" could be caused by the significant amount of noise in the system and should also be further evaluated.
The main finding that two NSIs can be used to couple the leg joints to create coordinated firing allows for either descending signals or sensory feedback to be easily added to the system. NSIs can receive spikes or current injection, making them well-suited to connect with spiking sensory neurons or translate analog sensory information. The identification of analogous interneurons in the biological system and possible sensory organs affecting their activity should be the focus of future research.
DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: [URL]/ single-leg-coordination.
AUTHOR CONTRIBUTIONS
BS and CM developed the main idea of the article. BS researched the biological neural network concepts, formulated a theory, and applied it in simulation. CM and DK performed the biological experiments. CM analyzed the associated data. The manuscript was written by BS and CM with support from LL, AB, and PM. AB supervised the project. LL and PM discussed the results. LL and AB provided the funding. All authors contributed to the article and approved the submitted version.
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Domain: Biology
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[{"added": "2022-01-24T16:15:41.774Z", "created": "2022-01-22T00:00:00.000", "id": "246219700", "metadata": {"extfieldsofstudy": ["Biology"], "oa_license": "CCBY", "oa_status": "GOLD", "oa_url": "https://www.frontiersin.org/articles/10.3389/finsc.2022.818449/pdf", "pdf_hash": "db1f82957e630ad92273323bd5d773a2f14b1631", "pdf_src": "Frontier", "provenance": "peS2o-0075.json.gz:39273", "s2fieldsofstudy": ["Biology"], "sha1": "42dce824a9b5b46dc84a00b685fe60ff0e643b5c", "year": 2022}, "source": "pes2o/s2orc", "version": "v3-fos-license"}]
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PRODUCTION AND CHARACTERIZATION OF MONOCLONAL ANTIBODIES TO ANOPHELES TESSELLATUS MIDGUT
The production of monoclonal antibodies (Mabs) against antigen derived from the midgut ofAnopheles tessellatus is described. Three cloned Mabs examined were found to be directed against conformational epitopes on midgut antigens. Ingestion of these Mabs in a bloodmeal did not affect mosquito mortality or fecundity. The intake of the Mabs, when compared to normal mouse IgG, with a bloodmeal containingPlasmodium v i v a gametocytes did not reduce the suscept~bility of the mosquito to parasite infection.
INTRODUCTION
The midgut of the mosquito is composed of a single layer of epithelial cells and plays a n important role in mosquito-pathogen interactions. The ingested bloodmeal is digested in the lumen ofthe midgut by proteolytic enzymes secreted by midgut epithelial cells.'The sexual stages and ookinetes of the malaria parasite Plasmodium can be damaged by mosquito t r y p ~i n .~ The formation of a peritrophic membrane by secretions from the epithelium and the presence of receptors on the epithelium for ookinetes also have a function in establishing a Plasmodium infection in the vector m o ~q u i t o .~ Cellular and humoral immune responses against arthropod vector antigens in vertebrate hosts influence vector physiology and modulate the transmission of pathogen^.^Midgut antigens have been implicated as the targets for such immune mechanism^.^Immune sera produced in rabbits5 and mice6 against mosquito tissues (including midguts from non-blood fed Anopheles tessellatus Theobald) reduced mosquito fecundity and its susceptibility to P. vivax.' Fecundity reduction has been attributed to antibodies binding to target antigens in the gut, causing it to become leaky and thereby permitting,other antibodies to enter the haemocoel to interfere with normal physiological processe~.~Mon~clonal antibodies (Mabs) produced by hybridoma cultures8 represent one of the many different antibodies produced by conventionally immunized animals. Mabs have been used as probes for investigatingmosquito trypsins and yolk protein^.^^-'^It is possible that Mabs to midgut antigens are more effective than polyclonal sera in influencing mosquito fecundity and its infectivity to malaria parasites because they can be used a t high concentrations against specific target molecules. Once biological activity is established characterization of target antigens is also more easily done with Mabs. This paper describes the production and characterization of three Mabs against midgut antigens of An. tessellatus.
METHODS AND MATERIALS
Midgut antigen: Midguts from 2-3 day old sugar fed female An.tessellatus maintained in the laboratory13 were dissected in phosphate buffered saline (PBS) pH 7.2 a t 4OC and stored a t -20°C until used for preparation of antigen. Midguts from 50 mosquitoes were pooled, homogenized and then lyophilized.
Immunization of mice:
Lyophilized antigen was reconstituted i n distilled water a t a concentration of approximately 0.5 mglml protein. Balblcmice were injected intra-peritoneally with 0.2 ml antigen as a 1:l emulsion with Freund's complete adjuvant. A second immunization with 0.2 ml antigen was performed as a 1:l suspension of Freund's incomplete adjuvant 3 wks later. The mice were boosted 12 wks later with 0.15 ml antigen without adjuvant and the spleen removed for fusion 3d later.
-Monoclonal antibody production and assay: Spleen cells from two immunized mice were pooled and fused with cells of the non-immunoglobulin secreting myeloma cell line, Ag8-653 in 38% polyethylene glycol according to established t e c h n i q ~e s .~J ~ The cells were cultured in medium containing hypoxanthine, aminopterin, thymidine (HAT) in 96 and 24 well plates. A dot immunobinding assay modified from Hawked5 was carried out to screen hybridoma culture supernatants for antibodies. Midgut antigen was applied directly (at 1-2 midgut equivalents/ spot) onto a nitrocellulose filter marked with a grid, air dried and then treated with 3% bovine serum albumin for 3h to block non-specific binding sites on the filter. Hybridoma culture supernatants (first antibody) were added individually onto each antigen spot for 2h a t 23OC; the filters were washed in PBS and thenincubated with peroxidase conjugated sheepanti-mouse immunoglobulin (Silenus, Australia) for l h a t 23OC. The reaction was visualized with chloronaphthol and H,0,.16Four hybridomas identified as positive in the immunobinding assay were cloned by limiting dilution in 96 well plates in a medium containing spleen cells. When clones were well grown, the culture supernatants were tested again by the immunobinding assay for specific antibodies. Strongly positive clones each derived from hybridomas PlG4,24C6 and 24D4 were grown in 24 well plates and then propagated in tissue culture flasks.
Ascites were produced by injecting 5 x lo5-lo6 cloned hybridoma cells into pristane or Freund's incomplete adjuvant14 primed Balblc mice. Control ascites were produced by injectingAg8-653 cells. Ascites fluid was tapped, and proteins .precipitated with ammonium sulphate, dialysed against 3 changes of 5mM sodium phosphate (pH 6.5) and immunoglobulin IgG purified by DEAE chromatography.14
Characterization of antigens:
Midguts from 300 mosquitoes in 3 0 0 ~1 PBS was mixed with 5 0 0 ~1 of 2 x concentrated Laemmli sample buffer17 and placed in boiling water for 3 min and then subjected to electrophoresis on a 10% polyacrylamide gel containing sodium dodecyl sulphate (SDS-PAGE).17Moleculer weight standards (Sigma, USA) were subjected to electrophoresis in parallel lanes. Following electrophoresis, the proteins were transferred to nitrocellulose membranes18 and strips of the blotted proteins probedlg with 1:5 dilutions of hybridoma culture supernatants, purified IgG a t 0.05 mg ml-l, immune mouse serum a t 1:500 dilution (prepared from the midgut immunized mice from which spleens were removed for fusion), rabbit anti-midgut serum5 (1:500 dilution), and unimmunized mouse and rabbit sera (at 1:50 dilution). Peroxidase conjugated rabbit anti-mouse IgG or goat anti-rabbit IgG (Sigma, U. S. A.) a t 1500 dilution was used as the second antibody. Antigenantibody binding was visualized using a chemiluminescent detection system (Amersharn, UK) or chloronaphthol and H,O,.
Assays for effects ofMabs on mosquito fecundity, mosquito mortality and parasite transmissibility:
Mice producing ascites were restrained and used to blood feed groups of 3-4 day old An.tessellatus on two consecutive days. These mosquitoes were held for 72h post bloodmeal and dissected to determine the numbers of mature oocytes (eggs) pr~duced.~Mortality was recorded daily during this period.
Mab I g G (100 p1) was reconstituted in 200 pl normal rabbit serum, mixed a t 50% haematocrit with human erythrocytes parasitized with P. v i v a garnatocytes and fed to 3-4d old An.tessellatus through a membrane feeder as described previ~usly.~The finai concentration of IgG in the bloodmeal was 0.7 mg mI-'. The susceptibility of P. vivax to An. tessellatus was determined by counting the numbers of oocysts on the midgut of the mosquito, 10d post-infection.
RESULTS
After fusion, hybrid cells were observed in 58% of wells. Hybridomas PlG4, 24D4,24C6,24C5, PlC11, PlF10, P2C8 produced antibodies that reacted with midgut antigens in immunobinding assays. Of these, the 3 hybridomas that gave the strongest antibody responses, PlG4,24D4 and 24C6 were cloned by limiting dilution. Over time, the many other hybridomas lost their reactivity with midgut antigen. A typical result obtained in an immunobinding assay with PlG4,24C6 and other Mabs is shown in Figure 1.
The presence of IgG from the Mabs, 24D4C4 and 24C6E9 in the bloodmeal reduced the susceptibility of An. tessellatus to P. vivax (Table 2) ; in these experiments, the presence of IgG antibodies from Ag8-653 ascites also reduced the susceptibility of the vector, when compared to the absence of mouse IgG. Immunoglobulin (IgG) from the Mab PlG4E7 did not have a significant effect on the susceptibility of the vector to P. vivax (data not shown). The mortality of An. tessellatus was not increased by ingesting Mabs -(either from ascites or IgG) in a bloodmeal (data not shown).
the target antigens was made difficult by the conformational nature of the epitopes involved. Identifying biologically useful Mabs involves quite difficult technical procedures and also may require the screening of a large number of Mabs for biological activity. The production of monospecific, polyclonal sera directed against specific purified proteins of the gut may be a n alternative approach towards the same objective.
Figure 2 :
Figure 2 : Western blots of An. tessellatus midgut antigens reacting with A. rabbit anti-midgut serum,B.non-immune rabbit serum, C. mouse anti-midgut serum and D. non-immune mouse serum Migration positions of pre-stained molecular weight markers (Sigma, USA) are indicated in kDa.df=bromophenol blue dye front.
Table 1 : Immunobinding capacity of denatured midgut antigens.
Denaturation was carried out for l5min a t each temperature. Ascites fluid was used as the first antibody. RTroom temperature. Intensity of colour development: 0 (no colour) to +4 (maximum).
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Domain: Biology
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[{"added": "2018-11-18T17:22:52.931Z", "created": "1995-03-30T00:00:00.000", "id": "53690670", "metadata": {"extfieldsofstudy": ["Biology"], "oa_license": "CCBY", "oa_status": "HYBRID", "oa_url": "http://jnsfsl.sljol.info/articles/10.4038/jnsfsr.v23i1.5567/galley/4440/download/", "pdf_hash": "6b3fdaa995f76e304a014ae53f74608d7f396751", "pdf_src": "ScienceParseMerged", "provenance": "peS2o-0075.json.gz:36652", "s2fieldsofstudy": ["Biology"], "sha1": "6b3fdaa995f76e304a014ae53f74608d7f396751", "year": 1995}, "source": "pes2o/s2orc", "version": "v3-fos-license"}]
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SOME FAUNISTICAL REMARKS ON SPIDERS OF THE GENUS HAPLODRASSUS ( ARANEAE : GNAPHOSIDAE ) FROM TURKEY
The spider fauna of Turkey, despite its outstanding zoogeographical situation, is rather poorly studied compared to other regions of the world. Gnaphosidae is however the most studied spider family in Turkey. The known gnaphosid fauna of Turkey includes 126 species and 30 genera (Topçu et al., 2005; Seyyar et al., 2008; 2009; 2010; Kovblyuk et al., 2009; Panayiotou et al., 2010; Seyyar and Demir, 2010).
INTRODUCTION
The spider fauna of Turkey, despite its outstanding zoogeographical situation, is rather poorly studied compared to other regions of the world. Gnaphosidae is however the most studied spider family in Turkey. The known gnaphosid fauna of Turkey includes 126 species and 30 genera (Topçu et al., 2005;Seyyar et al., 2008;2009;2010;Kovblyuk et al., 2009;Panayiotou et al., 2010;Seyyar and Demir, 2010).
Haplodrassus is one of the dominant gnaphosid genera in Turkey. This genus belongs to the subfamily Drassodinae. Haplodrassus can be distinguished from all other drassodine gnaphosids by their posterior median eyes being separated by their radius or less, by the flattened retrolateral tibial apophysis in males, and by the presence of paired lateral epigynal arms in females.
In the present study, two new records, H. mediterraneus Levy, 2004 andH. silvestris (Blackwall, 1833), are recorded for the first time from Turkey. Digital photographs of the genitalia and a detailed description of these species are presented. Localities of the material examined and world distribution of species are given in the text. This paper will provide new data about Haplodrassus of Turkey.
MATERIALS AND METHODS
The specimens were collected from different places in the south of Turkey. The specimens were preserved in 70% ethanol and deposited in the Niğde University Arachnology Museum.
In SEM photograph studies, the male palp was mounted using double-sided tape on SEM stubs, coated with gold in a Polaron SC 502 Sputter Coater, and examined with a JEOL JSM 5600 Scanning Electron microscope at 15 kW. All measurements are in millimeters. Abbreviations used in the text are as follows: E, embolus; MA, median apophysis; TA, terminal apophysis; NUAM, Niğde University Arachnology Museum.
DESCRIPTION
The total length of the animals is 6.2-7.2 mm (n=2) in males and 8.0-9.2 mm (n=2) in females. The carapace is elongate-oval, flattened, with the ocular area narrowed, yellowish red posteriorly, darker anteriorly; the thoracic groove is distinct; the anterior eye row is slightly pro-curved; the posterior eye row is pro-curved; anterior eyes are circular, posterior median eyes are irregularly triangular, posterior lateral eyes are oval. The posterior median eyes are largest; the chelicerae possess two pro-marginal and two retro-marginal teeth. The endites medially excavate with the serrula; the labium is wide and triangular; the sternum is rounded and lighter than carapace; leg formula 4123. The abdomen is light grey. The male palp (Figs.1-4) and epigyne (Figs.5-6) resemble the description of Levy (2004).
Comment: Adult males and females of this species were collected from under the bark of Verbascum sp. in October from the south of Turkey. We observed specimens of this species at only one collection site in research area. Although this species is very common and found nearly the year round on Israel (Levy, 2004), it is rarely found in Anatolia. The morphomet-Figs.1-6. Photographs male palp and epigyne of H. mediterraneus: 1-3 -ventral view of male palp, 4 -retrolateral view of male palp, 5 -epigyne, 6 -vulva.ric measurements and other characteristic features of our samples are similar to the Israel specimens. The discovery of this species from Turkey is important to its zoogeographical distribution World distribution: Israel (Platnick, 2011).
Comment: This species may either be uncommon or it's locally distributed in Anatolia because we did not otherwise find it during our collecting trips. The characteristic features of our H. silvestris samples are not different from the European specimens.
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Domain: Biology
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Bayesian divergence-time estimation with genome-wide SNP data of sea catfishes (Ariidae) supports Miocene closure of the Panamanian Isthmus
The closure of the Isthmus of Panama has long been considered to be one of the best defined biogeographic calibration points for molecular divergence-time estimation. However, geological and biological evidence has recently cast doubt on the presumed timing of the initial isthmus closure around 3 Ma but has instead suggested the existence of temporary land bridges as early as the Middle or Late Miocene. The biological evidence supporting these earlier land bridges was based either on only few molecular markers or on concatenation of genome-wide sequence data, an approach that is known to result in potentially misleading branch lengths and divergence times, which could compromise the reliability of this evidence. To allow divergence-time estimation with genomic data using the more appropriate multi-species coalescent model, we here develop a new method combining the SNP-based Bayesian species-tree inference of the software SNAPP with a molecular clock model that can be calibrated with fossil or biogeographic constraints. We validate our approach with simulations and use our method to reanalyze genomic data of Neotropical army ants (Dorylinae) that previously supported divergence times of Central and South American populations before the isthmus closure around 3 Ma. Our reanalysis with the multi-species coalescent model shifts all of these divergence times to ages younger than 3 Ma, suggesting that the older estimates supporting the earlier existence of temporary land bridges were artifacts resulting at least partially from the use of concatenation. We then apply our method to a new RAD-sequencing data set of Neotropical sea catfishes (Ariidae) and calibrate their species tree with extensive information from the fossil record. We identify a series of divergences between groups of Caribbean and Pacific sea catfishes around 10 Ma, indicating that processes related to the emergence of the isthmus led to vicariant speciation already in the Late Miocene, millions of years before the final isthmus closure.
with a molecular clock model that can be calibrated with fossil or biogeographic 148 constraints. SNAPP is well suited for analyses of genome-wide data as it infers the species 149 tree directly from single-nucleotide polymorphisms (SNPs), through integration over all absolute divergence times when properly calibrated with fossil or biogeographic evidence. 163 We evaluate the accuracy and precision of our approach using an extensive set of 164 simulations, and we compare it to divergence-time estimation based on concatenation. We Table 1. Based on the results of experiments 1-5, 188 we developed recommendations for divergence-time estimation with SNP data, and we then 189 applied this approach to infer timelines of evolution for Neotropical army ants and sea Table S1). All simulated data sets were based 196 on the same set of 100 species trees generated with the pure-birth Yule process (Yule 1925) 197 for all branches. These population sizes were set to N = 25 000 diploid individuals for most 208 analyses, but we also used the larger population sizes N = 100 000 and N = 400 000 in the 209 simulations conducted for experiment 2 (Table 1). For each simulated gene tree, between 2, 210 4, or 8 terminal lineages were sampled per species, corresponding to 1, 2, or 4 diploid individuals per species (Table 1) resulting data sets of close to 10 000 unlinked SNPs were further subsampled randomly to 228 generate sets of 300, 1 000, and 3 000 bi-allelic SNPs for each species tree (see Table 1).
229
For the analyses in experiments 1-4, each of the 100 data sets of 300, 1 000, and 230 3 000 SNPs was translated into the format required for SNAPP, where heterozygous sites 231 are coded with "1" and homozyguous sites are coded as "0" and "2". Per site, the codes "0" 232 and "2" were randomly assigned to one of the two alleles to ensure that the frequencies of 233 these codes were nearly identical in each data set. For experiment 4 in which we tested for 234 the effect of ascertainment bias in SNAPP analyses, the data sets of 1 000 SNPs were also 235 modified by adding invariant sites. To each set of 1 000 SNPs, between 12 184 and 32 740 236 invariant sites (alternating "0" and "2") were added so that the proportion of SNPs in 237 these data sets matched the mean proportion of variable sites in the alignments initially 238 generated for the respective species tree. Finally, for analyses using concatenation in 239 experiment 5, we added the same numbers of invariant sites to the data sets of 1 000 SNPs; 240 however, in this case we used the untranslated versions of these data sets with the original 241 nucleotide code, and also used nucleotide code for the added invariant sites (randomly 242 selecting "A" , "C", "G", or "T" at each site). This assumption was met in our simulated data sets but may often be violated by Finally, as we were interested in SNAPP's ability to infer divergence times rather 284 than the species-tree topology (which has been demonstrated previously; Bryant et al.
285
2012), we fixed the species-tree topology to the true topology. We provide a script written 286 in Ruby, "snapp_prep.rb", to generate XML input files for SNAPP corresponding to the 287 settings described above (with or without a fixed species tree). Note that these settings, 288 including the use of scale-invariant prior distributions, were deliberately not tailored 289 towards our simulated data sets, but were instead intended to be generally applicable for 290 divergence-time estimation with any SNP data set. As a result, the XML files produced by 291 our script should be suitable for analysis without requiring further adjustments from the 292 user. Our script is freely available at [URL] on operators used in our analyses are provided in Supplementary Text S1.
294
As SNAPP is specifically designed for the analysis of bi-allelic SNPs, its algorithm for or invariant sites were added to data sets of 1 000 SNPs (see Table 1). This option did 301 not apply to the analyses of concatenated data in experiment 5 as these were not analyses of 100 data sets of 300, 1 000, and 3 000 SNPs with node-age constraints on either 317 the root or a younger node, is shown in Figure 1 and summarized in containing the true node age was always slightly higher in analyses with root-node 324 constraints even though the width of these HPD intervals was generally smaller. Notes: Accuracy was measured as the percentage of 95% HPD intervals containing the true node age. Precision was measured as the mean width of 95% HPD intervals for node-age estimates. Both measures are presented separately for young (true node age < 10 myr) and old (true node age > 10 myr) nodes. Ex. Results are based on 100 species trees and 300 to 3 000 SNPs generated per species tree. a) Node ages estimated with an age constraint on the root. b) Node ages estimated with an age constraint on a node that is approximately a third as old as the root. Mean age estimates of constrained and unconstrained nodes are marked with red and gray circles, respectively, and vertical bars indicate 95% HPD intervals. Table 3). While both parameters were 355 underestimated roughly by a factor of three when ascertainment bias was corrected for, 356 leaving this bias unaccounted led to parameter overestimation by more than an order of 357 magnitude. Importantly, however, when ascertainment bias was accounted for, the Results are based on data sets of 1 000 SNPs generated for each of 100 species trees, analyzed with and without SNAPP's ascertainment-bias correction or after adding invariant sites to the data sets. Gray circles indicate mean estimates and 95% HPD intervals are marked with vertical bars. The visualization of node-age estimates in a) is equivalent to the illustration in Fig. 1, except that only unconstrained nodes are shown. Note that logarithmic scales are used for estimates of the clock rate (a) and Θ (b).
325
resulting estimates of the population size N (calculated as N = Θ/4µ with µ being the mutation rate per generation, i.e., the estimated clock rate divided by the number of 360 generations per myr) accurately recovered the true population size used for simulations 361 (N = 25 000 in all simulations conducted for experiment 4; see Table 1), as 95% of the 95% 362 HPD intervals included the true parameter value (Fig. 2c, Table 3). In contrast, the 363 population size was underestimated when ascertainment bias was not corrected for: Mean 364 estimates were on average 17.4% lower than the true population size and 35% of the 95% 365 HPD intervals did not include the true parameter value (Fig. 2c, Table 3).
366
Our results of experiment 4 also showed that when invariant sites were excluded, True node age (myr) Results are based on analyses of 100 data sets of 1 000 SNPs, simulated with population sizes N = 25 000, N = 100 000, and N = 400 000. Gray and red dots indicate node-age estimates obtained with the MSC implemented in SNAPP and with BEAST analyses of concatenated data, respectively. Node-age error is measured as the ratio of the estimated node age over the true node age. Solid lines represent mean node-age errors in bins of 0.2 myr. Only nodes with true ages up to 10 myr are shown to highlight differences between the two methods. Note that a logarithmic scale is used for node-age error.
recovered reliably in these analyses and were included in 97.2% of the 95% HPD intervals 378 (Fig. 2d). Notes: Mean node-age error is presented separately for nodes with young (true node age < 10 myr) and old (true node age > 10 myr) nodes. Note that very young nodes (true node age < 0.5 myr) are excluded from this comparison.
concatenation, the mean age estimate for a node with a true age around 3 Ma (±0.2 myr) Fresh fin tissues were preserved in 96% ethanol for subsequent DNA extraction.
following the manufacturer's instructions. RNase treatment after digestion (but before precipitation) was performed in order to improve the purity of the samples. DNA All fossils used for phylogenetic analyses are summarized in Supplementary Table S6. almost exclusively from the width of the calibration density (Fig. 1). In addition to data-set 600 size, the placement of the node-age calibration also had an effect on the precision of 601 divergence-time estimates, which was improved when the root node was calibrated instead 602 of a younger node. This suggests that future studies employing divergence-time estimation with SNAPP should make use of constraints on the root node if these are available from 604 the fossil record, from biogeographic scenarios, or from previously published time-calibrated 605 phylogenies (as in our analyses of empirical SNP data of Neotropical army ants and sea 606 catfishes). While we did not test the performance of multiple calibration points with 607 simulated data, the use of additional calibration points can be expected to further improve 608 the precision of divergence-time estimates; therefore these should be used if available.
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It should be noted that even though all our analyses of both simulated and 610 empirical data sets were calibrated through node-age constraints, this so-called "node implemented. This means that particularly in clades that may be expected to have 644 different mutation rates in different lineages, the precision of divergence-time estimates 645 may be exaggerated, which should be considered in the interpretation of such results.
646
Our experiment 4 revealed that when SNP data sets are used without the addition 647 of invariant sites, SNAPP's estimates for the clock rate and Θ did not match those used in 648 simulations (Fig. 2a,b, Table 3). While this mismatch might appear as a weakness of our 649 approach, we do not consider it unexpected that these estimates change when\===
Domain: Biology. The above document has 2 sentences that end with 'the true node age',
2 sentences that end with 'age > 10 myr) nodes',
2 sentences that end with 'the true parameter value (Fig'. It has approximately 1977 words, 72 sentences, and 20 paragraph(s).
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The H3.3K27M oncohistone antagonizes reprogramming in Drosophila
Development proceeds by the activation of genes by transcription factors and the inactivation of others by chromatin-mediated gene silencing. In certain cases development can be reversed or redirected by mis-expression of master regulator transcription factors. This must involve the activation of previously silenced genes, and such developmental aberrations are thought to underlie a variety of cancers. Here, we express the wing-specific Vestigial master regulator to reprogram the developing eye, and test the role of silencing in reprogramming using an H3.3K27M oncohistone mutation that dominantly inhibits histone H3K27 trimethylation. We find that production of the oncohistone blocks eye-to-wing reprogramming. CUT&Tag chromatin profiling of mutant tissues shows that H3K27me3 of domains is generally reduced upon oncohistone production, suggesting that a previous developmental program must be silenced for effective transformation. Strikingly, Vg and H3.3K27M synergize to stimulate overgrowth of eye tissue, a phenotype that resembles that of mutations in Polycomb silencing components. Transcriptome profiling of elongating RNA Polymerase II implicates the mis-regulation of signaling factors in overgrowth. Our results demonstrate that growth dysregulation can result from the simple combination of crippled silencing and transcription factor mis-expression, an effect that may explain the origins of oncohistone-bearing cancers.
Author summary
The differentiation of cell fates in multicellular organisms requires that certain genes be activated, and genes for alternative cell fates are repressed by chromatin silencing. Specific histone mutations that cripple silencing have been found associated with brain cancers in human patients, and these cancers may originate from instability of cell fates. We tested this idea by expressing a wing specification factor in the Drosophila eye to reprogram cell fates and create winged eyes. To test if defects in chromatin silencing increased cell reprogramming, we simultaneously expressed a crippling mutant histone. Contrary to expectations, we found that wing-to-eye reprogramming no longer occurs and instead the eye overgrows, a phenotype reminiscent of the cancers where the histone mutation was first identified. We suggest that reprogramming requires chromatin silencing of the previous
Introduction
Developmental programs in multicellular organisms are specified by transcription factors that activate and repress batteries of genes, thereby determining cell fate. Ectopic expression of specific transcription factors can drive changes in cell fate, either by inducing pluripotency from a differentiated state [1], or by transforming one cell type to another in a process referred to as 'transdetermination' or as 'direct reprogramming' [2]. Aberrant reprogramming induced by transcription factor misexpression underlies some developmental and malignant diseases.
In eukaryotes transcription factors interact with chromatin, where genomic DNA is wrapped around histone octamers in nucleosomes. Silencing histone modifications on nucleosomes inhibit factor binding and transcription, and so modulate gene expression programs. Silencing has been suggested to impose directionality and reliability to developmental progression [3]. A key chromatin mechanism is mediated by trimethylation of the lysine-27 residue of histone H3 (H3K27me3) which is bound by Polycomb proteins. Mutations of Polycomb proteins derepress developmental transcription factor genes and thereby induce aberrant fate transformations in animals, plants, and fungi, highlighting the conserved importance of this chromatin system [4][5][6].
In Drosophila, mutation of the histone H3-K27 residue recapitulates Polycomb transformations [7]. In humans, screening of cancer cells identified mutations of this residue in certain pediatric glioblastomas [8,9]. These oncohistone mutations are lysine-to-methionine (K27M) mis-sense substitutions that dominantly inhibit the EZH1/2 histone methyltransferases and reduce chromatin methylation [10][11][12][13][14]. The oncohistone is not tumorigenic on its own but may precondition cells to later oncogenic mutations [15,16]. However, since the critical window for tumorigenesis is in early developing lineages, the sequence of initiating events is not accessible to analysis.
Histones and histone modifying enzymes are conserved across eukaryotes, and expression of the H3K27M oncohistone in Drosophila cells recapitulates chromatin and silencing defects seen in gliomas [17]. Here, we use Drosophila to show that the H3K27M oncohistone blocks direct reprogramming induced by ectopic expression of the wing master regulator transcriptional activator Vestigial (Vg). While oncohistone production on its own inhibits cell proliferation, co-production with Vg results in overgrowth of cells, and these cells retain eye identity. Chromatin profiling by CUT&Tag [18] shows widespread reduction in H3K27me3 histone modification that may cripple silencing during reprogramming and identifies changes in gene expression that may induce neoplastic growth. The effect of the H3K27M oncohistone is distinct from that of eliminating the E(z) histone methyltransferase, demonstrating that a moderate defect in chromatin silencing combined with aberrant transcription factor expression can be sufficient to induce neoplastic growth, with implications for the developmental origins of gliomas.
The H3K27M oncohistone blocks direct reprogramming
To probe the interaction between reprogramming and chromatin silencing, we used inducible transgenes for the Vg master regulator transcription factor and for the H3.3K27M oncohistone. Vg encodes transcriptional activation domains and-when heterodimerized with the DNA-binding Scalloped (Sd) protein-determines the identity of cells in the pouch of wing imaginal discs [19]. Vg is a master regulator of wing development, as ectopic production of Vg converts tissue into wing structures [20]. We used the eyeless-GAL4 (eyGAL) driver to induce transgenes during development of the eye [21]. Expression of Vg in the eye is semi-lethal ( Table 1), and many dying pupae have small heads that lack eye tissue (Fig 1A and 1B). Those adults that survive show varying degrees of transformation: some animals lack eyes, while other have reduced numbers of eye ommatidia and have darkly pigmented outgrowths resembling wing tissue with small bristles and trichome-like hairs instead of the setae of normal eyes . About half of all adults with >Vg production display pigmented wing-like projections, while the rest lack eyes entirely. (D,E) Eye-antennal imaginal discs from wildtype eyGAL >RFP larvae (D) and from eyGAL >RFP >Vg larvae (E). Discs were immunostained for the Vg protein and for ELAV, a marker of differentiating photoreceptors. The antennal (a) and eye (e) portions of the disc are indicated, and the morphogenetic furrow that separates mitotically active undifferentiated cells in the anterior of the eye from differentiating photoreceptors in the posterior is marked with an orange arrowhead. The eye portions of discs with Vg production are distorted, with detectable Vg, low RFP signal, and very few differentiating photoreceptors at the posterior edge. [URL]001 ( Fig 1C and Table 1). These wing-like outgrowths result from reprogramming of the eye by the Vg master regulator. We dissected and immunostained developing eye-antennal imaginal discs to determine the cellular effects of ectopic Vg and H3.3K27M. The eyGAL driver induces an RFP reporter gene, marking the eye anlagen of the imaginal disc (Fig 1D). Inducing Vg production reduces and distorts the eye portion of discs to variable degrees, ranging from many to very few photoreceptors developing at the posterior edge of the eye disc (Fig 1E). While the amount of Vg protein detectable between discs varied, the RFP reporter was reduced in all discs, consistent with the inactivation of the eyGAL driver as Vg reprograms the eye disc.
We used a transgene encoding an inducible H3.3K27M gene [22] and the same eyGAL driver to produce the oncohistone in the eye. The oncohistone decreases the size of the adult eye in proportion to dosage (Fig 2A-2C). Thus, while the oncohistone is associated with proliferation in cancers, on its own it inhibits tissue growth. In imaginal discs, the oncohistone is detectable throughout the eye portion of the disc (Fig 2D and 2E) and results in reduced staining for H3K27me3, whereas H3K27me3 staining in the antennal portion is unaffected (Fig 2G and 2H). These discs are slightly smaller than wildtype discs, but with normal morphology and developing photoreceptors. In contrast, the H3K27me3 modification is unaffected by ectopic expression of Vg, showing high levels in both the eye and antennal portions of discs (Fig 2I).
To test the importance of H3K27me3 chromatin silencing in reprogramming, we coinduced production of Vg and the K27M oncohistone together in developing eyes. Surprisingly, this completely suppressed eye-to-wing reprogramming ( Table 1). H3.3K27M production rescues the lethality of Vg production, but the eclosing animals have grossly disrupted and expanded eyes with a variety of projections, stalks, and folds (Fig 2K and 2L). These convoluted eyes contain ommatidia with no wing-like tissue, implying that co-production of the oncohistone blocks the reprogramming effects of Vg, thereby maintaining the eye identity of these cells. This is apparent in developing imaginal discs, where the eye portion of discs is greatly expanded where the oncohistone is produced, with developing photoreceptors along a convoluted edge of the disc (Fig 2F and 2J). Suppression of reprogramming is specific to the K27M oncohistone, because a transgene with a wildtype H3.3 histone, with an H3.3K27R substitution, or with an H3K9M substitution [17] do not block reprogramming ( Table 1). We tested additional independent eyGAL4 driver constructs and insertions to induce the H3.3K27M oncohistone and to induce Vg (see S1 Text), all of which reproduced the phenotypes of Vg-induced reprogramming and oncohistone-induced overgrowth. Thus, H3K27me3-mediated silencing is required both for transcription factor-induced reprogramming of the eye and to limit factor-induced neoplastic growth.
Vg and H3K27M induce cell death and proliferation
The sizes of imaginal discs and adult eyes are consistent with decreased proliferation with either Vg or with H3.3K27M production, but increased proliferation with the two proteins coproduced. We examined cell division rates and cell death rates by staining eye imaginal discs with a mitotic marker histone (H3S10-phosphorylation) and with a cell death marker (cleaved DCP-1). In wildtype controls, mitoses are scattered throughout the anterior portion of the eye disc, but mostly absent in the posterior region once photoreceptors start to differentiate ( Fig 3A and 3A') with negligible cell death (Fig 3E). Expression of H3.3K27M does not affect mitosis in the disc (Fig 3B and 3B'), but a stripe of cell death appears across the disc where cells transition from proliferating to differentiating regions (Fig 3F). Thus, the reduction in eye size with H3.3K27M production is at least in part due to reduced cell viability. In contrast, induction of Vg is associated with increased mitosis and increased cell death throughout the disc (Fig 3C and 3C' and 3G). Thus, reduced cell viability limits the size of the reprogrammed tissue. Finally, co-production of H3.3K27M and Vg results in very large discs with patches of mitosis frequently apparent (Fig 3D and 3D'), but these discs show extensive cell death in undifferentiated regions (Fig 3H). The hyperproliferation of some regions accounts for overgrowth in spite of extensive oncohistone-induced cell death in other regions.
Gene expression in Vg-reprogrammed tissues
Reprogramming of the eye inactivates the eyGAL driver once cells transform, and this is apparent by reduced RFP production when Vg is induced (Fig 1E). Inactivation of the eyGAL driver implies that the Vg transgene will also be inactivated, and so the endogenous vg gene must be activated for successful reprogramming. To test this, we constructed animals with the eyGAL driver and the inducible Vg transgene but lacking the endogenous vg gene. As expected, these animals have very small eyes with no wing tissue outgrowths (Fig 4A). This implies that transient production of Vg activates endogenous genes for wing specification. The endogenous vg gene is also required for overgrowth of eye discs with oncohistone and ectopic Vg expression, as a dominant negative vg U allele reduces eye size (Fig 4B). >H3 To characterize activation of wing-specific genes further, we stained discs for proteins specific for eyes or for wings. These discs continue to express the eye factor Dachshund (Dac), although the normal striped pattern of this factor is distorted by the gross disorganization of the reprogrammed disc (Fig 4C-4F). The wing specification factor Nubbin (Nub) in reprogrammed eye discs is detectable but weaker than in wing discs (Fig 4G-4I), implying that the wing determination program is not efficiently activated. Notably, there is no detectable production of Nub in >H3.3K27M >Vg overgrown discs (Fig 4J), while RFP and dac are highly expressed. Thus, in this setting the ectopic production of Vg does not activate wing determination and eye factors continue to be expressed.
Chromatin profiling of H3K27me3 domains in reprogrammed tissues
To profile chromatin domains in Vg-reprogrammed eyes, we adapted the CUT&Tag method [18] for dissected imaginal discs from Drosophila larvae. CUT&Tag works by first soaking unfixed cells with a factor-specific antibody which binds to chromatin sites, followed by decoration with a secondary antibody. Next, a protein-A-Tn5 (pA-Tn5) transpososome loaded with adapter sequences is soaked in, binding to the chromatin-bound antibodies. Activation of the tethered transpososome by adding magnesium then integrates the adapters around binding sites, and PCR enrichment and sequencing of the resulting library thus maps the targeted chromatin protein. We have previously adapted the micrococcal nuclease-based CUT&RUN method for dissected imaginal discs [22], and adjusting buffers for CUT&Tag works reliably. For CUT&Tag with tissue samples, we dissect wing or eye imaginal discs, coat them with Concanavalin A magnetic beads for handling, lightly permeabilize them with digitonin, and sequentially incubate with antibodies and then with pA-Tn5. Resulting libraries are subjected to Illumina paired-end sequencing and mapped to the Drosophila dm6 genome assembly. Imaginal discs from 2-3 larvae were sufficient to generate chromatin profiles, although the capacity of CUT&Tag to profile very small sample sizes should work with even less tissue [17,23].
We first mapped the H3K27me3 silencing modification in eye and wing imaginal discs from wildtype larvae (S1 Text and S1A Fig). Previous studies found that H3K27me3-marked domains are shared between larval tissues but with quantitative differences in chromatin methylation that correspond to expression of included genes [22,24]. For example, a 350 kb H3K27me3 domain encompasses the ANTENNAPEDIA-COMPLEX (ANTP-C) cluster of homeobox genes in both eye and wing imaginal disc cells (Fig 5A). In eye imaginal discs the Antp gene is silenced and heavily coated with H3K27me3-marked chromatin, while in wing imaginal discs the Antp gene is transcribed, and H3K27me3 across this gene is correspondingly depleted. To quantify changes in chromatin landscapes, we measured the average H3K27me3 signal across 166 annotated H3K27me3 domains (S1 Data) in wing and eye discs and compared these on a scatter plot (Fig 5B). Domains that encompass genes involved in wing specification have moderately high chromatin methylation in eye discs and lose signal in wing discs when the genes are active. The opposite trend occurs for eye specification genes. Thus, as expected, activation of tissue-specific genes in H3K27me3 domains is accompanied by the reduction of histone methylation.
For discs producing Vg, H3.3K27M, or co-producing Vg and H3.3K27M, we dissected away the antennal portion of the disc and profiled only eye tissue. Production of Vg in eye production of H3.3K27M and Vg (H-K) immunostained for the wing-specific Nub protein and for ELAV. The wing pouch is heavily labeled with Nub, and eye discs with Vg production have patches with low level Nub staining. No Nub staining is visible in eye discs with H3.3K27M production or in discs with H3.3K27M and Vg co-produced. [URL] GENETICS
H3.3K27M blocks reprogramming in Drosophila discs results in limited changes in H3K27me3 across domains compared to eye discs (Fig 5C and 5D and S1 Data). The domains including the vg and the nub genes are noticeably less methylated but retain more methylation than in wing imaginal discs. Thus, reprogramming of eye cells is not accompanied by widespread changes in chromatin methylation patterns, and the more limited changes at wing-specific genes than in wing discs suggests that Vg expression does not fully rewire gene expression programs.
We next looked at the effect of the H3.3K27M oncohistone on H3K27me3 patterns in the eye. The oncohistone is incorporated into chromatin by histone H3.3-specific chaperones at active promoters and enhancers, and at lower levels by DNA replication throughout the genome [25], and production of H3.3K27M in eye discs gives a similar promoter enrichment with background throughout the genome when profiled using an antibody to the K27M epitope (S1B Fig). The oncohistone reduces H3K27me3 staining in the eye (Fig 2D-2F), and quantitative chromatin profiling of glioma cell lines has previously shown that H3K27me3 is globally reduced but a few domains remain [25]. In the eye imaginal disc, H3K27me3 still coats domains when the oncohistone is produced, although domains with less methylation are more depleted with more dispersion than strong domains (Fig 5E). Moderate depletion at weak domains is also apparent when the oncohistone and Vg are co-produced (Fig 5F). Additionally, we noted that the domain including the vg gene retains methylation, implying the endogenous gene is not being efficiently activated, and H3K27me3 signal at other wing and eye determination genes are not significantly altered (S1 Data).
We attributed the effects on weak domains to the overall lower H3K27me3 signal in oncohistone-producing cells. However, even in strong domains H3K27me3 distribution is affected. In Drosophila H3K27me3 domains are nucleated at nucleosome-depleted Polycomb Response Elements (PREs), and chromatin methylation then spreads out across the domain [26]. The H3.3K27M oncohistone specifically interferes with methylation spreading [27,28], and changes in the pattern of H3K27me3 methylation is apparent at individual domains such as the ANTP-C (Fig 5A). In this domain PREs are still heavily marked with the histone modification but intervals between PREs are reduced, consistent with the oncohistone inhibiting spreading of the modification from PREs. To assess spreading, we used published sites of Polycomb binding within H3K27me3 domains ( [24] and S1 Data] to define the positions of PREs and display H3K27me3 signals around those sites. This analysis confirmed a consistent loss of methylation around PRE peaks (Fig 5E and 5G). H3K27me3 profiling of eye disc portions coproducing Vg and H3.3K27M recapitulate reduced spreading around PREs in strong domains while weak domains are more dispersed (Fig 5E). Thus, oncohistone production in the eye does not ablate H3K27me3 domains, but does reduce the density of methylation across all domains.
Transcriptome profiling of reprogrammed tissues
We profiled wing and eye imaginal discs for the histone H3-K4 dimethylation (H3K4me2) histone modification to identify active promoters, and for the phosphorylated elongating form of
PLOS GENETICS
H3.3K27M blocks reprogramming in Drosophila RNA polymerase II (RNAPII-S2p) to measure transcription in each tissue. While RNA-seq profiles mature mRNA abundance in a cell, histone modification and RNAPII measurements directly correspond to changes in gene activation and silencing. Genome-wide patterns of both H3K4me2 and RNAPII-S2p are very similar between wing and eye discs, reflecting their similar expression profiles of housekeeping genes (Fig S1B). In contrast, wing and eye specific genes show increased promoter H3K4me2 and gene body RNAPII-S2p signals in the tissue where they are active (Fig 6A and 6B). Additionally, H3K4me2 signal identifies the active TSS of genes with alternative promoters (Fig 6B). To identify differentially expressed genes, we assigned promoter activity scores by binning H3K4me2 signal ±500 bp around each annotated promoter and assigned gene transcription scores by binning RNAPII-S2p signal across each annotated gene (S1 Data). Since RNAPII profiling integrates signal over the gene, we focused on this as a robust measure of gene transcription. We performed 4-6 biological replicates for each tissue with up to 10 million mapped reads per replicate and compared normalized count tables to discriminate genes in wing and in eye discs (Fig 6C and S1 Data). With a stringent threshold (FDR�0.05), we identified 50 genes up-regulated in wing imaginal discs, including 15 transcription factors such as vg, Antp, ap, Dr, nub, Sox15, rn, and zfh2 (S1 Data and Fig 6C). In contrast, 233 genes are up-regulated in eye imaginal discs, including many eye-specific transcription factors such as Optix, dac, dan, eya, lz, lab, so, B-H2, ey, toy, gl, and oc. The number of recovered differential tissue-specific transcription factors genes is slightly better than that in published RNA-seq data between eye and wing imaginal discs [29], demonstrating that RNAPII profiling efficiently detects expression changes in tissue specification genes.
We then profiled RNAPII in dissected eye discs producing Vg. There are 30 genes up-regulated in Vg-producing samples, while 138 genes are down-regulated compared to wildtype eye imaginal discs (S1 Data and Fig 6D). While the promoter of the endogenous vg gene gains H3K4me2 and is transcribed, only 51 (30%) of differentially-expressed genes are shared with wing disc samples. This may be due to the mosaicism of Vg-expressing samples or to incomplete activation of wing specification genes, but the more limited changes indicate that reprogramming of the eye by ectopic Vg is incomplete.
We then profiled RNAPII in eye discs producing the H3.3K27M oncohistone. Production of the oncohistone has little effect on genome-wide transcription (Fig 6E and S1 Data), consistent with the largely normal development of this genotype. Profiling of samples co-producing H3.3K27M and Vg discriminated 14 up-regulated genes and 45 down-regulated genes (Fig 6F and S1 Data). Notably, the endogenous vg gene is not induced, and only 28 differentially transcribed genes are similarly regulated in wing disc samples, consistent with the failure of reprogramming in these samples. There are 31 genes uniquely differentially transcribed in H3.3K27M and Vg co-producing discs (S1 Data). Of the 11 over-transcribed genes in this sample, two genes (hppy and Stlk) encode signaling kinases in the STE20 subfamily: the happyhour (hppy) kinase in particular has been linked to positive regulation of multiple developmental signaling pathways, including JNK, Egfr, hippo, TORC1, and TOR pathways [30][31][32][33], and influences both cell division and apoptosis in imaginal discs [32]. A third over-transcribed gene is Rnf146, which affects Wnt signaling. It is striking that human pediatric tumors with H3.3K27M mutations are associated with up-regulation of developmental signaling receptor PDGFRA [34].
Down-regulated genes might also lead to overgrowth; of 18 genes uniquely down-regulated in H3.3K27M-and Vg-co-expressing discs, Eip78C is part of the ecdysone-response program, while other like the transcription factor pre-lola-G have no known function. Finally, certain common changes in gene regulation may drive neoplastic growth of H3.3K27M-and Vg-coexpressing tissues. Two intriguing up-regulated genes are IntS3, a component of the general transcriptional Integrator complex [35], and Haspin, which encodes a mitotic histone kinase
PLOS GENETICS
H3.3K27M blocks reprogramming in Drosophila that also influences Polycomb-mediated silencing [36,37]. Further studies will be required to identify which of these genes drives neoplastic growth of eye discs co-regulating Vg and the H3.3K27M oncohistone.
H3K27M and Vg co-expression imitates PRC1 mutants
Tissue reprogramming has been associated with a number of components in the Polycomb silencing system [38]. H3K27 trimethylation is catalyzed by the E(z) histone modifying enzyme, a component of the Polycomb Repressive Complex 2, while the Polycomb Repressive Complex 1 (PRC1) complex binds the H3K27me3 mark on nucleosomes [39]. PRC1 also binds many promoters independently of PRC2 or H3K27me3, implying that it has additional functions [24]. Indeed, mutation of PRC1 components result in overgrowth of imaginal discs, while mutations in PRC2 components inhibit cell growth [24,40]. We examined the roles of PRC1 and PRC2 in reprogramming of the eye. A previous study characterized eyGAL-induced knock-down of PRC1 and PRC2 components [41], which we repeated here. The effects of knocking down the PRC2 histone methyltransferase E(z) is much more dramatic than the expression of H3.3K27M, resulting in the near elimination of the eye portion of imaginal discs (Fig 7A). These animals die as pupae lacking all head structures derived from the eye-antennal imaginal disc (Fig 7C). Simultaneous knockdown of E(z) and expression of Vg similarly results in small discs and no head structures (Fig 7D). Thus, growth inhibition by H3.3K27M expression resembles intermediate reduction of the E(z) enzyme, and these intermediate levels separate the requirement of E(z) for reprogramming from its requirement for cell viability.
Eye-specific knockdown of Polycomb is also lethal, but with a phenotype distinct from E(z) knockdown. Previous work has also shown that Polycomb knockdown reprograms part of the eye to wing-like tissue and induces overgrowth in the disc [41]. Eye discs with Polycomb knockdown have an outgrowth on the dorsal side of the disc [41] with high expression of the wing-specific Nub transcription factor (Fig 7B), and the dorsal edge of the eye disc is predisposed to reprogramming [41,42]. Dying pupae have both eye and wing tissue in the head, with wing-like outgrowths out the dorsal side of the eye [41] (Fig 7E). Reprogramming is enhanced by simultaneous Polycomb knockdown and Vg expression, where the amount of eye tissue is reduced and the wing tissue expands in a more flattened structure (Fig 7F).
We profiled histone modifications and RNAPII from Polycomb knockdown discs (S1 Data). These discs are mosaic for eye and reprogrammed fates, nevertheless induction of some genes are detected, including Antp and Ubx (Fig 7G and S1 Data). This matches transformation of tissues: Antp is activated when eye cells are reprogrammed to a wing fate [41], and Ubx expression is probably due to transformation of the antennal portion of the disc to a leg fate [Fig 7E]. We did not expect antennal effects since the eyGAL4 driver is limited to the eye portion of the disc in late larval stages, but this driver does transiently express in the antennal portion in earlier stages [43]. This early expression accounts for the changes in the antennal portion of discs upon E(z) or Polycomb knockdown ( [41]; Fig 7A and 7B). Finally, immunostaining of reprogrammed eye discs detected additional induction of wing-specification genes (Fig 7B and [41]) which were not detected by RNAPII profiling, possibly because of low expression of these transcription factors or because of the heterogeneity of discs limits detection.
While there are similarities between our results and published effects of PRC1 and PRC2 mutations, there are key differences in the regulatory consequences when reprogramming occurs. When eyGAL4-induced Vg expression reprograms the eye, the eyGAL4 driver shuts off. Similarly, successful reprogramming by Polycomb knockdown inactivates the eyGAL4 driver (Fig 7B), and therefore Polycomb expression will be restored in reprogrammed cells.
PLOS GENETICS
This is distinct from cells co-expressing H3.3K27M and Vg, or from genetic mutants of PRC1 or PRC2 [24,40]. In these cases cells must proliferate with crippled silencing. In other words, transient loss of Polycomb results in reprogramming, while permanent loss results in neoplastic growth. Partial reduction of E(z) activity allows cells to live, and thus reveals its requirement for reprogramming by a single transcription factor.
Discussion
The idea that cell fate programs are reinforced by chromatin silencing of alternative pathways implies that reducing epigenetic barriers that restrict cell fates will stimulate cell fate transformations. We find the opposite-using ectopic expression of the Vg master regulator factor, we find that compromised silencing does not enhance transformation of the eye; instead cells of the eye disc hyperproliferate. It is startlingly simple to create overgrowth tumors in the Drosophila developing eye: expression of only two proteins-the H3.3K27M oncohistone and a transcription factor-are sufficient. Commitment to a developmental fate requires both the expression of genes for determinative transcription factors and the silencing of genes for alternative fates. Coordinated activation and silencing may shape and stabilize developmental trajectories. Our results highlight that chromatin silencing is essential for transcription factorinduced developmental reprogramming. Cell identity in the Drosophila eye is determined by a network of self-reinforcing transcription factors. To reprogram this tissue expression of Vg must both induce wing specification genes and silence eye-specifying factors. Surprisingly, inhibition of H3K27 methylation does not enhance reprogramming, implying that the activation of silenced wing specification genes is not limiting. Instead, our results argue that H3K27me3-mediated silencing of eye-specific factors is needed for successful reprogramming. This requirement may result from the inducing Vg in a setting where the eye determination program has already been established. Further, the differentiation of retinal cells even when Vg is expressed indicates that eye determination factors must be dominant to wing specification factors, similar to what has been observed after tissue damage in Drosophila [44]. Intriguingly, overexpression of the histone gene transcription factor Wge can also drive eye-to-wing reprogramming, but this case requires chromatin silencing mediated by the histone H3K9 methyltransferase Su(var)3-9 [29,45]. Thus, reprogramming appears to require silencing of established developmental programs by either H3K27me3-or H3K9me3-mediated pathways.
Why does inhibiting reprogramming with the H3K27M oncohistone result in neoplastic growth? Genetic studies in mammalian systems have demonstrated that H3K27M oncohistones are not sufficient to induce tumorigenesis on their own [46]. Instead, secondary mutations are necessary, and the low mutational burdens of H3K27M-bearing cancers in patients implies that specific mutations are sufficient for malignancy. Some mutations are in classical tumor suppressor genes like TP53, thereby enhancing malignancy. In pediatric midline gliomas, additional activating mutations are often in specific signaling receptors such as PDGFRA or ACVR1 [8,34,[47][48][49], and the hyperactivation of developmental signaling probably induces the mis-expression of developmental transcription factors. Co-expression of the H3.3K27M oncohistone and Vg appears to imitate this effect, perhaps through the increased expression of the STE20-like kinases we identified by transcriptome profiling. The Vg factor both promotes a wing cell fate and stimulates cell growth [50], thus suppressing reprogramming may result in unrestrained proliferation. While mutation of the four Vg homologs in humans is not observed in gliomas, mis-expression of three paralogs are associated with other cancers [51]. More generally, the fact that simply mis-expressing a developmental transcription factor and an oncohistone stimulates proliferation suggests that other transcription factors might also do so, which would explain the cell-type-and stage-specificity of oncohistone-driven cancers.
It is striking that mutations in PRC1 components stimulate uncontrolled proliferation, while mutation of PRC2 components do not [24,40]. These two complexes normally work together to silence domains in genomes, but PRC1 also regulates gene expression of some developmental targets [52,53]. Our results suggest that mutations in PRC1 components both lose chromatin silencing and mis-express a developmental regulator, driving cell proliferation.
Fly strains
All crosses were performed at 25˚C. All mutations and chromosomal rearrangements used here are described in Flybase ( [URL]) and sources are listed in the S1 Text. The eyGAL4-3-8 driver was used for all experiments shown here, although identical results were obtained with other eyGAL4 constructs and insertions. [55]; integrants at the 25C landing site were used in this study.
Imaging imaginal discs
Imaginal discs from late 3rd instar larvae were dissected and fixed for 10 minutes in 4% formaldehyde/PBST (PBS with 0.1% triton-X100), and then incubated twice in 0.3% sodium deoxycholate/PBST for 20' each. Samples were incubated with primary antiserum diluted in PBST supplemented with 10% goat serum at 4˚overnight, and finally with fluorescently labeled secondary antibodies (1:200 dilution, Jackson ImmunoResearch). All tissues were stained with 0.5 μg/mL DAPI/PBS, mounted in 80% glycerol on slides, and imaged by epifluorescence on an EVOS FL Auto 2 inverted microscope (Thermo Fisher Scientific) with a 10X objective. At least 20 discs were imaged for each genotype. Pseudo-colored images were adjusted and composited in Adobe Photoshop and Adobe Illustrator. All antibodies used are listed in the S1 Text.
Imaging adult eyes
Adults were euthanized in a freezer and then imaged using a Sony digital camera mounted on a Nikon SMZ1500 stereomicroscope. Images were color-corrected with Adobe Photoshop and composited in Adobe Illustrator.
Chromatin profiling and sequencing
We dissected imaginal discs from 3rd instar larvae in Wash+ buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 0.5 mM spermidine with Roche cOmplete protease inhibitor). We used 4 wing imaginal discs and 6 eye-antennal imaginal discs for each chromatin profiling experiment. Experiments were performed in duplicate and in parallel to minimize technical variation. We used immuno-tethered CUT&Tag chromatin profiling [18] with antibodies to histone H3K27me3 (C36B11, Cell Signalling Technology) and to histone H3K4me2 (13-0027, Epicypher) modifications. To adapt CUT&Tag for whole tissues, we coated imaginal discs with Bio-Mag Plus Concanavalin-A-conjugated magnetic beads (ConA beads, Polysciences, Inc) in 8-tube PCR strips, and exchanged solutions on a magnetic stand (MSR812, Permagen). Tissues were incubated with primary antibody in dbe+ buffer (20 mM HEPES pH 7.5, 150 mM NaCl, 0.5 mM spermidine, 2 mM EDTA, 1% BSA, 0.05% digitonin with Roche cOmplete protease inhibitor) overnight at 4˚, incubated with secondary antibody in dbe+ buffer for 1 hour at room temperature, and then incubated with protein-A-Tn5 loaded with adapters in 300Wash + buffer (20 mM HEPES pH 7.5, 300 mM NaCl, 0.5 mM spermidine with Roche cOmplete protease inhibitor) for 1 hour. After one wash with 300Wash+ buffer, samples were incubated in 300Wash+ buffer supplemented with 10 mM MgCl 2 for 1 hour at 37˚to tagment chromatin. Reactions were stopped by addition of SDS to 0.16% and protease K to 0.3 mg/mL, incubated at 58˚for 1 hour, and DNA was purified by phenol:chloroform extraction and ethanol precipitation.
Libraries were prepared as described [23], with 14 cycles of PCR with 10 second combined annealing and extension for enrichment of short DNA fragments. Libraries were sequenced for 25 cycles in paired-end mode on the Illumina HiSeq 2500 platform at the Fred Hutchinson Cancer Research Center Genomics Shared Resource. Paired-end reads were mapped to release r6.30 of the D. melanogaster genome obtained from FlyBase using Bowtie2, and to the E coli genome for spike-in normalization. A step-by-step protocol is posted: [URL]. io/view/cut-tag-with-drosophila-tissues-bnx5mfq6
Data analysis
Track screenshots were produced using the UCSC Genome browser ( [URL]) [56]. We manually annotated H3K27me3 domains as enriched blocks in either wing or eye imaginal discs, or in profiling of larval brains [22], and are listed in S1 Data. We used EPDnew for promoter locations in dm6 genome assembly [57], and FB 2020_03 for gene annotations [58]. Analysis and display were done using deepTools v3.3.2 and bedTools v.2.29.2 in usegalaxy.org and in MS Excel. Promoter signals were extracted as fragment counts +500 bp around EPDnew annotated gene TSSs using 'bedtools MultiCovBed' in bedTools. Values for each dataset are provided in S1 Data. H3K27me3 signals in domains signals were extracted as fragment counts per kilobase per million reads (CPKM) across annotated H3K27me3 domains using 'bedtools MultiCovBed' in bedTools. Values for each dataset are provided in S1 Data. Differentially-expressed genes were defined counting fragments forCUT&Tag of elongating RNAPII-S2p across gene lengths using 'bedtools MultiCovBed' in bedTools. Count tables were imported into degust v. 4.1.1 (degust.erc.monash.edu), normalized with limma-voom and displayed as a volcano plot. Genes, fold-changes, FDRs are listed in S1 Data. For promoter heatmaps, profiling coverage was binned by 10 bp -1 kb to +2 kb around annotated TSSs of EPDnew promoters, ordered by mean signal and plotted using deepTools. The display range was adjusted to the maximum and minimum signals for each heatmap. For average H3K27me3 coverage around PREs, we used 2000 called Polycomb-bound sites in eye imaginal discs [24] and selected the top 700 sites that fall within H3K27me3-marked domains. This list is provided in S1 Data. We extracted and displayed mean H3K27me3 signal +20 kb around these sites using the 'computematrix' and 'plotHeatmap' functions of deepTools with 10 bp binning. Plots were individually scaled to the maximum mean signal in each dataset. For rankordering of domains, CPKM for H3K27me3 across domains was sorted for each sample and heatmapped using three-color conditional formatting in MS Excel with Green-Yellow-Red for low-to-high values. The full list is provided in S1 Data.
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Domain: Biology
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In Silico Analysis of SARS-CoV-2 Spike Proteins of Different Field Variants
Coronaviruses belong to the group of RNA family of viruses that trigger diseases in birds, humans, and mammals, which can cause respiratory tract infections. The COVID-19 pandemic has badly affected every part of the world. Our study aimed to explore the genome of SARS-CoV-2, followed by in silico analysis of its proteins. Different nucleotide and protein variants of SARS-CoV-2 were retrieved from NCBI. Contigs and consensus sequences were developed to identify these variants using SnapGene. Data of the variants that significantly differed from each other was run through Predict Protein software to understand the changes produced in the protein structure. The SOPMA web server was used to predict the secondary structure of the proteins. Tertiary structure details of the selected proteins were analyzed using the web server SWISS-MODEL. Sequencing results showed numerous single nucleotide polymorphisms in the surface glycoprotein, nucleocapsid, ORF1a, and ORF1ab polyprotein while the envelope, membrane, ORF3a, ORF6, ORF7a, ORF8, and ORF10 genes had no or few SNPs. Contigs were used to identify variations in the Alpha and Delta variants of SARS-CoV-2 with the reference strain (Wuhan). Some of the secondary structures of the SARS-CoV-2 proteins were predicted by using Sopma software and were further compared with reference strains of SARS-CoV-2 (Wuhan) proteins. The tertiary structure details of only spike proteins were analyzed through the SWISS-MODEL and Ramachandran plots. Through the Swiss-model, a comparison of the tertiary structure model of the SARS-CoV-2 spike protein of the Alpha and Delta variants was made with the reference strain (Wuhan). Alpha and Delta variants of the SARS-CoV-2 isolates submitted in GISAID from Pakistan with changes in structural and nonstructural proteins were compared with the reference strain, and 3D structure mapping of the spike glycoprotein and mutations in the amino acids were seen. The surprisingly increased rate of SARS-CoV-2 transmission has forced numerous countries to impose a total lockdown due to an unusual occurrence. In this research, we employed in silico computational tools to analyze the SARS-CoV-2 genomes worldwide to detect vital variations in structural proteins and dynamic changes in all SARS-CoV-2 proteins, mainly spike proteins, produced due to many mutations. Our analysis revealed substantial differences in the functionality, immunological, physicochemical, and structural variations in the SARS-CoV-2 isolates. However, the real impact of these SNPs can only be determined further by experiments. Our results can aid in vivo and in vitro experiments in the future.
Introduction
On 31 December 2019, COVID-19 was initially discovered in Wuhan, China. The condition became severe when many infected cases were reported in the "Huanan Seafood Market" [1]. HKU1, HCoV229E, HCoVOC43, HCoVNL63, and HCoV229E are coronaviruses generally responsible for only minor common cold and respiratory infections in newborn infants and the elderly [2]. Based on genetic material properties, the coronavirinae family contains four genes: Alpha, Beta, Gamma, and Delta coronavirus. Coronaviruses are RNA viruses that can trigger an infection in mammals, humans, and birds. They cause Vaccines 2023, 11, 736 2 of 12 respiratory infections such as the common cold, SARS, and MERS [3]. SARS-CoV-2 is a positive-polarity single-stranded RNA virus. It spread extremely rapidly, and became a worldwide pandemic within a few months. Its transmission between individuals and populations is relatively easy because of its transmission pattern in direct body contact and through respiratory droplets from an infected individual [4]. The virus has a 2 to 14-day incubation period and causes severe respiratory problems. Its symptoms are high-grade fever, non-productive cough, pharyngitis, muscle joint pains, runny nose, diarrhea, and shortness of breath. In certain circumstances, loss of sensations such as taste and smell are lost [4].
The four critical structural proteins found in the virion are the N-Protein (nucleocapsid), M-protein (transmembrane), E-Protein (envelope), and S-Protein (spike). However, the direct assembly of structural proteins is not required to form the whole virion infection in some coronaviruses; other proteins with overlapping compensatory roles may be expressed [5][6][7]. The SARS-CoV-2 genome comprises two enormous linear ORFs: ORF1a and ORF1b ORF1a are transcribed into polyproteins 1a and 1b, produced via a one ribosomal frameshift. For replication and transcription, there are 16 non-structural proteins (NSPS) [8].
However, the worldwide spread of COVID-19 has raised significant concerns about viral evolution and adaptation in terms of how it spreads worldwide, encountering various host immune systems and countermeasures determined by mutations, deletions, and recombination.
SARS-CoV-2 varies from earlier strains by having numerous hazardous residues in the coronavirus receptor-binding region (especially Gln493), which delivers valuable communication with ACE2 human receptors [9]. Understanding surface receptor variations in the field are crucial for developing a stable vaccine strain. Therefore, we employed in silico screening of the whole genome for significantly spiked protein SARS-CoV-2 of coronavirus variants from the published data worldwide to understand the viral variation patterns. As a result, SARS-CoV-2 positive samples were sequenced to investigate the genetic diversity during the fourth wave of the epidemic in Pakistan.
There is a pressing need to combat COVID-19, and we need quick and reliable approaches. SARS-CoV-2 varies from earlier strains by having numerous residues in the coronavirus receptor-binding region, which enables its binding with ACE2 human receptors. The change in closeness perhaps explains why this virus is more transmissible than other viruses. Using in silico techniques, we studied the pattern of variation in the proteins, especially the spike proteins, of various SARS-CoV-2 strains reported across the globe. This research will help lay the foundation for future research.
Viral Strain Selection and Retrieval of Protein Sequence
First, the proteome of the SARS-CoV-2 virus was taken from the NCBI GenBank (www.ncbi.nlm.nih.gov), and for further analysis, sequences of amino acids were obtained in the FASTA format. The target proteins consisted of membrane proteins, nucleocapsid phosphoproteins, surface glycoproteins, envelope proteins, and open read fragments including ORF1a polyprotein, ORF1ab polyprotein, ORF3a, ORF6 protein, ORF7a protein, ORF7b protein, ORF8 protein, and ORF10 protein.
Secondary structure prediction and solvent accessibility; 2.
Comparison of Pakistani Variants with Reference Strain
The GISAID database was used to retrieve genome sequences from Pakistan. All relevant sequences from the search results were retrieved in FASTA format (accessed 1 November 2021). CoVsurver was used, which was authorized by GISAID, to analyze our sequences in the FASTA format. These tools are online-based bioinformatics software that have been validated to identify and reassemble new coronavirus isolates. In comparison to SARS-CoV-2, we discovered nucleotide and amino acid mutations as well as correlations. The GISAID CoVsurver software was used to identify the GISAID clade of the sequences. The CoVsurver tool conducts sequence alignments and annotations as well as 3D structure mapping and mutations in amino acids.
Mutations Identified in the Sequenced SARS-CoV-2 Genomes
The sequencing results demonstrated numerous single nucleotide polymorphisms (SNPs) in surface glycoproteins, nucleocapsids, ORF1a polyprotein 1ab (ORF1ab), and ORF1ab polyproteins. These mutation hotspots may be particularly important in adapting SARS-CoV-2s to the human host. The envelope, membrane, ORF3a, ORF6, ORF7a, ORF8, and ORF10 genes had no or few SNPs, so limited alterations in these proteins might mean that they have conserved activities that are required for viral transmission.
Protein Secondary Structure
The secondary structures of the envelope protein, membrane glycoprotein, nuc ocapsid phosphoprotein, ORF10 protein, ORF1a polyprotein, ORF1ab polyprotein, ORF protein, ORF6 protein, ORF7a protein, ORF7b protein, ORF8 protein, and surface glyc protein of SARS-CoV-2 were predicted by using the Sopma secondary structure predicti method [URL] accessed on 26 April 2022. By keeping the default parameters, several conformation states: 4 (helix, sheet, turn, coil); similarity threshold: 8; window width: 17 were furth compared with the reference strain of SARS-CoV-2 (Wuhan) proteins, which all show similarity and variance to each other, as shown in Table 1 [10]. PredictProtein ( [URL] dictprotein.org/ accessed on 17 May 2022) was used to predict the protein structural a functional features. It was only used for those proteins with a drastic change in the s ondary structure, as shown in Figure 2 and others are shown in Supplementary Materia
Protein Secondary Structure
The secondary structures of the envelope protein, membrane glycoprotein, nucleocapsid phosphoprotein, ORF10 protein, ORF1a polyprotein, ORF1ab polyprotein, ORF3a protein, ORF6 protein, ORF7a protein, ORF7b protein, ORF8 protein, and surface glycoprotein of SARS-CoV-2 were predicted by using the Sopma secondary structure prediction method [URL], accessed on 26 April 2022. By keeping the default parameters, several conformational states: 4 (helix, sheet, turn, coil); similarity threshold: 8; window width: 17 were further compared with the reference strain of SARS-CoV-2 (Wuhan) proteins, which all showed similarity and variance to each other, as shown in Table 1 [10]. PredictProtein ( [URL]/ accessed on 17 May 2022) was used to predict the protein structural and functional features. It was only used for those proteins with a drastic change in the secondary structure, as shown in Figure 2 and others are shown in Supplementary Materials.
Tertiary Structure
Tertiary structural details of the spike proteins were analyzed using the online software SWISSMODEL ( [URL], accessed on 10 June 2022) and Ramachandran plots ( [URL], accessed on 10 June 2022). Through the Swiss model, a comparison of the tertiary structure model of the SARS-CoV-2 spike protein of the Alpha and Delta variants was made with the reference strain (Wuhan), as shown in Figures 3 and 4 [11].
Tertiary Structure
Tertiary structural details of the spike proteins were analyzed using the online software SWISSMODEL ( [URL], accessed on 10 June 2022) and Ramachandran plots ( [URL], accessed on 10 June 2022). Through the Swiss model, a comparison of the tertiary structure model of the SARS-CoV-2 spike protein of the Alpha and Delta variants was made with the reference strain (Wuhan), as shown in Figures 3 and 4 [11].
Tertiary Structure
Tertiary structural details of the spike proteins were analyzed using the online software SWISSMODEL ( [URL], accessed on 10 June 2022) and Ramachandran plots ( [URL], accessed on 10 June 2022). Through the Swiss model, a comparison of the tertiary structure model of the SARS-CoV-2 spike protein of the Alpha and Delta variants was made with the reference strain (Wuhan), as shown in Figures 3 and 4 [11].
Comparison of Pakistani Variants with Reference Strain
The Alpha and Delta variants of the SARS-CoV-2 isolates submitted in GISAID ( [URL]233, accessed on 1 November 2021) from Pakistan with changes in the amino acids in the structural and nonstructural proteins compared with the reference strain (hCoV-19/Wuhan/WIV04/2019), as shown in Table S1 in Supplementary Materials, and the 3D structure mapping of spike glycoprotein and mutations in amino acid are shown in the Figure 5.
Discussion
In this research, we studied two complete sequences of the Alpha and Delta variants of SARS-CoV-2 with the Wuhan variant as the reference sequence. Several mutations have been noticed in the COVID-19 proteins as new variants emerge. These variations have several adverse effects on the structure and function of COVID-19 proteins, making it challenging to administer the COVID-19 complex. Such mutations are discussed here.
Coronaviruses have the biggest genome size ranging from 26.4 to 31.7 kb of all RNA viruses [12,13]. Its enormous gene size allows for greater flexibility in integrating and modifying genes [12][13][14]. In RNA viruses, the mutation frequency is relatively high, increasing virulence and developing new species [15]. The greater rate of mutation within the genomes of viruses in various geographical areas is also one reason that COVID-19 is liable for the changes in the death rate and disease symptoms [16]. In COVID-19, we found a few additional single amino acid changes in the Alpha and Delta variants compared to the reference strain (Wuhan), as shown in the above figures.
Virus particles contain the RNA genetic material and the structural proteins required for host cell entry. Once within the cell, the infecting RNA encodes structural proteins that form viral particles, non-structural proteins that regulate viral assembly, transcription, replication, and control of the host cell, and accessory proteins whose role is unknown. The large genome, ORF1ab, has overlapping open reading frames that encode the polyproteins PP1ab and PP1a. The polyproteins are degraded into 16 non-structural proteins known as NSP1-16. A-1 ribosomal frameshifting occurrence is required to produce the longer (PP1ab) or shorter (PP1a) protein. Based on similarities to other coronaviruses, the proteins include the papain-like protein (NSP3), 3C-like proteinase protein (NSP5), RNAdependent RNA polymerase (NSP12, RdRp), helices (NSP13, HEL), endoRNAse (NSP15), 2 -O-ribose-methyltransferase (NSP16), and other non-SARS-CoV-2 non-structural proteins and have the functions of viral transcription, replication, proteolytic processing, host immune response suppression, and host gene expression suppression.
Previous research has revealed that mutations in non-structural proteins 2 and 3 play a crucial role in infectious capacity and are mainly accountable for the SARS-CoV-2 differentiation process [16], and that the coronavirus nucleocapsid protein is required for RNA replication, genome packing, and transcription [17]. In addition, the envelope protein is involved in the viral genome assembly and development of ion channels (IC), which are critical for virus-host connection and are primarily related to pathogenesis [5,18].
The spike protein is about 180 to 200 kDa and has 1273 amino acids [19]. To avoid the host's immunological reaction, many polysaccharide molecules cover the spike protein's surface [20]. The RBD of the spike protein is the area that mainly interacts with ACE2, leading to virus entry into the host cell [16,21]. For several years, theoretical or experimental techniques have been used to predict protein stability [22]. According to prior studies, a single point mutation in RBD disrupts the antigenic structure, affecting RBD binding to ACE2 [23,24]. Furthermore, in silico investigations have demonstrated that point mutations inside the RBD of spike glycoprotein had a stabilizing impact on the spike protein and were discovered to enhance the protein stability.
Some mutations may play a vital role in binding to human ACE2 receptors and many mutations may have enhanced the binding affinity of the surface glycoproteins. Additional beta strands and hydrogen bonds were shown in both the predictions and the 3D models. These extra mutations may produce structural conformational alterations and a greater binding affinity. Biologists and others can utilize these methods to gain a preliminary knowledge of SARS-CoV-2 mutations and their links to SARS-CoV and other related viruses. The initial observations can then be further investigated using various specialized tools and methods. In particular, computational techniques are promising. Machine learning, artificial intelligence, data integration and mining, visualization, computational and mathematical modeling of critical biochemical interactions, and disease control mechanisms can provide cost-and time-efficient solutions.
Researchers are collecting data related to coronavirus to fully understand the spread of the disease, its pathogenesis, and biology to eliminate it [25]. The explosive growth of structural and genomic databases, combined with computational approaches, contributes to discovering and manufacturing novel vaccination candidates. In addition, modern breakthroughs in immunological bioinformatics have resulted in various tools and web servers that can help cut the time and cost of manufacturing traditional vaccinations.
In our findings, forty substantial mutations were seen in the Alpha and ninety-two substantial mutations in the Delta variant in genomic sequences of Pakistani SARS-CoV-2 strains compared with the Wuhan reference strain, covering the whole viral genome. The additional evaluation found that most of these changes were associated with a few viral genomic regions including the spike, nucleocapsid protein, NS3, NSP2, and NSP6, as structural protein integrity is critical for the immune response. Hence, we looked for mutations in the viral proteins. The nucleocapsid protein is an immunogenic phosphoprotein that helps in genome replication and regulation and the cell signaling pathway. The protein structure is disrupted due to the mutation G204R in the nucleocapsid and D614G in the spike protein. Several studies have revealed that the mutation enhances viral infectivity. Moreover, the D614G mutation expands the spike protein, which might result in protein instability and lead to increased viral infection.
We found 29 mutations in the spike protein of the Delta variant and 14 mutations in the Alpha variant in the genomic sequences of the Pakistani SARS-CoV-2 strains compared with the reference strain. The SARS-CoV-2 spike protein is a prominent target for therapeutic and vaccine development due to its interaction in the host cell receptor identification, attachment, and entrance [26,27]. In the spike protein of both the Alpha and Delta variants, we discovered a D614G (aspartic acid to glycine) mutation. A recent study revealed that the D614G variant is more pathogenic, with infected individuals having a higher viral load; however, there was no correlation with disease severity [28]. In silico studies using pseudoviruses by Li et al. revealed that the D614G mutation significantly enhanced the infection [29]. Similar findings were reported, with the D614G mutant having the highest cell entrance among the spike variations [30]. Furthermore, Hou et al. observed that a change in D614G improves the SARS-CoV-2 infection rate and transmission, primarily in humans and animals [31]. The D614G mutation is becoming a more common strain around the world.
The host diversity of coronaviruses and the variation in tissue tropism is primarily because of changes in the surface glycoprotein. The S1 subunit is linked to functions of the receptor binding domain, while the S2 subunit helps in facilitating virus fusion with cell membranes.
In Pakistan, there was a 66% increase in SARS-CoV-2 cases and 64.8% increase in deaths in June 2020 compared to February-May 2020 (72,460 confirmed cases and 1543 deaths) [32]. The significant rise in the incidence cases and the number of deaths may point toward the widespread distribution of the D614G mutation, which must be further examined [33].
Additional molecular epidemiological investigations are required to track the DG614 strain's circulation in Pakistan, which could also serve to understand the impact of SARS-CoV-2 gene mutations on disease severity. Moreover, tracing variations in the SARS-CoV-2 spike glycoprotein is critical because of its function in cell receptor interaction, entrance in the host cell, and triggering antibody responses, as the widespread distribution of the D614G variation throughout the world has an influence on vaccination effectiveness. This has been a major concern, as most vaccines were designed on the D614G variation. The same issue has been conveyed through the results of Weissman et al., who showed that the D614G mutation is neutralized at a higher level by serum from vaccinated mice, nonhuman primate, and humans [34]. Furthermore, regular monitoring of SARS-CoV-2 spike protein gene mutation is essential for detecting escape variants and in the future, for vaccine development.
This emphasizes that the SARS-CoV-2 strains circulating in Pakistan have mutated and have a genetic variation from their origins. Therefore, we suggest the whole-genome sequencing of strains found throughout the country for a better understanding of the viral evolution and identify strains with distinctive mutational changes.
Conclusions
The surprisingly increased rate of SARS-CoV-2 transmission has forced numerous countries to impose a total lockdown due to an unusual occurrence. Therefore, there is an immediate need to tackle COVID-19, so we want rapid and practical measures. In silico techniques are based on analyzing biological data and using refined predictions and calculations to create a scientific database. In this research, we employed in silico computational tools to analyze SARS-CoV-2 genomes worldwide to detect vital variations in structural proteins and dynamic changes in all SARS-CoV-2 proteins, mainly spike proteins, produced due to a large number of mutations.
Our analysis revealed substantial differences in functionality, immunological, physicochemical, and structural variations in the SARS-CoV-2 isolates. However, the real impact of these SNPs can only be determined by further experiments. Our results can aid in vivo and in vitro experiments in the future.
Current developments in immunological bioinformatics areas have resulted in different servers and tools that can save the cost and time of traditional vaccine development. However, suitable antigen candidates remain a hurdle for researchers. To design a multiple epitope vaccine, the antigenic epitope prediction of a relevant protein by immunoinformatic methods is very helpful. By using in silico cloning, we will acquire a harmless SARS-CoV-2 vaccine that could trigger immune responses: cellular, innate, and humoral. Although the production and manufacture of vaccines are expensive and takes more time, immunoinformatic approaches can decrease this load. Nowadays, researchers are finding different methods to develop multi-epitope subunit vaccines. With the development of computational tools, epitope prediction for antibodies has become more meaningful.
Supplementary Materials:
The following supporting information can be downloaded at: [URL]:// www.mdpi.com/article/10.3390/vaccines11040736/s1. Figure S1: Figure 1 Schematic view of contigs of the envelope protein of 1; Figure S2: Schematic view of contigs of the membrane glycoprotein of 1; Figure S3: Schematic view of contigs of the Nucleocapsid phosphoprotein of 1; Figure S4: Schematic view of contigs of the ORF10 protein of 1; Figure S5: Schematic view of contigs of the ORF1a polyprotein of 1; Figure S6: Schematic view of contigs of the ORF1ab polyprotein of 1; Figure S7: Schematic view of contigs of the ORF3a protein of 1; Figure S8: Schematic view of contigs of the ORF6 protein of 1; Figure S9: Schematic view of contigs of the ORF7a protein of 1; Figure S10: Schematic view of contigs of the ORF7b protein of 1; Figure S11: Schematic view of contigs of the ORF8 protein of 1; Figure S12: Viewer lays out predicted features of protein structural and functional features of Membrane Glycoproteins; Figure S13: Viewer lays out predicted features of protein structural and functional features of Nucleocapsid phosphoprotein; Figure S14: Viewer lays out predicted features of protein structural and functional features of ORF1a Polyprotein; Figure S15: Viewer lays out predicted features of protein structural and functional features of ORF1ab Polyprotein; Figure S16: Viewer lays out predicted features of protein structural and functional features of ORF7a protein; Table S1:
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Domain: Biology
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SPIDR: a highly multiplexed method for mapping RNA-protein interactions uncovers a potential mechanism for selective translational suppression upon cellular stress
RNA binding proteins (RBPs) play crucial roles in regulating every stage of the mRNA life cycle and mediating non-coding RNA functions. Despite their importance, the specific roles of most RBPs remain unexplored because we do not know what specific RNAs most RBPs bind. Current methods, such as crosslinking and immunoprecipitation followed by sequencing (CLIP-seq), have expanded our knowledge of RBP-RNA interactions but are generally limited by their ability to map only one RBP at a time. To address this limitation, we developed SPIDR (Split and Pool Identification of RBP targets), a massively multiplexed method to simultaneously profile global RNA binding sites of dozens to hundreds of RBPs in a single experiment. SPIDR employs split-pool barcoding coupled with antibody-bead barcoding to increase the throughput of current CLIP methods by two orders of magnitude. SPIDR reliably identifies precise, single-nucleotide RNA binding sites for diverse classes of RBPs simultaneously. Using SPIDR, we explored changes in RBP binding upon mTOR inhibition and identified that 4EBP1 acts as a dynamic RBP that selectively binds to 5’-untranslated regions of specific translationally repressed mRNAs only upon mTOR inhibition. This observation provides a potential mechanism to explain the specificity of translational regulation controlled by mTOR signaling. SPIDR has the potential to revolutionize our understanding of RNA biology and both transcriptional and post-transcriptional gene regulation by enabling rapid, de novo discovery of RNA-protein interactions at an unprecedented scale.
INTRODUCTION
RNA binding proteins (RBPs) play key roles in controlling all stages of the mRNA life cycle, including transcription, processing, nuclear export, translation, and degradation [1][2][3][4][5] . Recent estimates suggest that up to 30% of all human proteins (several thousand in total) bind to RNA 6-10 , indicative of their broad activity and central importance in cell biology. Moreover, mutations in RBPs have been causally linked to various human diseases, including immunoregulatory and neurological disorders as well as cancer [2][3][4]11 . Yet, we still do not know what specific roles most of these RBPs play because the RNAs they bind remain mostly unknown.
In addition, there are many thousands of regulatory non-coding RNAs (ncRNAs) whose functional roles remain largely unknown 12,13 ; understanding how they work requires defining the proteins to which they bind [13][14][15] . For example, uncovering the mechanism by which the Xist long noncoding RNA (lncRNA) silences the inactive X chromosome required identification of the SPEN/SHARP RBP that binds to Xist 16-20 -a process that took >25 years after the lncRNA was discovered 14 . Given the large discrepancy between the number of ncRNAs and putative RBPs identified, and the number of RNA-protein interactions demonstrated to be functionally relevant, there is an urgent need to generate high-resolution binding maps to enable functional characterization 14 .
Currently, the most rigorous and widely utilized method to characterize RBP-RNA interactions is crosslinking and immunoprecipitation followed by next generation 2 June 6, 2023 sequencing (CLIP-seq) [21][22][23][24][25][26] . Briefly, CLIP works by utilizing UV light to covalently crosslink RNA and directly interacting proteins, followed by cell lysis, immunoprecipitation under stringent conditions (e.g., 1M salt) to purify a protein of interest followed by gel electrophoresis, transfer to a nitrocellulose membrane, and excision of the protein-RNA complex prior to sequencing and identification of the bound RNAs. CLIP and its related variants have greatly expanded our knowledge of RNA-RBP interactions and our understanding of gene expression from mRNA splicing to microRNA targeting 21-26 .
Yet, CLIP and all of its variants (with one recent exception 27 which we discuss in more detail below; see Note 1) are limited to mapping a single RBP at a time. As such, efforts to generate reference maps for hundreds of RBPs in even a limited number of cell types have required major financial investment and the work of large teams working in international consortiums (e.g., ENCODE) 23,28,29 . Despite these herculean efforts and the important advances they have enabled, there are critical limitations: (i) Only a small fraction of the total number of predicted RBPs have been successfully mapped using genome-wide methods (ENCODE has so far characterized the binding patterns of < 10% of known RBPs); (ii) Of these, most have been mapped in only a small number of cell lines (mainly K562 and HepG2); (iii) Because each protein map is generated from an individual experiment, a large number of cells is required to map dozens, let alone hundreds, of RBPs -this is particularly challenging for studying primary cells, disease models, or other populations of rare cells. Further, because these datasets are highly cell type-specific, the generated maps are not likely to be directly useful for studying these RBPs within other cell-types or model systems (e.g., patient samples, animal models, or perturbations). Thus, it is critically important to enable the generation of comprehensive RBP binding for any cell type of interest in a manner that is accessible to any individual lab.
To overcome these challenges, we developed SPIDR (Split and Pool Identification of RBP targets), a massively multiplexed method to simultaneously profile the global RNA binding sites of dozens to hundreds of RBPs in a single experiment. SPIDR is based on our split-pool barcoding strategy that maps multiway nucleic acid interactions using high throughput sequencing 30-32 ; the vastly simplified version of split-pool barcoding we present here, when combined with antibodybead barcoding, increases the throughput of current CLIP methods by two orders of magnitude. Using this approach, we can reliably identify the precise, single nucleotide RNA binding sites of dozens of RBPs simultaneously and can detect changes in RBP binding upon perturbation. Using this approach, we uncovered a mechanism driven by dynamic RBP binding to mRNA that may explain the specificity of translational regulation controlled by mTOR signaling. Thus, SPIDR enables rapid, de novo discovery of RNA-protein interactions at an unprecedented scale and has the potential to transform our understanding of RNA biology and both transcriptional and posttranscriptional gene regulation.
SPIDR: A highly multiplexed method for mapping RBP-RNA interactions
We developed SPIDR to enable highly multiplexed mapping of RBPs to individual RNAs transcriptomewide. Briefly, SPIDR involves: (i) generating highly multiplexed antibody-bead pools by tagging individual antibody-bead conjugates with a specific oligonucleotide (tagged bead pools), (ii) performing RBP purification using these tagged antibody-bead pools in UV-crosslinked cell lysates, and (iii) linking individual antibodies to their associated RNAs using split-andpool barcoding ( Figure 1A and Supplemental Figure 1).
We first devised a highly modular scheme to generate hundreds of tagged antibody-beads such that each unique bead population is labeled with a specific oligonucleotide tag and all bead populations are combined to generate an antibody-bead pool ( Figure 1A and Supplemental Figure 1). Because this approach does not require direct chemical modification of the antibody, we can utilize any antibody (in any storage buffer) and rapidly link it to a defined sequence on a bead at high efficiency using the same coupling procedure utilized in traditional CLIP-based approaches (see Methods). Using this pool, we perform on-bead immunopurification (IP) of RBPs in UV-crosslinked lysates using standard conditions and assign individual protein identities to their associated RNAs using splitand-pool barcoding, where the same barcode strings are added to both the oligonucleotide bead tag and immunopurified RNA ( Figure 1A). We dramatically simplified our split-and-pool tagging method such that the entire protocol can be performed without the need for specialized equipment in ~1 hour (see Methods). 3 June 6,2023 To ensure that IP using a pool containing multiple antibodies can successfully and specifically purify each of the individual proteins, we performed an IP in K562 cells using a pool of antibodies against 39 RBPs and measured the purified proteins by liquid chromatography tandem mass spectrometry (LC-MS/ MS). We confirmed that 35 of the 39 targeted RBPs enriched at least 2-fold relative to a negative control, showing that multiplexed enrichment of several RBPs simultaneously is possible (Supplemental Figure 2).
The few exceptions were RBPs that were simply not detected (neither in the pooled IP nor under control conditions) and likely reflect either a poor antibody or lack of RBP expression in this cell line.
SPIDR accurately maps dozens of RBPs within a single experiment
To test whether SPIDR accurately maps RBPs to RNA, we performed SPIDR in two widely studied human Figure 1: SPIDR (Split and Pool Identification of RBP targets) -a highly multiplexed method to map protein-RNA interactions. (A) Schematic overview of the SPIDR method. The bead pool is incubated with UV crosslinked lysate in a single tube. After immunopurification, each bead is uniquely labeled by split-and-pool barcoding. The complexity of the barcode generated depends on the number of individual tags used in each split-pool round and the number of split-pool rounds. For example, after 8 rounds of split and pool barcoding, using 12 barcodes in each round, the likelihood that two beads will end up with the same barcode is ~ 1 in 430 million (1/12 8 ). Oligos and RNA molecules and their linked barcodes are sequenced and RNAs are matched to proteins based on their shared barcodes. (The bead labeling strategy was adapted from ChIP-DIP, a protocol for multiplexed mapping of proteins to DNA, [URL]/ technologies/). (B) Schematic list of the different RBPs mapped by SPIDR in K562 and/or HEK293T cells, functional assignments based on literature review. (C) An example of the raw alignment data for the pool (all reads before splitting by bead identities) and for specific RBPs (all reads assigned to specific RBP beads) across the XIST RNA. Blocks represent exons, lines introns, and thick blocks are the annotated XIST repeat regions (A-E). (D) Raw alignment data for SLBP across the H3C2 histone mRNA. Top track is pooled alignment data; tracks below are reads assigned to SLBP or other RBPs and controls. Specifically, we generated antibody bead pools containing 68 uniquely tagged antibody-beads targeting 62 distinct RBPs across the RNA life cycle, including splicing, processing, and translation factors (Figure 1B, Supplemental Tables 1, 2). As negative controls, we included antibodies against epitopes not present in endogenous human cells (GFP and V5), antibodies that lack affinity to any epitope (mouse IgG), and oligonucleotide-labeled beads lacking any antibody (empty beads).
Using these pools, we performed SPIDR on 10 million UV-crosslinked cells. Focusing on the K562 data (which were sequenced at greater depth), we generated a median of 4 oligonucleotide tags per SPIDR cluster with the majority of clusters (>80%) containing tags representing only a single antibody type (Supplemental Figure 3), indicating that there is minimal 'crosstalk' between beads in a SPIDR experiment. This specificity enables us to uniquely assign RNA molecules to their corresponding RBPs. After removing PCR duplicates, we assigned each sequenced RNA read to its associated RBP and identified high confidence binding sites by comparing read coverage across an RNA to the coverage in all other targets in the pooled IP (Supplemental Figure 4, Supplemental Figure 5; see Methods for details). Using this approach, we detected the precise binding sites for SAF-A, PTBP1, SPEN, and HNRNPK on the XIST RNA 17,20,23 ( Figure 1C). Although most proteins (38/53 RBPs in K562) contained more than 2 million mapped RNA reads (Supplemental Figure 4), we observed specific NOLC1 SMNDC1 FUS TAF15 PCBP2 DDX52 RPS3 LARP1 ADAR1 DDX55 ILF3 LARP7 TARDBP LIN28B to 100nt windows across each classical non-coding RNA (columns). Each bin is colored based on the enrichment of read coverage per RBP relative to background. (B) Sequence read coverage for LSM11 binding to U7 snRNA. For all tracks, "pool" refers to all reads prior to splitting them by paired barcodes (shown in gray), and individual tracks (shown in teal) reflect reads after assignment to specific antibodies. (C) Enrichment of read coverage relative to background for WDR43 and LIN28B over the 5' ETS region of 45S RNA. (D) Sequence reads coverage for LIN28B binding to let-7 miRNAs. (E) Sequence reads coverage for DROSHA/DGCR8, UPF1, SPEN, and TARDBP to their respective mRNAs. (F) Sequence reads coverage for two distinct antibodies to HNRNPL in a single SPIDR experiment. For comparison, HNRNPL coverage from the ENCODE-generated eCLIP data is shown (bright green). 5 June 6, 2023 binding to known target sites even for RBPs with lower numbers of reads. For example, SLBP (Stem Loop Binding Protein) had only 1.5 million mapped reads yet displayed strong enrichment specifically at the 3' ends of histone mRNAs as expected 29 (Figure 1D).
To systematically assess the quality, accuracy, and resolution of our SPIDR binding maps and the scope of the SPIDR method, we explored several key features: (i) Accurate mapping of classical RNPs. We targeted RBPs of diverse functionality, such as those which bind preferentially to RNAs coding for proteins and/or lncRNAs, to introns, exons, miRNAs, etc., as well as more "classical" ribonuclear protein (RNP) complexes, such as the ribosome or spliceosome (Figure 2A). We observed precise binding to the expected RNAs and binding sites. For example, we observed binding of: • LSM11 to the U7 small nuclear RNA (snRNA) 33 and the telomerase RNA component (TERC) 34 (Figure 2A and 2B).
• WDR43, a protein that is involved in ribosomal RNA (rRNA) processing, to the 45S pre-rRNA and the U3 small nucleolar RNA (snoRNA), which is involved in rRNA modification 35 (Figure 2A and 2C).
• LIN28B to a distinct region of the 45S pre-rRNA, consistent with recent reports of its role in ribosomal RNA biogenesis in the nucleolus 36 (Figure 2A and 2C).
• DDX52, a DEAD-box protein that is predicted to be involved in the maturation of the small ribosomal subunit 39,40 and RPS3, a structural protein contained within the small ribosomal RNA subunit, to distinct sites on the 18S rRNA (Figure 2A).
• FUS and TAF15 to distinct locations on the U1 snRNA 41,42 (Figure 2A) • SMNDC1 specifically to the U2 snRNA 43 (Figure 2A) • SSB (also known as La protein) binding to tRNA precursors consistent with its known role in the biogenesis of RNA Polymerase III transcripts 44,45 (Figure 2A).
(ii) Many RBPs bind their own mRNAs to autoregulate expression levels. Many RBPs have been reported to bind their own mRNAs to control their overall protein levels through post-transcriptional regulatory feedback 52-54 . For example, SPEN protein binds its own mRNA to suppress its transcription 55 , UPF1 binds its mRNA to target it for Nonsense Mediated Decay 56 , TARDBP binds its own 3'-UTR to trigger an alternative splicing event that results in degradation of its own mRNA 57,58 , and DGCR8, which together with DROSHA forms the known microprocessor complex, binds a hairpin structure in DGCR8 mRNA to induce cleavage and destabilization of the mRNA 59 ( Figure 2E). In addition to these cases, we observed autoregulatory binding of proteins to their own mRNAs for nearly a third of our targeted RBPs (15 proteins) (Supplemental Figure 6).
(iii) Different antibodies that capture the same protein or multiple proteins within the same complex show similar binding. We considered the possibility that including antibodies against multiple proteins contained within the same complex, or that otherwise bind to the same RNA, within the same pooled sample could compete against each other and therefore limit the utility of large-scale multiplexing. However, we did not observe this to be the case; in fact, antibodies against different proteins known to occupy the same complex displayed highly comparable binding sites on the same RNAs. For example, DROSHA and DGCR8, two proteins that bind as part of the microprocessor complex, showed highly consistent binding patterns across known miRNA precursors with significant overlap in their binding sites (odds-ratio of 316-fold, hypergeometric p-value < 10 -100 ). Similarly, when we included two distinct antibodies targeting the same protein, HNRNPL, we observed highly comparable binding profiles for both antibodies ( Figure 2F) and significant overlap in defined binding sites (odds-ratio of 15-fold, hypergeometric p-value < 10 -100 ). Taken together, our results indicate that SPIDR can be used to map different RBPs that bind to the same RNA targets and can successfully map multiple antibodies targeting the same protein. As such, SPIDR may be a particularly useful tool for directly screening multiple antibodies targeting the same protein to evaluate utility for use in CLIP-like studies.
(iv) Transcriptome-wide SPIDR maps are highly comparable with CLIP. Because K562 represents the ENCODE-mapped cell line with the largest number of eCLIP datasets, we were able to benchmark our SPIDR results directly to those generated by ENCODE.
To do this, we compared the profiles for each of the 33 RBPs that overlap between SPIDR and ENCODE datasets in K562 cells 23,28,29 (see Methods). We observed highly overlapping binding patterns for most RBPs, including: HNRNPK binding to POLR2A ( Figure 3A), PTBP1 binding to AGO1 (Figure 3B), RBFOX2 to NDEL1 ( Figure 3C) and the binding of several known nuclear RBPs to XIST ( Figure 3D). To explore this data on a global scale, we compared RNA binding sites for each RBP and observed significant overlap between 6 June 6, 2023 SPIDR-and ENCODE-derived binding sites for the vast majority of proteins (29/33, p<0.01, Figure 3E). Moreover, we observed that in virtually all cases each RBP preferentially binds to the same RNA features (e.g., introns, exons, CDS, miRNAs, 5' and 3'UTRs) in both datasets ( Figure 3F, Supplemental Figure 7). Finally, the binding motifs identified within the significant SPIDR-defined binding sites match those defined by CLIP and in vitro binding assays 29 (e.g., RNA Bind-N-Seq, Figure 3G).
(v) SPIDR enables high-resolution RBP mapping at single nucleotide resolution. Next, we explored whether SPIDR can provide single nucleotide resolution maps of precise RBP-RNA binding sites, as is the case for some current CLIP-seq approaches. Specifically, UV crosslinking creates a covalent adduct at the site of RBP-RNA crosslinking, which leads to a preferential drop-off of the reverse transcriptase at these sites ( Figure 4A). To explore this, we computed the number of reads that end at each position of an RNA (truncations) and compared these counts to those expected by chance. We observed strong positional enrichments at known protein binding sites. For example, we observe strong enrichment for RPS2 and RPS6 -two distinct structural components of the small ribosomal RNA subunit -at the precise locations where these proteins are known to contact the 18S rRNA in the resolved ribosome structure ( Figure 4B). Moreover, examining individual mRNAs bound by HNRNPC Figure 3: SPIDR data is highly comparable to previous eCLIP datasets. Examples . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint June 6, 2023 ( Figure 4C) or PTBP1 ( Figure 4D) showed that the precise binding site corresponds to the known motif sequence. When we computed this enrichment more globally, we observed that HNRNPC ( Figure 4E) and PTBP1 ( Figure 4F) reads tend to terminate immediately proximal to these well-known binding sequences 29 . . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made within the cytoplasm (e.g. UPF1).
LARP1 binds to the 40S ribosome and mRNAs encoding translation-associated proteins
In addition to the three known structural components of the small ribosomal subunit (RPS2, RPS3, and RPS6), we noticed that LARP1 also showed strong binding to the 18S ribosomal RNA (Figures 2A, 5A). LARP1 is an RNA binding protein that has been linked to translational initiation of specific mRNAs. It is known to bind to the 5' end of specific mRNAs, primarily those encoding critical translation proteins such as ribosomal proteins and initiation and elongation factors, via recognition of a terminal oligopyrimidine (TOP) sequence in the 5' UTR of these transcripts 60 . The exact role of LARP1 in translation has been debated because it has been reported to both promote and repress translation of mRNAs containing a TOP-motif 60-66 .
Although LARP1 is known to bind TOP-motif containing mRNAs, how it might promote translation initiation of these mRNAs is mostly unknown. Because we identified a strong binding interaction between LARP1 and the 18S ribosomal RNA, we explored where in the initiating ribosome this interaction occurs. Interestingly, the LARP1 binding site on the 18S ribosomal RNA (1698-1702 nts) is at a distinct location relative to all other 18S binding proteins that we explored and corresponds to a position within the 48S structure that is directly adjacent to the mRNA entry channel ( Figure 5B). More generally, we observed strong binding of LARP1 at the TOP-motif sequence within the 5' UTR of . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint June 6, 2023 translation-associated mRNAs ( Figure 5C).
These results suggest that LARP1 may act to promote increased translational initiation of TOP-motif containing mRNAs by directly binding to the 43S pre-initiation complex and recruiting this complex specifically to mRNAs containing a TOP-motif. Because LARP1 is positioned immediately adjacent to the mRNA in this structure, this 43S+LARP1 complex would be ideally positioned to access and bind the TOP motif to facilitate efficient ribosome assembly and translational initiation at these mRNAs. This mechanism of direct ribosome recruitment to TOP-motif containing mRNAs through LARP1 binding to the 43S ribosome and the mRNA would explain why the TOP-motif must be contained within a fixed distance from the 5' cap to promote translational initiation 67 ( Figure 5D).
4EBP1 binds specifically to LARP1-bound mRNAs upon mTOR inhibition
Translation of TOP motif-containing mRNAs is selectively repressed upon inhibition of the mTOR kinase, which occurs in conditions of physiological stress [68][69][70][71] . Recent studies have shown that under these conditions, LARP1 binds the 5'-UTR of TOP-containing mRNAs, and it has been postulated that this binding activity is responsible for the specific translational repression of these mRNAs 60,72 . Yet, the mechanism by which LARP1 binding might repress translation remains unknown.
The canonical model for how mTOR inhibition leads to translational suppression is through the selective phosphorylation of 4EBP1 69,71 . Specifically, when phosphorylated, 4EBP1 cannot bind to EIF4E, which is the critical initiation factor that binds to the 5' mRNA cap and recruits the remaining initiation factors through direct binding with EIF4G 73,74 . When 4EBP1 is not phosphorylated (i.e., in the absence of mTOR), it binds to EIF4E and prevents it from binding to EIF4G and initiating translation. While this differential binding of 4EBP1 to EIF4E upon mTOR modulation is well-established and is central to translational suppression, precisely how it leads to selective modulation of TOP mRNA translation has remained unclear. Specifically, direct competition between 4EBP1 and EIF4G for binding to EIF4E should impact translation of all EIF4E-dependent mRNAs, yet the observed translational downregulation is specific to TOP-containing mRNAs 69,71,75 and this specificity is dependent on LARP1 binding 60 .
To explore the mechanism of translational suppression of TOP-containing mRNAs upon mTOR inhibition, we treated HEK293T cells with torin, a drug that inhibits mTOR kinase. We adapted SPIDR to map multiple independent samples within a single split-and-pool barcoding experiment (Figure 6A, Supplemental Table 2, see Methods) and used this approach to perform SPIDR on >50 distinct RBPs, including LARP1, numerous translational initiation factors, and 4 negative controls in both torin-treated and untreated conditions.
To ensure that mTOR inhibition robustly leads to translational suppression of TOP-containing mRNAs, we quantified global protein levels in torin-treated and untreated cells using quantitative mass spectrometry (see Methods) to determine protein level changes globally. Although the level of most proteins does not change upon torin-treatment, we observed a striking reduction of proteins encoded from TOP motif-containing mRNAs. Indeed, this translational suppression was directly proportional to the strength of the TOP-motif contained within the 5'-UTR of each mRNA ( Figure 6B).
Next, we explored changes in RBP binding upon mTOR inhibition. We measured the number of RNA reads observed for each protein upon torin treatment relative to control. While the majority of proteins showed no change in the number of RNA reads, the sole exception was 4EBP1, which showed a dramatic increase (>20-fold) in the overall number of RNA reads produced upon mTOR inhibition ( Figure 6C). Interestingly, this increase corresponded to increased binding specifically at mRNAs containing a TOP-motif (p-value < 8 x 10 -10 , Mann-Whitney, Figure 6D and 6E). Notably, this did not simply reflect an increased level of 4EBP1 binding at the same sites, but instead corresponded to the detection of many statistically significant binding sites only upon mTOR inhibition that were not observed in the presence of mTOR activity (control samples). Consistent with these observations, a previous study observed that 4EBP1 can be in proximity to translationally suppressed mRNAs upon mTOR inhibition 76 .
In contrast to 4EBP1, which showed a dramatic transition in binding activity to mRNA upon mTOR inhibition, we did not observe a global change in the number of RNA reads purified by LARP1 upon mTOR inhibition ( Figure 6C). Indeed, in both torin-treated and untreated samples we observed strong binding of LARP1 to TOP motif mRNAs as well as to the 18S ribosomal RNA suggesting that this interaction with the 40S ribosome and TOP mRNAs occurs independently of mTOR activity. However, we did observe a 1.7-fold increase in levels of binding of LARP1 at TOP mRNAs upon mTOR inhibition (p-value < 5.4 x 10 -16 , Mann-Whitney, Figure 6D and 6F). This increased enrichment at TOP mRNAs could reflect more LARP1 binding at these specific mRNAs or could reflect the fact that the LARP1 complex might be more stably associated with each mRNA due to translational repression.
Together, our results suggest a model that may reconcile the apparently divergent perspectives about the role of LARP1 as both an activator and . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint repressor of translational initiation and explains how selective mTOR-dependent translational repression is achieved ( Figure 6G). Specifically, LARP1 binds to the 40S ribosome and 5' untranslated region of mRNAs containing a TOP motif regardless of mTOR activity. In the presence of mTOR (Fig 6G, right side), this dual binding modality can act to promote ribosome recruitment specifically to TOP-containing mRNAs and promote translation of these mRNAs. In the absence of mTOR (Fig 6G, left side), 4EBP1 can bind to TOP-containing mRNAs, potentially via the LARP1 protein already bound to these mRNAs. Indeed, most of the significant 4EBP1 binding sites are also bound by LARP1 under Torin treatment (60% overlap, odds-ratio of 12-fold, hypergeometric p-value < 10 -100 ). By binding selectively to these TOP-containing mRNAs, 4EBP1 can bind to EIF4E and prevent binding between EIF4E and EIF4G, a necessary requirement Model of mTOR-dependent repression of mRNA translation. LARP1 binds to the 40S ribosome and to 5' untranslated region of TOP-containing mRNAs independent of mTOR activity. When mTOR is active (i.e., in the absence of torin; right side), this dual binding modality can recruit the ribosome specifically to TOP-containing mRNAs and promote their translation. When mTOR is inactive (i.e., in the presence of torin), 4EBP1 can bind to TOP-containing mRNAs (possibly through an interaction with LARP1) and to EIF4E. The interaction between 4EBP1 and EIF4E prevents binding between EIF4E and EIF4G, which is required to initiate translation. In this way, LARP1/4EBP1 binding specifically to TOP-containing mRNAs would enable sequence-specific repression of translation.
. CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. June 6, 2023 for initiation of translation. In this way, LARP1/4EBP1 binding to specific mRNAs would enable sequencespecific repression of mRNA translation. This model would explain the apparently divergent roles of LARP1 as both an activator and repressor of translation as it indicates that LARP1 may act as a selective recruitment platform that can either activate or repress translation through the distinct factors that co-bind in the presence or absence of mTOR activity.
DISCUSSION
Here we present SPIDR, a massively multiplexed method to generate high-quality, high-resolution, transcriptome-wide maps of RBP-RNA interactions. SPIDR can map RBPs with a wide-range of RNA binding characteristics and functions (e.g., mRNAs, lncRNAs, rRNAs, small RNAs, etc.) and will enable the study of diverse RNA processes (e.g., splicing, translation, miRNA processing, etc.) within a single experiment and at an unprecedented scale.
While we show that SPIDR can accurately map dozens of RBPs within a single experiment, the numbers used mostly reflect the availability of high-quality antibodies. As such, we expect that this approach can readily be applied to even larger pool sizes for hundreds or thousands of proteins simultaneously. Because of this, we expect that SPIDR will represent a critical technology for exploring the many thousands of human proteins that have been reported as putative RNA binding proteins but that remain largely uncharacterized [6][7][8][9][10] . Similarly, we expect that this technology will be crucial for assessing the putative functions of the >20,000 annotated ncRNAs which have remained largely uncharacterized.
Because the number of cells required to perform SPIDR is comparable to that of a traditional CLIP experiment, yet a single SPIDR experiment reports on the binding behavior of dozens (and likely hundreds) of RBPs, this approach dramatically reduces the number of cells required to map an individual RBP. Accordingly, SPIDR will be a valuable tool for studying RBP-RNA interactions in many different contexts, including within rare cell types and patient samples where large numbers of cells may be difficult to obtain.
We showed that SPIDR generates single nucleotide contact maps that accurately recapitulate the RNA-protein contacts observed within structural models. This suggests that SPIDR will also be well-suited to add high-resolution binding information for entire RNP complexes in a single experiment, as it will allow simultaneous targeting of all proteins within a complex. We envision that, in conjunction with more traditional structural biology methods, this approach will help elucidate the precise structure of various RNP complexes, including for mapping proteins that are not currently resolved within these structures (e.g. LARP1 binding within the 48S ribosome).
In addition to accurately measuring multiple proteins simultaneously, because of the nature of the splitand-pool barcoding strategy used, this approach also allows for multiple samples to be pooled within a single experiment. This ability to simultaneously map multiple proteins across different samples and conditions will enable exploration of RBP binding patterns and their changes across diverse biological processes and disease states. Until now, systematic comparative studies of RBP-RNA interaction changes at scale have been impossible, even for large consortia (e.g., ENCODE), which have invested massive amounts of time and effort to generate CLIP-seq data for only two cell lines. Our 4EBP1 results highlight the critical value of SPIDR for enabling exploration of RBP dynamics across samples. Specifically, 4EBP1 was not commonly thought to directly bind to mRNA, nonetheless, including 4EBP1 within our larger pool of target proteins allowed us to uncover changes across two different experimental conditions that may explain how specificity of mTOR-mediated translational suppression is achieved.
Although we focused on the differential RNA binding properties of 4EBP1/LARP1, there are many additional insights into RBP biology that we expect can be uncovered from exploration of this dataset. For example, we observe that TARDBP (TDP43) shows strong binding to U6 snRNA and to multiple scaRNAs, a class of ncRNAs that play critical roles in spliceosome-associated snRNA biogenesis 77 . TDP43 is an RBP of great interest because of its well-known genetic link to various neurodegenerative disorders, such as amyotrophic lateral sclerosis (ALS) [78][79][80][81] . These observations could provide new mechanistic insights into how disruption of this RBP impacts splicing changes and pathogenesis in neurodegeneration.
Thus, we expect that SPIDR will enable a fundamental shift for studying mechanisms of transcriptional and post-transcriptional regulation. Rather than depending on large consortium efforts to generate reference maps within selected cell-types, SPIDR enables any standard molecular biology lab to rapidly generate a comprehensive and high-resolution genome-wide map within any cell-type or experimental system of interest without the need for specialized training or equipment.
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NOTES Note 1: Comparison to a previous multiplexed CLIP method
A recent study reported a variant of CLIP called Antibody-Bead eCLIP (ABC) that utilizes direct chemical conjugation of an oligo sequence to an antibody followed by proximity ligation between the antibodyoligo and RNA to enable multiplexed mapping of 10 proteins simultaneously 27 . Our approach differs from this strategy in several key practical and conceptual ways.
Antibody labeling:
The ABC method utilizes direct chemical modification of each antibody. First, this requires large excess of each antibody and selective purification of each conjugate to generate each labeled reagent. This necessitates a more elaborate, multi-step chemical modification and purification procedure for each antibody and therefore is not readily accessible for labeling large numbers of distinct antibodies. Second, because ABC utilizes chemical modification of the antibody using NHS chemistry, the precise site of oligo conjugation on each antibody is random. This could impact both epitope recognition (when modified within the recognition site) and protein-G binding to the FC region of the antibody; both will decrease the efficiency of IP. In contrast, SPIDR utilizes labeling of the protein G bead instead of direct modification of the antibody. As such, SPIDR is a rapid, efficient, and highly modular strategy already utilized in standard IP strategies to couple antibodies to beads. Because of this distinction, the SPIDR approach can work with the same amounts of antibody used in standard approaches and with antibodies produced and stored in any buffer condition, without the need for specific purification or chemical modification.
Proximity-ligation versus split-pool detection:
The ABC method utilizes proximity-ligation to link an antibody sequence to its RNA target. There are several conceptual limitations to this strategy. First, the efficiency of proximity-ligation is limited as ligation must occur between each oligo and RNA end at 1:1 stoichiometry, resulting in many failed ligation events. This will decrease the efficiency of the overall RNA detection rate, an issue that will primarily impact RNAs of low abundance or in low cell numbers. Second, proximity-ligation methods are highly sensitive to distance constraints between the two ligating components. Accordingly, the success of this approach will depend on the distance between the RBP and the RNA and where on the RBP the specific antibody binds. Therefore, there are likely to be antibodies for which this approach will not produce comparable results to standard CLIP. Moreover, this approach is highly sensitive to the RNA fragment size generated. If fragments are too long, this will be problematic because there might be multiple proteins that can ligate; if the fragments are too short, the ends might not be capable of ligation. In contrast, the SPIDR method utilizes split-pool barcoding which is not dependent on the distances between the antibody, RBP, and RNA and therefore not susceptible to these distance constraints. Because of this, SPIDR could be used for analyzing RBP-RNA interactions within higherorder assemblies and in the presence of additional crosslinkers beyond UV. Finally, because SPIDR utilizes barcodes on the beads and because split-pool barcoding is not limited to pairwise contacts, a single barcode can provide information on the identity of multiple RNAs simultaneously thereby increasing the resolution detected per sequenced read.
For these reasons, we believe that SPIDR will be more broadly applicable to wide-range of antibodies and is readily accessible to any molecular biology lab.
Note 2: Features and Limitations of the Method
First, similar to CLIP and other immunoprecipitation methods, SPIDR is constrained by the availability of antibodies that have been validated to specifically enrich for RBPs of interest. We note that SPIDR may offer the opportunity to partly alleviate this problem as its multiplexing capability allows for the inclusion of several distinct antibodies, including those that may . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint not have been previously validated, against an RBP of interest without increasing the experimental burden. Second, the SPIDR protocol requires that each experiment is performed under the same IP conditions for all RBPs. Although we show that standard conditions work for many diverse proteins, they may not be suitable for all RBPs. One possible solution is to match antibodies (and target RBPs) by similar IP conditions.
Finally, in the current protocol, we used the same antibody amount for each RBP of interest, which may in part explain the uneven coverage of RNA reads measured for each RBP. Although we do identify well-known binding sites for nearly all RBPs targeted, higher sequencing coverage might be needed for RBPs at the lower end of this distribution. An alternative solution would be to adapt the antibody amount to equalize coverage for each RBP after performing an initial pre-screen and low-depth sequencing run.
. CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. Populations of biotinylated protein G beads are incubated with a streptavidin-biotin oligo complex. Each population of beads is labeled with an oligo with a specific sequence and then incubated with one type of capture antibody such that each population has a unique capture antibody and a corresponding oligo tag that can be recognized after sequencing. Populations are combined to create the bead pool. . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Observed distributions of labeled beads after sequencing. Each bead is defined in sequencing by a particular, unique combinatorial barcode acquired during split-pool. A SPIDR cluster represents any set of molecules, oligo or RNA, that share the same bead combinatorial barcode. Left: CDF plot showing the number of independent oligos matched within an individual SPIDR cluster. Right: CDF plot describing the degree of heterogeneity of these detected oligos within each SPIDR cluster, as determined by oligos with a shared combinatorial barcode. X axis represents the homogeneity of the oligo types with 1 indicating that all oligos are of the same type.
. CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. An example of our background correction method that utilizes the total read coverage across all proteins to normalize each individual protein. Shown are example tracks on RN7SK before and after background correction. Left: Raw alignment data for the entire pooled dataset (top track) and for representative antibodies against U2AF1, TARDBP, SHARP, LARP7 and HNRNPK on RN7SK. Right: Background corrected data for the same set of antibodies. Signal that was not antibody-specific has been normalized out. The reads in the right are binned in 5 nucleotide windows. RN7SK is known to be bound by LARP7 Ref.51 .
. CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; June 6, 2023 T T A1 F AQR BUD13 PUM1_Bethyl TRA2A LIN28B U2AF1 DHX30 RPS3 SLBP WDR43 LARP7 IGF2BP1 NOLC1 DDX55 PUM1_CST IGF2BP2 UPF1 KHSRP ARDBP EWSR1 HNRNPK CPSF6 RBF OX2 HNRNPL_Bethyl ILF3 PTBP1 HNRNPL_CST HNRNPM LSM11 SMNDC1 PCBP1 RBM15 FUS AF15 SAFB SSB HNRNPC HNRN P ASTKD2 DGCR8 DDX6 DROSHA percentage table and keeping the original ENCODE table or vice versa, meaning shuffling the columns of the ENCODE table and keeping the original SPIDR table. This was done 1000 times in each direction and every time the Euclidean distance was calculated. The values are represented by the two histograms. The Euclidean distance of all of the randomly shuffled 2000 comparison was always larger than of the true pair, which shows that the two original annotation tables from SPIDR and ENCODE are highly significantly similar (p-value < 0.0005).
. CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint June 6, 2023
UV-crosslinking
Crosslinking was performed as previously described 23 . Briefly, K562 cells were washed once with 1X PBS and diluted to a density of ~10 million cells/mL in 1X PBS for plating onto culture dishes. HEK293T cells were washed once with 1X PBS and crosslinked directly on culture dishes. RNA-protein interactions were crosslinked on ice using 0.25 J cm -2 (UV 2.5k) of UV at 254 nm in a Spectrolinker UV Crosslinker. Cells were then scraped from culture dishes, washed once with 1X PBS, pelleted by centrifugation at 330 x g for 3 minutes, and flash-frozen in liquid nitrogen for storage at -80°C.
Torin-1 treatment
HEK293T cells were treated at a final concentration of 250 nM Torin-1 (Cell Signaling Technology, #14379) in standard HEK293T media for 18 hours prior to UV-crosslinking and harvesting.
Bead biotinylation
The bead labeling strategy was adapted from ChIP DIP, a Guttman lab protocol used for multiplexed mapping of hundreds of proteins the DNA ( [URL]/). Specifically, 1 mL of Protein G Dynabeads (ThermoFisher, #10003D) were washed once with 1X PBST (1X PBS + 0.1% Tween-20) and resuspended in 1mL PBST. Beads were then incubated with 20 μL of 5 mM EZ-Link Sulfo-NHS-Biotin (Thermo, #21217) on a HulaMixer for 30 minutes at room temperature. Following NHS reaction, beads were placed on a magnet and 500 μL of buffer was removed and replaced with 500 μL of 1M Tris pH 7.4 to quench the reaction for an additional 30 minutes at room temperature. Beads were then washed twice with 1 mL PBST and resuspended in their original storage buffer until use.
Labeling biotinylated beads with oligonucleotide tags
Unique biotinylated oligonucleotides were first coupled to streptavidin (BioLegend, #280302) in a 96-well PCR plate. In each well, 20 μL of 10 μM oligo was added to 75 μL 1X PBS and 5 μL 1 mg/mL streptavidin. The 96-well plate was then incubated with shaking at 1600 rpm on a ThermoMixer for 30 minutes at room temperature. Each well was then diluted 1:4 in 1X PBS for a final concentration of 227 nM.
For each experiment, the appropriate amount of biotinylated Protein G beads (10 μL beads per capture antibody) was washed once in 1X PBST. Beads were then resuspended in oligo binding buffer (0.5X PBST, 5 mM Tris pH 8.0, 0.5 mM EDTA, 1M NaCl). 200 μL of the bead suspension was aliquoted into individual wells of a 96-well plate, followed by addition of 4 μL of 227nM streptavidin-coupled oligo to each well. The 96-well plate was then incubated with shaking at 1200 rpm on a ThermoMixer for 30 minutes at room temperature. Beads were then washed twice with M2 buffer (20 mM Tris 7.5, 50 mM NaCl, 0.2% Triton X-100, 0.2% Na-Deoxycholate, 0.2% NP-40), twice with 1X PBST, and resuspended in 200 μL of 1X PBST.
Binding antibody to labeled Protein G beads
2.5 μg of each capture antibody was added to each well of the 96-well plate containing labeled beads in 1X PBST. The plate was incubated with shaking at 1200 rpm on a ThermoMixer for 30 minutes at room temperature. After incubation, beads were washed twice with 1X PBST + 2 mM biotin (Sigma, #B4639-5G), resuspended in 200 μL of 1x PBST + 2mM biotin, and left shaking at 1200 rpm for 10 minutes at room temperature. All wells containing beads were then pooled together and washed twice with 1 mL 1X PBST + 2 mM biotin. At this stage, each bead in the bead pool contains a single type of capture antibody with a corresponding unique oligonucleotide tag.
Pooled immunoprecipitation
For each experiment, 10 million cells were lysed in 1 mL RIPA buffer (50mM HEPES pH 7.4, 100mM NaCl, 1% NP-40, 0.5% Na-Deoxycholate, 0.1% SDS) supplemented with 20 μL Protease Inhibitor Cocktail (Sigma, . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint #P8340-5mL), 10 μL of Turbo DNase (Invitrogen, #AM2238), 1X Manganese/Calcium mix (2.5 mM MnCl 2 , 0.5 mM CaCl 2 ), and 5 μL of RiboLock RNase Inhibitor (Thermo Fisher, #EO0382)). Samples were incubated on ice for 10 minutes to allow lysis to proceed. After lysis, cells were sonicated at 3-4 W of power for 3 minutes (pulses 0.7 s on, 3.3 s off) using the Branson sonicator and then incubated at 37°C for 10 minutes to allow for DNase digestion. DNase reaction was quenched with addition of 0.25 M EDTA/EGTA mix for a final concentration of 10 mM EDTA/ EGTA. RNase If (NEB, #M0243L) was then added at a 1:500 dilution and samples were incubated at 37°C for 10 minutes to allow partial fragmentation of RNA to obtain RNAs of approximately ~300-400 bp in length. RNase reaction was quenched with addition of 500 μL ice cold RIPA buffer supplemented with 20 μL Protease Inhibitor Cocktail and 5 μL of RiboLock RNase Inhibitor, followed by incubation on ice for 3 minutes. Lysates were then cleared by centrifugation at 15000 x g at 4°C for 2 minutes. The supernatant was transferred to new tubes and diluted in additional RIPA buffer such that the final volume corresponded to 1 mL lysate for every 100 μL of Protein G beads used. Lysate was then combined with the labeled antibody-bead pool and 1 M biotin was added to a final concentration of 10 mM as to quench any disassociated streptavidin-coupled oligos. Beads were left rotating overnight at 4°C on a HulaMixer. Following immunoprecipitation, beads were washed twice with RIPA buffer, twice with high salt wash buffer (50 mM HEPES pH 7.4, 1 M NaCl, 1% NP-40, 0.5% Na-Deoxycholate, 0.1% SDS), and twice with Tween buffer (50 mM HEPES pH 7.4, 0.1% Tween-20).
Ligation of the RNA Phosphate Modified ("RPM") tag
After immunoprecipitation, 3' ends of RNA were modified to have 3' OH groups compatible for ligation using T4 Polynucleotide Kinase (NEB, #M0201L). Beads were incubated at 37°C for 10 minutes with shaking at 1200 rpm on a ThermoMixer. Following end repair, beads were buffer exchanged by washing twice with high salt wash buffer and twice with Tween buffer. RNA is subsequently ligated with an "RNA Phosphate Modified" (RPM) adaptor (Quinodoz et al 2021) using High ConcentrationT4 RNA Ligase I (NEB, M0437M). Beads were incubated at 24°C for 1 hour 15 minutes with shaking at 1400 rpm, followed by three washes in Tween buffer. After RPM ligation, RNA was converted to cDNA using SuperScript III (Invitrogen, #18080093) at 42°C for 20 minutes using the "RPM Bottom" RT primer to facilitate on-bead library construction and a 5' sticky end to ligate tags during split-and-pool barcoding. Excess primer is digested with Exonuclease I (NEB, #M0293L) at 37°C for 15 minutes.
Split-and-pool barcoding to identify RNA-protein interactions
Split-and-pool barcoding was performed as previously described 31 with minor modifications. Specifically, beads were split-and-pool ligated over ≥ 6 rounds with a set of "Odd," "Even," and "Terminal" tags. The number of barcoding rounds performed for each SPIDR experiment was determined based on the complexity of the given bead pool. All split-and-pool ligation steps were performed for 5 minutes at room temperature and supplemented with 2 mM biotin and 1:40 RiboLock RNase Inhibitor to prevent RNA degradation. We ensured that virtually all barcode clusters (>95%) represented molecules belonging to unique, individual beads.
Compared to previously published approaches, we reduced the number of barcodes per round, but increased the rounds of split and pool barcoding as we optimized the ligation step. Therefore, the barcoding procedure was significantly simplified in contrast to previous versions. For example, for the K562 cells pooled experiment, 6 rounds of 24 barcodes were used for combinatorial barcoding (with a scheme of Odd, Even, Odd, Even, Odd, Terminal tag). For the HEK293T cells mTOR inhibition experiment, 6 rounds of 36 barcodes were used for combinatorial barcoding to achieve sufficient barcode complexity. Of the 36 barcodes used in round one of the ligations, 18 were used to label the control condition and the remaining 18 were used to label the torin treated condition. The samples were then pooled together for the remaining 5 rounds of ligation.
Library preparation
After split-and-pool barcoding, beads were aliquoted into 5% aliquots for library preparation and sequencing. RNA in each aliquot was degraded by incubating with RNase H (NEB, #M0297L) and RNase cocktail (Invitrogen, #AM2286) at 37°C for 20 minutes. 3' ends of the resulting cDNA were ligated to attach dsDNA oligos containing library amplification sequences using a "splint" ligation as previously described (Quinodoz et al 2021) 31 . The "splint" ligation reaction was performed with 1X Instant Sticky End Master Mix (NEB #M0370) at 24°C for 1 hour with shaking at 1400 rpm on a ThermoMixer. Barcoded cDNA and biotinylated oligo tags were then eluted from beads by boiling in NLS elution buffer (20 mM Tris-HCl pH 7.5, 10 mM EDTA, 2% N-lauroylsarcosine, 2.5 mM TCEP) for 6 minutes at 91°C, with shaking at 1350 rpm.
Biotinylated oligo tags were first captured by diluting the eluant in 1X oligo binding buffer (0.5X PBST, 5 mM Tris pH 8.0, 0.5 mM EDTA, 1M NaCl) and subsequently binding to MyOne Streptavidin C1 Dynabeads (Invitrogen, #65001) at room temperature for 30 minutes. Beads were placed on a magnet and the supernatant, containing cDNA, was moved to a separate tube. Biotinylated oligo tags were amplified on-bead using 2X Q5 Hot-Start Mastermix (NEB . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made #M0494) with primers that add the indexed full Illumina adaptor sequences.
To isolate barcoded cDNA, the supernatant was first incubated with a biotinylated antisense ssDNA ("anti-RPM") probe that hybridizes to the junction between the reverse transcription primer and splint sequences to reduce empty insertion products. This mixture was then bound to MyOne Streptavidin C1 Dynabeads at room temperature for 30 minutes. Beads were placed on a magnet and the supernatant, containing the remaining cDNA products, was cleaned up on Silane beads (Invitrogen, #37002D) as previously described 83 . Finally, cDNA was amplified using 2X Q5 Hot-Start Mastermix (NEB #M0494) with primers that add the indexed full Illumina adaptor sequences.
Sequencing
Paired-end sequencing was performed on either an Illumina NovaSeq 6000 (S4 flowcell), NextSeq 550, or NextSeq 2000 with read lengths ≥ 100 x 200 nucleotides. For the K562 data, 37 SPIDR aliquots were generated and sequenced from two technical replicate experiments. The two experiments were generated using the same batch of UV-crosslinked lysate processed on the same day. For the HEK293T data, 9 SPIDR aliquots were generated from a single technical replicate. Each SPIDR library corresponds to a distinct aliquot that was separately amplified with different indexed primers, providing an additional round of barcoding as previously described 31 . Minimum required sequencing depth for each experiment was determined by the estimated number of beads and unique molecules in each aliquot. For oligo tag libraries, each library was sequenced to a depth of observing ~5 unique oligo tags per bead on average. For cDNA libraries, each library was sequenced with at least 2x coverage of the total estimated library complexity.
Read processing and alignment
Paired-end RNA sequencing reads were trimmed to remove adaptor sequences using Trim Galore! v0.6.2 and assessed with FastQC v0.11.8. Subsequently, the RPM (ATCAGCACTTA) sequence was trimmed using Cutadapt v3.4 from both 5' and 3' read ends. The barcodes of trimmed reads were identified with Barcode ID v1.2.0 ( [URL]:// github.com/GuttmanLab/sprite2.0-pipeline) and the ligation efficiency was assessed. Reads with or without an RPM sequence were split into two separate files to process RNA and oligo tag reads individually downstream, respectively.
RNA read pairs were then aligned to a combined genome reference containing the sequences of repetitive and structural RNAs (ribosomal RNAs, snRNAs, snoRNAs, 45S pre-rRNAs, tRNAs) using Bowtie2. The remaining reads were then aligned to the human (hg38) genome using STAR aligner. Only reads that mapped uniquely to the genome were kept for further analysis.
Barcode matching and filtering
Mapped RNA and oligo tag reads were merged, and a cluster file was generated for all downstream analysis as previously described. MultiQC v1.6 was used to aggregate all reports. To unambiguously exclude ligation events that could not have occurred sequentially, we utilized unique sets of barcodes for each round of split-and-pool. All clusters containing barcode strings that were out-of-order or contained identical repeats of barcodes were filtered from the merged cluster file. To determine the amount of unique oligo tags present in each cluster, sequences sharing the same Unique Molecular Identifier (UMI) were removed and the remaining occurrences were counted. To remove PCR duplication events within the RNA library, sequences sharing identical start and stop genomic positions were removed.
Splitting alignment files by protein identity
Barcode strings from filtered cluster files were then used to assign protein identities to the alignment file containing all mapped RNA reads. Because each cluster represents an individual bead, the frequency of oligo tags (each representing unique protein type) was used to determine protein assignments. Specifically, for each cluster we required ≥ 3 observed oligo tags and that the most common protein type represented ≥ 80% of all observed tags. RNA reads were then split into separate alignment files by barcode strings corresponding to protein type.
Background correction and peak calling
In order to determine what portion of the observed signal is specific to a particular capture antibody, rather than . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint June 6, 2023 common pileups regardless of the protein captured, we normalized coverage for each protein relative to the coverage detected for all other proteins. Specifically, for a protein of interest, we computed the number of reads that were mapped to that protein. We then randomly downsampled all reads not assigned to that protein such that it had a comparable number of reads as the protein of interest. To measure the expected variance in the control sample, we repeated this downsampling procedure at least 100 independent times. We then computed read counts per window across the transcriptome (either 10nts or 100nts) for the protein of interest and each of the randomized control samples. We computed a normalized enrichment as the number of observed reads within the window (observed) divided by the average of the read counts overt that window across the >=100 permutations (expected). To assess the significance of this enrichment score, we measured how often the observed score was seen in the >=100 permutations. A p-value was assigned as the number of random scores greater than or equal to the observed scores divided by the number of random permutations used (we included the actual observed score in the numerator and denominator). All windows that had at least 10 observed reads and a p-value less than 0.05 were considered significantly enriched.
Peak annotation
Enriched windows were first filtered to only include regions resulting from reads that could be uniquely mapped in the second STAR alignment, and then poor alignments to rRNA regions (chr21: 88206400-8449330) were removed. These filtered peaks were then annotated based on overlap with GENCODE v41 transcripts. In the case of overlapping annotations, the final assigned annotation was chosen based on the following priority list: miRNA, CDS, 5'UTR, 3'UTR, proximal intron (within 500 nt of the splice site region), distal intron (further than 500 nt of the splice site region), non-coding exon, and finally non-coding intron. Windows for which the primary gene annotation was a miRNA host gene were marked as miRNA proximal.
ENCODE datasets
43 of the proteins included in SPIDR also had a matched K562 ENCODE eCLIP experiment with paired-end sequencing data. The raw FASTQ files for these datasets were downloaded from the ENCODE website ( [URL]:// www.encodeproject.org/) and aligned to the genome using the same parameters as in the SPIDR dataset.
For comparison of matched SPIDR and ENCODE datasets, the larger of the pair of alignment files was downsampled to the depth of the smaller alignment file. Windows of enrichment in ENCODE datasets were then determined using the same background correction strategy and thresholding as in SPIDR (minimum read count of 10, p-value < 0.05). As was done in the SPIDR data, all ENCODE datasets were used as negative controls for one for one another when determining background correction factors and calling windows of enrichment.
Motif enrichment analysis
Filtered SPIDR peaks were used to subset the corresponding SPIDR alignment files, such that only reads that fell within enriched windows were kept. These reads were then used as input for de novo motif analysis by HOMER ( [URL]/). Motifs with a reported p-value < 10 -40 were considered significant.
Comparison of bound RNA features
Enriched windows for both SPIDR and ENCODE, as determined using the SPIDR workflow of background correction and thresholding, were annotated based on overlap with GENCODE v41 transcripts. Peaks annotated as intergenic were removed, and then both the SPIDR and ENCODE datasets were filtered to include only proteins that had greater than 100 peaks.
The likelihood of seeing a similarity between SPIDR and ENCODE in the region annotations is visualized by comparing the observed values to randomly shuffled values. The inputs for this method are two matrices, one for SPIDR and one for ENCODE, with the percentage of annotations observed for a given region type for a given RBP. Shuffling is performed by randomly switching percentages across RBPs, keeping the relative values between regions constant. This can be thought of as randomly shuffling the columns of one of the input matrices. A distance is calculated by flattening the two input matrices into vectors, taking the difference between the two vectors, and calculating an L2-norm on that difference. In Supplemental Figure 7 the histogram of L2-norms shows the distribution we would expect if RBPs had no effect on the L2-norm between SPIDR and ENCODE. The dashed vertical line represents the L2-norm when the input matrices were flattened but not shuffled.
The basic algorithm is as follows: . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made Calculate the true L2-norm between SPIDR and ENCODE Keeping SPIDR constant, randomly switch probabilities between RBPs while keeping the percentages within an RBP the same for ENCODE Repeat step 2, shuffling SPIDR and keeping ENCODE constant Repeat steps 2 and 3 for 1000 samples
Single nucleotide resolution analysis
We computed the frequency of reads ending at the 3' end of the cDNA. We computed enrichment for each of these counts by randomly downsampling all reads not assigned to the specific protein and computing the same 3' end coverage. Enrichments and p-values were computed as described above and as previously reported in (Banerjee et al. 2020) 84 .
mTOR analysis
Background corrected bedgraphs were generated from control and +Torin conditions for each RBP in each condition. These bedgraph values were then mapped on to Refseq genes using the bedtools map command (arguments: -c 4 -o absmax). Where multiple isoforms were present for the same gene, the isoform with the highest map count was used. To normalize for possible detection bias due to fewer antibody beads in one condition versus the other we adjusted the map value by the ratio of antibody beads as determined by number of bead clusters corresponding to each antibody in each respective condition. Number of antibody (bead clusters) were defined and calculated using the same values used to generate the split bam files for each protein (options: minimum number of oligos=3,fraction unique=0.8, max number of RNAs in clusters=100). The ratio of cluster-corrected values for each gene across the two conditions was then compared per gene and separated based on TOP score. Published TOP scores 60 were used to generate categories for violin plots.
For the protein changes CDF plots, we first selected for the 2000 highest expressed genes based on previous RNA-seq data 84 . Input TPM values for HEK293 cells were taken from input CLAP (sub_input.merged.bam) data from HEK293T cell in (Banerjee et al. 2020) 84 . The input samples were downsampled to 20M reads prior to TPM calculation. Featurecounts was used to calculate read overlaps with hg38 protein coding refseq genes and further converted to TPM values. The top 2000 expressed genes (based on HEK293 input TPM) were used to plot the average protein log2 fold changes (Torin versus control) vs TOP score. Published TOP scores 60 were used to plot CDF values.
Multiplexed Immunopurification (IP) for mass spectrometry
10 million K562 cells were lysed in 4mL of RIPA on ice for 10 minutes. The lysate was clarified by centrifugation at 15000g for 2 minutes, and then split in half for either the pooled IP with 39 antibodies or the negative control IP with an anti-V5 antibody. Each half of the lysate was combined with 10ug total antibody (0.25ug per each antibody for the pooled IP) and 100uL of Protein G beads and left rotating at 4C overnight. The beads were then washed twice with RIPA, twice with High Salt Wash Buffer, twice with Clap-Tween, and finally three times with Mass Spec IP Wash Buffer (150mM NaCl, 50mM Tris-HCl pH 7.5, 5% Glycerol). Each sample was then reduced, alkylated, Trypsin digested, and desalted as described in (Parnas et al, 2015) 85 . Peptides were reconstituted in 12uL 3% acetonitrile/0.1% formic acid. mTOR proteomics 5 million cells each of control and 250nM Torin-1 treated HEK cells were lysed in 250uL Mass Spec Lysis Buffer (8M urea, 75mM NaCL, 50mM Tris pH 8.0, 1mM EDTA) for 30min at room temperature. Samples were then clarified by centrifugation at 23000g for 5 minutes, and the protein content in the supernatant was measured by BCA assay (ThermoFisher, #PI23227). 40ug of protein for each sample was reduced with 5mM final dithriothreitol (DTT) for 45 minutes at room temperature and subsequently alkylated with 10mM final iodoacetamide (IAA) for 45 minutes in the dark at room temperature. 50mM Tris (pH 8.0) was then added to each sample such that the final concentration of urea was less than 2M. Samples were digested overnight with 0.4ug Trypsin (Promega, #V5113) for a 1:100 enzyme to protein ratio. Peptides were desalted on C18 StageTips according to (Rappsilber et al., 2007) 86 .
LC-MS/MS
LC-MS/MS analysis was performed on a Q-Exactive HF. 5uL of total peptides were analyzed on a Waters M-Class UPLC using a C18 25cm Thermo EASY-Spray column (2um, 100A, 75um x 25cm) or IonOpticks Aurora ultimate . CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint column (1.7um, 75um x 25cm) coupled to a benchtop ThermoFisher Scientific Orbitrap Q Exactive HF mass spectrometer. Peptides were separated at a flow rate of 400 nL/min with a linear 95 min gradient from 5% to 22% solvent B (100% acetonitrile, 0.1% formic acid), followed by a linear 30 min gradient from 22 to 90% solvent B. Each sample was run for 160 min, including sample loading and column equilibration times. Data was acquired using Xcalibur 4.1 software.
The IP samples were measured in a Data Dependent Acquisition (DDA) mode. MS1 Spectra were measured with a resolution of 120,000, an AGC target of 3e6 and a mass range from 300 to 1800 m/z. Up to 12 MS2 spectra per duty cycle were triggered at a resolution of 15,000, an AGC target of 1e5, an isolation window of 1.6 m/z and a normalized collision energy of 28.
The Torin treated and control total lysate samples were measured in a Data Independent Acquisition (DIA) mode. MS1 Spectra were measured with a resolution of 120,000, an AGC target of 5e6 and a mass range from 350 to 1650 m/z. 47 isolation windows of 28 m/z were measured at a resolution of 30,000, an AGC target of 3e6, normalized collision energies of 22.5, 25, 27.5, and a fixed first mass of 200 m/z.
Database searching of the proteomics raw files
Proteomics raw files were analyzed using the directDIA method on SpectroNaut v16.0 for DIA runs or SpectroMine (3.2.220222.52329) for DDA runs (Biognosys) using a human UniProt database (Homo sapiens, UP000005640), under BSG factory settings, with automatic cross-run median normalization and imputation. Protein group data were exported for subsequent analysis.
. CC-BY 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (which this version posted June 7, 2023. ; [URL]/10.1101/2023.06.05.543769 doi: bioRxiv preprint
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Domain: Biology
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Transgenic plants as green factories for vaccine production
1 Department of Microbiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia. 2 KMR College of Pharmacy, Perundurai – 638052, Tamilnadu, India. 3 Department of Botany, GDC Anantnag 192102, Jammu and Kashmir, India. 4 Molecular Biology and Genetics Laboratory, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia. 5 Department of Botany, Faculty of Science, South Valley University, 83523 Qena, Egypt. 6 Department of Biochemical Engineering and Biotechnology, IIT, Delhi, Hauz Khas, New Delhi 110016, India. 7 Pharmaceutical Biotechnology, Amity Institute of Pharmacy, Amity University, NOIDA, Uttar Pradesh 201303. 8 Department of Botany, S. P. College, Srinagar 190001, Jammu and Kashmir, India.
INTRODUCTION
Transgenic plants are the plants in which foreign genes of desired characters have to be inserted. Transgenic plant have been found to have many advantages like, development of high yielding varieties of crop plants and disease resistant, and are plants with improved tolerance to biotic and abiotic stress (Ahmad et al., 2008;2010a;b;2011;Ahmad and Umar, 2011;Ahmad and Prasad, 2012a;b;Sarwat et al., 2012). Apart from the above, transgenic plants have been employed for the production of vaccines for the treatment of various infectious diseases (Kant et al., 2011;Vianna et al., 2011;Yoshida et al., 2011;Sharma and Sood, 2011;Twyman et al., 2012). Infectious diseases are major cause of mortality and morbidity worldwide (Goldblatt and Ramsay, 2003) and one-third of the deaths are caused by the infectious agents. Vaccine is an immuno-biological substance, used for specific protection against both infectious and noninfectious diseases (reviewed by Ahmad et al., 2012;Twyman et al., 2012). Vaccine is responsible for the stimulation of protective antibody and other immune mechanisms. The vaccines can be made from live or killed inactivated organisms, extracted cellular fractions, toxoid or combination of these. Recent preparations are sub-unit vaccines and recombinant vaccines. The main limitation with vaccines is their dependence on cold chain system, which is used to store and transport the vaccine under strict controlled conditions (Park, 2005). Other limitations are risk of adverse reactions such as reactions inherent to inoculation, reactions due to faulty techniques etc (Goldblatt and Ramsay, 2003). Thus, for the implementation of a successful global vaccination strategy, a well designed subunit oral vaccine system should satisfy the following criteria (Chargelegue et al., 2005;Levine et al., 2006;Nochi et al., 2007): (a) Produce sufficient quantities of desired antigen; (b) preserve the expressed antigen for a long time at room temperature; (c) induce protective immunity; (d) be protected from enzymatic digestion in the gastrointestinal tract.
Therefore, in the 1990s, an International campaign was initiated to immunize all the world's children against six devastating diseases. The target was to reach 80% of infants and reduce the annual death toll from these infections by roughly three million. Still, 20% of infants are un-immunized by six vaccines against polio, measles, diphtheria, pertusis, tetanus and tuberculosis. In many developing countries, millions of children still die from infectious diseases due to immunizations being nonexistent, unreliable or too costly (Ramsay et al., 1999). None will be entirely safe until every child has routine access to vaccines. Hence, there is an urgent need to search for vaccines which are easy to administer, easy to store, cost effective, easy to transport and possess readily acceptable delivery system. Hence, there is a lot of scope in developing plant derived vaccine (Streatfield et al., 2001;Ahmad et al., 2012). Now the question arises what is plant derived vaccine?Advances in transgenic research have made use of crop plants to serve as bioreactor for the production of recombinant molecules (Raskin et al., 2002;Kant et al., 2011;Vianna et al., 2011;Yoshida et al., 2011;Sharma and Sood, 2011). This means that transgenic plants are used to express antigen proteins induced by plant transgenic vectors and to produce certain special vaccines with high anti-disease ability (reviewed by Mei et al., 2006;Malabadi et al., 2012) (Figure 1). Plant derived vaccines significantly increase availability of vaccines in places where maintenance of cold chain system is difficult (Webster et al., 2002;Kant et al., 2011;Vianna et al., 2011;Yoshida et al., 2011;Sharma and Sood, 2011;Twyman et al., 2012). Important examples on the development of plant bioreactors are shown in Table 1.
The immunogenicity and safety of plant derived vaccines was declared in phase I clinical studies (Tacket, 2009). During the last decade, different types of efficient plantbased expression systems have been studied and more than 100 different types of recombinant proteins including plant-derived vaccine antigens have been successfully expressed in different types of plant tissues (Tiwari et al., 2009;Rybicki, 2010;reviewed by Ahmad et al., 2012). Positive effects of edible vaccines include decrease in potential hazards such as toxic compounds, responses to allergy and risk of attenuated strains reverting to pathogenic strains associated with established production technologies that use bacteria, yeast and mammalian cells (Pelosi et al., 2012).
TRANSGENIC PLANTS FOR THE PRODUCTION OF PLANT DERIVED VACCINES
Through recombinant DNA technology, different level of antigen expression for each independent line has been observed in plants (Karg and Kallio, 2009;Shih and Doran, 2009;Wilken and Nikolov, 2012). In 1990 first edible vaccine, surface protein A from Streptococcus mutans was expressed in tobacco (Curtis and Cardineray, 1990). Plant derived vaccine in the form of seed or fruit can be easily stored and transported from one place to another without the worry of its degradation or damage. A large amount of plant derived vaccine can be easily produced by cultivation in fields with relatively few inputs. Autoimmune disorders like Type I diabetes, multiple sclerosis, rheumatoid arthritis etc., can also be suppressed by using plant derived vaccines (Prakash, 1996).
Plants are selected which expresses highest level of antigen and least number of adverse effects. Till date various types of antigens are successfully expressed in different plants (Mason and Arntzen, 1995;reviewed by Ahmad et al., 2012). With the development of plant genetic engineering, the expression system for transgenic plants are no longer limited to model plants, but extended to some orally or high protein content plants. Various plant plateforms have been demonstrated for production of recombinant proteins in plants, including leafy crops, cereals and legume seeds, oil seeds, fruits, vegetables, higher plant tissue and cell cultures, hydroponic systems, algae and halobios (reviewed by Mei et al., 2006). Coexpression of adjuvant along with antigen has also been done in the same plant (Lal et al., 2007). The use of rice storage protein gene promoters to express transgenes in rice grain is well documented (Nicholson et al., 2005). Furtado et al. (2008) compared use of storage protein gene promoter and non-storage gene promoter with regard to spatial and temporal control of expression from barely, wheat and rice. Storage protein promoter from barley and wheat directed the expression in endosperm but not in embryo; expression was leaky, as it was observed in seed maternal tissues, leaf and root tissues; whereas, rice promoters directed the endosperm-specific expression in transgenic rice (Furtado et al., 2008). Alfalfa (Medicago sativa) is considered as a good bioreactor for production of recombinant proteins as it contains high levels of protein content and low levels of secondary metabolites (Dus Santos et al., 2002). Cereal crops can be the most suitable candidate and can be used to enhance the antigen concentration and help to reduce oral dose as they have ample amount of soluble protein in endosperm (Ahmad et al., 2012). Potato, tomato and carrot have been successfully reported to express vaccine candidates (Walmsley and Arntzen, 2000). Antigen genes encoding HBsAg, HIVgag and Rabies Capsid Proteins have been successfully transformed to tomato (Sala et al., 2003). High levels of recombinant protein expression were observed in proplastids of cultured carrot cells (Daniell et al., 2005). Oral delivery of the therapeutic proteins via edible carrot preserved the structural integrity of their target proteins as no cooking is needed (Muller et al., 2003). Other vegetable crops like lettuce (Lactuca sativa), celery cabbage (Brassica rapa var.pekinensis), cauliflower (Brassica oleracea var.botrytis) are under study for the production of vaccines. The only problem in these vegetables is low expression levels (Koprowski, 2005;Tacket and Mason, 1999). The earliest fruit used for the plant transgenic programme is banana (Musa acuminate) (Mason et al., 2002).
According to Trivedi and Nath (2004) papaya (Carica papaya) is another ideal plant species for vaccine production. Apart from fruit, vegetable and cereal crops scientists have used algae to produce metabolites and heterologous proteins for pharmaceuticals applications (Mayfield and Franklin, 2005). The species under study are: Chlamydomonas reinhardii (Sun et al., 2003), Phaeodactylum tricornutum (Zaslavskaia et al., 2000), Amphidinium carterae, Symbiodinium microadriaticum (ten Lohuis and Miller, 1998) and Cylindrotheca fusiformis (Fischer et al., 1999). Exciting progress has been made with the chloroplast based production of two particularly important classes of pharmaceuticals, vaccines and antibodies (Bock and Warzecha, 2010;Scotti et al., 2012). Extraordinarily high expression levels and the prospects of developing edible pharmaceuticals make transgenic chloroplasts a promising platform for the production of next-generation vaccines and antimicrobials (Waheed et al., 2012). During the past few years, several vaccine candidates have been produced successfully via plastid transformation, which emphasizes that transplastomic plants, as a second generation expression system, have great potential to fill gaps in conventional production platforms. A salient feature of plastids is that they combine characteristics of prokaryotic and eukaryotic expression systems, which is exemplified by the production of virus like particles and of bacterial antigens (reviewed by Bock and Warzecha, 2010). Successful expression of antigens in plants was carried out for Escherichia coli, heat labile enterotoxin B subunit (LT-B) in tobacco and potato (Hirst and Holmgren, 1987), Rabies virus G protein in tomato (Mc Garvery et al., 1995), Hepatitis B virus surface antigen in tobacco and potato (Thanavala et al., 1995), Norwalk virus capsid protein in tobacco and potato (Mason et al., 1996) and cholera toxin B subunit (CT-B) in potato (Arakawa, 1997).
Antigen expressed in plant or plant products can be administered orally or by intramuscular or by intravenous injection. Homogenized leaves, fruits or vegetables are used through oral route. Purified antigen containing plant tissue can be delivered in a capsule or powder (pill) form. Capsule may be suitable because capsule coating can be modified in such a way that coating material dissolves in particular area of stomach, and vaccine can be released in a specific area of the body. Purified component can also be used by intramuscular and intravenous administration. Oral administration of plant derived vaccine induces both mucosal and systemic immunity. When antigen is administered orally, it induces more mucosal response than intramuscular or intravenous injections. So, more importance has been given to those antigens, which induce mucosal immune response to produce secretory Ig A at mucosal surfaces. Mucosal immunity is very effective in diarrhoeal diseases caused by rotavirus, Norwalk virus, Vibrio cholerae, entero toxigenic E. coli (ETEC) and also in respiratory diseases such as pneumonia.
Second generation plant derived vaccines are known as multi component vaccines, provides protection against several pathogens. Both Enterotoxigenic Escherichia coli (ETEC) heat-labile enterotoxin (LT-B) and the capsid protein of Norwalk virus were successfully expressed in plants and induced immune response against both E. coli and Norwalk virus in mice (Huang et al., 2001).
ADVANTAGES OF EDIBLE VACCINES OVER INJECTED VACCINES
Edible vaccines have many advantages over the injected vaccines like: 1. Edible vaccines are cost effective, have low risk of contamination and no cost for transportation. Pharmaceutical companies spend million dollars for the production of vaccines and to preserve vaccines. Transgenic plants does not need cold chain storages.2. Pharmaceutical companies need the hitech machines for the production of vaccines. In the case of edible vaccines production we need soil rich land instead of machines.3. Long distance transportation is not required in the case of edible vaccines.4. The cost of materials needed for field grown plants is lower compared to cell culture grown in bioreactors (Xu et al., 2011). 5. Edible vaccines have a low cost for medical equipment as well, because needles and syringes are not needed for delivery (Streatfield, 2006;Xu et al., 2011).6. Medical professionals are not needed for oral delivery (Streatfield, 2006).7. Transgenic plants have low contamination risks as compared to injected vaccines 8. Needles and syringes are responsible for spreading of second hand diseases (Nochi et al., 2007).9. Oral delivery has efficiency to provoke a mucosal immune response, which produces cell mediated responses (Streatfield, 2006).
Edible vaccines have multi-component ability that is possible due to the crossing of 2 plant lines (Lal et al., 2007). These vaccines with multi-component abilities are known as second generation edible vaccines as they allow for several antigens to approach M cells (microfold cells) simultaneously (Lal et al., 2007). The multicomponent edible vaccines can prevent multiple diseases for example ETEC, chlorea and ratovirus (Lal et al., 2007). Injected vaccines do not have this property, so there are less effective than edible vaccines (Ramessar et al., 2008a;b;Naqvi et al., 2011).
Chimeric viruses
Over-coat and epi-coat technology is used to produce chimeric viruses. Over-coat technology provides expression of entire protein, whereas epi-coat technology permits the plant to produce only the foreign proteins ( [URL] viruses redesigned to carry the desired genes and used to infect differently in different parts of the plant. Alfalfa mosaic virus, CaMV (Cauliflower mosaic virus), CpMV (Cow pea mosaic virus), TMV (Tobacco mosaic virus), Tomato bushy stunt virus and Potato virus are redesigned to express fragments of antigens on their surface. There are reports that they produce plant based chimeric virus such as foot and mouth disease virus; mint enteritis virus. Fragment of gp41 surface protein of HIV virus put into CpMV could evoke a strong neutralizing antibody response in mice (Moffat, 1995;Wang et al., 2012).
APPROACHES TO PRODUCE PLANT DERIVED VACCINES
Plants serve as an important source to produce costeffective vaccine derivatives. Plant based production of vaccine candidates can help to reduce the economic burden on the developing countries and can be made easily available to every individual. Various models to produce vaccine candidates are described below.
Enterotoxigenic Escherichia coli (ETEC)
Enterotoxigenic Escherichia coli strains are a major cause of enteric diseases in live stock and humans. ETEC is attached to specific receptors on the surface of enterocytes in the intestinal lumen by fimbriae. ETEC produces a heat-stable enterotoxin (ST) which consists of five B subunits and one A subunit. B subunit binds to sugar residues of ganglioside Gm1 on the cells lining the villi and crypts of the small intestine. Insertion of the B subunit into the host cell membrane forms a hydrophilic transmembrane channel through which the toxic A subunit can pass into the cytoplasm (Roy et al., 2010). Raw transgenic potato expressing LT-B were fed to 11 volunteers, out of which 10(91%) developed neutralizing antibodies and 6(55%) of individuals also showed mucosal response (Tacket et al., 1998). Different reports are there on synthetic heat-labile enterotoxin (LT-B) gene and their expression in plants such as potato, banana, tobacco and tomato; and all were tested in mice (Mason et al., 1998). Expression of E. coli fimbrial subunit protein in transgenic plants can be used to vaccinate against these diseases. Joensuu et al. ( 2006) evaluated transgenic plants to produce Fae G protein and adhesion of F4 fimbriae. Oakes et al. (2007) reported the edible transgenic soyabean plant producing E. coli fimbrial subunit proteins. Tacket (2009) discussed early human studies of oral transgenic plant-derived vaccines against enterotoxigenic Escherichia coli. Genetic combination of gene coding for an LTB:ST protein in tobacco by Agrobacterium mediated transformation displays antigenic determinants from both LTB and ST. Presence of mucosal and systemic humoral responses in mice when dosed orally with transgenic tobacco leaves also confirmed that plant-derived LTB:ST can lead to immunogenicity development via oral route (Rosales-Mendoza et al., 2011).
Vibrio cholera
Cholera is due to contaminated food or water which triggers an acute intestinal infection by V. cholera (López-Gigosos et al., 2011). Enterotoxin such as cholera toxin (CT) was expressed in tobacco plant (Arakawa et al., 1998). Nochi et al. (2007), showed oral immunization with transgenic rice encoding the cholera toxin B subunit (CTB) which stimulates secretory Ig A, shows resistant to gastrointestinal digestion. Karaman et al. (2012) introduced synthetic gene encoding for CT-B by the control of a γ-zein promoter in maize seeds. CT-B levels were checked via ganglioside dependent ELISA. Anti-CTB IgG and anti-CTB IgA were found in the sera and fecal samples of the orally immunized mice protected against holotoxin challenge with CT.
Anthrax
Anthrax is a disease most commonly occur by inoculation of B. anthracis through the skin of infected animals, their products and inhalation of spores in dust or wool fibers. Virulence factors is a toxin complex, which consists of three proteins. The protective antigen (PA) binds the complex receptors on the macrophage surface. After proteolysis, oedema factor and lethal factor are released which after endocytosis, blocks the adenyl cyclase pathway within the cell. The main effect of this toxin complex is to increase vascular permeability, which leads to a shock. Protective antigen was expressed in transgenic tobacco chloroplasts by inserting the pag A gene into the chloroplast genome. Cytotoxicity measurements in macrophage lysis assays showed that chloroplast-derived PA was equal in potency to PA produced in B. anthracis. Chloroplastderived protective antigen provides cleaner and safer anthrax plant-derived-vaccine at a lower production cost (Koya et al., 2005). Koya et al. (2005) published for the first time the PA expression in plants from stable nucleartransgenic tobacco. Aziz et al. (2002) also reported the expression of PA in leaves of stable nuclear-transgenic tomato plants. Expression of PA in tobacco or tomato was enhanced in combination with a second B. anthracis protein, lethal factor (LF), and showing cytolytic activity when applied to macrophage-like cell lines. Also, when tomato leaf material was injected into mice, antisera could be recovered with neutralizing activity to anthrax lethal toxin (LT), which is a combination of PA and LF.
Porphyromonas gingivalis
Periodontal diseases are caused by oral anaerobic bacterium Porphyromanas gingivalis. It is thought to be initiated by the binding of P. gingivalis fimbrial protein to saliva coated oral surfaces. Shin et al. (2009) has successfully transferred FIM A protein producing gene into potato tuber tissues and produced native FIM A protein in edible plant cells.
Norwalk virus
Calci viruses are a major cause of food and water associated outbreaks of diarrhoea and vomiting, affecting individuals of all age groups. A capsid protein of Norwalk virus was expressed in transgenic tobacco and potato plants. Potato tubers expressing Norwalk virus antigen were fed to mice, it developed serum IgG specific for Norwalk virus (Mason et al., 1996). According to Tacket et al. (2000) volunteers fed with transgenic potato expressing Norwalk virus antigen showed seroconversion.
Hepatitis B virus
It is estimated that 3 to 6% of the world population has been infected with Hepatitis B virus (HBV) and there are 300 to 400 million carriers in the world. India alone has over 40 million carriers. In the acute stage there are signs of inflammation in the portal triads: the infiltrate is mainly lymphocytic. In the liver parenchyma, single cells show ballooning and form acidophilic (councilman) bodies as they die. In chronic hepatitis, damage extends out from the portal tracts, giving the piecemeal necrosis appearance. Some lobular inflammation is also seen. As the disease progresses fibrosis develops and eventually, cirrhosis. Hepatitis B virus replicates in the hepatocytes, reflected in the detection of viral DNA and HBs Ag in the nucleus and HBs Ag in the cytoplasm and at the hepatocytomembrane (Simmonds and Peutherer, 2003). Hepatitis B virus is carried in the blood and blood derived bodily fluids of infected persons and can be transferred through contact with a carrier's blood caused by unsafe injections or transfusions, sexual contact and tattooing. Long term protection against Hepatitis B virus is possible with vaccine. HBs Ag was expressed in transgenic potato plant and tested in mice for production of antibodies (Richter et al., 2000). Pniewski et al. (2011) has shown the production of small surface antigen for HBV (S-HBsAg) in genetically modified glufosinate-resistant lettuce. They orally immunised mice by using lyophilised form of plant material and showed the presence of secretory IgA (S-IgA) and total serum antibodies. Li et al. (2011) also demonstrated the transformation of HBsAg (hepatitis B surface antigen) gene in to tomato mediated by Agrobacterium tumifaciens. Lou (2007) has experimentally expressed hepatitis B virus large surface antigen in transgenic tomato plant. Transgenic lettuce plant carrying recombinant hepatitis B virus antigen HBs Ag was demonstrated in Brazil (Marcondes and Hansen, 2008). Tacket (2009) has discussed early human studies of oral transgenic plantderived vaccines against hepatitis B virus. A phase I clinical trial with plant derived hepatitis B vaccine has boosted antigen-specific serum antibodies titer (Tacket, 2009).
Measles
Millions of people live in areas where measles are endemic and resources are scare. Measles are transmitted from person to person by respiratory droplets. Measles in an acute febrile illness, the onset is flu-like with high fever, cough and conjunctivitis, red spots with a bluish-white centre on the buccal mucosa called Koplik's spots. Measles antigens expressed in plants have been shown to be antigenic and immunogenic both after invasive and oral vaccination (Marcondes and Hansen, 2008). Crude Quillaja saponin extracts stimulates measles' virus specific immune responses in mice, following oral immunization with plant based measles virus haemagglutinin protein (Pickering et al., 2006). Webster et al. (2002) confirmed that the transgenic tobacco plants-derived MV-H protein vaccine, which when, modified to MV-H DNA vaccine, to prime-boost vaccination strategy demonstrated the MV hemagglutinin protein (MV-H) expression. Orally immunized mice with plant-derived MV-H showed MV-specific IgG.
Japanese encephalitis
JE virus is a single stranded positive sense RNA virus belonging to family flaviviridae transmitted through a zoonotic cycle between mosquitoes, pigs and water birds. It causes encephalitis all over the world especially in Eastern and South-eastern Asia. JE affects some primary organs like thalamus, corpus striatum, brainstem and spinal cord. With the absence of specific antiviral therapy, it is managed mainly by its symptom and by supportive therapies along with preventive measurements (Misra and Kalita, 2010). Transgenic rice expressing the envelope protein of Japanese encephalitis virus (JEV), under control of a dual cauliflower mosaic virus (CaMV 35s) promoter, was generated. JEV specific neutralizing antibody was detected in mice after immunization of mice with protein extracts from transgenic rice plant by intraperitoneal or oral immunization (Wang et al., 2009). Appaiahgari et al. (2009) showed the expression of Japanese encephalitis viral envelope protein (E) in transgenic tobacco can produce immunogenic response in mammalian system. Shoji et al. (2009) described the production of hemagglutinin from A/Indonesia/05/05 strain of H5N1 influenza virus by transient expression in plants. The results indicate that immunization of ferrets with plant-derived hemagglutinin elicited serum hemagglutinin-inhibiting antibodies and protected the ferrets against challenge infection with a homologous virus. Plant derived vaccine may be the solution in the rapid, large scale production of influenza vaccine in the face of pandemic. Kalthoff et al. (2010) showed the expression of fulllength recombinant hemagglutinin (rHA0) of H5N1 in Nicotiana benthamiana with optimize expression levels. Their results showed to provide an immunogenic protection protect chicken against lethal challenge infection with heterologous HPAIV H5N1 of 96% homology to rHA0 by plant-expressed hemagglutinin. Jul-Larsen et al. (2012) demonstrated recombinant influenza haemagglutinin antigen (HAC1) that was derived from the 2009 pandemic H1N1 virus and expressed in tobacco plants. They showed that the tobacco derived recombinant HAC1 antigen is a promising vaccine candidate recognized by both B-and T cells. Shoji et al. (2011) showed the advantages provided by the plant system for influenza vaccine antigen production is their independence from pathogenic viruses, and cost and time efficiency. They produced large-scale of recombinant hemagglutinin proteins from A/California/04/09 (H1N1) and A/Indonesia/05/05 (H5N1) strains of influenza virus in N. benthamiana plants, and tested their immunogenicity (serum hemagglutination inhibition and virus neutralizing antibodies), and safety in animal models. Madhun et al. (2011) produced influenza subunit antigen in transient plant expression systems as an alternative. A needle-free intranasal influenza vaccine is an attractive approach to be followed. Plant-derived influenza H5N1 (A/Anhui/1/05) antigen, alone or formulated with bis-(3', 5')-cyclic dimeric guanosine monophosphate (c-di-GMP) as adjuvant induces strong mucosal and systemic humoral immune responses. Search for safe and effective adjuvant to enhance H5N1 intranasal vaccine with extracts of mushroom mycelia was found to be good (Ichinolhe et al., 2010).
LIMITATIONS
Before the commercial production of plant derived vaccines, there is urgent need to consider the following points; 1. Searching for suitable plant which will give ideal antigen expression.2. Identification of proper dosage (whether plant parts, plant products, pill, intramuscular or intravenous injection of purified antigen) can produce proper dose.3. Verification of allergens in the plant and plant products.4. Study the impact of plant derived vaccines on the environment and human health.5. Genetically altered crops producing plant derived vaccine could get mixed with human food supply or animal feed, causing potential threat to public health.6. Cross pollination and their problems.7. Effects on insects and soil microbes.8. Regulation of plant derived vaccines in the form of food, drug or agricultural product.9. Cultivation of plant derived vaccines and their delivery in capsule or pill form.
Risks of plant derived vaccines
Plant derived vaccines pose serious risks to the public if they are not handled with care. Safety of transgenic plants includes many aspects like ecology, agronomy and molecular biology which focus on food and environmental safety (Ahmad et al., 2012). Environmental issues and biodiversity concern are raised because of the transgenic seeds or plants that escape into the wild. Moreover, plant derived vaccines cannot be distinguished from non-plant derived vaccines of the same plant. Plant derived vaccine tomato plant looks like a traditional tomato. There is always a risk of mis-administration.
Although, plant derived vaccine technology can save many lives in developing countries. At the same time, there is an urgent need to address proper commercia-ization of plant derived vaccine technology and to prevent misuse of technology because it possesses great risk on environment and human health. Development of vaccine into a stable seed form or production in leaf is mostly favoured but its to spoilage to prevent loss/leaking out of antigen into environment is to be checked. The amount of plant which can be taken up as raw food is to be strictly monitored as over dose may cause toxic/allergic reactions. Most of the edible crops are destroyed by attack of insects and hence their effect on vaccine producing plant has to be evaluated. Even though plant derived vaccines have shown promising results but evaluation of their tolerance needs in-depth study (Ahmad et al., 2012).
CONCLUSION AND FUTURE PERSPECTIVE
Edible plant derived vaccine may lead to a future of safer and more effective immunization. They would overcome some of the difficulties associated with traditional vaccines like production, distribution and delivery and they can be incorporated in to the immunization plans. Edible vaccines have lot of advantages over injected vaccines like, well established cultivation, low cost of production, no need for "cold chain" delivery, rapid scaleup, simple distribution by seeds, ease of genetic manipulation, oral delivery and low health risks from human pathogen and toxin contamination, etc. Significant progress has been achieved in employing plants as vaccine expression system, for example vegetables, fruits, cereal crops, etc. Tobacco, tomato, maize, rice are leading production plateforms for recombinant protein production. The basic advantage of using plants as vaccine production system is that plants being higher eukaryotes provide opportunities for unlimited production, the range and diversity of recombinant molecules namely peptides, polypeptides and complex multimeric proteins that cannot be made in microbial systems. Plant production system provides a wider flexibility in designing of new pharmaceutical proteins. Days are not too far when we eat delicious vegetables, fruits etc, to prevent ourselves from infectious diseases. Developing and under-developed countries will be benefited more by this edible vaccine production system because the methods in production are reasonably affordable and the vaccine products would be more openly accessible to the population.
One of the most important bottlenecks in edible vaccine technology is yield improvement, as this factor has a major impact on economic feasibility. Different strategies in hand which can lead to improved production of edible vaccines include the development of novel promoters, improvement in protein stability by protein engineering approach, targeted expression of protein of interest and last but not least the improvement in downstream processsing. The potential concern of edible vaccine technology is differential glycosylation of proteins in in vitro systems or in non-native species. Strategies should be devised to humanize the plant glycosylation machinery by inhibiting glycosylation enzymes. The use of plastids as vaccine production platform is quite promising to prevent transgene escape through pollens or seed dispersal and it needs an extensive research to improve expression levels and prevention of proteolysis in plastids.
Figure 1 .
Figure 1. Strategy for the production of candidate vaccine antigen in plants
Table 1 .
Representative plant-based vaccines: under clinical development or in market.
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Domain: Biology
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The Accumulation of Man6GlcNAc2-PP-dolichol in theSaccharomyces cerevisiae Δalg9 Mutant Reveals a Regulatory Role for the Alg3p α1,3-Man Middle-arm Addition in Downstream Oligosaccharide-lipid and Glycoprotein Glycan Processing*
N-Glycans in nearly all eukaryotes are derived by transfer of a precursor Glc3Man9GlcNAc2 from dolichol (Dol) to consensus Asn residues in nascent proteins in the endoplasmic reticulum. The Saccharomyces cerevisiae alg(asparagine-linked glycosylation) mutants fail to synthesize oligosaccharide-lipid properly, and thealg9 mutant, accumulates Man6GlcNAc2-PP-Dol. High-field 1H NMR and methylation analyses of Man6GlcNAc2released with peptide-N-glycosidase F from invertase secreted by Δalg9 yeast showed its structure to be Manα1,2Manα1,2Manα1,3(Manα1,3Manα1,6)-Manβ1,4GlcNAcβ1,4GlcNAcα/β, confirming the addition of the α1,3-linked Man to Man5GlcNAc2-PP-Dol prior to the addition of the final upper-arm α1,6-linked Man. This Man6GlcNAc2 is the endoglycosidase H-sensitive product of the Alg3p step. The Δalg9Hex7–10GlcNAc2 elongation intermediates were released from invertase and similarly analyzed. When compared withalg3 sec18 and wild-type core mannans, Δalg9 N-glycans reveal a regulatory role for the Alg3p-dependent α1,3-linked Man in subsequent oligosaccharide-lipid and glycoprotein glycan maturation. The presence of this Man appears to provide structural information potentiating the downstream action of the endoplasmic reticulum glucosyltransferases Alg6p, Alg8p and Alg10p, glucosidases Gls1p and Gls2p, and the Golgi Och1p outerchain α1,6-Man branch-initiating mannosyltransferase.
With the exception of some protists, all eukaryotes co-translationally transfer Glc 3 Man 9 GlcNAc 2 from a Dol 1 intermediate to selected Asn residues designated by the sequon, Asn-Xaa-Ser/Thr, where Xaa is any amino acid except Pro. We are beginning to understand why eukaryotes expend so much en-ergy synthesizing this evolutionarily conserved tetradecasaccharide, only to modify it by removal of the entire glucotriose unit and one or more of the core mannose residues. It is intuitive that the biochemical intermediates formed provide information necessary for downstream oligosaccharide processing events, and recent evidence for this has been provided by studies on the Alg10p ␣1,2-glucosyltransferase (1). Early work with mammalian systems provided much of what is currently known concerning N-linked glycosylation (2,3), including a fair picture of the distribution of oligosaccharide-processing activities through subcellular fractionation, lectin binding, and immunolocalization techniques (4). However, with the exception of the Lec Chinese hamster ovary cells, the genetic approach to glycoprotein study in somatic cells has proven difficult.
Yeast have provided a more amenable system of study with the development of three classes of mutants. In Saccharomyces cerevisiae they are alg, mnn, and sec mutants. N-Glycan synthesis in S. cerevisiae proceeds much like that of higher eukaryotes up through the early ER processing events, including removal of the glucotriose moiety and the central arm ␣1,2linked mannose (5,6). Although subsequent processing differs from that found in higher eukaryotes, this yeast provides a good model system to investigate the structural information contained in the Glc 3 Man 9 GlcNAc 2 -PP-Dol precursor transferred to protein, since nearly the entire phosphorodolichol and much of the Golgi processing pathways have been dissected by the generation of the alg and mnn mutants (7)(8)(9)(10).
Genes for a number of steps in OSL biosynthesis were not readily identified in yeast, because their absence produced no apparent phenotype in the presence of wild-type OST. However, in recent work, Aebi's group has isolated ALG3, ALG6, ALG8, ALG9, and ALG10 loci by rescuing through complementation the respective synthetically lethal phenotypes occurring in conjunction with mutant subunits of OST (1,(11)(12)(13). Subsequent to their isolation, strains for each deletion have been generated in a wild-type OST background. These mutants provide an advantage for investigating Golgi-processed N-glycan structures, because gene ablation prevents N-linked glycan structures associated with leaky alleles, for example alg3-1 (14).
The alg9 mutants accumulate endo H-sensitive Man 6 GlcNAc 2 -PP-Dol, the glycan of which is shown in this work to be Man␣1,2Man␣1,2Man␣1,3(Man␣1,3Man␣1,6)-Man1,4GlcNAc1,4GlcNAc␣,, differing from Man 5 GlcNAc 2 -PP-Dol of alg3 mutants only by the addition of the central arm-initiating ␣1,3-linked Man (see residue 7 in Scheme I). In addition, we define downstream processing of ⌬alg9 glycans found on secreted invertase as was done previously on wildtype and alg3 sec18 invertase as a model secreted glycoprotein (15,16). This study reveals the strong regulatory influence addition of this central arm ␣1,3-linked Man plays in subsequent ER and Golgi processing events. Findings here provide implications regarding the in vivo substrate specificities of the ER glucosyltransferases and glucosidases and of the Golgi Och1p ␣1,6-Man branch-initiating enzyme.
EXPERIMENTAL PROCEDURES
Materials-S. cerevisiae haploid strain yg414 (Mat␣ ade2-101 ura3-52 his3D200 lys2-801⌬alg9::G418), was supplied by S. te Heesen (ETH, Zurich, Switzerland). The ⌬alg9 strain was transformed with the pRB58 multicopy yeast invertase plasmid for overexpression using the lithium acetate procedure (17). Bio-Gel P-4 was from Bio-Rad. Sephacryl S-300 Superfine was a product of Amersham Pharmacia Biotech, and DE52-cellulose was obtained from Whatman. Cellulose TLC plates were purchased from EM Separations Technology. The [2-3 H]Man was obtained from American Radiolabeled Chemicals, Inc. Ecolume scintillation mixture was purchased from ICN Pharmaceuticals, Inc. Sigma was the source of 99.8% and 99.96% D 2 O, while 99.996% D 2 O was from Cambridge Isotopes Laboratories. Man 3 GlcNAc[ 3 H]ol was from a previous study (16). Endo H and PN-Gase F were prepared as described previously (18,19). Silica-gel 60A TLC plates were purchased from Whatman. Castanospermine was obtained from Sigma. All solvents were American Chemical Society "reagent grade" or better.
Yeast Microsomes-Microsomal membranes were prepared as described previously (20). Briefly, 10 g of ⌬alg9 S. cerevisiae yeast cells were resuspended in 20 ml of buffer (50 mM Tris-Cl, pH 7.5, 5 mM MgCl 2 , 10 mM -mercaptoethanol) and transferred to a Bead Beater TM (Biospec Products Bartlesville, OK) along with 20 ml of acid-washed Ballantoni glass beads (0.45-0.55 mm). The yeast suspension was beaten for 60 s and cooled on ice for 5 min. This was repeated six times or until the yeast cells were more than 90% disrupted as determined in the light microscope (magnification, ϫ440). The slurry was removed from the blender, rinsed with buffer, and centrifuged at 2,000 ϫ g for 10 min at 4°C in 40-ml polycarbonate tubes to remove the nuclei and cell walls. The supernatant was transferred into a 40-ml polycarbonate tube and centrifuged at 40,000 ϫ g for 60 min at 4°C. The supernatant was discarded, and the microsomal pellet without the brownish mitochondrial upper layer was gently resuspended in buffer and dispersed in a small Dounce homogenizer with a Teflon pestle. The final protein concentration was determined by the method of Bearden (21) using bovine serum albumin (Sigma) as a standard. Microsomes (30 -40 mg of protein/ml) were frozen as 30-l beads in liquid N 2 and stored at Ϫ70°C.
Glycolipid Biosynthesis in Vitro-Glycolipids were synthesized as described previously (22). Incorporation of [ 3 H]Man from GDP-[ 3 H]Man or [ 14 C]Man from GDP-[ 14 C]Man into endogenous yeast acceptors was performed in a final reaction volume of 110 l. For synthesis of preparative amounts of ⌬alg9 OSL, the reactions were incubated for 20 min at 27°C. All reactions were terminated by addition of 2 ml of CHCl 3 : CH 3 OH (2:1 v/v). The glycolipids were extracted by a modified procedure of Waechter et al. (20). The CHCl 3 /CH 3 OH (2:1) and CHCl 3 / CH 3 OH/dH 2 O (1:1:0.3) extracts contained Man-P-Dol and OSL, respectively, based on their behavior on silica-gel and cellulose TLC. For preparation of large amounts of OSL, reactions were scaled up 2-fold. To reduce OSL glucosylation, some reactions contained 10 M UDP. The resulting OSL was purified on cellulose TLC plates using Man-P-Dol and/or OSL standards. Purified OSL was scraped from TLC plates, resuspended in 4 ml of CHCl 3 /CH 3 OH/dH 2 O (1:1:0.3), the matrix removed by centrifugation, the supernatant concentrated under reduced pressure at 28°C, and the residue resuspended in a small volume of fresh CHCl 3 /CH 3 OH/dH 2 O (1:1:0.3).
Glycosidase Digestions-To isolate transferred oligosaccharides, microsomal pellets were solubilized in 1% SDS in 50 mM sodium phosphate buffer (pH 8.5) with heating. For endo H digestion, the pH was adjusted to 5.5 with 1.0 M phosphoric acid and endo H added at 50 milliunits/ml. The reactions were incubated at 37°C for 16 h, and endo H activity was verified by hydrolysis of Man 6 GlcNAc 4 -Asn-dansyl, followed by paper chromatography of the released GlcNAc-Asn-dansyl moiety (23). For PNGase F digestions, SDS was removed from the solubilized pellet protein by extraction with 80% acetone. The resulting pellets were resuspended in 50 mM sodium phosphate buffer, pH 8.5, boiled to resolubilize them, and PNGase F was added to 100 milliunits/ ml. The reaction was incubated at 30°C for 16 h, and activity was verified by hydrolysis of [ 3 H]dansyl-fetuin-pentaglycopeptide, followed by paper chromatography (24).
Purification of External Invertase and Oligosaccharide Isolation-Secreted invertase was purified from crude cell extracts by 35% ammonium sulfate and pH 4 precipitations, followed by DE-52 and Sephacryl S-300 chromatography as described previously (15). A typical purification from 225 g of cells yielded 47 mg of external invertase. The Nlinked oligosaccharides were hydrolyzed from invertase by treatment with PNGase F as described above, isolated by solvent precipitation as described previously (25), then chromatographed on a calibrated column of Bio-Gel P-4 (95 cm ϫ 16 mm) with 0.1 N acetic acid, 1% 1-butanol as the eluant at 8.8 ml/h and room temperature. Fractions of 0.73 ml were collected, and aliquots were assayed for total hexose and radioactivity, which included an internal marker of Man 3 GlcNAc-[ 3 H]ol. Mass Spectrometry-MALDI-TOF mass spectrometry was performed on a Bruker Reflex instrument. Samples of 25-50 pmol were prepared with 2,5-dihydrobenzoic acid as matrix. Data accumulated for 10 -50 3-ns pulses of the 337 nm laser were averaged for each sample. Analyses were performed in linear and reflective mode.
HPAEC Branch Isomer Analysis-Pooled aliquots were chromatographed on a Dionex HPAEC system using a voltage PAD response detector and a PA-100 column. Samples were separated using 100 mM NaOH accompanied by the following sodium acetate gradient: isocratic at 35 mM for 5 min, then 35-170 mM over 45 min. Individual runs included raffinose or known glycans as internal standards.
Invertase Assay-External invertase activity was followed through the purification scheme using a modification of the method of Goldstein and Lampen (26).
Methylation Linkage Analysis-Samples were analyzed by methylation as described (27) using the NaOH/Me 2 SO method. Briefly, the free hydroxyls of the oligosaccharides were deprotonated with NaOH/ Me 2 SO. Then CH 3 I was added to replace the free hydroxyls with methoxy groups. The methoxylated oligosaccharide was hydrolyzed in strong acid, evaporated under low pressure, and applied to Whatman Silica-gel 60A TLC plates. The plates were developed twice with CH 3 CN-CHCl 3 -CH 3 OH, 3:9:1 (v/v/v), thoroughly dried between each ascent, and rapidly dipped into a solution containing 3 g of N-(naphthyl)ethylenediamine and 50 ml of concentrated H 2 SO 4 in 1 liter of CH 3 OH. The plates were dried and placed in an oven for 10 min at 120°C. All saccharide standards were purchased from Sigma. 1 H NMR Spectroscopy-Oligosaccharides (0.2-0.5 mg) were exchanged three times by rotary evaporation from 99.8% D 2 O and twice from 99.96% D 2 O, followed by lyophilization. Lyophilized samples were dried over P 2 O 5 in vacuo for a day or more, then reconstituted in 0.5 ml of 99.996% D 2 O to a final concentration of 0.25-0.70 mM. Samples were quickly transferred to 5-mm tubes (Wilmad Co., no. 535pp, previously washed and exchanged with 99.8% D 2 O), flame-sealed, and examined at 300 K by 1D and 2D DQF-COSY phase-sensitive 1 H NMR spectroscopy at 500 MHz as described previously (28 -30). Spectral width in the 11.7-tesla field was 1502 Hz for all experiments. For acquisition of 1D data, 1024 scans were collected over 4096 data points. The limit of resolution was 0.0045 ppm based on the ratio of the width of the widest peak at half-height (2.26 Hz) to the number of Hz per ppm (500.13 Hz/ppm). For homonuclear 2D DQF-COSY, 1.5 s of low power presaturation on residual HDO at 4.79 ppm was applied. Data collection for the 2D experiments was 4096 data points in t 2 and 512 complex data points in the indirect t 1 dimension.
Glucosylation in Vivo-⌬alg9 cells were grown overnight to stationary phase and collected by centrifugation for 5 min at 3000 rpm and room temperature in a Sorvall TC6 centrifuge equipped with an H400 rotor. The yeast were washed twice in glucose-free YP medium by centrifugation, and incubated in the presence or absence of 5 mM castanospermine (Sigma) for one 1.5 h in YP ϩ 1% glucose. The yeast were again washed twice in glucose-free YP medium by centrifugation, and cells (2 ϫ 10 9 ) were resuspended in 400 l of YP ϩ 0.15% glucose containing 500 Ci of [2-3 H]Man (20 Ci/mmol) (American Radiolabeled Chemicals Inc.). After 2 min of labeling 2000-fold excess of unlabeled Man was added to reactions. Aliquots of 125 l were taken at 0, 1, and 10 min, and reactions were terminated by rapid addition to 4 ml of CHCl 3 /CH 3 OH (2:1) while vortexing.
General Methods-Neutral hexose was determined by a scaled-down version of the phenol-sulfuric assay with mannose as the standard (31). Protein was determined using either the method of Bearden (21), with bovine serum albumin as standard, or in the case of purified invertase by absorbance at 280 nm (25). Radioactivity was measured with a Beckman LS-3801 scintillation spectrometer in Ecolume (ICN) scintillation fluor.
Characterization of ⌬alg9
Invertase N-Linked Glycans-⌬alg9 yeast were transformed with pRB58 (see "Experimental Procedures"), and several transformants producing invertase activities of over 700 IU/g (wet weight) were accessioned. The best overproducer of invertase, JCY362, was chosen for external invertase purification from the ⌬alg9 background, which yielded high specific activity enzyme (79 mg, 3990 units/mg protein) with an overall recovery of 72% from 535 g of cells.
Bio-Gel P-4 size exclusion chromatography of the PNGase Freleased glycans from this invertase preparation provided four major pools, labeled A through D in Fig. 1, which eluted on the calibrated column in volumes consistent with Hex 6 GlcNAc 2 (fractions130 -136), Hex 7 GlcNAc 2 (fractions 124 -129), Hex 8.5 GlcNAc 2 (fractions 118 -123), and Hex 10 GlcNAc 2 (fractions 113-117), respectively. Each pool was rechromatographed, and the central 85% of the resulting peaks were re-pooled for further analysis (data not shown). Fig. 2A shows the MS spectrum of pool A, revealing one size isomer with masses of 1420 and 1436 Da for the sodium-and potassium-adducted species, respectively, the expected M r for a Hex 6 GlcNAc 2 oligosaccharide. The HPAEC data (Fig. 3) confirm that pool A consisted of a single Hex 6 GlcNAc 2 isomer. The inset in Fig. 2A shows that endo H at 50 milliunits/ml for16 h at 37°C, conditions that will not hydrolyze the reducing-end GlcNAc from the alg3 Man 5 GlcNAc 2 precursor (25), removed a mass equivalent to the reducing-end GlcNAc (204 Da). This result is consistent with the notion that the Hex 6 GlcNAc 2 is the first endo H-sensitive structure in the N-linked glycosylation pathway, the expected product of the Alg3p step shown in Scheme IC (32).
MALDI-TOF MS Analysis of Bio-Gel P-4 Pools A-D-
Pool B contained two glycan sizes; major ions of Hex 7 GlcNAc 2 appeared at 1581 and 1597 Da for the sodium and potassium forms, respectively, while lesser intensities were seen for ions at 1743 and 1759 Da, consistent with the same ion forms of Hex 8 GlcNAc 2 (Fig. 2B). Although MS is not wholly quantitative, signal intensities from the size neighbors in a homologous structural series should be relatively proportional. On this basis, Hex 7 GlcNAc 2 represented approximately 90% of pool B while Hex 8 GlcNAc 2 represented approximately 10%. As Fig. 2C shows, pool C contained both Hex 8 GlcNAc 2 and Hex 9 GlcNAc 2 , the former comprising approximately 20% of the pool while the latter made up the remaining 80%. Pool D contained a single glycan size consistent with Hex 10 GlcNAc 2 sodium and potassium forms at 2067/2083 Da, respectively.
HPAEC Analysis-In order to estimate the number of branch isomers present in the four oligosaccharide pools, each was analyzed by HPAEC using an analytical Dionex CarboPak PA-100 column (4 ϫ 250 mm). As indicated above, pool A gave a single peak (Fig. 3) and co-eluted with the smallest ⌬alg9 glycan from OSL (data not shown), consistent with the hypothesis that it is the core alg9 isomer unmodified by further processing after transfer to protein and transport through the secretory pathway. Quantitation of pool A from the Bio-Gel P-4 column indicated this to be the most abundant isomer among the Hex 6 -10 GlcNAc 2 structures, representing 24% of the N- glycan chains found on secreted invertase. Fig. 3 shows that pool B yielded five peaks corresponding to the presence of a minimum of five branch isomers, with the most abundant glycan species representing 53% of the profile area. The remaining four minor peaks contained 24%, 16%, 4%, and 2% of the total profile area, respectively.
Pool C provided six peaks, where the major species represented approximately 68% of the total area of the analytes (Fig. 3). The other peaks were 10%, 10%, 6%, 4%, and 2% of the profile area, respectively. Pools B and C contained one common peak eluting at 11 min, which represented 2% of the area in pool B and 10% of the area in pool C. Since pools B and C both have a Man 8 GlcNAc 2 component, this peak likely represents an isomer common to both pools. Finally, in pool D five glycan peaks were separated. The main species represented 61% of the chromatogram area. Other isomers represented 24%, 7%, 4%, and 4% of the total peak area present in the HPAEC profile.
NMR Analysis of ⌬Alg9 Core Oligosaccharides-In order to determine what structures were present in each of the Bio-Gel P-4 pools A-D, 1D 1 H NMR spectra were collected for each. These are shown as a montage in Fig. 4, and integrations of proton intensities for established reporter groups present in expansions of the spectra are summarized in Table I. Also present in Table I is the assignment of the proton intensities for the monosaccharide constituents in the Man 5 GlcNAc 2 oligosaccharide that accumulates in alg3 yeast, the precursor of the ALG3 step, whose structure is shown in Scheme IB. All structures assigned for ⌬alg9 glycans in the current work had as their core GlcNAc residues 1 and 2 and Man residues 3, 4, 5, 8, and 11 (Scheme IB). Fractional molar proton intensities in pools A-D (Table I) were assigned as additions to this Man 5 GlcNAc 2 core, which resulted in 18 structurally related members of a homologous biosynthetic series summarized in Scheme II. The isomer identifications in Scheme II relate the hexose number present and the order in which fractional proton intensities were used to deduce the structures, e.g. isomer 7a denotes the first Hex 7 GlcNAc 2 configuration assigned in pool B.
Within each pool, the number and amount of each isomer assigned agreed closely with the number and area of HPAEC peaks present (Fig. 3) and size proportions estimated by MALDI-TOF MS (Fig. 2). The subsequent paragraphs explain how each assignment was made. Some assignments required DQF-COSY experiments for validation, which are presented in Figs. 5 and 6.
Pool A: Hex 6 GlcNAc 2 -The smallest oligosaccharide found SCHEME I. Representative glycan structures and their anomeric 1 H NMR resonances (␦, ppm). A, the Glc 3 Man 9 GlcNAc 2 transferred to protein in wild-type cells (boxed area indicates the residues conserved in the core alg9 glycan); B, alg3 Man 5 GlcNAc 2 ; C, alg9 Man 6 GlcNAc 2 ; D, the alg9 glycan containing all Golgi modifications seen in this study. The Man added by Och1p is boxed (36). on secreted invertase in ⌬alg9 yeast was Hex 6 GlcNAc 2 , which was completely endo H-sensitive to removal of the reducing end GlcNAc ( Fig. 2A, inset). Given the substrate specificity of endo H, the ⌬alg9 Hex 6 GlcNAc 2 structure should be the Alg3p product, the cytosolic Man 5 GlcNAc 2 precursor with the added middle arm ␣1,3-linked Man residue 7 (Scheme I, B and C, respectively). The determination of the glycan structures in a homologous series by high field proton NMR is quite straightforward, because the addition of subsequent glycosidic linkages results in characteristic chemical shift resonances that can be compared with large libraries of related glycan proton chemical shifts available in the literature and data bases. Importantly, the 1D spectra can be accurately integrated to assign the components of complex mixtures (33).
Integration of signals in Fig. 4A ( Table I, pool A) between 4.70 and 5.40 ppm provides 6.00 mol of hexose protons, including 1 mol for residue 3 obscured by residual HDO signal centered at 4.78 ppm. Note the 1-mol resonance intensity integrated at 5.11 ppm, which is indicative of terminal ␣1,3-linked Man residue 7 (33,34). This resonance is the only detectable difference on comparing the ⌬alg9 Man 6 GlcNAc 2 NMR spectrum with that of alg3-1 Man 5 GlcNAc 2 (14) described previously and included in Scheme I. A 2D DQF-COSY experiment (data not shown) showed J 1,2 cross-peak for residue 7 at 5.11(C1-H)/4.05(C2-H) ppm, confirming this assignment (34). In addition, residue 4's C2-H shifts downfield on 3-O substitution from 3.97 to 4.12 ppm, verifying addition of residue 7 to residue 4 (Scheme IC). As will be seen, this resonance is present in all glycans Hex 7 GlcNAc 2 and larger, which means the ⌬alg9 Man 6 GlcNAc 2 is an invariant core component of all the Man Ͼ6 GlNAc 2 structures elucidated.
In one preparation of ⌬alg9 Hex 6 GlcNAc 2 , Glc 1 Man 5 GlcNAc 2 was found to be a minor species (8%). This assignment was made on the basis of the total anomeric proton integration and presence of ϳ0.08 mol of Glc anomeric resonance intensity at 5.25 ppm J 1,2 coupled (3.5 Hz) with its C2-H at 3.54 ppm as expected for an ␣-anomeric proton with an axial C2-H as found in Glc (14 -16, 25, 34, 35). This species was present in the shoulder of the P-4 Bio-Gel fractions most distant from the V o and is not considered further when assigning Hex 7-10 GlcNAc 2 structures.
Pool B: Hex 7-8 GlcNAc 2 -Pool B integrated to a total of 7.10 mol of anomeric protons (Table I), assuming 1 mol of intensity for residue 3 obscured by the HDO signal. This indicates that 90% of the pool is Hex 7 GlcNAc 2 and 10% is Hex 8 GlcNAc 2 , in good agreement with MALDI-TOF MS distribution observed in Fig. 2B. C1-H resonance intensity in excess of the core Man 6 GlcNAc 2 structure was present at chemical shifts 4.89, 5.03, 5.12, and 5.25 ppm (Table I). At 4.89 ppm, 1.20 mol of resonance intensity was present. One mol was from residue 4 of the core structure (Scheme IB), and 0.20 mol was assigned to the branch-forming unsubstituted ␣1,6-linked Man residue 12. The resonances for 4 and 12 correlate with their definitive C2-H resonances at 4.13 and 3.98 ppm, respectively (14 -16, 25, 34). Thus, 20% of Pool B is isomer 7a (Scheme II), which is the expected product of the Och1p outer chain initiating enzyme (36).
At 5.03 ppm 1.06 mol of resonance intensity was integrated. One mol of this signal was assigned to residue 11 of the core structure and the additional 0.06 mol to residue 13, the ␣1,2-Man cap on residue 12 (see Scheme ID). The predicted 2D DQF-COSY cross-peaks for these residues were observed at 5.03(C1-H)/4.08(C2-H) ppm (Fig. 5A) as expected for all terminal ␣1,2-linked mannoses. Together with the size constraint placed on the pool by total integrated C1-H resonance intensity, mass spectrometry data, and the above resonance assignments, ϳ6% of pool B contain residues 12 and 13 as their Golgi modification to the ⌬alg9 Man 6 GlcNAc 2 core to yield isomer 8c (Scheme II).
The total resonance intensity between 5.11 and 5.15 ppm was 1.74 mol. One mol is from ␣1,3-linked Man residue 7 in the core structure (Scheme IC), while the other 0.74 mol represents ␣1,3-linked Man residue 14 and the 2-O-substituted residue 12 in isomer 8c assigned above. Both of the 3-O-substituting Man's C2-Hs were observed in the 2D DQF-COSY spectrum at 4.08 ppm (Fig. 5A) (15,25,37). Subtracting 0.06 mol of residue 12 present in 8c from the 0.74 mol integrated leaves 0.68 mol of ␣1,3-linked Man C1-H resonance, which is seen as residue 14 on the basis of the intense 2D DQF-COSY cross-peak at 5.14(C1-H)/4.06(C2-H) ppm (Fig. 5A). This defines isomer 7b (Scheme II) as a major constituent of pool B. Furthermore, A very weak base-line 5.12(C1-H)/4.22(C2-H) ppm resonance was also observed, but is not apparent in the projection in Fig. 5A. This cross-peak is assigned to residue 16, which was previously shown to be due to a novel 3-O-substituting mannose in alg3 sec18 invertase oligosaccharides (15,25). As will be shown below, components of the Hex 8 -9 GlcNAc 2 and Hex 10 GlcNAc 2 pools also contain the Man␣1,3Man␣1,3-disaccharide. Furthermore, a minor component of pool B (2%) eluted at 11 min on HPAEC with a component of the Hex 8 -9 GlcNAc 2 isomers in pool C (see Fig. 3), also predicted to contain residue 16 (see Table I, Scheme II, and Fig. 5B). These two are most probably the same isomer, and its presence in pool B may have resulted from combining overlapping fractions from multiple Bio-Gel P-4 runs. Taken together, these calculations are consistent with about 60% of pool B being isomer 7b and about 4% isomer 8d (Scheme II).
The resonance intensity at 5.25 ppm was 0.10 mol (Table I) and indicates the presence of residue G 1 . This assignment is supported by the 2D DQF-COSY cross-peak at 5.25(C1-H)/ 3.54(C2-H) ppm (Fig. 5A), which is in the region expected for this axial ring proton (25,35). The resonance peak indicates that 10% of pool B is the core ⌬alg9 glycan, assigned isomer 7c, which retained the ␣1,3-Glc on transport through the Golgi after leaving the ER (Scheme II).
For the oligosaccharide structures studied here, a C2-H resonance intensity can appear at 4.22 ppm from residue 3 in the absence of the through space effect caused by residue 12's substitution of residue 5 (15,37), the 3-O substitution of residue 11 by 14 or G 1 (25), or the 3-O substitution of residue 14 with 16 (see Scheme ID). In pool B, 1.52 mol of resonance intensity was present at 4.22 ppm (Table I), which, as noted above, reveals a principle J 1,2 cross-peak in 2D DQF-COSY spectrum with residue 11's C1-H at 5.04 ppm, already assigned to the major constituent of this pool, isomer 7b. Isomers 7b and Pool C: Hex 8 -9 GlcNAc 2 -The total Man and Glc anomeric proton resonance intensity of Pool C was 8.82 mol, including 1 mol of resonance intensity for HDO-obscured residue 3, which corresponds to 82% Hex 9 GlcNAc 2 and 18% Hex 8 GlcNAc 2 (Table I). These values are in agreement with mass spectrometry profile (Fig. 3C). At 4.89 -4.91 ppm there was 1.18 mol of resonance intensity for ␣1,6-linked Man residues 4 and 12 (see Scheme ID). Subtracting 1 mol of intensity for the common core residue 4 leaves 0.18 mol by difference, indicating that 18% of the pool isomers had an unsubstituted residue 12, verified (see Fig. 5B) by the 2D DQF-COSY 4.91(C1-H)/3.97(C2-H) ppm cross-peak (15). Because the upper arm is abbreviated due to the alg9 lesion and pool C glycans are limited to 14. c Core residue 3 could not be integrated accurately due to water suppression, but must be present in all N-linked glycan cores. Hex 8 -9 GlcNAc 2 size, all isomers with unsubstituted 12, or without 12 altogether, must contain a lower arm substitution on residue 11. These can be 3-O substitutions by G 1 or 14 (15). Residue 14 can also be 3-O-substituted with 16. Representations of these structures are shown in Scheme II as isomers 8a, 8b, 8d, and 9a.
Between 5.03 and 5.05 ppm, 1.72 mol of ␣1,2-linked terminal Man anomeric protons were integrated (Table I). One mol is from the core residue 11 (Scheme IC), and the other 0.72 mol results from residue 13, which 2-O-substitutes 12. Between 5.11 and 5.14 ppm, there was 2.82 mol of proton resonance intensity. One mol is from core residue 7 (Scheme ID). Residue 12's cross-peak, when substituted with 13, is found at 5.14(C1-H)/4.02(C2-H) ppm (37) (Fig. 5B), and indicates that 72% of the pool contains structures in which the ␣1,6-Man outer chain branch initiation has an ␣1,2-Man cap. Thus, residue 7 and 2-O-substituted 12 account for 1.72 of the 2.82 mol of resonance intensity between 5.11 and 5.14 ppm, which leaves 1.10 mol to assign.
Subtracting isomer 8d's contribution (ϳ0.10 mol) to the 1.20 mol of intensity at 4.22 ppm leaves 1.10 mol, which correlates exactly with the residual 1.10 mol of 5.14 ppm resonance intensity calculated above (2.82 mol total minus 1.72 mol for 7 and 2-O-substituted 12) and assignable to terminal ␣1,3-linked SCHEME II. Interrelationship of ⌬alg9 Hex 6 -10 GlcNAc 2 species deduced in this study. The identifiers for structures are those used in the text. The regions of the secretory pathway in which the reactions occur are indicated at the top as bracketed areas denoted ER and Golgi. Glycans of interest are indicated as: A, alg3-type Glc 1 Man 5 GlcNAc 2 ; B, the family of N-glycans exhibiting persistent Glc; C, mature wild-type core-filled isomers or substrates for elongation to "mannan"; D, pathways leading to structures containing 16 (see text); E, structures leading to both normal core-filling or isomers containing 16. The asterisk appears above the glucosylated structures deduced from in vivo [2-3 H]Man labeling studies (see text). Glycans 8x and 9x were not seen but are implied by the presence of 10d. The Alg3p substrate Man 5 GlcNAc 2 from which all structures in this study are derived is indicated by Ќ.
Man. These are assigned collectively to 3-O-substituted residues 14, and/or 15, whose C1-, C2-, and C3-H cross-peaks have been identified above. Structure 9c can be assigned as the major component (68%) of pool C on the basis of its Man 9 GlcNAc 2 size, the large signal for 2-O-substituted 12 (Fig. 5B), and one ␣1,3-linked terminal Man distributed between residues 11 and 13. Accounting for all integrated protons, 2D DQF-COSY cross-peak intensities, and Hex 8,9 GlcNAc 2 size distribution of the pool, the remaining isomers in pool C are estimated to be: 8a, 6%; 8b, 2%; 8d, 10%; 9a, 10%; and 9b, 4%, which are in very good agreement with the number of separated peaks and their areas seen in pool C by analytical HPAEC (Fig. 3).
Pool D: Hex 10 GlcNAc 2 Pool-The total integrated Man and Glc anomeric proton intensity of this pool was 10.0 mol, which agrees with a single MALDI-TOF MS mass for a Hex 10 GlcNAc 2 sized glycan (Fig. 2D). Increased C1-H resonance intensity above that provided by the core Hex 6 GlcNAc 2 was observed at 5.25, 5.14, 5.04, and 4.89 ppm (Table I).
At 4.89 -4.91 ppm 1.07 mol of resonance intensity for 2-Ounsubstituted ␣1,6-linked Man was integrated. Subtracting 1 mol for core residue 4 leaves 0.07 mol. Since 12 is fully substituted by 13, in order to satisfy the Hex 10 GlcNAc 2 size constraint, the additional 0.07 mol of 4.89 -4.91 ppm resonance is likely to be present as an additional ␣1,6-linked Man on the new lower arm ␣1,6-linked branch and is assigned as residue 17 substituting residue 12. This structure is supported by the presence of a 2D DQF-COSY cross-peak of low intensity at 4.91(C1-H)/4.04(C2-H) ppm (not apparent in Fig. 5C) and assigns 7% of pool D as isomer 10d (Scheme II).
At 5.25 ppm 0.03 mol of resonance intensity was observed for G 1 , and the 2D DQF-COSY cross-peak at 5.25(C1-H)/ 3.54(C2-H) ppm confirms its presence. Thus, 3% of pool D is 10e, Glc 1 Man 9 GlcNAc 2 , which appears to retain an untrimmed glucose on exit from the ER to the Golgi (Table I).
At 5.11-5.15 ppm 3.90 mol of resonance intensity was integrated. Of this value, 1 mol is from core residue 7 and 1 mol from 2-O-substituted residue 12 documented above. This leaves 1.90 mol of resonance to be assigned to ␣1,3-linked residues 14, 15, and 16 (Scheme ID), and, indeed a strong cross-peak is seen in the 2D DQF COSY spectrum of Pool D (Fig. 5C) ppm, which is more intense in pool D than seen in pool C (Fig. 6, compare panels B and C).
At 4.22 ppm 1.93 mol of C2-H proton resonance intensity was integrated. As already described for the other pools, this is due to the 3-O substitution of 11 with G 1 or 14, of 13 with 15, or of 14 and/or 15 with 16 (Scheme ID). As described above for pool C, cross-peaks for all of these residues were observed in pool D's 2D DQF-COSY spectra (Figs. 5C and 6C, respectively). Distribution of the integrated protons, consistent with relative crosspeak resonance intensities, and the Hex 10 GlcNAc 2 size constraint, allows assignment of the two major isomers as 10a (61%) and 10b (24%) (Scheme II). These structures account for 1.70 mol of 1.93 mol of resonance intensity at 4.22 ppm leaving 0.23 mol, and isomers 10d and 10e, assigned above, account for an additional 0.13 mol, leaving 0.10 mol to assign. Similarly, isomer 10a, 10b, 10d, and 10e account for 1.80 of the 1.90 mol of ␦ 5.13 resonance intensity calculated in the previous paragraph. Isomer 10c (5%) accounts for the remaining 0.10 mol of 3-O-substituted Man at 4.22 ppm, the 0.10 mol of ␣1,3-Man protons at 5.13-5.15 ppm, and the 0.05 mol of unsubstituted residue 11 at 5.03 ppm. These assignments are in very good agreement with the pool's HPAEC profile, both in the number of peaks found and their respective areas (Fig. 3).
In Vitro Characterization of alg9 N-Linked Oligosaccharides-Because Man 6 GlcNAc 2 was the smallest glycan released from ⌬alg9 secreted invertase, either this or its glucosylated form would be expected to be present on the ⌬alg9 OSL precursor. Fig. 7 shows comparative permethylation analysis of in vitro and in vivo-synthesized alg9 Hex 6 GlcNAc 2 . The former was released by mild acid hydrolysis from OSL synthesized in vitro under limiting glucosylation conditions (see "Experimental Procedures"), while the latter was the pool A Man 6 GlcNAc 2 .
In Vivo Glucosylation-NMR-derived structures of secreted invertase glycans in ⌬alg9 yeast indicate that ϳ7% of Hex 6 -10GlcNAc 2 isomers retained residue G 1 (Scheme II). To ascertain whether the persistence of G 1 is a product of nominal glucose addition of this residue, as in alg3 yeast (14), or a remnant of full glucosylation and processing, ⌬alg9 cells were pulse-chase labeled with [2-3 H]Man in the absence (Fig. 8, A-C) or presence (Fig. 8, D-F) of CST, and the labeled glycans were released from glycoprotein pellets by endo H and analyzed as described under "Experimental Procedures." At 0 min of chase, greater than 95% of endo H released glycans migrated as Glc 3 Man 6 GlcNAc (Fig. 8A, peak centered at 31 min) and ϳ5% migrated as Man 6 GlcNAc (Fig. 8A, peak centered at 8 min) in the absence of CST. The glycans migrated as the expected sizes, Hex 11 and Hex 8 , respectively, on Bio-Gel P-4 (data not shown). The Glc 3 Man 6 GlcNAc oligosaccharide lost the equivalent of two hexose units upon digestion with jack bean ␣-mannosidase as revealed by Bio-Gel P-4 chromatography (data not shown), all data being consistent with its assigned identity. In the presence of CST, 0 min of chase revealed that virtually all detectable glycans released from protein by endo H were Glc 3 Man 6 GlcNAc (Fig. 8D, peak centered at 31 min). The lack of Man 6 GlcNAc in the presence CST and its trace amount in the absence of CST clearly demonstrate that Glc 3 Man 6 GlcNAc 2 is the main if not the only glycan transferred to protein in the ⌬alg9 background.
At 1 min of chase in the absence of CST smaller glycans form, Aliquots from each reaction were terminated by addition to CHCl 3 / CH 3 OH (2:1) at 0, 1, and 10 min of chase. All pellets were solubilized and N-glycans released with endo H, which were characterized on the Dionex PA-100 column, previously calibrated with authentic oligosaccharide standards from this and a previous study (15). Panels A and D, 0 min of chase; panels B and E, 1 min of chase; panels C and F, 10 min of chase. Additional details are given under "Experimental Procedures." while in the presence of CST only a trace amount of these smaller forms are detectable. From these results it is evident that the smaller glycan species (Fig. 8B, peak centered at 20 min and shoulder at 16.5 min) are largely generated by glucosidases I and II in the absence of CST but cannot be formed in the presence of the glucosidase inhibitor. In the presence of CST at 1 min of chase larger glycans are formed (Fig. 8E, peak centered at 43 min), which is expected as the Glc 3 Man 6 GlcNAc present on accessible regions of the peptide backbone would be expected to be elongated by Golgi mannosyltransferases.
At 10 min of chase nearly all of the [2-3 H]Man-labeled Glc 3 Man 6 GlcNAc (Fig. 8C, peak centered at 30 min) has been processed to Man 6 GlcNAc (Fig. 8C, peak centered at 8 min) in the absence of CST, while in the presence of CST the majority of endo H-released oligosaccharides remain Glc 3 Man 6 GlcNAc (Fig. 8F, peak centered at 30 min). This result clearly demonstrates that Glc 3 Man 6 GlcNAc 2 is transferred to protein and largely processed to Man 6 GlcNAc 2 in the ⌬alg9 background.
DISCUSSION
The structure of the alg9 core oligosaccharide and its Golgimodified elongation products on secreted invertase have been defined through methylation analysis, HPAEC, MALDI-TOF MS, endo H sensitivity, pulse-chase[2-3 H]Man labeling, and high field 1 H NMR spectroscopy. The ⌬alg9 core Man 6 GlcNAc 2 N-glycan transferred to protein in vitro, via its dolichol-linked intermediate, and that present on secreted invertase, are one and the same structure. This is the first endo H-sensitive oligosaccharide formed during OSL synthesis and is the Alg3pdirected product (Scheme IC). Examining the synthesis and processing of ⌬alg9 N-linked glycans reveals an important role for the ALG3 phosphorodolichol pathway step in downstream processing events leading to mannan on secreted S. cerevisiae invertase.
Previous studies have shown that a significant proportion of the glycan chains on alg3 sec18 invertase retained one or more Glc residues transferred from OSL during glycosylation in the ER (14,15,25). Furthermore, the level of OSL glucosylation in the alg3 background was very low, with only ϳ7% of the chains transferred to protein containing the normal glucotriose unit. Thus, the Man 5 GlcNAc 2 -PP-Dol translocated into the yeast ER in alg3 is both poorly glucosylated, and the portion that is glucosylated becomes a poor substrate for glucosidases I and II once transferred to protein. Although little residual Glc survived on ⌬alg9 invertase glycans, initially making it difficult to assess the potential level of glucosylation and subsequent processing, [2-3 H]Man pulse-chase labeling demonstrated convincingly that the ⌬alg9 fully glucosylates the truncated Man 6 GlcNAc 2 -PP-Dol, and that Glc 3 Man 6 GlcNAc 2 is the major glycan transferred to protein from OSL in this mutant (Fig. 8). Furthermore, the complete absence of NMR signals for the terminal ␣1,2-linked Glc residue, G 3 , or the penultimate ␣1,3 Glc residue, G 2 , and the only small residual level of G 1 (Table I) on the invertase oligosaccharides imply that both glucosidases I and II have nearly wild-type activity on the glucosylated alg9 oligosaccharides. It is worth noting that wild-type N-glycans retain a similar amount of G 1 as seen here in the ⌬alg9 strain (34). Thus, an important conclusion of the current work is that the middle arm ␣1,3-linked Man specified by the ALG3 locus provides structural information that potentiates the Alg6p, Alg8p, and Alg10p ER glucosyltransferases and Gls1p and Gls2p trimming glucosidases.
The NMR data show the complete absence of the upper arm Man␣1,2Man␣1,6-residues, indicating that, without the addition of the ␣1,2-linked Man 10 to residue 7 by Alg9p (Scheme IA), no further Man additions can occur on the OSL precursor by downstream Man-P-Dol dependent mannosyltransferases.
The reason such strict dependence on the Alg9p step evolved is not known with certainty, but may relate to the order of subsequent processing reactions. It is known that this disaccharide, consisting of the upper arm Man␣1,2Man␣1,6-, residues 9 and 6, respectively, is required for efficient ER ␣1,2-mannosidase (Mns1p) trimming of the middle arm ␣1,2-Man (38), and removal of this residue in conjunction with glucose trimming appears to be an integral part of protein quality control editing in the S. cerevisiae ER (39).
Mannan outer chain synthesis begins with the addition of ␣1,6-linked Man 12 to the lower arm core ␣1,3-linked Man 5 (Scheme ID) (7, 10) catalyzed by Och1p in the cis-Golgi, whose in vitro substrate specificity has been defined (36). Pyridylaminated Man 5 GlcNAc 2 -PA derived from the alg3⌬och1mnn1 triple mutant (Scheme IB), formed only 9% of the Och1p product compared with Man 8 -9 GlcNAc 2 -PA as substrate. Another glycan, a Man 7 GlcNAc 2 -PA, differing from the core alg9 structure (Scheme IC) by the presence of the upper arm ␣1,6-linked Man (residue 6, see Scheme IA), converted 60% of input to product compared with the wild-type Man 8,9 GlcNAc 2 -PA substrate in this study. Although the ⌬alg9 core Man 6 GlcNAc 2 was not tested as a substrate in this study (36), it appears from the current work that the addition of residue 7 does help potentiate the activity of Och1p. When comparing the alg3 and ⌬alg9 glycan pools that have one Golgi-type hexose addition (Hex 6 GlcNAc 2 for alg3; Hex 7 GlcNAc 2 for ⌬alg9), only 3% of the former's pool isomers have the Och1p addition (15), while 22% of the latter's have this addition (Scheme II). This clearly implies that in vivo the addition of Man residue 7 to the alg3 core Man 5 GlcNAc 2 (Scheme IC) significantly increases the proportion of glycans that are substrates for Och1p activity compared with those without this residue.
A novel series of core-filled structures was assigned containing terminal Man␣1,3Man␣1,3-disaccharides (see Scheme II). These structures have been identified previously on S. cerevisiae wild-type Man 14 GlcNAc (16) and to a greater extent on larger alg3 core glycans (15). Currently, little is known regarding the enzyme activity catalyzing this addition. Studies on the mnn1 mutant noted the absence of terminal Man␣1,3Man␣1,3disaccharide on O-linked glycans (40), suggesting that at least the penultimate ␣1,3-linked Man is added by the MNN1 encoded mannosyltransferase (40,41). Three genes in the protein sequence data base, yil014w, ygl257c, and ynr059w, closely related to MNN1, have been implicated in the terminal ␣1,3-Man addition to O-glycans. 2 Furthermore, a new family of enzymes that add ␣1,3-Man to ␣1,3-Man termini in O-linked glycans has been described recently in S. cerevisiae (42). It is possible that one of these genes, yil014, ygl257, and/or ynr059w, may be responsible for decorating existing N-linked ␣1,3-Man caps with a 3-O-substituting Man under certain metabolic conditions or in isolated genetic backgrounds.
The interrelationship of the alg9 structures deduced in this study is summarized in Scheme II. Kinetically, those glycans that are good substrates for mannan elongation will be depleted preferentially from the isomer pools. Outer chain elongation to mannan begins with the Och1p addition of ␣1,6linked Man 12 to the core ␣1,3-linked Man residue 5 (Scheme ID). The major difference between wild-type core-filled mannan and that of alg9 is the accumulation of isomer 7b (Scheme II), which escapes the Och1p ␣1,6-Man addition (residue 12), although to a much lesser extent than described earlier for alg3 glycans (15,25). While the ALG3-directed Man␣1,3-addition enhances to nearly wild-type levels OSL glucosylation and subsequent removal of those added Glc residues from glycoprotein, the Och1p activity clearly did not reach wild-type levels on the basis of residual isomers 6a, 7b, and 8d (Scheme II). This suggests that completing the upper arm Man␣1,2Man␣1,6branch (residues 9 and 6; Scheme IA) provides additional structural information recognized by the Och1p enzyme. An important next step in understanding the role of a given mannose addition on the subsequent OSL processing steps will be to analyze glycan structures in alg mutants that accumulate OSL intermediates further down the phosphorodolichol pathway. To this end, we have begun a structural analysis of the alg12 mutant (39), which accumulates the alg9 product.
While yeast are primitive eukaryotes, the conserved aspects of their N-glycosylation pathways and homology of many biosynthetic components with those of higher eukaryotes has yielded important understanding concerning errors in metabolism leading to OSL truncation in humans. This has proven to be the case in several forms of carbohydrate-deficient glycoprotein syndrome. Indeed, a lesion in the human homolog of the ALG6 defines a new form of carbohydrate-deficient glycoprotein syndrome type I (43)(44)(45). It is likely that, while diagnosis of CDGS may fall into a limited range of recognizable phenotypes, the underlying genomic lesions may be numerous, and defects in many of the ALG genes are clear candidates to be causative agents in this emerging disease.
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Domain: Biology
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Aerobic Batch Degradation of Cresol by Newly Isolated Pseudomonas monteilii Cr13
Microorganisms have broad spectrum of applications varying from metabolite production to breakdown of complex carbon sources. Cresols are one such complex that has drawn attention due to its disposal in un-degraded or partially degraded form that enters into air, water and soil polluting the environment; its extremities extend to surrounding ecosystem and destroying the aquatic and human life forms. Cresol possesses special affection towards blood plasma leading to kidney, liver and heart disorders. Many have reported biological degradation of hazardous chemical using different organisms with fine degradation efficiency. The current investigation adds another strain Pseudomonas monteilii which was found to exhibit the potential to utilize cresol as its major carbon source for growth and proliferation. Further, the Cresol inhibitory concentration was checked by varying concentration from 100 to 1500 ppm. The remnant concentration over the time period was analyzed by aminoantipyrine assay. The strain was capable of >99% removal at neutral pH, 150 rpm and 30p C in 24h. The phylogeny of the strain was analyzed post 16S rRNA sequencing using in silico tools. The strain can be optimized for degrading higher cresol dose without compromising on the biodegradation efficiency.
The methylphenols are the aromatic organic compounds that have variable melting points decided by the surrounding temperatures. These methylphenols have various applications in manufacturing of pesticides, petroleum products, dyes and also in a few pharmaceutical products and are generally known as cresols 1,2,3,4,5 . Cresols are generally found dumped in regions with petroleum or dye dumping sites. They are severely toxic for humans if inhaled or ingested, even at a very low concentration that often leads to consequences varying from irritation of eyes, mouth, throat and skin, vomiting, liver and heart damage, paralysis, coma and death 1,6,7 . In human, cresol finds more affinity towards the ligand binding protein, Human Serum Albumin (HSA). HSA contributes as carrier proteins for various steroids, fatty acids and helps in maintaining the antioxidants in the body. Cresol binds to albumin reducing the carrier profile of protein resulting in kidney and blood disorders 8,9,10,11 . It is not only toxic for human life but equally deleterious for the aquatic forms, thus, direct disposal in water, air and soil is strictly prohibited 12,13,14 .
The environment exposure to cresols has been observed in terms of petroleum leakage, sludge from dye, pharmaceutical or other industries using derivatives of cresols as raw material 1 . There have been a lot of investigation on degradation and sequestration of the hazardous compound, where Sucrose Fermentation Positive (+ve) 10 Lactose Fermentation Negative (-ve) 16 , Ralstonia 17 etc. have been predominantly isolated from the contaminated sites. These microorganisms possess a strong specific mechanism to metabolize these phenols to a usable carbon source for their growth and proliferation. The enzymes such as monooxygenases, hydroxylases and dehydrogenases play a major role in formation of cresol intermediates and further, direct to Tricarboxylic acid cycle (TCA). The end product thus, remains carbon dioxide and ATP, energy providing molecules 18,19 .
In the current study, a novel strain of Pseudomonas has been isolated form petroleum contaminated site, which has been found quite suitable for cresol degradation. The isolate has thus, been phylogentically analyzed.
Isolation and Screening of Cresol Degrading Bacteria
The soil samples were collected from the various localities in and around Calicut which were contaminated with petroleum, including, petrol pumps, automobile workshops, Petroleum transfer areas to the reservoir, etc. The 30 different soil types were inoculated in to Minimal mineral medium without carbon source as a negative control, whereas, for the test samples, various isomers of cresol were fed as carbon source in minimal mineral medium. The concentration of cresol isomers were maintained as low as 100 ppm in 1L of the medium. 1% of each soil sample was inoculated into the control and test samples and was incubated for24h at 30p C with 150 rpm. Depending on the microbial growth the concentration of cresol variants was gradually increased to 500 ppm for the consortium showing a good affinity towards the cresol minimal medium. 20 The cresol acclimatization by the grown consortium was tested at higher concentration ranging from 500 ppm to 1200 ppm. Based on the responses of each bacterial culture, the strain with best cresol tolerance and efficiency of survival on the cresol medium was isolated for phenotypic characterization. 21
Morphological and Phenotypic Characterization
The isolated strain was undergone a series of biochemical and morphological identification. The strain was Gram stained and observed under the microscope to understand morphological characteristics. Further, the pigment producing efficiency, colony shape, aerobic requirements and other sugar utilizing efficiencies were tested. 22 The study of phenotypical characterization was initiated with DNA isolation. The obtained sample was analyzed for purity and integrity on 2% agarose gel prepared in 1X TE buffer. PCR was run to amplify the product. Amplified product was sequenced using 16S rRNA sequencing. 23 Phenotypical characterization was done in the Department of Life Sciences, Kristu Jayanti College, Bangalore. Phylogenetic analysis was performed using clustalW.
Cresol Degradation Study
The cresol degradation was checked by evaluating the remnant cresol concentration in the medium after the optimum incubation period.
The evaluation of cresol content was performed by a colorimetric assay using 4-amino antipyrine method. 24 Cresol was treated with 4-aminoantipyrine at alkaline pH around 10.00 in presence of an oxidizing agent, potassium ferricyanide that results in the formation of stable reddish-brown colored antipyrine dye. The dye can be estimated by taking O. D. at 460 nm. The intensity of color or the optical density is directly proportional to the concentration of cresol. The standard graph for the assay was plotted with known concentrations of cresol between 10 to 50µg/L. So, the O. D. values obtained for known concentration of cresol were extrapolated or interpolated on the standard curve to the quantitative cresol concentration. 25,26,27
Isolation and Screening of Cresol Degrading Bacteria
The thirty different soil types were inoculated in to minimal medium with and without cresol variants to grow the bacterial consortium possessed by each. The grown culture was found to have a good population of bacteria that was screened further based on the tolerance at high cresol concentration. Among the cresol variants used 28 Thereafter, CR-13 was characterized with various microscopic features, (Table 1) colony characters ( Table 2) and biochemical tests ( Table 3). The strain was found to be Gram negative, rodshaped motile organism, without spore and capsule.
[ Figure 1 (A)]. It possessed circular colonies with smooth and wet texture, [ Figure 1 (B)]. This aerobic bacterium was found catalase and oxidase positive.
Phylogenetic Characterization
The sequence, received from the sequencer was converted into FASTA file using bioinformatics software. The sequence then used for Blast search against NCBI and phylogenetic analysis was performed using clustalW. The phylogenetic analysis predicted that the sequence, obtained was found to have 99% similarity with Pseudomonas monteilii strain MBG2 16S ribosomal RNA gene. (Figure 2) The growth profile of Pseudomonas monteilii CR-13 at different concentration of o-Cresol was studied in the presence and absence of standard carbon source, cresol and without any carbon source. The growth of the cells was studied using UV-Vis spectrophotometer at 600nm. ( Figure 3). All the varied initial concentrations of o-Cresol were consumed by P. monteilii CR-13. The P. monteilii CR-13 could survive and utilize upto 1500ppm of o-Cresol with a low growth profile than the standard carbon source (glucose). Similar studies have reported by Ahamad, P. Y. A. et al. 29 ,and Mamma D 30 where acclimatized Pseudomonas putida cells could overcome the inhibitory effect of phenol by the addition of glucose, a conventional carbon source.
Degradation Efficiency of P. monteilii CR13
The biodegradation study of cresol by P. monteilii CR13 was performed by 4-aminoantipyrine assay. The biodegradation study in terms of residual concentration of o-Cresol was performed and the unknown concentration was calculated based on the optical density of the standard curve for 4-aminoantipyrine assay. The maximum degradation was affected by the initial cresol concentration in the medium. The amount of remnant cresol concentration was determined by interpolating the O. D. values obtained after 4-amino antipyrine assay. The values of O. D. at different time intervals between 0 to 30 h have been plotted over the standard curve to determine the unknown concentrations. So, by the end of each experimental set, the concentrations calculated was 0.25, 2.5, 2.75 and 5 ppm, respectively with initial cresol dose of 100, 500, 1000 and 1500 ppm. The degradation efficiencies achieved are 99.75, 99.5, 99.35 and 98.28, respectively for 100 ppm, 500 ppm , 1000 ppm, and 1500 ppm respectively. (Figure 4)
CONCLUSION
The isolated Pseudomonas monteilii CR-13 is an efficient bacterial strain to biodegrade o-Cresol. The P. monteilii CR-13 is efficiently degrading 1500 ppm of o-Cresol and shows good growth profile. Further investigations are recommended for immobilization and optimizing the growth conditions for enhancing the degrading capacity of P. monteilii CR-13. In the light of results that were gathered from our batch culture, the organism is showing high promise in degradation of o-Cresol.
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Domain: Biology
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Biomineralization of Magnetosomes: Billion-Year Evolution Shaping Modern Nanotools
Biomineralization in the microbial realm usually gives origin to finely structured inorganic nanomaterials. Perhaps, one of the most elegant bioinorganic processes found in nature is the iron biomineralization into magnetosomes, which is performed by magnetotactic bacteria. A magnetosome gene cluster within the bacterial genome precisely regulates the mineral synthesis. The spread and evolution of this ability among bacteria are thought to be a 2,7-billion-year process mediated by horizontal gene transfers. The produced magnetite or greigite nanocrystals coated by a biological membrane have a narrow diameter dispersibility, a highly precise morphology, and a permanent magnetic dipole due to the molecular level control. Approaches inspired by this bacterial biomineralization mechanism can imitate some of the biogenic nanomagnets characteristics in the chemical synthesis of iron oxide nanoparticles. Thus, this chapter will give a concise overview of magnetosome synthesis’s main steps, some hypotheses about the evolution of magnetosomes’ biomineralization, and approaches used to mimic this biological phenomenon in vitro.
Introduction
Among everything that is known in Microbiology, magnetotactic bacteria (MTB) are known to perform one of the finest examples of a controlled biomineralization process. MTB were first observed in the late 1950s, by the medical Doctor Salvatore Bellini in the Italian city of Pavia and later described in Massachusetts by Richard Blakemore in the 1970s [1,2]. MTB are known to align its motility axis to the geomagnetic field and use it for orientation. When observed under the light microscope, MTB present unidirectional swimming to the North or South Magnetic Poles from an applied external magnetic field (a magnet); this behavior is called magnetotaxis [3]. This behavior occurs due to the presence of magnetic nanocrystals-the magnetosomes-, usually aligned in single or multiple chains within the bacterial cytoplasm (Figure 1), and flagellar propulsion guided by chemotaxis [3]. In a simple way, chemotaxis in MTB is assisted by bacterial orientation along Earth's magnetic field (magnetotaxis). Therefore, magnetotaxis allows MTB to find the optimum position for survival and growth in a chemically stratified water column, seeking for an optimum environment where proton motive driving force reaches maximum potential. For MTB, which are frequently microaerophilic or anaerobic microrganisms, this environment is near the oxic/anoxic interface [4].
Magnetosomes are composed of a magnetic nanoparticle in most cases composed of magnetite (Fe 3 O 4 ) and sometimes greigite (Fe 3 S 4 ) with species specific shapes and sizes, and enveloped by a phospholipid bilayer with associated proteins, which constitutes the magnetosome membrane (MM) [3]. The gene regulation of magnetosome biomineralization (MB) and organization within the cell will be discussed in more detail in the sections ahead. Based on the total iron amount within a magnetotactic bacterium cell, MTB appear to play a major role in the biogeochemical cycling of iron [5]. MTB through magnetosome synthesis, Biomineralization of Magnetosomes: Billion-Year Evolution Shaping Modern Nanotools DOI: [URL] /10.5772/intechopen.94465 assimilate the iron solubilized in the environment to an inorganic crystal. After cell lysis, the magnetosome is deposited in the sediment, forming what is known as magnetofossils [6]. Besides, MTB can be ingested by protozoans, and the iron from magnetosomes is, then, incorporated in the food chain [7]. Apart from iron and based on their physiology, MTB seem to have relevant roles in other biogeochemical cycles of sulfur, nitrogen, and carbon [8].
MTB are an extremely diverse group of Gram-negative bacteria with a variety of morphotypes (i.e., rods, vibrios, spirilla, coccoid, and ovoid) and species affiliated to Proteobacteria (Alpha-, Beta-, Gamma-, Delta-, and Ca. Etaproteobacteria class), Omnitrophica and Nitrospirae phyla [9]. MTB affiliation to other taxa have been proposed based on metagenomics studies, but observation of the magnetosomes was not performed to confirm this matter. This great diversity is reflected in MTB ubiquity in almost all aquatic habitats across the Earth (Figure 2), including extreme environments such as thermal trenches and saline-alkaline lakes [6,10]. More than being interesting species for their unique evolutionary process and ecological importance, MTB are also proving to be of interest for biotechnological applications. Their unique physiology makes MTB potential bioremediators of heavy metals and magnetosomes can be extracted and used as nanotools for magnetic controlled drug targeting, contrast agents for magnetic resonance imaging, enzyme immobilization and many more industrial and biomedical applications [11].
Steps of magnetosome biomineralization in MTB
MB is highly regulated at the genetic level [12]. Magnetosome gene clusters (MGCs) [13], structured as operons, are responsible for MB in MTB. MTB genomes contain: (i) conserved mam genes, encountered in all MTB; and (ii) restricted genes encountered in some phylogenetic groups of MTB [14]. Examples of genes restricted to certain MTB are: (i) mms (from magnetosome membrane specific) genes found in magnetotactic Proteobacteria; (ii) mad (from magnetosome associated Deltaproteobacteria), which were first reported in magnetotactic deltaproteobacteria [15] and recently encountered in MTB affiliated to Omnitrophica and Nitrospirae phyla [9]; and (iii) man (from magnetosome genes in Nitrospirae), which are genes reported in MTB affiliated to Nitrospirae phylum [16]. Comprehension of MB were inferred by mam and mms genes deletion in the cultured magnetotactic alphaproteobacteria Magnetospirillum magneticum strain AMB-1 and Magnetospirillum gryphiswaldense strain MSR-1 [14]. Precise man and mad genes roles in MB remain unclear as they were studied in uncultured MTB [16], thus genetic systems to test gene function is not available.
As previously described, MTB are capable of biomineralizing magnetosomes, an organelle with a ferrimagnetic mineral core surrounded by a biological membrane [3]. A series of complex mechanisms occur in order to transform the environmental bioavailable iron into a complete and fully functional magnetic organelle. MB process involves different steps such as iron uptake, magnetosome vesicle formation, specific protein recruiting, crystal nucleation, redox balance, and pH control in magnetosome vesicle, size and crystalline morphology control and magnetosome vesicle docking in the bacterial cytoskeleton [3]. Mam [3]. MB involves four major steps as they are: (i) MM formation (participation of MamI, MamL and MamAB proteins) [3,14]; (ii) crystal nucleation (which include MamE, Mms6, MamB and MamM) [3,14]; (iii) crystal maturation (participation of MamE, MmsF, MamGFDC and Mam P, S, T) [3,14]; and (iv) magnetosome chain alignment within cell body (participation of MamJ, MamK and MamY) [14,20]. Mam and Mms protein functions involved in MB are described in Table 1 and Figure 3.
Evolutionary history of MGCs within Bacteria domain
MGC origin and evolution within the Bacteria domain is a constantly discussed topic in the literature. The scattering of MGCs and the magnetotactic behavior raises questions as MTB encompasses high diversity regarding their ecology, metabolism, and phylogeny. The first proposed hypothesis was the polyphyletic origin of magnetite and greigite MB [53]. According to this hypothesis, biomineralization of greigite and magnetite magnetosomes would have evolved without sharing a last universal common ancestor of magnetotactic bacteria (LUCA MTB). At that time MGCs were not discovered. Thus, this assumption relied on the information that the biochemical and nutritional parameters for greigite and magnetite biomineralization are different. Likewise, all known MTB affiliated to Alphaproteobacteria synthesized magnetite magnetosomes, while the ones affiliated to Deltaproteobacteria synthesized greigite magnetosomes, thus permitting the inference the polyphyletic hypothesis. Years later, after the discovery of MGCs, similarities between mam genes of magnetite and greigite MTB showed a common ancestor for both minerals synthesis in MTB [54]. It is speculated that greigite MGCs originated after events of duplication and divergence from magnetite MGCs in sulfate-reducing bacteria like the multicellular magnetotactic prokaryote (MMP) Ca. Magnetoglobus multicellularis strain Araruama affiliated to Deltaproteobacteria [54].
On behalf of that, Lefèvre and colleges [55] hypothesized a monophyletic origin of MGCs concerning magnetotactic proteobacteria. The comparison of 16S rRNA gene and conserved Mam proteins evolution showed a convergence of both phylogenetic inferences. It was suggested that MTB affiliated to Proteobacteria phyla shared a LUCA MTB and over time, some proteobacteria would have lost the MGC, resulting in the inability of biomineralizing magnetosomes [55]. Opposing all previous statements, a considerable number of authors proposed the importance and influence of horizontal gene transfer (HGT) events on the evolution and scatter of MGC in Bacteria domain [9,13,[56][57][58][59]. In light of these events, different non-MTB would have received MGCs by HGT, granting them the capacity of biomineralizing magnetosomes [9].
The origin of MB was dated, by molecular Bayesian clock, before the divergence of the Nitrospirae and Proteobacteria phyla during the Archean eon [13]. The divergence happened 2.7 billion years ago before the appearance of phototrophs and Great Oxygenation at the time of Paleoproterozoic on the Proterozoic eon (Figure 4). This hypothesis is supported by: (i) low pressure or absence of O 2 in the atmosphere and anoxic oceans in Archean [60]; (ii) abundant dissolved Fe 2+ as concentrations of 40 [66], mesophilic [8] and moderately thermophilic MTB [47]. Alongside these conditions, Earth's magnetic field originated 4.2 billion years ago enduring several inversions until the present time [67]. Considering this panorama, it is plausible that MTB and the geomagnetic fields have coevolved selecting the ones capable of undergoing all the continuous biotic and abiotic variations [13]. Large scale metagenome approach of MTB diversity demonstrated two possible routes concerning MGC evolution over time [9]. It is hypothesized that a LUCA MTB contained magnetite or an unknown MGC followed by events of MGC duplication, divergence, and loss combined with ancient and recent HGT events could explain the scattering of the magnetotactic behavior in the Bacteria domain [9] ( Figure 4). The unending studies regarding MTB diversity and ecology are indispensable for an accurate decipherment of MGC evolution in the Bacteria domain.
Influence of the medium on biomineralization
The fact that related magnetotactic strains synthesize magnetosomes with significant differences in sizes and elongation is a clue that, despite a rigorous genetic control, environmental factors may influence the characteristics of the biomineralized nanocrystals [68]. Extensive experiments performed in cultures of MTB have pointed out temperature, pH, iron concentration, oxygen concentration, external magnetic fields, and nutrient concentrations as important factors driving physical changes in magnetosomes [69].
Ferric iron concentrations exert an important influence on the magnetic properties of Magnetospirillum magnetotacticum strain MS-1 cells due to alterations within biogenic magnetite [70]. The coercive force (H C ), probably the most important criterion in the selection of magnetic nanoparticles for technological applications, is significantly affected [70]. The H C was increased from 216 Oe when cells were cultured at 12 μM Fe 3+ to 238 Oe at 68 μM [70].
In another study, it was shown that reducing conditions leads to an increase in magnetosomes crystals of Ms. magneticum strain AMB-1 in culture [71]. An oxidoreduction potential of 0 mV (neutral condition) led to a crystal diameter of 31.5 ± 1.3 nm, which augmented to 37.2 ± 0.6 nm when the culture was carried out at -500 mV (reducing condition) [71]. The reducing condition also caused an increase in the total magnetite mass per cell as 9.1 ± 1.9 magnetosomes were observed per μm (cell length), in contrast to 5.48 ± 1.3 in neutral condition.
The evidence that characteristics of biogenic magnetite can be modified is of great interest for practical applications because certain purposes may require specific particle properties. Therefore, the knowledge of the interplay between environmental conditions and process regulation by biomolecules in biomineralization can help develop methods for the in vitro biomimetic preparation of magnetic nanoparticles with tunable properties.
Microbes inspire chemistry: biomimetic synthesis of artificial nanoparticles
Understanding MB is key not only for the in-depth learning of microbial physiological phenomena, but it can teach us valuable insights for the fabrication of technological materials. Magnetic nanoparticles have emerged as functional materials since the 1940s, when iron oxide powders, with crystals ranging from 60 nm to 1 μm, were used to impregnate recording tapes [72]. In that media, recorded information was engraved through changes in magnetization of the impregnated nanoparticles. Similarly, the biogenic magnetosomes can carry paleomagnetic signals, which can be detected, for instance, through the measurement of their magnetic properties in marine sediments [73]. The roles of bacterial magnetite as magnetofossils is only possible due to their stable single magnetic domain, caused by their controlled size range (20-100 nm) [73,74]. This magnetic property also permits the utilization of biogenic nanomagnets in research on anticancer and antimicrobial therapy-as drug carriers, contrast agents, and hyperthermal agents-, enzyme immobilization-as recyclable supports-, cell labeling and other applications [11].
Biological materials are precisely arranged at the nanoscale. Hence, biomimetics, which is the art of imitating biological process to architecture novel materials, is proving profitable for nanotechnology industries [75]. One of the foundations of biomimetics is the biodiscovery and bioengineering of surface-binding proteins and peptides [76]. The regular structures present in such biomolecules enables the recognition and the interaction with atomic patterns on the surface of synthetic polymers, semiconductors, and metal oxide crystals [76]. In the case of metal oxides, these interactions occur basically via non-covalent weak bindings like hydrogen bonds and electrostatic dipoles.
In chemical syntheses, the shape-and size-controlled nanoparticles generally are obtained with high temperatures and organic solvents [74]. These consumptions are related to high production costs and environmental impacts during the life cycle of the nanoparticles [74]. One of the simplest and widely utilized techniques for making iron oxide nanoparticles is coprecipitation [74]. In this technique, ferrous and ferric salts are dissolved, and the cations are precipitated in an alkaline aqueous medium. For the synthesis of magnetite, a fixed molar proportion of 2:1 (Fe 3+ /Fe 2+ ), is precipitated, following the stoichiometry: This molar proportion is mandatory because it is the same ferrous/ferric ratio within magnetite [77]. In MTB, iron is accumulated inside the magnetosome vesicle in it ferrous form before being oxidized to ferric ion by magnetochromes-oxidizing domains of MamP, MamX, MamT and MamE [77]. This is an example of naturally occurring partial oxidation of ferrous ion. Partial oxidation is also used to obtain artificial, biomimetic magnetite [78]. In this case, the ferrous cation is precipitated to form ferrous hydroxide (Fe(OH) 2 ). After that, a strong oxidizing agent, usually nitrate, partially transform Fe 2+ to Fe 3+ , leading to magnetite: While coprecipitation leads to nanoparticles of an irregular shape, partial oxidation magnetite has a well-defined faceted morphology and a larger size [78]. Due to its low solubility, Fe(OH) 2 tends to form larger precipitates. This is not the case for the coprecipitation of Fe 3+ and Fe 2+ , which tends to form multiple, smaller precipitates [78].
Complementary to oxidation control, the surface interaction of the forming magnetic crystal with biomolecules is the main strategy for synthesizing magnetosome-like nanoparticles. A summary of biomolecule-supplemented chemical syntheses of magnetic nanoparticles is in Table 2.
MamC protein from Magnetococcus marinus strain MC-1 has an effect of enlarging magnetite precipitates [79,84]. Due to its effect over synthesis, this protein has been expressed for use in different biomimetics studies (Figure 5). Different coprecipitation experiments have shown an increase from ~10-25 nm, in control synthesis, to ~30-40 nm, when recombinant MamC from strain MC-1 is added in concentrations over 10 μg/mL [79,84].
In another study, Ms. magneticum strain AMB-1-derived Mms6 displays a negative effect on average particle size -20 nm length down from 32 nm in the control experiment -in partial oxidation and coprecipitation-derived magnetite [80]. Instead, its addition to the reactional medium narrows size distribution regardless of the chemical route. The presence of recombinant Mms6 derived from strain AMB-1 imprints the cubo-octahedral morphology of the naturally occurring magnetosomes onto chemically precipitated crystals. From experiments using mutant clones of strain AMB-1, it has been demonstrated that the anionic residues Asp123, Glu124, and Glu125 effectively participate as key residues of Mms6 for defining crystal morphology are in the protein binding to magnetite [88]. The interactions between these C-terminal side-groups and the magnetite surface ultimately respond for the strong morphology and size controlling character of Mms6 either in biologic or biomimetic mineralization [89].
To modulate/improve magnetite chemical synthesis by the use MB proteins, magnetite-interacting components (MICs) of three magnetite-associated proteins (MamC, Mms6, and Mms7) have been subjected to NMR studies to investigate their affinity and binding to the ferrous ion during coprecipitation [81]. In all cases, it has been a clear role of aspartate and glutamate residues to the affinity to the cation [81]. The strong binding of ferrous cation to four anionic residues is related to confinement of iron by Mms6-and Mms7-MICs and, consequently, to the initiation of magnetite nucleation by these proteins. Besides ferrous ion, Mms6 glutamate residues positions 44, 50, and 55 at C-terminal region shows a strong binding affinity to ferric ion [90]. MamC-MIC, in turn, displays a weaker iron-binding but a stronger effect on magnetite size [81]. Thus, the ionotropic (i.e. iron-affinity) effect of MamC does not give sufficient ground for the role of this protein in biomineralization [84,91,92]. MamC must exert a template effect in magnetite formation [84]. In the MM, MamC is constituted by two transmembrane domains connected by alpha-helical looping, which contacts the forming magnetite within the magnetosome vesicle lumen [92]. The distance between iron-interacting residues Glu66 and Asp70 of the alpha-helical looping matches the iron interatomic distance within the magnetite surface plane. The alpha-helical conformation of the MamC-MIC ensures the proper positioning of the points of interaction with iron [91]. The complementary roles of MamC and Mms6 can be combined in a biomimetic synthesis, yielding large magnetosomes (30 ± 10 nm) with well-defined crystal faces [84].
Other MM proteins are also good candidates for use in biomimetics. MamF controls the size monodispersity of nanocrystals. In aqueous solution, this protein forms a self-aggregative proteinosome of approximately 36 nm [82]. When used as an additive in coprecipitation, homogeneously sized nanocrystals are obtained. As in MamC, Mms13 and MmsF have their active loops located between the two transmembrane domains [83]. These active loops were expressed in a chimeric coiled-coil scaffold protein, which was called Mms13cc and MmsFcc. The MmsFcc construct regulated the cuboidal morphology of the produced nanocrystals. Taking the inspiration of the interaction between anionic residues and nascent magnetite, the addition of acidic polypeptides is an alternative to recombinant proteins [78]. In the presence of poly-aspartate, partial oxidation synthesis resulted in narrower size distribution of nanocrystals [78]. Using a classical partial oxidation synthesis, 65% of magnetite nanoparticles assumed a facetted shape with a size distribution between 20 and 60 nm. When the synthesis was supplemented with poly-aspartate, a drastic change of the morphology occurred, with 85% of the nanoparticles showing a more rounded shape. However, the size distribution became significantly narrower, with most particles ranging 15-30 nm.
As discussed, biomimetic synthesis of magnetite with recombinant magnetosome proteins involves electrostatic interaction between anionic aminoacids with iron cations. Nevertheless, the use of cationic polymers and aminoacids also has been proven successful in imitating characteristics of magnetosomes into artificial magnetite. In those cases, the one accepted chemical mechanism is the dipole stabilization of the negatively charged surface of magnetite crystals by positive side groups, namely amino and guanidine, present in alkaline aminoacids [85,86]. This phenomenon is supported by the phosphatidylethanolamine composition of the magnetosome vesicle, which exposed positively charged amino groups to the nucleation sites [86,93].
In one experiment performed at the Max Planck Institute of Colloids and Interfaces, Germany, a wide array of randomly-generated peptides was expressed in phage display and had their binding capacity tested against a magnetite powder [86]. The primary structure of magnetite adhering peptides was then compared to the proteomes of several MTB species, but no significant similarity was spotted. However, of the five magnetite-interacting peptides identified in that study, three had arginine as half the residues in the sequence. The cationic poly-arginine was used as an additive to the iron precipitation. The resulting nanoparticles possessed a fine size distribution (30-40 nm), reproducible -despite irregular -morphologies and colloidal stability. These characteristics were not achieved in the control of conventional precipitation. Poly-arginine also improves the tuneability of the biomimetic synthesis. In the presence of the additive, the average diameters of the magnetite precipitates could be adjusted from 10 to 40 nm when the reaction occurred in pHs from 9 to 11, respectively [94].
As polyaminoacids, single aminoacids can promote control over magnetic nanoparticle syntheses [85]. When arginine and lysine were tested for that purpose, the latter was able to control the particle size according to its concentration ( Table 2) [85]. The side-chain amino group in lysine can perform a steadier stabilization of the anionic oxyhydroxide precursor of magnetite. Then, further growth of lysine-stabilized nuclei enables a larger crystal size with a better-defined hexahedral shape. The control over size and shape also reflects in the magnetic properties of the nanomaterial. The obtained nanoparticles displayed a superparamagnetic behavior, with a large magnetic moment and magnetization saturation (67 emu/g).
Not only is the size dispersity and morphology better controlled in biomimetic synthesis, but the colloidal stability of bioinspired nanomagnets is generally improved. The magnetic core of bare nanomagnets exerts an attractive force, possibly leading to instability to the colloidal suspension [78,85]. When peptides are added to the precipitation media, functional groups of the same charge become exposed on the nanoparticle surface and counterbalance the attractive force with electrostatic repulsion [78,85]. Due to the interaction of cationic amino groups with magnetite, carboxyl groups become exposed during coprecipitation with lysine [85]. Thus, the zeta-potential of those nanoparticles was -31 mV at physiological pH, while the control nanoparticles showed a 0 value. The synthesis of magnetite supplemented with poly-aspartate led to nanoparticles with surface-exposed carboxyl groups [78]. Therefore, the measured zeta potential was approximately -30 mV. Because suspension stability in aqueous media is crucial for biomedical applications, the colloidal stability obtained in biomimetic nanoparticles is a fundamental property.
The knowledge gained from biomimetic approaches was used to construct a double-stimuli-responsive nanoformulation consisting of a nanomagnet bound to the antiproliferative drug oxaliplatin [95]. The nanocrystal was synthesized by co-precipitation of iron ions in the presence of recombinant MamC. The magnetiteoxaliplatin bond was stable at pH 7.2. In acidic pH, the release of oxaliplatin was triggered. This release was further boosted by the application of an alternating magnetic field and the cytotoxicity against colorectal cancer cells was improved [95]. The responsive to alternating magnetic fields also enables MamC-derived magnetic nanoparticles to be used in hyperthermia treatments [96]. A 25 mg/mL suspension of the biomimetic nanoparticles exposed to an alternating field of 226 Oe at a 280 kHz frequency can cause a temperature increase of 16.7 °C (specific absorption rate = 47 W/g).
Another functional magnetic nanoparticle was coprecipitated in the presence of a bifunctional polypeptide and ginger extract [87]. The fourteen-residue-long polypeptide was designed from two heptapeptides: a magnetite binding domain and a cell-targeting domain with specificity to ovarian carcinoma cells. The metalreducing and chelating activity of the ginger extract leads to nanoparticles averaging 10 nm in length and 48.9 emu/g of magnetization saturation. When different cell lines -A2780 (ovarian carcinoma) and L929 (mouse fibroblast) -were treated with the functional nanoparticle, the first group exhibited a particle uptake almost 5 times more intense.
Conclusion
In this chapter, we have summarized how the basic-science knowledge gained through molecular biology, phylogenetics, and metagenomics of MTB can be translated into tools of technological interest. Although the authors had not the pretentiousness of gathering extensive information available on the topic, the chapter evidences how cross-disciplinary research is crucial for understanding and applying such a complex biological phenomenon. This is especially true in a field in which intriguing discoveries are made at a fast pace.\===
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SPATIAL DISTRIBUTION OF FIBROBLAST GROWTH FACTOR RECEPTOR 2 IN NORMAL AND LESIONED CENTRAL NERVOUS SYSTEM OF PLEURODELES WALTLII
Fibroblast Growth Factors (FGFs) have been implicat ed in numerous cellular processes including proliferation, migration, differentiation and neuro nal survival. One of these growth factors, Fibrobla st Growth Factor 2 (FGF2), is apparently implicated in the ability of the adult salamander ( Pleurodeles waltlii) to recover locomotion following complete transectio n of the spinal cord . In a previous study, we reported up regulation of FGF2 during regeneration of damaged a xons and recovery of hind limb locomotion. In this study reported here, we investigated the spatial distribu tion of FGFR2-one of the receptors that mediate the effects of FGF2-using a variety of techniques, namely, western blot, immunohistochemistry and in situ hybridization. We find that in intact animals FGFR2 is mainly expr essed in the most posterior part of body Spinal Cor d (SC3) specifically in neurons. However, lesioning t he spinal cord produces increased expression in Bra i stem (BS) neurons and decreased expression in posterior parts of the spinal cord not only in neurons but al so in the neuroglial ependymal cells lining the central canal . This suggests that FGF2 simultaneously activates FGFR1 and 2, perhaps at different points in the regenerat ion process and thus FGFR2 might play at least an i ndirect role in the spontaneous regeneration observed in th is species and might be relevant to the treatment o f spinal cord lesions in humans. Verification of this possib ility will require studies of additional time point s.
INTRODUCTION
Unlike mammals and most other vertebrates, the adult salamander (Pleurodeles waltlii) has the ability to spontaneously recover locomotion following complete transection of the spinal cord (Piatt, 1955;Davis et al., 1990;Chevallier et al., 2004). Recovery of function, which takes approximately 3 months, is accompanied by the functional regeneration of lesioned brainstem and spinal fibers crossing the transection (Davis et al., 1990;Chevallier et al., 2004). Although the underlying molecular mechanisms remain unclear, it is known that Fibroblast Growth Factor 2 (FGF2) and its receptors can contribute to the proliferation of neural progenitor cells . It seems likely therefore that FGF2 plays a role in the regeneration process. Studies have observed up regulation of FGF2 mRNA and protein of complete cord transection, not only in salamanders (Moftah et al., 2008) but also in young rat (Qi et al., 2003). In both cases, observations have been linked to the ability of descending axons to regenerate in the transected spinal cord, leading to at least partial recovery of locomotion (Inoue et al., 1998;Wakabayashi et al., 2001). This suggests a possible therapeutic role for FGF2 and FGF2 receptors in human cases of spinal lesion (McDonald and Sadowsky, 2002).
A number of studies have investigated regeneration of the salamander spinal cord following transection of Science Publications AJN the spine at the cervical, thoracic and lumbar levels (Piatt, 1955;Davis et al., 1990) for review). In each case, the regenerated tail contains a complete spinal cord with sensory ganglia, motor neurons and interneurons, spinal projection neurons and spinal tracts (Nordlander and Singer, 1978;Zhang et al., 2003). FGF signaling is mediated by four types of receptors (FGFR1,FGFR2,FGFR3 and FGFR4), which all belong to the high affinity tyrosine kinase receptor family (Dionne et al., 1990;Keegan et al., 1991;Trueb, 2011). They are known to trigger the phosphorylation of signaling cascades and regulate protein synthesis and neuronal excitability. Studies in mice showed that the proper formation of the medial prefrontal cortex depends on the function of FGFR2 genes since they are required for up regulating the self renewal of radial glial precursor cells (Stevens et al., 2010). The function of FGF signaling in maintaining the self renewal and undifferentiated state of cortical stem cells is reminiscent of its role in the spinal cord stem zone (Kang et al., 2009). In comparison, following peripheral nerve injury in rats, studies suggest that FGFR2 expression in the Dorsal Root Ganglion (DRG) is reduced (Yamanaka et al., 2007). However, relatively little is known about FGFR 2 distribution in the CNS. Previous studies provide conflicting evidence (Pettmann et al., 1986;Finklestein et al., 1988;Grothe et al., 1991;Yamamoto et al., 1991;Gomez-Pinilla et al., 1992;Matsuda et al., 1992;Matsuyama et al., 1992;Woodward et al., 1992;Eckenstein et al., 1994) or are limited to specific areas of the brain Frautschy et al., 1991;Matsuda et al., 1992;1993;Stock et al., 1992;Tooyama et al., 1992;Tooyama et al., 1993a;1993b;Gonzalez et al., 1994;Saarimaki-Vire et al., 2007).
In this study therefore, we used in situ hybridization to examine the distribution of mRNA for FGFR2 in the entire spinal cord and brainstem of healthy and transected Pleurodeles in order to get deeper insight as to the role of FGFR2 in spontaneous spinal cord regeneration in this species.
Animals
Experiments were carried out as previously described (Moftah et al., 2008). In brief, 25 Urodele amphibians (Pleurodeles waltlii) were obtained from Blades Biological ltd (Kent, UK) and kept in aquaria at 19°C. Surgical procedures, handling and housing of the animals were in accordance with protocols approved by the INSERM Ethics Committee and conformed to NIH guidelines.
Spinalization
Surgery was performed in aseptic conditions under general anesthesia as previously described (Moftah et al., 2008). In short, anesthetized animals were operated by completely cutting the spinal cord between segments 12 and 13. The wound was sutured and wound healing was complete 8 to 10 days post operative. Sham-operated animals were exposed to laminectomy but not spinal cord transection.
In Situ Hybridization
All animal groups were anaesthetized and treated for in situ hybridization as previously described (Moftah et al., 2008). In summary, brainstem and spinal cord were exposed by craniectomy and laminectomy, respectively spinal cord was then divided into three segments (SC1, SC2 and SC3) corresponding respectively to the anterior, middle and posterior regions of trunk spinal cord. SC1 and SC2 were pooled to represent the pre-lesional part of the cord (in spinalized animals). Brainstem (BS) and spinal cord were immediately but separately frozen by immersion in-50°C isopentane (Merck) without fixation. All samples were stored in embedding medium (Tissue Tek, Sakura) to be sectioned and processed later on.
Oligonucleotide Probes
For in situ detection of FGFR2 mRNA, we used the following two fifty-mer oligonucleotide probes (Eurogentech, Seraing, Belgium) based on previously published gene sequences (Moftah et al., 2002): The probes were chosen from regions presenting few homologies with related mRNA sequences and were checked against the GenBank database.
As previously described (Landry et al., 2000), oligonucleotides were labeled in cobalt containing buffer with 35 S-dATP (Amersham) to a specific activity of 1-4×10 9 cpm/µg and purified by ethanol precipitation.
In Situ Hybridization
Spinal cord and brainstem sections (265 section/region/animal/experiment) were 14µm thick and processed as described earlier (Landry et al., 2000). Briefly, sections were incubated at 42°C with 0.5 ng of each of the radioactively labeled probes. After hybridization, they were rinsed for four times at 55°C followed by 30 min at room temperature. Radioactivity
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was revealed by dipping sections into Ilford K5 nuclear emulsion (Ilford, Mobberly, Cheshire, UK), diluted 1:1 with distilled water, developing them in Kodak D19 and fixing them in Kodak 3000. Sections were then counterstained with 0.25% cresyl violet acetate (pH 4) (Sigma) and mounted in glycerol.
Immunohistochemistry
Anaesthetized animals (n = 5/experiment) were perfusion-fixed via the ascending aorta. Brainstem and spinal cord were dissected out, post-fixed in the same fixative and rinsed for at least 24 h in 0.1 M PB (pH 7.4) containing 15% sucrose and 0.02% sodium azide (Sigma) for cryoprotection. Sections were triple labeled with mouse anti-FGFR2 IgM, mouse anti-NeuN (Abcam, Paris, France) and rabbit anti-GFAP (Dako SA, Trappes, France). This procedure made it possible to avoid cross reactivity between same species-raised antibodies (as previously described in Moftah et al. (2008).
Imaging
Slides were examined; bright field light microscopy micrographs and triple immunostainings were taken using a Zeiss Axiophot 2 microscope (Zeiss, Jena, Germany). Digital images were optimized for image resolution (300 dpi final resolution), brightness and contrast using Adobe Photoshop CS3 (Adobe System, San Jose, CA, USA).
Data Analyses
The number of labeled cells and the labeling intensity were quantified as previously described (Landry et al., 2000). Cellular profiles containing three times more grains than mean background grain densities were considered labeled. Cell profiles were manually outlined as previously described (Moftah et al., 2008). Delineation was based exclusively on staining and not on shape, size or other measurable quantities. The number of silver grains per cresyl violet counterstained cell was counted semi automatically using MetaMorph Offline 6.1 software (Universal Imaging Corporation). Data were expressed as grain density per µm 2 ± SEM in calibrated photomicrographs.
Transmitted light photomicrographs were taken for at least 500 fields (700 µ m² each), in each experiment.
Data were imported into a spreadsheet program (Sigma Plot software, Jandel Scientific) that calculated and graphed the density of FGFR2 mRNA expression and the number of labeled cells. Data were compared using one way ANOVA tests and processed using standard statistical analysis techniques (Sigma Stat software). Differences were considered to be significant when p≤0.05.
FGFR2 in Brainstem and Body Spinal Cord in Intact Animals
In intact animals, western blot assays showed significantly less FGFR2 protein in brainstem than in the Science Publications AJN posterior portion of trunk spinal cord (SC3) (Fig. 1 A and B) (p = 0.01). This was even more evident in the case of the pooled anterior spinal cord regions (SC1, 2) where FGFR2 protein decreased (p = 0.014). To verify if these differences relate to changes in transcriptional activity, we studied the distribution of FGFR2 mRNA by using in situ hybridization ( Fig. 2 and 3 A, B and E, F). Analysis of grain density confirmed the western blot data. More specifically, the highest levels of FGFR2 mRNA were found in SC3 as seen in Fig. 2A. Grain density had, however, a complex rostrocaudal profile since it first decreased passing from BS to SC1, 2 (p = 0.005) then increased reaching around 50% more in SC3 (p = 0.001). Meanwhile, the number of labeled cells shown in Fig. 2B indicates that around 20% of the total cell number was labeled in the entire neural axis. These findings suggest the presence of a rostrocaudal gradient in the number of cells expressing the receptor in intact animals.
Histologically, FGFR2 mRNA was mainly found along the midline (Fig. 3, A and B) in the intact brainstem. However, in spinal cord sections, receptors were obvious in the ventrolateral region as shown in Fig. 3E and F.
FGFR2 in Brainstem in Spinal-Transected Animals
Based on results from previous studies (Chevallier et al., 2004;Moftah et al., 2008) we measured FGFR2 mRNA density at 1-2 weeks after transection, when the animals were unable to display hind limb locomotor movements. A second set of measurements was taken 15 weeks post op., when the animals had fully recovered locomotion and reinnervation of the spinal cord below the lesion was complete (Piatt, 1955;Davis et al., 1990;Chevallier et al., 2004). As controls, we examined the distribution of FGFR2 mRNA in sham-operated animals. Tissue sections of BS ( Fig. 3C and D) and SC3 ( Fig. 3G and H) were examined 15 weeks post operatively to assess the distribution of FGFR2 mRNA. Positive and negative cell profiles were found, the former being more heavily labeled than in intact animals (Fig. 3B), mainly in brain stem (Fig. 3D). Spinal cord, however, shows no clear increase in the amount of FGFR2 mRNA grains, which are prominent in a ventrolateral position (Fig. 3H).
Analysis of BS from sham-operated animals 15 days post op. showed no significant difference in grain density with respect to intact animals (p = 0.3) (Fig. 4A, white bars), though the number of labeled cells was slightly higher (Fig. 4B, white bars). There were no significant differences in grain density between spinalized, shamoperated and intact animals. This suggests that the slight increase in the number of FGFR2 positive (FGFR2 + ) cells was due to the surgical operation per se rather than to spinal cord lesion.
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15 weeks after surgery, sham-operated animals still had higher grain densities for FGFR2 than intact animals (Fig. 4A, left grey bar). Spinalized animals also had higher densities; though lower than those in sham operated animals (p<0.001) (Fig. 4A, right grey bar). There was a slim increase in labeled cells numbers in sham-operated and intact animals (p = 0.9). However spinalized animals (Fig. 4B, right grey bar) had slightly lower numbers of labeled cells than intact (P = 0.01) or sham operated animals (p = 0.008). Together these results suggest that observed increases in FGFR2 expression were at least partially an effect of laminectomy. Vice versa, the decrease in the number of FGFR2 + cells in spinalized animals was probably a result of cord injury, perhaps reflecting the death of descending brainstem neurons projecting below the transection.
FGFR2 in Spinal Cord in Spinal-Transected Animals
Pooling SC1 and SC2, we investigated only one prelesional portion of spinal cord. We found that in shamoperated animals FGFR2 grain density and numbers of labeled cells dramatically increased 1-2 weeks after surgery (Fig. 5, sham white bars). In spinalized animals grain density did not differ significantly from the levels observed in intact animals while the number of cells slightly fell (Fig. 5, spinal white bars). 15 weeks after surgery grain densities in sham-operated and spinalized animals (Fig. 5A, grey bars) were almost similar (0.0625±0.0034 grains/µm 2 and 0.0533±0.0051 grains/µm 2 respectively). In both sets of animals, densities were significantly higher than in intact animals (p<0.006) but much lower than those recorded in shamoperated animals, 1-2 weeks after surgery. Moreover, cell numbers were not altered in both sham-operated and lesioned animals (p = 0.084 and 0.07 respectively) after 15 weeks of the operation (Fig. 5B, grey bars). This suggests that the observed effects were due to the surgical operation and not to spinal cord lesion. In SC3, sham-operated and spinalized animals both showed a large decrease in FGFR2 grain density, 1-2 weeks after surgery (Fig. 6A, white bars). In shamoperated animals grain density increased slightly 15 weeks after surgery (Fig. 6A, grey bars) while in spinalized animals, it continued to decrease (p<0.001). Numbers of labeled cells were similar to those observed in intact animals in both sets of animals at all time points (p = 0.257 in shams and 0.28 in spinals) (Fig. 6). Altogether, these findings suggest that at least part of the observed drop in grain densities was due to the spinal lesion and that FGFR2 + expression in spinalized animals is down regulated throughout the recovery process. Taken together, these findings suggest that the spinal lesion caused a decrease in FGFR gene activation.
To examine whether the observed reduction in the expression of mRNA from FGFR2 after spinal cord injury led to reduced protein synthesis, we used immunohistochemistry to detect FGFR2 protein in neuronal and glial cells in BS (Fig. 7 A-D) and in spinal cord (Fig. 7I-L). The assay showed that, in intact animals, FGFR2 protein was present in both types of cell in BS ( Fig. 7D), but that in spinal cord it was essentially limited to neurons (Fig. 7L). The positioning of FGFR2 protein was similar to that of the FGFR2 mRNA observed in the in situ hybridization study. 15 weeks after spinal lesion, the main FGFR2 + cells in BS were neurons ( Fig. 7E-H).
In the spinal cord, however, neuroglial ependymal cells lining the central canal were the main containers of FGFR2 protein ( Fig. 7M-P).
DISCUSSION
Compared to our previous study (Moftah et al., 2008), we show here that, in intact Pleurodeles, the pattern of distribution of FGFR2 mRNA is opposite to that of its ligand (FGF2). Increased expression of FGFR2 in the posterior part of the spinal cord may be correlated with decreased levels of FGF2. However, histologically, they are seen in the same ventrolateral position in spinal cord cross sections. Judged by their morphology, size and position, the main cells expressing FGFR2 appear to be motor neurons.
In the present study, we analyzed the spatial distribution of FGFR2 mRNA in brainstem and spinal Science Publications AJN cord from adult Pleurodeles. Our findings show that in normal animals, expression of FGFR2 mRNA is significantly higher in the most posterior part of the spinal cord than in the BS. The rostrocaudal gradient in the number of cells expressing the receptor suggests the existence of an increasing gradient of grain density in individual cells.
We go on to show that spinal injury at the mid trunk level modifies this distribution. In lesioned animals, the highest levels of expression are observed in BS with no significant drop in SC1 and 2. By contrast, expression of FGFR2 in posterior segments of the cord is reduced. This finding is evidence in favor of the suggestion by Chevallier et al. (2004) that the recovery of locomotor activity is underlined by descending BS axons regeneration. It is probable that the observed post lesion increase in rostral FGFR2 facilitates the high regenerative capacity of reticulospinal neurons already seen in previous studies (Davis et al., 1990;Chevallier et al., 2004). Increased rostral expression of FGFR2 matched the increased expression of FGF2 reported in our previous work (Moftah et al., 2008). This is evidence that FGF2 contributes to spinal cord regeneration, in Pleurodeles and that this effect is mediated by FGFR2.
If we consider spinal cord results together with findings from BS we observe an overall decrease in FGFR2 expression in spinalized as opposed to shamoperated animals. This appears to be matched by a fall in the overall number of FGFR2 expressing cells. The observed drop in the number of labeled cells in BS and SC1 & 2 suggests that in some individual cells FGFR2 expression may in fact be up regulated.
Given that lesioned animals show a decrease in FGFR2 expression in SC3, it is probable that the direct onsite regenerative effect of FGF2 depends on other receptors. It is nonetheless possible that FGFR2 contributes to the proliferation of the surrounding tissue, including neuroglial cells in the ependymal tube. This possibility is supported by the observed increase in the receptor's mRNA in sham-operated animals, where no spinal cord lesion was performed and only the surrounding tissue healed and is consistent with the findings of our previous FGF2 study in sham-operated salamanders (Moftah et al., 2008).
Our immunohistochemical data shows that in spinalized animals, FGFR2 protein is mainly located in neuroglial cells lining the ependymal canal. This is a major finding, since (i) the ependymal canal epithelium has been demonstrated to harbor neural stem cells (Johansson et al., 1999) and (ii) FGF2 is one of the two necessary mitogens for adult neural stem cells, especially in spinal cord (Weiss et al., 1996). The role of the FGFRs in the proliferation of neural progenitors in BS and spinal cord is currently unknown. Our findings of high levels of FGFR2 expression in BS and lower levels in the posterior segment of spinal cord support previous suggestions stating that FGF2-induced proliferation of neural progenitor cells in BS is mediated primarily by FGFR2 while proliferation in spinal cord mainly depends on FGFR1 (Zhang et al., 2003). An alternative is that FGF2 simultaneously activates FGFR1 and 2, perhaps at different points in the regeneration process. Verification of this possibility will require studies of additional time points and might be relevant to the treatment of spinal cord lesions in humans.
CONCLUSION
The present study shows that, in intact animals, FGFR2 is mainly expressed in the most posterior part of body spinal cord specifically in neurons. However, lesioning the spinal cord at the mid-trunk region produces increased expression of FGFR2 in brainstem neurons and decreased expression in posterior parts of the spinal cord, not only in neurons but also in the neuroglial ependymal cells lining the central canal. This suggests that FGF2 simultaneously activates FGFR1 and 2, perhaps at different points in the regeneration process and thus FGFR2 might play at least an indirect role in the spontaneous regeneration observed in this species and might be relevant to the treatment of spinal cord lesions in humans.
ACKNOWLEDGMENT
This study was supported by «Région Aquitaine» contract no. 20040301208N and N€uromed FP7 project no. 245807. Postdoctoral grants were offered to MM by the AUF (Agence Universitaire de la Francophonie) and the Fondation Singer Polignac.
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Domain: Biology
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Virion structure and mechanism of propagation of coronaviruses including SARS-CoV 2 (COVID -19 ) and some meaningful points for drug or vaccine development
SARS-CoV-2 or COVID-19, a new seventh human corona virus, has out-broken in Wuhan, China since 31st December 2019, and quickly escalated to take the form of pandemic which killed many human beings throughout almost all countries across continents. The rapidity of its transmission from human to human is far greater than all previous human corona viruses which came into existence like SARS-CoV, MERS-CoV, etc. The nucleotide sequence of SARS-CoV-2 (isolates Wuhan-Hu-1) is 29,875 bp in ss-RNA. Symptoms of SARS-CoV-2 infected pneumonia include from asymptomatic to high fever and/or respiratory illnesses. Coronavirus virion (spherical/round /elliptical in shape) consists of three partsouter membrane or envelope, nucleocapsid and genome (RNA). SARS-CoV-2 was shown to use receptor, angiotensin converting enzyme 2 (ACE2) for attachment to the cells through its surface spike (S) protein (S1), and the virion enters into the host cell through two routesdirect membrane fusion and endocytotic pathway. The RNA of SARS-CoV acts directly as mRNA and here minus(-) 1 programmed ribosomal frameshift (-1PRF) is being operated by slippery sequence and pseudoknot, so it translates 16 nonstructural proteins including RNA dependent RNA replicase. Then genomic RNA replicated continuously on – strand RNA template and subgenomic RNA transcribed discontinuously on –RNA template to sgmRNA. Subgenomic RNAs/sgmRNAs synthesize all structural proteins. This article takes into consideration the details of established theories of viral structure, viral attachment, mode of entry into human cells, different models of replication and transcription of virus genome proposed by eminent scientists over the years, and makes an in depth examination highlighting meaningful points or important target cites of viral propagation or synthesis, which are conserved, for prompt development of potent drugs or vaccine to counter COVID-19 for which human race is anxiously and eagerly waiting.
INTRODUCTION
also noted in some patients in Germany (19). Although, SARS-CoV-2 has highest rapidity of transmission from human to human but mortility rate of it is below 5 % (20, 21), and it may cause asymptomatic infections. The innate immune responses in human lung tissues due to SARS-CoV-2 was not significantly triggered the expression of any types of IFN at all. In addition, SARS-CoV-2 infection upregulated only 63.16% of inflammatory mediators (pro-inflammatory cytokines and chemokines) while much expression of all types of IFN and 84.62% upregulation of inflammatory mediators were noted in SARS-CoV infection (22). Probably due to this suboptimally induced innate immune response of patient, SARS-CoV-2 replicates quickly and accumulates in large quantity in the respiratory tract early and that is why this virus transmits efficiently from person-to-person via droplets or contact with secretions of patients containing high viral loads (23,24). It was noted that within the 48-hour interval, SARS-CoV-2, produced 3.20 times greater virion than that of SARS-CoV from the infected lung tissues (22). COVID-19 patients, in some cases, showed very mild or no symptoms. This may be the result of having poor level of innate immune response in the system of concerned patients. Infections in such patients, actually, makes it difficult to break the chain of viral transmission and keep the situation under control (22). This virus takes entry into us through the mucosa of the respiratory tract, mouth and the eyes. It is important to note that person-to-person transmission of SARS-CoV2 is happening and this is the reason why it took the form of pandemic quickly in almost all countries through rapid spreading (11,(25)(26)(27)(28). However, the actual mechanism which confers this viral characteristics of high transmissibility in all geographically different countries and asymptomatic infection is now under intense research. As of 10 June, 2020, a total of 7145539 cases have been reported to be infected and 408025 patients died among them globally(29). The estimated incubation period of SARs-Cov2 in human is claimed to have been recorded from the studies of some scientists (30)(31)(32). From their works. it was realized that maximum incubation period is 14 days (30) and therefore, Leung(30) suggested that quarantine period should be of three weeks (21days) instead of 14 days. Now, its highly contagious nature and mortality rate have brought the entire world to its knees and challenged the very existence of human beings. The concerned governments of all countries have deployed war-like emergency strategies, and the leaders in charge are leaving no stones unturned to reduce the speed of transmission of the disease, so that the infectivity curve of COVID-19 could be brought under control. WHO is monitoring the epidemiological, symptomological characteristic data from time to time and circulating these data through its website under situation reports. The situation compelled us to increase our safety measures to the highest level and maintain necessary personal and collective hygine procedures properly in order to keep this deadly virus away. Moreover, in a bid to curb rapid transmission governments are taking extreme precautionary measures, and some of them are being imposition of lock down for minimum 21 days, social distancing or "physical distancing", which is to keep safe distance of at least two meters between one person and the other while in public place frequent hand sanitization by alcohol based sanitizer or washing hands by soap or water, and covering mouth and nose by using a face mask while venturing outside of their home (33). Under such grim situation, potential drug and vaccine discovery to combat COVID-19 have become extremely urgent if human beings want to leave this depressing and disturbing period behind and move forward to see the light of a better future. Therefore, the objectives of this article are to have in depth discussion of virion structure, mode of attachment to human cell, entry into the cell, replication and transcription of RNA of corona virus including SARS-CoV-2 (COVID- 19), and simultaneously this article wants to highlight some significantly meaningful points which will further help in the gradual development and final discovery of appropriate drugs and vaccines as soon as possible.
VIRION STRUCTURE
The virions of coronaviruses are round /elliptical/spherical having diameters of 60-140 nm (34,35). Like other corona viruses, each virion of SARS-CoV-2 consists of three parts -a) outer membrane or envelope, b) nucleocapsid and c) genome or nucleic acid. The very diagnostic character of envelope is the club-shaped spike (S) projecting from outer side of virion. Nucleocapsid, having helically symmetrical, resides within the envelope of the virion.,and it is is not common among +sense RNA viruses (Fig 1B)., The carboxy-terminal region of the M protein developes an extra internal layer, so, the viral membrane is unusually thick (34). Each virion of SARS-CoV2 contains four structural proteins -i) the spike (S), ii) membrane (M), iii) envelope (E) and iv) nucleocapsid (N) proteins. These proteins are encoded within the 3′ end of the viral genomic RNA. i)The S protein -It is made up of 1,273 amino acids that construct spikes on the surface of SARS-CoV2(14) (NC_045512.2) and S of SARS-CoV has 23 N-linked glycosylation sites (36). Its molecular weight (mwt) is about 150 kDa, and it is made up of trimeric glycoprotein. It has two parts-S1 and S2. Former makes the receptor protein domain and later forms the stalk of spike. The trimeric S glycoprotein behaves as a class I fusion protein (37) and attaches to the host receptor (38,39). In SARS-CoV-2, the S2 subunit is highly conserved. ii) The M protein-the topological model of M protein was presented byArmstrong et al (40). It is a smaller protein having three transmembrane domains and mwt rangs between 25-30 kDa (35,40,41). The differential maturation and subcellular localization of SAR-CoV surface proteins S, M and E were reported by Nal et al, (41). and they found that it (M) is divided into a small ectodomain(N-terminal) and a larger endodomain(C terminal) which expands 6-8 nm into the virion. The work of Neuman et al. (35) gave a clear structure and function of the M protein which has two different conformations, as it promotes membrane curvature and on the other hand it binds to the nucleocapsid. The M proteins of SARS-CoV2 contain 222 amino acids14(NC_045512.2 ), iii) The E protein-SARS-CoV2 has 75 amino acids in N-proteins( NC_045512.2). Its mwt ranges from 8-12 kDa having in small quantities and it is a transmembrane protein. The E protein of SARS-CoV consists of a C-terminal endodomain (CTEND) and an N-terminal ectodomain(NTED) and shows ion channel activity (42) and iv) The N protein -The highly phosphorylated N proein binds with g RNA maks a flexible, probably helical, nucleocapsid. The SARS-CoV2 shows that it contains 419 aminoacids (NC_045512.2 ). Chang et al. (43) exhibited a modular organization of SARS-CoVs nucleocapsid protein and consists of two separate domains, an N-terminal domain (NTD) and a C-terminal domain (CTD), both bind RNA, but mechanism of binding are different, and it was reported that N protein also binds with M proteins (44). Another very important role of N protein is to encounter the interferon (IFN) and viral encoded repressor of RNA interference, as a result a microenvironment is created for smooth synthesis of virion (45.46). SARS-CoV-2 bears structural characteristics like other coronaviruses as described above.
The whole genomic analysis of SARS-COV2 (isolate Wuhan-Hu-1) shows that it includes 29903 nucleotide(nt) bp (accession No NC_045512.2 ) in non-segmented ss-RNA (14), while other few isolates of this virus have been also submitted and published in genbank and they are 29881 bp (isolate 2019-nCoV WHU01, GenBank: the accession no MN988668), 29838 bp (isolate 2019-nCoV_HKU-SZ-002a_2020. the accession no MN938384), etc. The isolate Wuhan-Hu-1 of SARS-CoV2 exhibits that it contains many genes and they are ORF1ab (21,290nt), ORF1a (13,218nt), S gene (3822nt), ORF3a (828nt), E gene (228nt), M gene (669nt), ORF6 (186nt), ORF7a (366nt), ORF7b (132nt), ORF8 (366nt), N gene/ORF9 (1260nt) and ORF10 gene (117nt). At 5′ end UTR (untranscribed region) contains 265nt while at 3′ end UTR has 229nt (GenBank: accession No NC_045512.2). This polycistronic genome of SARs-CoV2 can be categorized into two parts: the first 2/3 rd of the genome (ORF1ab & 1a genes) can translate to 16 non-structural proteins and while the remaining part of the genome(3-terminal) encodes the structural proteins (S, M, E and N) and some accessory proteins that are translated from a nested set of subgenomic mRNAs (sgmRNAs)47(GenBank: accession No NC_045512.2)( Fig 1C). Previously, same thing is also recorded in other corona viruses (8,48). Notable thing is that SARS-CoV-2 bears 79% identity with SARS-CoV and 50% identity with MERS-CoV while 88% identity to two bat-derived SARS-like bat coronaviruses [bat-SL-CoVZC (45) and bat-SL-CoVZXC (21)] collected and identified in 2018 in Zhoushan, eastern China. SARS-CoV-2 is much divergent from SARS-CoV and it has been recognized as a new human-infecting betacoronavirus (15), so it is known as SAR-CoV-2. In general, a coronavirus genome (CoVs) contains a non-segmented, +ssRNA genome of 30 kb having a 5′ 7mG( methylated guanine) cap structure along with a 3′ polyadenylated (A) tail, permiting its function as an mRNA for synthesis of the replicase polyproteins polyprotein 1a/1ab (pp1a/pp1ab) in the infected cells. The genome encodes five open reading frameS(ORFs) that are replicated to genomic RNA and transcribed to six + subgenomic mRNA (+sgmRNA). The 5' end of the genomic RNA has the untranslated leader (UTL) sequence with the TRS-L( Transcription Regulation Sequence) in the downstream part. The TRS-L is very similar to sequences (TRS-B) that can be found in front of each open reading frame. It is well known that almost all nsps (nsp1 -16) have their specific function in the replication of Coronairuses but the role of few nsps are unclear.
Viral binding to host cell
It is the first and foremost important step for infection or entry of virus into host cell. Here ligand and receptor interaction happens. The specific receptor present on the cell surface is to interact with specific virus or viral part. The presence of receptors are also tissue or organ specific. The angiotensin-converting enzyme 2 (ACE2) of human acticing as receptor binds with spike of SARS-CoV and SARS-CoV-2(18) A list of corona viruses along with receptor is given in table 1. The RBD(receptor binding domain) of the S1 protein i.e ligand interact with ACE2 for binding. Both SARS-CoV and MERS-CoV deploy the S1 CTD as RBD to recognize the receptor ACE (55). The region in SARS-CoV-2 S protein that is responsible for ACE2 interaction remains under research but recent work of Wang et al. (56) that utilized flow cytometry and immunostaining assays, first revealed that the S1 CTD serves as the main location in SARS-CoV-2 that binds to the ACE2 receptor. The S protein of SARS-CoV-2 exists in the form of a trimer, each monomer contains approximately 1,300 amino acids (57,58). Wrapp et al. (57) and Walls et al (58) have just reported the cryo-Electron Microscopic structure of spike trimer of this virus in two independent studies. The spike proteins of SARS-CoV-2 (SARS2-S; 1,273 residues, strain Wuhan-Hu-1) and SARS-CoV (SARS-S, 1,255 residues, strain Urbani) are 77.5% similar bysequences of amino acid, are very identical in structure (57,58) and easily bind the human ACE2 protein as a host receptor (59,60) through their S1 B domain. Modern research also stresses on the key function of ACE2 in facilating the entry of SARS-CoV-2 (58,61,62). The S protein is next to become broken by host proteases into a S1 subunit(N-terminal) and a S2 subunit (membrane-bound C-terminal) (63). The research on crystal structures of SARS-CoV RBD complex with ACE2 exhibited that SARS-CoV RBD has a core structure and a receptorbinding motif (RBM) and that the RBM interacts with the outer surface of the claw-like structure of ACE2 (64,65). Importantly, scientists noted two virus-binding hot spots on human ACE2 (64,65) and also recorded mutations on these two hot spots, and these regions select the host range of SARS-CoV. Moreover, Wan et al (60) (60). The gross similarities between SARS-CoV-2 spike and SARS-CoV spike (isolated from human, civet, or bat) are found from 76 to 78% for the whole protein,while it was near 73% to 76% for the RBD, and 50% to 53% for the RBM(60). Furthermore, they reported that RBD has 14 ACE2-contacting residues, but 4 are partially conserved and 9 are fully conserved among SARS-CoV2 and SARS-CoV from human. Similarly, Lan et al. (66) presented gross topology of SARS-CoV-2 spike monomer citing Nterminal domain (NTD), receptor-binding domain (RBD, 333-527 aa), receptor-binding motif (RBM, 438-506 aa), fusion peptide (FP), subdomain 1 (SD1), subdomain 2(SD2), heptad repeat 1 (HR1), heptad repeat2 (HR2), intracellular domain(IC) and transmembrane region (TM), (Fig 2A). Moreover, we came to know from their important work on the SARS-CoV-2 RBD that it has a twisted five-stranded anti-parallel β sheet (β1-β4 and β7) with loops and short connecting helices. The extended insertion ( β5 and β6 strands, α4 and α5 helices and loops) is the receptorbinding motif (RBM) containing most of the contacting residues of SARS-CoV-2 for ACE2 binding. The RBD contains nine cysteine aminoacids. The N-terminal peptidase domain of ACE2 consits of two lobes which form the site which binds with substrate between them ( Fig 2B). Lan et al. (66) reported that the ACE2-interacting aminoacids on the SARS-CoV-2 was 17 amino acid positions and their positions are K418, G447, Y450, Y454, L456, F457, A476, F486, N487, Y 489, Q 493, G496. Q498, T500, N501, G502 and Y505( Fig 2C ). Wang et al. (56) have done an atomic comparison of the two viral ligands (SARS-CoV-2-CTD and SARS-RBD) binding the receptor ACE2. It revealed more interactions in SARS-CoV-2-CTD/ACE2 than in SARS-RBD/ACE2. It was also validated by other workers (57). Walls et al.(58) also reported that the SARS CoV 2-S1B receptor binding domain (residues 338-506) has a core domain and a subdomain (residues 438-498) for receptor binding and looping out from the antiparallel beta sheet core domain structure that directly engages the receptor. An irreversible conformational change of spike proteins is noted to be triggered due to receptor interaction which results membrane fusion.
The ACE2, was recorded as ACE homologue. The protein encoded by the gene is grouped to the angiotensin-converting enzyme family of dipeptidylcarboxydipeptidases. Genomic structure comparison suggests that ACE2 and ACE genes have been created by duplication of a common ancestor (67). ACE2 has 805 amino acids and is a type I transmembrane glycoprotein (metalloproteinase). The ACE2 gene is located on the X chromosome. ACE2 consists of two domains: an amino-terminal catalytic domain and a carboxy-terminal domain. The catalytic domain has an functional zone known as the zinc metallopeptidase domain (HEXH motif) and exhibits 41.8% sequence similarity with the amino domain of ACE(49) (Fig. 2B). The affinity of binding of ACE2 with the RBD (receptor binding domain) of SARS-CoV-2 is many times higher than its affinity with the RBD of the SARS-CoV (49). In addition, some workers (68) reported that the M protein also play a vital role during early stages of HCoV-NL63 infection, and that the concerted action of the two proteins (M and S) is a prerequisite for effective infection. The variation of binding capacity at different temperatures demonstrated that at both 4 and 37 0 C, virions can bind to the surface of host cells as noted for the LPV(B-lymphotropic papovavirus)(69) while in case of HIV-170, the affinity of virion-binding at 37 0 C is higher than that at 4 0 C but internalization only occurs at 37 0 C. Kuba et al. (71) using a flow cytometry assay exhibited that SARS-CoV S protein is internalized by VeroE6 cells together with ACE2 at 37 0 C.
The correlation between receptor expression in human and infectivity of coronavirus was searched by some scientists. ACE2 expression in human tissues has a positive correlation with possibility of SARS-CoV infection, including lung and intestine (72,73). SARS-CoV has much tendency to infect ciliated epithelial cells expressing ACE2(74). Wang and Cheng (75) noted that SARS-CoV-2 upregulated the expression of ACE2 in lung tissue, a s a results viral replication and transmission becomes increase and but a negative correlation between ACE2 expression and SARS-CoV-2 (COVID- 19) severity and fatality at a population level was reported by Chen et al. (47). More research is required to determine the actual receptor-ligand interaction. More than one receptor may play here. The proper understanding of this process can quicken the development of effective vaccines and antiviral drug development.
Entry into host cell
After attachment on host receptor, next important step in viral disease is the invasion of the viral RNA into the host cells. As we know that ACE2 is common receptor for both SARS-CoV and SARS-CoV2, so here pathway of entry of SARS -CoV2 will be discussed along with other corona viruses. Now, it is confirmed that genomic RNA of CoVs invades the host cells via two paths: (i) the direct membrane fusion or non-endocytic pathway-here viruses put their genomes into the cytoplasm of host cell after their envelopes join or fuse with the plasma membrane at the cell surface and it is pH independent (ii) endocytotic pathway (76). It occurs through endocytic machinery. It is influenced by the acidic endosomal pH, which triggers the fusion of viral and endosomal membranes and ultimately of the viral gRNA enters into the cytoplasm.. SARS-CoV has adapted to enter into cells by direct fusion at the plasma membrane(77) (Fig 3A). In this case, virus fusogenic mechanism was described by Liu et al. (78). According to them, as soon as the S1 subunit of SARS-CoVS protein attaches to ACE2, the S2 subunit rearranges conformation by invading the fusion peptide(FP) into the plasma membrane. The HR2 domain reacts with the HR1 trimer to make 6-HB core, directing the fusion between the cell membrane and envelope of CoV (78). Further work has confirmed that entry of SARS-CoV is pH-dependent (79), and that the endosomal protease cathepsin L(80) is engaged here, advocating that this virus follows endocytosis. So, this pathway has been vigorously worked out.
Some scientists worked out by flow cytometry internalization assay to acertain that the RBD spike protein binding directs the endocytosis of SARSCoV by inected cells. So they confired that cellular endocytosis is a mechanism for SARS-CoV entry (81,82). Wang et al. (82) propose that the RBD S-protein induces the ACE2 directed cellular endocytosis signal pathway, as a result, gRNA of SARS-CoV invades the host cells. As the binding of SARS-CoV RBD spike protein triggered ACE2 internalization, now question may arise whether N-linked glycosylation on the RBD could affect ACE2 internalization or not. It was noticed that deletion of N-glycans from RBD-Fc can still induce ACE2 internalization (83). It is very interesting to note that after internalization of spike S or virus, down regulation of receptor occurred. For instance, the downregulation of the receptor ( CD46) occurred due to attachment of measles hemagglutinin, to imbalance the complement pathways and immune systems (84). So, after utilizing the receptors for cell entry, some viruses influence down-regulation of the receptor to disturb its normal function, causing severe disease.
The entry of SARS-CoV has been also recorded through Clathrin-mediated as well as clathrinand caveolae-non-mediated pathway (81,82). The study of Inoue et al (81) indicated that the pathway of SARS-CoV entry into host cells is medicated by clathrin. Initially viral S1 binds to receptor ACE2 on plasma membrane, next a pit is formed by invagination of plasma membrane along with S1-receptor complex and its membrane is mounded by clathrin triskelions. These pits form early endosomes which are acidic (pH 6.5 to 6.0) and early endosomes becomes late endosomes and more acidic pH 5.5)( Fig 3A). The acidic condition of endosomes facilitates viral infection (85,86). The endosomal acid proteases (cathepsin L) mediates the cleavage of S into S1 and S2 to enhense its fusion activity. After the fusion peptide (FP) inserts into the endosomal membrane, a six-helix bundle (6-HB) core is build by interacting among the heptad repeat 1 and 2 (HR1 and HR2) domains and the S2 protein. This core structure facilitates the the cellular plasma membrane and viral envelope tocome close to each other for fusion ( Fig 3B). As a result the ACE2/ virus complex becomes translocated to endosomes, and the virus is uncoated by the help of endosomal acid protease, cathepsin L (80,77). Finally, Wang et al. (82) suggest that SARS-CoV penetrate cells through receptor-mediated, clathrin-independent, caveolae-independent endocytosis, likely through a process which is associated with lipid rafts. In different cell types, same virus invates through different endocytotic pathways. For example, the entry of SARS-CoV is mediated by the clathrin-dependent endocytosis pathway into HepG2 cells (81) while it relies on a novel clathrin-and caveolae-independent endocytotic pathway for entry into VeroE6 cells (82). According to Burkard et al, (87) coronaviruses take enty into host cell through Endo/Lysosomal pathway in a proteolytic processing of fusion proteins by lysosomal protease, They proposed two models like early (e.g. HCoV NL 63 ) and late coronavirus fusion(e.g. HMV). At present, questions may arise whether CoV induces autophagy, and whether the autophagy machinery or ATG proteins are engaged for the infection and replication of CoVs. Viral attack was not arrested by the knockdown of ATG5 gene (88), so it confirmed us that the autophagy was not involved in the viral infection or replication.
Replication of genome, subgenomic RNA or m RNA formation,nsps and structural protein and RTC formation
The whole genome sequences, replication, transcription and translation to proteins or enzymes of SAR -CoV2 have been recorded since December 2019 (accession No NC_045512.2) (22,24,65,56,47) but actual mechanism of these processesof this virus have not been reported.
As it belongs to same group of SARS-CoV, we assume that this virus follows same mechanism of replication, transcription and translation of SARS-CoV or other corona viruses, so, mechanism of synthesis of coronaviruses including SARs-CoV has been reviewed here. The replication or synthesis entirely happens in the cytosol of host cell. In the first step of viral synthesis, translation of pp1a and pp1ab for production 16 non-structural proteins or enzymes that are neccessary for viral further biosynthesis. As corona viruses including SARS-CoV 2 belongs to + SS RNA, their genome can be directly utilized as mRNA and they use RNA directed RNA transcription in their replication.
Translation of pp1a and pp1ab and nsps formation
After entry and uncoating, RNA genome attaches with host ribosome and exploits host RNA, amino acid pool and other factors, and initiates translation of the corona virus genome at the replicase-(RdRp) ORF1a start codon and translation of ORF1a gives polyprotein 1a (pp1a), and -1RFS(1 ribosomal frameshifting) directs the translation of ORF1b to produce pp1ab, ultimately through co-and posttranslational processing or cleaving by virus-encoded proteinases like papain-like (PLpro) that reside in nsp3 and chymotrypsin-like main proteinase (Mpro) in nsp5 of pp1a, into 16 nonstructural proteins(nsps); which are utilized later for replication of genomic RNA and translation of six structural proteins. ORF1a and 1b are linked by a -1ribosomal frameshift site (89). During synthesis, nsp1 to nsp11 are encoded in ORF1a, and nsp12 to nsp16 are encoded in ORF1b(90) ( Fig 4A). Nsps 4-16 are conserved in CoVs (91). The functions of 16 nsps are listed in table 2. (108) compared the SAR-CoV-2 gene sequence with that of SARS-CoV. An genetic analysis of SAR-CoV-2 and SAR-CoV, revealed that the transmembrane helical segments in the ORF1ab encoded 2 (nsp2) and nsp3 and exibited that at the position 723 a serine replaced a glycine residue, while in the position 1010 proline replaced isoleucine (108). The viral mutations is responsible for potential the relapse of this viral disease.
Programmed ribosomal framshifting
An alternative way of translation of merged proteins encoded by two overlapping ORFs is Programmed Ribosomal frameshifting (PRFS). In this case, the frameshift appears at low frequency and ribosome's slipping by one base in either the 3'(+1) or 5'(-1) directions during translation is key matter. Specifically, −1 ribosomal frameshifting appears more frequently in RNA viruses (Fig 4B, C). Eukaryotic ribosomal frameshift signals usually composed of two elements: a heptanucleotide (a 7-bp) slippery sequence (XX XY YYN) where X indicates any nucleotide, Y indicates A or U, and Z is A, U, or C (the frame of the initiator AUG is denoted by the spacing), and an H-type RNA pseudoknot (PK) located downstream (109,110). Each Pseudoknot of SARV-CoV contains 79 nucleotides of a secondary RNA substructure that contains two stacked stems 1(S1, 18nt) and stem2 ( S2, 30 nt) connected by a ssloop(single stranded) (L1, 3nt ) and a big stem-loop (SL1, 28 nt) (111) (Fig 4D). Later Plant et al. (112) also mentioned the third stem (S3) in SARS-CoV and MHV, and third pseudonot structures were analysis by mutation. A stem -loop SL1 with a proximal ds-segment (double -stranded ) segment which exists in SARS-CoV, is absent in other characterized coronavirus 1a/1b pseudoknots (11,113). The -/+ ribosomal frameshifting is generally occurred due to presence of slippery sequence and pseudoknot but, there are alternative ways for formation of " out-offrame proteins". Alternative splicing can cause frameshifting (113).
However, in programmed -1 ribosomal frame shifting, the ribosome is bound to move one nucleotide backwards into an overlapping reading frame and to translate an completely new amino acids sequence. It is very common SARS-CoVs (114). In SARS-CoVs, the stimulatory structure is to be an mRNA pseudoknot as described in the infectious bronchitis virus(IBV) where a 'slippery' sequence of the type UUUAAAC and a H-type pseudoknot are able to induce translation of the zone 1b of the polyprotein 1a/1b (115). The SARS-CoV genome has been found to be responsible for encoding an mRNA segment that directs −1 ribosomal frameshifting (36). Smith et al. (116) and Tu et al. (117) recorded that ribosomal movement was stalled at a pseudoknot neccessary for frameshifting (Fig 4B,C). Many models are present to analyze −1 ribosomal frameshifting operation by pseudoknots. It is found that RNA pseudoknot structures force to stall ribosomes over a slippery sequence, at which the ribosome-bound tRNAs realign in the -1 frame, but that pausing alone is not sufficient to operates frameshifting (111,118). To discuss the in-depth mechanism of it at the gene level, Namy et al (89) introduced a method of purifying "rabbit reticulocyte lysate (RRL) ribosomes". Cryo-EM (Cryo-electron microscopy) study of purified mammalian 80S ribosomes from rabbit reticulocytes halted at a coronavirus pseudoknot revealed an "intermediate of the frameshifting process". The translating 80S ribosome, halted at the IBV pseudoknot (Fig 4C), contains a P-site tRNA and elongation factor 2 (eEF2). Frank, and Agrawal (119) noted that the 70S complex exhibited "a ratchet-like rearrangement of the ribosome" which is connected to the trapping of eEF2 in E. coli, and in 80 S complex, "a ratchet like rearrangement of the ribosome" connected to the trapped eEF2 in yeast by sordain antibiotic treatment (120). So, they (119,120) proposed a model where "the ratchet-like rearrangement of the ribosome" is a part of a mechanism for moving the tRNAs during the translocation. Similarly, experimental results observed by Namy et al, (89) where no antibiotic was applied, also showed a ratchet like rearrangement of the ribosome in eukaryote. Plant et al.(121) have proposed a "golden mean" model in which viruses utilize both "programmed ribosomal frameshifting" and "translational attenuation" to maintain the relative ratios of their encoded proteins.
RTC formation
The SARS-CoV RTC (Replication -transcriptional complex) is the assemblage of many of the nsps including RNA-dependent RNA polymerase (RdRp) and unidentified host factor. RTCs are attached in double-membrane vesicles (DMVs) suitable for RNA synthesis to create an environment, and ultimately replication of RNA and transcription of the sub-genomic RNAs (sgRNAs) sequences are held. These DMVs are virus induced structure from intracellular membranes. The viral "RNA-dependent RNA polymerase (RdRp)" is the key enzyme of this 'replication/transcription complex' (RTC). RNA replication is seemed to happen on DMVs (122). One common feature among plus-strand RNA viruses is that RTCs is linked with cell membrane (123). The mechanism by which the DMVs are developed are yet to known. In the work of Bechill et al, (124) it was found that uPR(unfolded protein response)) may mediate DMV formation as it is promoted during coronavirus infections. Snijder et al. (122) and Snijder et al. (125) recorded that the DMVs of SARS-CoV are most likely derived from the endoplasmic reticulum (ER). The results of Knoops et al. (126) showed that DMVs are likely to originate from the part of a reticulovesicular network of modified ER membranes. Later on, these networks become a large single-membrane vesicles. gRNA ( genomic RNA) which are synthesisezed in infected cell, are packed into virions on membranes which are situated between the endoplasmic reticulum (ER) and the Golgi apparatus ( "ER-Golgi intermediate compartment (ERGIC)" (127). Proteins ( nsp3, nsp5 and nsp8) engaged in the replication of virus are situated in adjacent reticular structures but not inside of DMVs. RNA, which is either "replicative intermediates" or "dead end" double-stranded RNA, was found in DMVs. The non-ionic detergents disrupt all RTC and make SARS-CoV RNA to susceptible for breaking by nuclease, and this reminds us the intact membrane structure of RTC for RNA synthesis (128). It is found that pool of replicated genomic RNAs of SARS-CoV are connected with the RTC, while sg RNAs after synthesis come out from the RTC structures. Moreover, it has been recorded that RTC activity relies on a host factor which is present in cytosol, that indicates that crosstalk between RTC and cytosol, takes place via channels that help transport through membranes (128). The single DMV can move freely but when many are "captured" by the DMV/CM(convoluted membrane) assemblies, they are unable to move. It was also reported that disruption of microtubule-dependent transport of DMVs, did not interfered on RNA replication (129). In coronaviruses the RTC formation might be one of strategies for involving of cellular and viral proteins or enzymes for successful replication of g RNA and other parts of virus and also for creating a safe place from the attrack of host defense (129).
RNA replication and transcription of the sub-genomic RNAs (sgRNAs) sequences.
During genome replication and sgRNA synthesis, two replicative intermediate (RIs) such as RI -1 and RI-2 are operated. From RI-1, -sense full genome strand was formed on the template on + sense ss RNA by viral encoded the RNA dependent RNA polymerase (RdRp). In RI-2,sense RNA genome strand is used as template to generate + sense genome of virus by viral encoded RdRp ( Fig 5A) . The TIs (Transcription intermediates) and TFs(transcription forms) are main transcription structures that are responsible for synthesis of subgenomic mRNA (130). Here the exiting recent model of coronavirus RNA synthesis reveals that minus strand RNAs come from copying the + RNA continuously to form genomic templates and discontinuously to create subgenomic templates (131).
Formation of subgenomic RNA template(-) and subgenomic +stand mRNA
There are many models for formation of subgenomic RNA template(-) and subgenomic +stand mRNA. Taking consideration of transcription of MHV, three models were proposed over the years. They are as follows-1. Loof out model: It was the first model in which the RNA polymerase starts the replication of the leader RNA from the 3' end of the minus (-)-stranded RNA template. As soon as replication of leader RNA completed, the polymerase enzyme "jumps" over to the different initiation sites for several mRNA species, so, it is probable to create "loop out" of the RNA template. This model is not supported by maximum workers, 2.
Post-transcriptional processing model (Fig 5B). This model indictes that synthesis of the leader RNA and mRNAs are independant; after the finishing of synthesis, the leader RNA is then bound to the body sequences of the mRNAs by a mechanismwhich is yet to know. UV transcription inactivation studies were performed and the results indicated that the formation of mass of subgenomic RNAs were not possible post-transcriptionally by cis-splicing of a genome-length precursor molecule (132) and 3."leader primed transcription model" (Fig 5C): The this model exhibits that the leader RNA is replicated and "falls off" from the template. Then a viral RNA dependent RNA polymerase (RdRp) binds with this free leader RNA and initiates mRNA synthesis at several initiation points. This model indicates that the leader RNA serves as a primer for RNA synthesis. Lai et al. (133) gave this model and it is known as "leader primed transcription model" (134). Out of these three models, this one was accepted by many authors by few modification.
At the 5' end of the genome and all subgenomic mRNAs, there is an identical "leader" that has sequence which is 65 to 98 nt in many CoVs (135). As this leader sequence is located to the 5' end of the genomic RNA, viral RNA synthesis has a mechanism which directs the leader RNA to join the "body leader"of each sg mRNAs that are situated at the 3' end of the genomic RNA. Moreover, at the end of the leader and before the body of each ORF of the genomic or subgenomic mRNAs there is one TRS ("transcription regulating sequence") or "intergenic sequence," ("IGS" or IS) (135)(136)(137). TRS which is situated near the leader sequence at the 5′ end, then it known asTRS-L and TRS which is preceding each viral gene or ORF is called TRS-B. If we go in details regarding TRS , we found that each TRS has a conserved core sequence (CS) bearing 6-7 nt and variable 5′ and 3′ flanking sequences (the 5′ TRS and 3′ TRS, respectively) (138). The CS is similar for the genome leader (CS-L) and all mRNA coding sequences (CS-B). The study of secondary structure of the TRS-L region from TGEV(transmissible gastroenteritis virus) and BCoV( bovine coronavirus) exhibited that the CS-L is exted like a hairpin-like structure which is suitable for replication and transcription (139). The transcription mechanism is monitored by TRSs (140). The viral RdRp complex recognizes TRSs. At these locations, the polymerase cmplex either reads through to the next TRS or detaches from the template strand, then binds with the leader TRS, situated in the 5' UTR, and as a result, the synthesis of a set of sg-strand RNA bearing an antileader RNA sequence, become completed. The TRS of nidovirus is an AU-rich element ( eg 5' UCUAAAC3′ in MHV) that is situated at (i) the 3′end of the common leader, (ii) the 5' end of each mRNA 'body' segment, and (iii) fusion between leader and body in the sg mRNA, so here like MHV, it assumed that nucleotide base pairing between minus-and plus sense copies of this regulatory sequence might guide the co-transcriptional fusion of sg mRNA leader and body by discontinuous transcription manner (133,134). Although different models are present to analyze the fusion of the common 5' leader sequence to the different 3′ body segments in arterivirus and coronavirus sg mRNAs, but almost co-transcriptional fusion of leader and body is being reported in all models (135,141,142). The RNA-dependent RNA-polymerase has been seemed to halt after a body TRS(TRS-B) of a particular gene is replicated during (-) strand synthesis, therefore, switching to the TRS-L occurres and thus a common L sequence to each sg mRNA is added.
The gRNA and 6 sgmRNAs are formed during replication of MHV-A59. It was observed that 1stsgmRNA-6th sgmRNA are 9.6, 7.4, 3.4, 3.0, 2.4 and 1.7 kb respectively. All together form a 3′ co-terminal nested set. The sgmRNAs, in size, ranges from 1/3rd (1stsgmRNA) to about 1/20th (6 th sgmRNA) of the genomic RNA. It was also recorded that + strands (genomes and subgenomic mRNA) are formed in huge amounts but out of them about 1% minus strands of both genome-and subgenomic length act as the templates for genome and subgenomic mRNA synthesis (143.144). It was also reported that the newly synthesized viral genomic RNAs may act as template for the formation of sg-length minus strands. Experimental studies conducted by Stanley and Dorothea Sawicki and coworkers (130), where they used MHV as a model, revealed that both -strand gRNA and sg-length minus strands are formed during very early stage of infection. Each sg mRNA is formed from a corresponding transcription intermediate (TI) which bears the sg-length minus-strand template. Many sg mRNAs are synthesized from these complexes in constant amounts but in non-equimolar. It appears that the ratio of the synthesis of genomic RNA to sg mRNAs is constant in the whole replication stage (130,131).
Next question has been raised whether the discontinuous process of RNA replication occurres during minus-or plus strand synthesis. To solve the matter or question two prominent, models -i) 'leader-primed transcription' with discontinuous plus strand RNA synthesis ( Fig 5D) and ii)'discontinuous extension of minus-strand RNA synthesis' (Fig 6) and they are opposite to each other. In both cases the TRS elements acts as major role. A base-pairing interaction between the sense copy of the TRS in the genomic leader (leader TRS) and the antisense copy of the TRSs at the 5' end of each of the sg mRNA body segments (anti-body TRS) is a common phenomenonin these two models. The 'leader-primed transcription' model with discontinuous + strand RNA synthesis (133,134) exhibited that transcription is started from the 3′ end of the anti-genome(strand RNA) to create a leader primer of which the 3′-terminal leader TRS would base pair to the various anti-body TRSs in the anti-genome. Later on, the leader transcript would be elongated to develop the sg mRNA. Thus, this model gives us that the discontinuous proccess procceds during plus-strand synthesis and that the body TRS complements in the -strand g RNA or antigenome invariabily play as promoters for transcription. There was no detection of sg-lengthstrands in coronavirus-infected cells validated the leader-primed transcription model with discontinuous + strand RNA synthesis (133). Moreover, the subsequent discovery of such molecules, for both coronaviruses and arteriviruses (145,146), generated the idea of alternative models. Brian et al. first time first reported a nested set of sg -strand RNAs in TGEV infected cells, (145) and they gave us the idea that the sg-length -strands were intermediates in the production of sg mRNAs. Subsequently, Stanley and Sawicki (147) gave us discontinuous extension of -strand RNA synthesis as an alternative way (Fig. 6). They confirmed that minus-strand sg RNA synthesis was operated discontinuously, with attenuation of nascent strand RNA synthesis occurring in the different body TRS regions of the genomic template, and while plus strands were synthesized continuously. A small portion of the polymerase complex detaches from the +strand template when it comes to TRS to develop an -sg RNA. So, the synthesis of a longer -sg RNA, i.e., the product of the upstream TRS, is attenuated by the presence of a downstream TRS because only a little percentage of the polymerase complexes reach the upstream TRS. The nascent sg-length minus strand, bearing an anti-body TRS at its 3′ end, become moved to the leader TRS(TRS-L) in the genomic template and the CS-L could basepairing process procceds between CS-L and CS-B (cCS-B) as the CS is similar to the genome leader (CS-L) and all mRNA coding sequences (CS-B), and it allows for leader-body joining (Fig 6 ) and the polymerase(RdRp) would continue and dulicate the leader sequence to put an antileader (complementary to the 5′ plus-strand leader sequence) on the 3′ end of the nascent, subgenomic minus strand (Fig 6). Here the anti-TRS plays as a primer to finish transcription and duplicates the leader RNA (141). Ultimately the subgenomic minus strands are produced and they perform as templates for the transcription of subgenomic mRNAs (Fig 6). The genomic and subgenomic RNA synthesis take place in TRC/DVC. Later the RTCs employing in plus-strand synthesis became aged and released their minus-strand templates, which are then decayed, and fresh RTCs are produced in infection (131). Both genetical and biochemical researchs validated this model (130,131,148).
Accoding to some workers, the RNA synthesis of cononaviruses must have to involve by cisacting RNA elements, that are situated in structured 5′ and 3′ untranslated Regions (UTRs), and into the associated coding sequences (149)(150)(151). These RNA elements have stem-loops (SL1, SL2, SL3, SL4 and SL5) structures which are conserved but degree of conservation among different coronaviruses are variable, and all have some role in replication of RNA, discontinuous synthesis of sgRNA and sgmRNA (149,151,152). Very primary research work using defective interfering RNAs from alpha-, beta-, and gamma-coronaviruses limited the 3′ cis-acting RNA elements needed for synthesis of RNA of CoVs coronavirus to the 3′ UTR plus the poly(A) tail (150) but later it was found in other gene like at down stream N gene stop codon where BSL((Bulged Stem Loop) and H-type PK(pseudoknot) are involved. So, a model for the initiation of replication of negative-strand RNA of CoVs was postulated (140,153) (Fig 7).
It may be assumed that the production of subgenome length minus-strand (sgl-s) templates with the same 5' and 3′ ends takes place from the genome-length minus strands by splicing but this possibility was not supported by experimental results (131). Finally, many workers have supported discontinuous extension of minus-strand RNA synthesis' model and analyzed in detail in their reviews (141,148). But there are some limitations of this model to explain some facts or questions like what does constitute the signal that attenuates or arrest minus-strand synthesis at each of the body TRS motifs? How does "the relocation of the 3' end of the nascent minus strand to the 5' end of the genomic template" happen (131)? Sola et al. (140) proposed a three step model of transcription of corona virus on the basis of experimental data on corona virus transcription as shown by some workers (154)(155)(156). The steps are: (i) Complex formation (ii) Base pairing scanning and (iii) Template switch (for details ref 140).
The regulation of transcription process have suggested that it is regulated by many factors through monitoring the template switch frequency during discontinuous transcription (131,149). These factors are like complementarily between the leader TRS and the body TRS-B(154), TRS secondary structure to the 3′ end, RNA-RNA or protein-RNA interactions eg N-protien RNA chaperone (149,155), cell factor RNA helicase DDX1. (158), the two polymerases nsp8 and nsp12 (159), etc. It is very interesting to note that Nucleocapsid phosphorylation and RNA helicase DDX1 recruitment enable coronavirus transition from discontinuous to continuous transcription (158).
After synthesis of nested sgmRNAs (10 sgmRNSs in SARS-CoV-2), each of them undergoes translation by host ribosome, amino acids and other factors and express its gene as protein. As a results, four known structural proteins(sps) like S, E, M and N and other proteins in coronaviruses but in case of SARS-CoV2, in addition to sps, others are ORFs proteins (3a, 6, 7a,7b, 8 and 10) in SARS-CoV2 (NC_045512.2, NCBI, Baltimore) (Fig 6). Througout the discussion, we found that all RNAs and protein synthesis take place in solely cytoplasm, there is no involment of host nucleus. But researches of some workers reported that some viral proteins (e.g N, nsp 3, 6, 9b etc) have been located in nucleus. N protein enters into nucleus through nuclear pore complex and it later induces the cell cycle blockage and impediment of cytokinesis. (160,161). Protein 6 inhibits nuclear import of factors like STAT1(162) and nullifies IFN signaling pathways (163). Although viral proteins are observed in nucleus of deseased cells, its actual mechanism of entry into nucleus is unknown.
Assembly and release of virion
SARS-CoV structural proteins then moved to inside of the ERGolgi intermediate compartment (ERGIC). The envelope glycoproteins (E)after synthesis are attached on the membrane of the ER(endoplasmic reticulum) or Golgi, and the nucleocapsid is crated by N-proteins. Newly synthesized genomic RNA is then packed in nucleocapsid. The phosphorylation of N protein helps to induce structural change enhancing the affinity for viral versus nonviral RNA. N protein attaches the viral genome in a "beads-on-a-string type conformation". Specific interaction between leader RNA and nucleocapsid protein of CoVs has been reported (164) and the positive relationship between the nucleocapsid protein and the genomic packaging signal in MHV was also recorded (165). The C-terminal RNA binding domain is involved in this packaging signal (166). Then N-proteins are incorporated during budding to develop complete virion (127). Hurst et al (167) observed an interaction between the N protein and nsp3 which is associated with replicase-transcriptase complex (RTC) and showed that N protein also binds with M proteins (8). M proteins are found to play an important function during viral assembly by interacting with other structural proteins of SARS-COV. At last, the vesicles containing the virions join with the cell membrane and spread the virus through exocytosis from the diseased cells. E-proteins of SARS-CoV also serves a vital function on assembly and release of the virus and pathogenesis (42). Finally after assembly of viral parts and maturation of virion in ERGIC(Endoplasmic reticulum Galgi body intermediate compartment or Golgibody, buds with virions come out from Golgi body and form vesicles. These vesicles with virions approach to plasma membrane and by exocytosis virions are released from infected host cell (Fig 8).
Meaningful points to note for development of antiviral drugs and vaccines against SARS-CoV-2
Till now, no approved effective antiviral drug or vaccine has been developed against SARs-CoV2 (COVID-19). Scientists of many private companies and Government institutional laboratories of some countries putting their hard labour around the clock to develop effective remedies. After thorough search and rigorous reading of numerous research papers related to genomic structures, ligand receptor interaction, mode of entry in human cell, replication and transcription, RTC formation, assembly of viral parts, maturation and release of coronona virus, particularly human coronona viruses, such as SARS -CoV, MERS -CoV, SARs -CoV-2, we have attempted to highlight some meaningful points that may be utilized for the production of drugs or vaccines and developed drugs must target the conserved zone of virusi) A lot of compounds like polypeptides, peptides, antibiotics etc are able to interact with receptor ACE2 and inactivate the latter (168,169). We may use some of them against SARS-CoV2 because they block the S-protein-binding site, or change the conformation of ACE2 which is not suitable for binding or fusion. So, now we can remember the words of Li et al. (49)-" if SARS returns as a threat to human health, these studies may contribute to its control" (49). The chloroquine or hydroxychloroquine have been considered by in vitro trial in Vero E6 cell line as effective drug against Covid-19 as it disturbs the glycosylation of a virus cell surface receptor, ACE2 on Vero E6 cell line (170). So, hydroxy chloroquine sulfate (HCQS) has been approved as one of the important drugs for the treatment of severe SARSCoV-2 infections (171).
ii)The virus utilizes TMPRSS2, host serine protease to prime S protein as a result it facilitates the fusion of viral and cellular membranes and leads to the entry of virus into the cell. Here, scientists may develop or search already existing any serine protease inhibitor for inhibiting the viral entry (172,173). We may mention here serine protease inhibitors like "camostat mesylate" and K11777 , that inhibit TMPRSS2 and partially arrest SARS-CoV infection of lung epithelial cells. (174,175). Hoffmann et al. (62) recently reported that TMPRSS2 is hindered by a protease inhibitor which has been clinically tested. Some peptide inhibitors have been formulated which are active to destabilize HR regions of S2 and they have exhibited their effectiveness in both in vitro and in vivo trial (176,177).
iii) The increase of pH of endosome may be one target for drug development. Chloroquine when applied against Chikungunya virus in vitro assay, was succesfull to check the viral infection as this drug makes an alkaline environment in the cell (178). A recent work revealed that both chloroquine and the antiviral drug remdesivir are effective against SARS-CoV-2 in vitro and advocated that these drugs might be applied in human patients aganst this virus (171). Adedeji et al.(179) screened some of 14,000 compounds of the Maybridge Hit Finder small-molecule library. These compounds are at per with Lipinski's rule of five (180). Out of these compounds, they found three compounds-SSAA09E2, SSAA09E1 and SSAA09E30 arrest SARS-CoV entry by the following principles: (i) checking of early SARS-S-ACE2 interactions (ii) inactivation of cathepsin-L, and (iii) hindering fusion respectively. iv) Studies have shown that Mpro of different coronaviruses are highly conserved in terms of both sequences and 3D structures. The main protease of SARS-CoV-2 and SARS-CoV are almost similar ( 96.1%) (59). These features, together with its functional importance, have considered Mpro an important object for the design of anti-coronaviral drugs (172). Very recently, Li et al.(59) selected the structure of SARS-CoV-2 main protease as a homologous target for drug molecule screening on basis of bioinformatics analysis, and suggested that out of 8,000, four drugs such as Prulifloxacin (fluoroquinolone antibiotic), Tegobuvir, Bictegravir and Nelfinavir (anti-HIV drugs) exhibited maximum binding conformations with the main protease of virus (181). Similarly, Li et al. (182) shorted out available drugs which may be potential inactivator for SARS-CoV-2 M protease.and these drugs have higher mutation tolerance than widely used drugs lopinavir. In this respect, we may mention the review work of Ghosh et al (183) which highlighted the repurposing of drugs against SARS-CoV2,, SARS-CoV and MERS-CoV.
v) The functional domains exist in the replicase polyproteins are conserved in all CoVs, so, they must be good targets for anti viral drugs or vaccine (90). According to Amici et al.(184). Indothethacin showed antiviral activity against SAR-CoV blocking viral synthesis in early stage in Vero 6E cell line.
vi) Coronavirus RNA is synthesized in a RTC or DVC. Drugs may be developed for changing its micro-environment or degradation of this DMV (185) .
vii) The −1 ribosomal frameshifting is very essential for SARS-CoV to synthesize the replication-transcription complex and the 1a/1b ribosomal frameshift signal is conserved (111). So it can be attractive approach that this mRNA structure is an important target for drug development. ANXA2 has been suggested as an antiviral regulator which specifically binds to the frameshift signal as frameshift signal binding compounds186 and other compounds could be applied targeting this conserved framshift signal. viii) During RNA replication of SARS-CoV, nucleic acid unwinding by the viral helicase is a critical point , so this point may be targeted for inhibition of RNA replication. Adedeji et al. (187) applied some inhibitors to this point and become successful to block RNA replication .
ix) Here discontinuous RNA transcription may be a good target for drug design in such a way that a compound may be applied to inhibit this RNA transcription. Similarly, nucleoside inhibitors may be applied to arrest SARS-CoV-2 replication specifically without damaging the cell. Moreover, TRSs have immense role in regulating RNA synthesis and they have conseved sequences (CS), which may be targeted for anti-SARS-CoV2 drug development.
x) Chloroquine drug has a power for inhibiting proteolytic processing of the M protein and affects construction of virus and budding. Besides, this drug changing pH of cell can damage the viral protein (188) and interferes the recognition of viral antigen by dendritic cells, which operates by a Toll-like receptor-dependent pathway that needs changes of pH of endosomes to low acidic(189 ) Chloquine may be good drug against COVID-19 (171) .
xi) Viral proiens such as nsp12(RdRp), N protein, nsp14 etc have conserved zones, as these three proteins have vital role in RNA synthesis, drug designer may target their conserved sequences. As for example nsp14 of corona viruses is highly succeptible to ribavirin and 5fluorouracil agents (116).
xii) Bloking of entry of viral proteins (N, nsp3, nsp 6, etc ) into nucleus may be a good strategy to arrest.the viral synthesis and to restore our immune system which became antagonized by viral proteins. Ivermectin, an FDA-approved anti-parasitic and broad spectrum anti-viral drug , has been found as an inhibitor of SARS-CoV-2 isolate Australia/VIC01/2020) in cell line( Vero/hSLAM). It reduced nearly 5000-fold viral RNA synthesis at 48 h. It binds with the carrier( IMPα/β1) of viral protein which carries the protein inside the nucleus. So, binding to the viral protein becomes inhibited and preventing it from entering the nucleus. As a result our antiviral responses becomes normal or more efficient antiviral response. If patients are treated by this drug early in infection, it reduces the viral load, checks severity of COVID-19 and arrests "person-person transmission" (190).
xiii) The newly replicated genomic RNA and structural proteins of SARS-CoV are assembled into virions in ERGICor endoplasmic reticular or Golgicomplex membrane. Virions are shred off from infected cells by exocytosis, so these two works ( virion assembly and shredding ) may be arrested by any inhibitor. xiv) Proper and faithful vaccine development is most likely effective to control of SARS-CoV-2, learning from previous antiviral passive immunization ( like RBD based vaccine) and active immunization ( live attenuated vaccine). The convalescent sera after collecting from patients may be a target for passive immunization for treatment of SARS-CoV2. The antibodies against S1 from convalescent sera generated in SARS patients might have a chance to block SARS-CoV2 entry (62). Some research works exhibited that SARS-CoV S induced polyclonal antibodies in animal model like mice and patients effectively zeopartized SARS-CoV-2 Sdependent entry into cells (58,62) . Here Wang et al. (191) reported a human monoclonal antibody (47D11) that neutralized SARS-CoV-2. It firmly attaches a conserved epitope on the spike receptor binding domain. The RBD in the S1 subunit of the SARS-CoV spike (S) protein has CND(critical neutralizing domain) that is capable of triggering effective neutralizing antibody response and cross-protection against different isolates of SARS-CoV2, so scientists target it for vaccine development. Zhu etal (192), have compiled in their review paper all RBD based SARS vaccines that were the most effective and safest. Recently Fast and Chen (193) have applied bio-informatics tools to detect B-cell and T-cell epitopes in SARS-CoV-2 based on viral protein antigen presentation and antibody binding properties and reported that the spike protein of SARS-CoV2 bears a lot of both T-cell and B-cell epitopes. Neutralizing antibodies can clear virus or protect an uninfected host that is exposed to the virus. We may recall here the words of Li et al (49) " if SARS returns as a threat to human health, these studies may contribute to its control". Hence, this antibody application may prevent and/or treat COVID-19 (191). A recombinant SARS-CoV-2 spike protein vaccine combined with other top epitopes could be a meaningful step for development of vaccine against SARS-CoV-2. The ray of hope is coming from some laboratories for drug and vaccine production, examples may be cited here as the mRNA base vaccine (mRNA -1273) under a phase 1 clinical trial against SARS-Co-2 since 25th February, 2020 with 1st dose already applied on human on 16th March (195), etc. Previous antiviral vaccine like live attenuated vaccines was applied to arrest dangerous diseases caused by avian and porcine CoVs. By gaining experiences of previous vaccines, a live attenuated vaccine could be invented against COVID-19 or SARS-CoV-2, because we can multiply this virus in high titers in Vero 6E or other cell lines.
CONCLUSION
In conclusion, now the SARS-CoV-2 or COVID-19 pandemic appears to be out of control in some countries. Although the development of drugs and vaccine are very urgent, we have to remember it needs time and patience to discover the appropriate drug. In such a moment new quick tests to identify SARS-CoV-2 patients at the earliest stages of disease are also necessary as these tests will lead quarantine and isolation procedures to arrest the transmission of this disese. We are hopeful that the elaborate discussion of virion structure , molecular mechanism of propagation and clues for drug or vaccine development embedded in this article will help us for quick proper and effective drug or vaccine discovery. The whole world is anxiously waiting to defeat the COVID-19 pandemic and win the game of survival by discovering drugs and vaccines against SARS-CoV-2.
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Domain: Biology
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Adaptive evolution of DNA methylation reshaped gene regulation in maize
1Department of Agriculture and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583, USA. 2Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, NE 68583, USA. 3Department of Plant Biology, Microbial and Plant Genomics Institute, University of Minnesota, Saint Paul, MN 55108, USA. 4National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China. 5Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Nanxincun 20, Fragrant Hill, Beijing 100093, China. 6Division of Math and Sciences, Delta State University, Cleveland, MS 38733-0001, USA. 7Department of Evolution and Ecology, Center for Population Biology and Genome Center, University of California, Davis, CA 95616, USA. *Corresponding author
INTRODUCTION
Genomic DNA is tightly packed in the nucleus and is functionally modified by various chromatin marks such as DNA methylation on cytosine. DNA methylation is a heritable covalent modification prevalent in most species, from bacteria to humans [1,2]. In mammals, DNA methylation commonly occurs in the symmetric CG context with exceptions of non-CG methylation in specific cell types, such as embryonic stem cells [3], but in plants it occurs in all contexts including CG, CHG and CHH (H stands for A, T, or C). Genome-wide levels of cytosine methylation exhibits substantial variations across angiosperms, largely due to differences in the genomic composition of transposable elements [4,5], but broad patterns of methylation are often conserved within species [6,7]. Across plant genomes, levels of DNA methylation vary widely from euchromatin to heterochromatin, driven by the different molecular mechanisms for the establishment and maintenance of DNA methylation in CG, CHG, and CHH contexts [8,9].
DNA methylation is considered essential to suppress the activity of transposons [10], to regulate gene expression [11], and to maintain genome stability [8]. Failure to maintain patterns of DNA methylation in many cases can lead to developmental abnormalities and even lethality [12,13]. Nonetheless, variation of the DNA methylation has been detected both in natural plant [14] and human populations [15]. Levels of DNA methylation can be affected by genetic variation and environmental cues [16]. Additionally, heritable de novo epimutation -the stochastic loss or gain of DNA methylation -can occur spontaneously and has functional consequences [17,18]. Population methylome studies suggest that the spread of DNA methylation from transposons into flanking regions is one of the major sources of epimutation, such that 20% and 50% of the cis-meQTL (methylation Quantitative Trait Loci) are attributable to flanking structural variants in Arabidopsis [7] and maize [19].
In Arabidopsis, a multi-generational epimutation accumulation experiment [20] estimated forward (gain of DNA methylation) and backward (loss of methylation) epimutation rates per CG site at about 2.56 × 10 −4 and 6.30 × 10 −4 , respectively. Other than this Arabidopsis experiment, there are no systematic estimates of the epimutation rates in higher plants, making it difficult to understand the extent to which spontaneous epimutations contribute to methylome diversity in a natural population. Because the per base rates of DNA methylation variation are several orders of magnitude larger than DNA point mutation, conventional population genetic models which assume infinite sites models seemed inappropriate for epimutation modeling. As an attempt to overcome the obstacle, Charlesworth and Jain [21] developed an analytical framework to address evolution questions for a high order of mutations. Leveraging this theoretical framework, Vidalis et al. [22] constructed the methylome site frequency spectrum (mSFS) using worldwide Arabidopsis samples, but they failed to find evidences for selections on genic CG epimutation under benign environments. The confounding effect between DNA variation and methylation variation, as well as the high scaled epimutation rates become obstacles to further dissect the evolutionary forces in shaping the methylation patterns at different timescales under different environments.
Maize, a major cereal crop species, was domesticated from its wild ancestor Teosinte (Z. mays ssp. parviglumis) near the Balsas River Valley area in Mexico about 9,000 years ago. Genetic studies revealed that the selection of several major effect loci dramatically changed the morphology of teosinte to the modern maize [23]. About 4,000 years ago, maize was introduced to the Southwest of the United States from Mexico; after the initial introduction, however, for about 2,000 years, its territory was limited within the lowland desert. Through sequencing of ancient maize cobs from Turkey Pen Shelter in the temperate Southwest United States, evidence suggested that the ancient maize had finally adapted to the temperate environment in terms of flowering time and tillering phenotypes and eventually enabled this species to spread out to a broader area of the temperate environment [24]. Flowering time, a trait that directly affecting plant fitness, played a major role in this local adaptation process. Numerous QTLs and GWAS studies in maize suggested that flowering time in maize was predominantly controlled by a large number of additive loci [25,26]. Occasionally, genotype-by-environment interaction was detected for flowering time traits [27]. However, the roles of methylation variation for flowering time and other adaptation-related traits in a natural population, as well as the evolutionary forces in shaping the methylome patterns in different timescales remain largely elusive.
Here, we sequenced genomes from a set of geographically widespread Mexican landraces and a natural population of teosinte collected near Palmar Chico, Mexico [28], from which we generated genomic sequences and methylomes in base-pair resolution. Additionally, we profiled the teosinte (accession no. Ames 21809) interactome using HiChIP method with two antibodies of H3K4me3 and H3K27ac. Together with the analysis from previously published genome [29], transcriptome [30], methylome [6], and interactome [31] datasets, we estimated epimutation rates and the selection pressure in different timescales, investigated the DNA methylation landscapes and variations, detected differentially methylated regions (DMRs), characterized the genomic features that are related with DMRs, and functionally validated two DMRs that are associated with adaptation-traits. Our results suggested that DNA methylation is likely under weak selection during evolution. The inferred phenotypic effects from some DMRs demonstrated that varied methylation patterns may modulate the regulation of some domestication genes and thus affect maize adaptation.
Genomic distribution of methylation in maize and teosinte
To investigate genome-wide methylation patterns in maize and teosinte, we performed whole-genome bisulfite sequencing from a panel of wild teosinte, domesticated maize landraces, and modern maize inbreds (Table S1). Using the resequenced genome of each line, we created individual pseudo-references (see materials and methods) that alleviated potential bias of mapping reads to a single reference genome [32] and improved overall read-mapping ( Figure S1A). Using pseudo-references, on average about 25 million (5.6%) more methylated cytosine sites were identified than using the B73 reference ( Figure S1B). Across populations, average genome-wide cytosine methylation levels were about 78.6%, 66.1% and 2.1% in CG, CHG, and CHH contexts, respectively, which are consistent with previous estimations in maize [12] and are much higher than observed (30.4% CG, 9.9% CHG, and 3.9% CHH) in Arabidopsis [5]. We observed slightly higher levels of methylation in landraces, which may due to lower sequencing depth. We found no significant differences between teosinte and maize as a group ( Figure S2).
We found methylated cytosines in all contexts were significantly higher in the pericentromeric regions (0.54 ± 0.01 in a 10 Mb window) than in chromosome arms (0.44 ± 0.04) (Students' t test P-value < 2.2e − 16) (Figure S3). At gene level, we calculated the average methylated CGs (mCG) level across gene bodies (from transcription start site to transcription termination site, including exons and introns) in each population and observed a bimodal distribution of mCG in gene bodies ( Figure S4A), with approximately 25% of genes (N = 6, 874) showing evidence of gene body methylation (gbm). While the overall distribution of gbm did not differ across populations, genes with clear syntenic orthologs in Sorghum exhibited gbm ( Figure S4B-C), consistent with previous reports [5,7,33,34].
Genome-wide methylation is only under weak selection
As the frequency of methylation may be affected by both selections and epimutation rates, we implemented a novel MCMC approach to estimate these parameters using a population genetic model developed for highly variable loci [21]. We defined 100-bp tiles across the genome as a DNA methylation locus and categorized individual tiles as unmethylated, methylated, or heterozygous alleles for outcrossed populations (i.e., teosinte and landrace populations) and as unmethylated and methylated alleles for modern maize inbred lines (see materials and methods). To determine the thresholds for methylation calls, we employed the iterative expectation maximization algorithm to fit the data [35]. We then constructed methylome site frequency spectra (mSFS) for CG and CHG sites ( Figure 1A-B). And sensitivity test results suggested that the mSFS were insensitive to the cutoffs used for the methylation calls ( Figure S5). As the vast majority (> 98%) of CHH sites were unmethylated (Figure S6), we excluded CHH sites from population genetic analysis.
Because we found little differences among populations in genome-wide patterns, we estimated parameters using the combined data; estimates from individual populations were nonetheless broadly similar ( Figure S7). The predicted mSFS from our model was largely similar to the observed data ( Figure 1A-B), with differences likely attributable to deviations from the simple constant-size population assumed in the model [21]. Model estimates of the epimutation rate µ for both CG (1.2 × 10 −6 ) and CHG (2.5 × 10 −6 ) sites were more than an order of magnitude higher than the back-mutation rates (ν = 6.0 × 10 −8 and 1.0 × 10 −7 ), consistent with the observed prevalence of both types of methylation. Estimates of the genome-wide selection coefficient s associated with methylation of a 100-bp tile were 1.4 × 10 −5 and 1.6 × 10 −5 for CG and CHG tiles, respectively. Assuming an effective population size of ≈ 150, 000 for maize [36], the population-scaled selection coefficient Ne × s for CG and CHG tiles were 2.1 and 2.3, indicating relatively weak selection for methylation in each context according to classical population genetic theory [37].
Population level DMRs are enriched in selective sweeps
Average genome-wide methylation data revealed few differences between teosinte and maize, but masks differentiation due to selection at individual loci. To investigate whether individual genomic regions exhibit differential methylation among populations, we employed metilene method [38] to identify population-based differentially methylated regions (DMRs) across the genome. This approach allowed us to precisely define the boundaries of the DMRs by merging tiles recursively. Use this approach, we identified a total of 5,278 DMRs, or about 0.08% (1.8 Mb) of the genome, including 3,900 DMRs between teosinte and modern maize, 1,019 between teosinte and landrace, and 359 DMRs between landrace and modern maize (Table S2).
DNA methylation can have a number of functional consequences [14,39,40], and thus we tested whether differences in methylation among populations were associated with selection at individual locus. To test this hypothesis, we used SNP data from each population to scan for genomic regions showing evidence of selection (see materials and methods). We detected a total of 1,330 selective sweeps between modern maize and teosinte (Figure 2 and Table S3, see Figure S8 for results of teosinte vs. landrace and landrace vs. modern maize). Several classical domestication genes, e.g., tb1 [41], ZAG2 [42], ZmSWEET4c [43], RA1 [44], and BT2 [45] were among these selective signals.
DMRs at CG and CHG sites were highly enriched in regions showing evidence of recent selection ( Figure S9, P-value < 0.001), particularly in intergenic regions ( Figure S10A). These DMRs, both hypo-and hypermethylated in maize, exhibited significantly higher allele frequency differentiations between maize and teosinte ( Figure S10B, P-value < 0.001).
Fig. 2. Selection on differentially methylated regions.
Distributions of teosinte-maize selective sweeps, DMRs and other genomic features across ten maize chromosomes. From outer to inner circles were 1 Chromosome names, 2 selective sweeps detected between modern maize and teosinte, 3 the recombination rate, and the density of DMRs (number per 1-Mb) between modern maize and teosinte in 4 CG and 5 CHG contexts. Red dots in circle 3 denote the centromeres.
Hypomethylated regions in maize are involved in distal gene regulation
Further investigation indicated that teosinte-maize CG DMRs were significantly enriched in mappable genic and intergenic (i.e., nongenic excluding 5-kb upstream and downstream of genes and transposons) regions for both hyper-and hypomethylated regions in maize, but depleted from transposon regions ( Figure 3A). We detected maize hyper-and hypomethylated DMRs in 0.01% and 0.02% of mappable regions across the genome. In particular, 0.07% and 0.05% of maize hyper-DMR (DMR hypermethylated in maize) and hypo-DMR (DMR hypomethylated in maize) were located within mappable exonic regions, which were 14-fold and 5-fold higher than expected by chance (permutation P-values < 0.001, Figure S11A). These CG DMRs could be mapped to N = 229 unique genes (Table S4). After examining the mapping locations based on the collapsed gene model, we found DMRs peaked at 5' UTR ( Figure 3B), consistent with a pattern that was previously observed [47]. Using these DMR genes for a gene ontology (GO) analysis, we detected 15 molecular function terms were significantly enriched ( Figure S11B). Interestingly, 14/15 of these significant terms were associated with "binding" activities, including protein, nucleoside, and ribonucleoside binding. Furthermore, we found these exonic DMRs were enriched at transcription factor binding sites which were identified using DAP-seq [48] (Permutation P-value < 0.001).
Li et al., recently profiled genomic regions colocalized with H3K4me3 and H3K27ac, two well-known chromatin marks for promoters and enhancers [49,50], to define the interactome in maize [31]. We leveraged these interactome data to study the relationship between DMRs and physical interactions. First, we found the interactive anchor sequences were significantly enriched in DMRs that are hypomethylated in maize, especially in the regulatory regions, including upstream 5-kb, downstream 5-kb, and intergenic regions ( Figure 3A). We also found DMRs located in transposon elements that were hypomethylated in maize more likely overlap with interactive anchors than expected by chance (Permutation P-value < 0.001).
We hypothesized that the DMRs, especially the DMRs located within the intergenic regions, will alter the up-or downstream gene expression through physical interactions. To test this hypothesis, we mapped the interactive anchors harboring maize hypomethylated DMRs to their 1st, 2nd, and 3rd levels of contacts ( Figure S12A). Interestingly, genes (N = 60) directly contacted (or the 1st level contacts) with these maize hypomethylated intergenic DMRs (Table S5) showed significantly (Student's paired t-test, P-value < 0.05) increased expression levels in maize compared to teosinte using published data [30]. The results were insignificant for 2nd and 3rd levels contacts (Figure S12A). We found 5/60 genes (Enrichment test P-value < 0.01) were domestication candidate genes as reported previously [51][52][53][54]. Two of them were Zm00001d018036 gene associated with cob length (P-value = 6 × 10 −25 ) and Zm00001d041948 gene associated with shank length (P-value = 5.6 × 10 −10 ) [51]. Further investigation of these two candidates using recently published chromatin data [46] detected the STARR (Self-Transcribing Active Regulatory Region, a sequencing technology for identifying and quantifying enhancer activity [55]) and H3K27ac peaks at the DMR loci ( Figure 3C and Figure S13A). Consistently with the enhancer signals, the expression levels of these two genes had been significantly increased in maize relative to teosinte (Figure S12B and Figure S13B). with H3K27ac and H3K4me3 (middle panels), and STARR profiles (lower panels) around Zm00001d018036 gene in B73. STARR-seq data obtained from [46] showed the transcriptional output (STARR-RNA) and DNA input (STARR-input) around this region. Blue curly lines indicate the interactive contacts between DMR and the candidate gene and grey curly lines indicate other interactive contacts around the region. Horizontal thick blue lines denote the interactive anchors. Red and grey boxes indicate the DMR and gene model, respectively.
DMRs explained disproportionally larger phenotypic variance and altered differential flowering time genes regulation
Next, we employed a variance component analysis approach by using SNP sets residing in DMRs (DMR-SNPs) to evaluate the relative importance of DMRs (see materials and methods). By using NAM population with 41 publicly available phenotypes [56], we estimated the variance explained by DMR-SNPs and explained by SNPs mapped to the rest of the genome. Results suggested that teosinte-maize CG DMR-SNPs, although only accounting for 0.01% of the genome, could explain more than 1% of the phenotypic variances for 18 traits, including ear row number (12.1%), northern leaf blight (5.5%) and, stalk strength (8.9%) ( Figure 4A). For landrace-maize CG DMR-SNPs, we detected disproportionally larger phenotypic variances be explained for several yield and disease-related traits, including tassel branch number, ear row number, Southern and Northern leaf blight ( Figure 4B).
Interestingly, four flowering time related traits, i.e., photoperiod growing-degree days to silk, photoperiod growing-degree days to anthesis, days to anthesis, and days to silk, showed consistently strong signals that can be explained by over 3.7% heritabilities ( Figure 4A). We hypothesized that the disproportionally larger heritability explained by CG DMR-SNPs might be caused by some large effect flowering time related genes. To test this hypothesis, we examined several known genes in the flowering time pathway [57]. Indeed, we detected six DMRs located on four flowering time related genes ( Figure S14) (Enrichment test P-value = 0.02).
Additionally, one DMR located 40-kb upstream of the ZmRAP2.7 gene and 20-kb downstream of the vgt1 locus that was hypomethylated in modern maize and landrace but was hypermethylated in teosinte ( Figure 4C). A MITE transposon insertion in the vgt1 locus was considered as the causal variation for the down regulation of the ZmRAP2.7, which was a transcription factor in the flowering time pathway [58]. Reanalysis of the published ChIP data revealed that the DMR colocalized with H3K27ac, the chromatin activation mark, and there existed a physical interaction between the DMR and vgt1 locus in maize [31] (Figure 4D). To examine the interaction status in teosinte, we then generated HiChIP data for a teosinte sample using the same tissue and antibodies (see materials and methods). As expected, we failed to detect the physical interaction between vgt1-DMR and vgt1 itself in teosinte (Figure 4D), suggesting that methylation might play a functional role in affecting physical interaction at vgt1-DMR locus.
To further validate the potential enhancer function of the 209-bp vgt1-DMR, we incorporated the vgt1-DMR sequence amplified from B73 into a vector constructed as shown in (Figure 4E) and performed the dual-luciferase transient expression assay in maize protoplasts (see materials and methods). The results of the transient expression assay revealed that the maize cells harboring the DMR exhibited a significantly higher LUC and REN ratio than control (fold change= 2.2, P-value= 2.4e −8 , Figure 4F), revealing that the DMR might act as an enhancer to activate LUC expression.
A segregating tb1-DMR performed like a cis-acting element
One of the most significant teosinte-maize CG DMRs was located 30-kb upstream of the tb1 gene, which is a transcription factor acting as a repressor of axillary branching (aka tillering) phenotype [41]. This 534-bp tb1-DMR was hypomethylated in modern maize, hypermethylated in teosinte, and segregating in landraces ( Figure 5A). Phenotypic analysis indicated that the DMR was associated with the tillering phenotype using the phenotypic data observed for the 17 landraces (Fisher's exact test P-value < 0.05). And the phenotypic effect was consistent with previous observations that the hypermethylated (teosinte-like) genotypes were likely to grow tillers.
The causal variation for this locus was previously mapped to a Hopscotch TE insertion 60-kb upstream (Figure 5B) of the tb1 gene. The TE was considered as an enhancer, as shown by a transient in vivo assay [41]. The interactome data support this claim that there was a physical contact between Hopscotch and tb1 gene ( Figure 5B) as have also been shown [31]. Interestingly, we also detected a direct physical contact between the tb1-DMR and tb1 gene itself in maize line B73 but missing in teosinte using our newly generated HiChIP data ( Figure 5B). This observation was consistent with our previous result for the vgt1-DMR locus and suggested the DMR might result in differential interactive loops. The colocalization of tb1-DMR with chromatin activation marks in the region also suggested the tb1-DMR might perform like a cis-acting regulatory element (Figure 5B).
To understand the correlation among these genomic components, i.e., the tb1-DMR and tb1 gene, we conducted linkage disequilib- rium (LD) analysis using landrace genomic and methylation data, which were segregating at this tb1-DMR locus (see materials and methods). As a result, we failed to detect strong LD (R 2 = 0.1) in this region (Figure S15), indicating the tb1-DMR might be originated independently. Further, we found the highly methylated landraces were geographically closer to the Balsas River Valley in Mexico, where maize was originally domesticated from ( Figure S16A). As the landraces spread out from the domestication center, their CG methylation levels were gradually reduced ( Figure S16B). Additionally, we conducted a dual-luciferase transient assay by constructing a vector similar to ( Figure 4E). The results indicated that the tb1-DMR significantly increased the LUC/REN ratio as compared to control (Figure 5C), suggesting that the tb1-DMR was potentially act as a cis-acting element to enhance downstream gene expression.
DISCUSSION
In this study, we employed population genetics and statistical genomics approaches to infer the rates of epimutation, selection pressure on DNA methylation, and the extent to which SNPs located within DMRs contributed to phenotypic variations. Our results revealed that the forward epimutation rate (∼ 10 −6 ) was about 10 times larger than the backward epimutation rate (∼ 10 −7 ), which is several magnitudes larger than that of the DNA mutation rate (∼ 10 −8 ) in maize [59]. Our estimated epimutation rates were different from the rates estimated in Arabidopsis using the epimutation accumulation experiments [60]. Partially because in this study, we used 100-bp tile as an inheritance unit, while the Arabidopsis calculated the per base epimutation rates. Additionally, the genome-wide methylation levels were dramatically different between maize and Arabidopsis, which may result in different epimutation rates for these two species.
Although population methylome modeling suggested that genome-wide DNA methylation was not under strong selection, we detected a large number of DMRs by conducting population-wide comparisons. These DMRs are likely to explain adaptation and domestication related phenotypes, demonstrated by both the global quantitative genetics analyses using DMR-SNPs and the functional validations of two well characterized loci vgt1 and tb1. In both functional validation cases, evidences showed that methylation levels tend to affect the physical interactions. In particular, the domesticated alleles exhibiting low methylation levels in modern maize associated with newly formed interactive loops in B73 as compared to the wild ancestor teosinte. Transient expression assays demonstrated that two of these non-methylated alleles can increase the expression levels of the reporter genes. Accordingly, we identified a set of new genes (about 60) that differentially expressed between maize and teosinte, in which their exonic regions directly connect with hypo-DMRs. Taken together, these results suggested that methylations might modulate physical interactions and hence likely affect gene expression. This speculation of methylation variation affected distal regulation fitted well with the previous results from GWAS that 80% of explained variation could be attributable to trait-associated variants located in regulatory regions [61]. Our variance component analysis results further suggested that DMR-SNPs (largely mapped to the genic and intergenic regions) explained disproportionally larger phenotypic variations. Interestingly, teosinte-maize DMR-SNPs explained more phenotypic variances for domestication related phenotypes, while landrace-maize DMR-SNPs explained more for improvement related phenotypes.
Unlike the low rate of DNA mutation, the high rate of the DNA methylation might provide an alternative mechanism for plant adaptation. We showed that the hypomethylated regions tend to be involved in the distal regulations to activate or inactivate genes expressions. They likely to have greater effects on phenotypic traits and, therefore, could be serve as potential targets of recent selection. However, in this study, we could not rule out the possibility that DMRs might be the hitchhiking effects of the positive selection on genomic variations.
Collectively, the fact that a large number of DMRs overlapped with the selective sweeps during domestication and improvement processes, and the evidences that DMRs may function as cis-acting elements provide new insights into plant adaptive evolution. These naturally occurring adaptive DMRs could possibly be leveraged for understanding gene regulation. Since some of the DMRs exhibited favorable phenotypic consequences, they might also be the potential targets of artificial selection for further crop improvement.
Plant materials and DNA sequencing
We obtained a set of geographically widespread open pollinated landraces across Mexico (N = 17) from Germplasm Resources Information Network (GRIN) ( Table S1). The teosinte (Zea mays ssp. parviglumis; N = 20) were collected near Palmar Chico, Mexico [28]. We harvested the third leaf of the teosintes and Mexican landraces for DNA extraction using a modified CTAB procedure [62]. The extracted DNA was then sent out for whole genome sequencing (WGS) and whole genome bisulfite sequencing (WGBS) using Illumina HiSeq platform. Additionally, we obtained WGBS data for 14 modern maize inbred lines [6] and WGS data for the same 14 lines from the maize HapMap3 project [29].
Sequencing data analysis
The average coverage for the WGS of the 20 teosintes and 17 landraces lines was about 20 ×. For these WGS data, we first mapped the cleaned reads to the B73 reference genome (AGPv4) [63] using BWA-mem [64] with default parameters, and kept only uniquely mapped reads. Then we removed the duplicated reads using Picard tools [65]. We conducted SNP calling using Genome Analysis Toolkit's (GATK, version 4) HaplotypeCaller [66], in which the following parameters were applied: QD < 2.0, FS > 60.0, MQ < 40.0, MQRankSum < −12.5, and ReadPosRankSum < −8.0.
In order to improve the WGBS mapping rate and decrease the mapping bias, we replaced the B73 reference genome with filtered SNP variants using an in-house developed software -pseudoRef ( [URL]). Subsequently, we mapped reads to each corrected pseudo-reference genome using Bowtie2 [67] and kept only unique mapped reads. After filtering the duplicate reads, we extracted methylated cytosines using the Bismark methylation extractor and only retained sites with more than three mapped reads. The weighted methylation level was determined following the previously reported method [68].
Population epigenetics modeling
Spontaneous epimutation changes (i.e. gain or loss of cytosine methylation) exhibit higher rate than genomic mutation [20,60]. The standard population genetic methods designed for SNPs are thus inappropriate for population epigenetic studies. Here, we applied the analytical framework for hypermutable polymorphisms developed by Charlesworth and Jain [21]. Under this framework, the probability density of the methylated alleles was modeled as: where α = 4N e µ, β = 4N e ν, γ = 2N e s. N e , effective population size; q, frequency of the hypermethylation alleles; µ, forward epimutation rate (methylation gain); ν, backward epimutation rate (methylation loss); s, selection coefficient. The constant C is required so that 1 0 φ(q)dq = 1. We defined 100-bp tiles as a DNA methylation locus. To define the methylation status, we assumed that the methylation levels in a heterozygote individual falling into three mixture distributions (unmethylated, methylated, and heterozygote distributions). We employed an R add-on package "mixtools" and fitted the "normalmixEM" procedure to estimate model parameters [35]. Based on the converged results of the iterative expectation maximization algorithm (using the "normalmixEM" function), we decided to use 0.7 and 0.3 thresholds for heterozygote individuals (i.e., average methylation value> 0.7 for a 100-bp tile was determined as a methylated call and coded as 2; < 0.3 was determined as an unmethylated call and coded as 0; otherwise coded as 1). We also tested different cutoffs and found that the final methylation site frequency spectrum (mSFS) was insensitive to the cutoffs used. Similarly, we assumed two mixture distributions for inbred lines and used cutoff = 0.5 to determine methylated (coded as 1) and unmethylated (coded as 0) calls. With these cutoffs, we then constructed mSFS on genome-wide methylation loci. We also constructed interspecific (i.e., across maize, landrace, and teosinte populations) and intraspecific (i.e., within maize, landrace, and teosinte populations) mSFS.
Genome scanning to detect selective signals
We called SNPs using our WGS data and performed genome scanning for selective signals using XP-CLR method [71]. In the XP-CLR analysis, we used a 50-kb sliding window and a 5-kb step size. To ensure comparability of the composite likelihood score in each window, we fixed the number assayed in each window to 200 SNPs. We evaluated evidence for selections across the genome in three contrasts: teosinte vs landrace, landrace vs modern maize, and teosinte vs modern maize. We merged nearby windows falling into the 10% tails into the same window. After window merging, we considered the 0.5% outliers as the targets of selection.
We calculated F ST using WGS data using VCFtools [72]. In the analysis, we used a 50-kb sliding window and a 5-kb step size.
DMR detection and GO term analysis
We used a software package 'metilene' for DMR detection between two populations [38]. To call a DMR, we required it contained at least eight cytosine sites with < 300-bp in distance between two adjacent cytosine sites, and the average of methylation differences between two populations should be > 0.4 for CG and CHG sites. Finally, we required a corrected P-value < 0.01 as the cutoff. We conducted gene ontology (GO) term analysis on selected gene lists using AgriGO2.0 with default parameters [73]. We used the significance cutoff at P-value < 0.01.
HiChIP sequencing library construction
We constructed the teosinte HiChIP library according to the protocol developed by Mumbach et al. [74] with some modifications. The samples we used were two weeks aerial tissues collected from a teosinte accession (Ames 21809) that were planted in the growth chamber under the long-day condition (15h day time and 9h night time) at the temperature (25°C at day time and 20°C at night time). After tissue collection, we immediately cross-linked it in a 1.5 mM EGS solution (Thermo, 21565) for 20 min in a vacuum, followed by 10 min vacuum infiltration using 1% formaldehyde (Merck, F8775-500ML). To quench the EGS and formaldehyde, we added a final concentration of 0.15 mM glycine (Merck, V900144) and infiltrated by vacuum for 5 min. Then, cross-linked samples were washed five times in double-distilled water and flash-frozen in liquid nitrogen.
To isolate the nuclear from cross-linked tissues, we used the methods as described previously [31]. After obtaining the purified nuclear, we resuspended it in 0.5% SDS and used 10% Triton X-100 to quench it, and then performed digestion, incorporation, and proximity ligation reactions as previously described [74]. We used two antibodies H3K4me3 (Abcam, ab8580) and H3K27ac (Abcam, ab4729) to pull down the DNA. And then, we purified DNA with the MinElute PCR Purification Kit (QIAGEN) and measured the DNA concentration using Qubit. To fragment and capture interactive loops, we used the Tn5 transposase kit (Vazyme, TD502) to construct the library with 5 ng DNA. We then sent the qualified DNA libraries for sequencing using the Illumina platform.
ChIP-seq and HiChIP data analysis
We obtained ChIP-seq data from the B73 shoot tissue [31] and then aligned the raw reads to B73 reference genome (AGPv4) using Bowtie2 [75]. After alignment, we removed the duplicated reads and kept only the uniquely mapped reads. By using the uniquely mapped reads, we calculated read coverages using deepTools [76].
For the teosinte HiChIP sequencing data, we first aligned the raw reads to the B73 reference genome (AGPv4) using HiC-Pro [77], and then processed the valid read pairs to call interactive loops using hichipper pipeline [78] with a 5-kb bin size. After the analysis, we filtered out the non-valid loops with genomic distance less than 5 kb or larger than 2 Mb. By using the mango pipeline [79], we determined the remaining loops with three read pairs supports and the FDR < 0.01 as the significant interactive loops.
In the analysis, we mapped SNPs to the DMR and non-DMR regions. We partitioned SNPs into two sets -SNPs located within DMR and SNPs that were outside of DMRs (i.e., the rest of the genome). For each SNP set, we calculated an additive kinship matrix using the variance component annotation pipeline implemented in TASSEL5 [81]. We then fed these kinship matrices along with the NAM phenotypic data to estimate the variance components explained by SNP sets using a Residual Maximum Likelihood (REML) method implemented in LDAK [82].
Dual-luciferase transient expression assay in maize protoplasts
To investigate the effect of DMRs on gene expression, we performed a dual-luciferase transient expression assay in maize protoplasts. We used the pGreen II 0800-LUC vector [83] for the transient expression assay with minor modification, where a minimal promoter from cauliflower mosaic virus (mpCaMV) was inserted into the upstream of luciferase (LUC) to drive LUC gene transcription. In the construct, we employed the Renillia luciferase (REN) gene under the control of 35S promoter from cauliflower mosaic virus (CaMV) as an internal control to evaluate the efficiency of maize protoplasts transformation. We amplified the selected DMR sequences from B73 and then inserted them into the control vector at the restriction sites KpnI/XhoI upstream of the mpCaMV, generating the reporter constructs.
We isolated protoplasts from the 14-day-old leaves of B73 albino seedlings following the protocol [84]. Subsequently, we transformed 15 ug plasmids into the 100 ul isolated protoplasts using polyethylene glycol (PEG) mediated transformation method [84]. After 16 hours infiltration, we measured the LUC and REN activities using dual-luciferase reporter assay reagents (Promega, USA) and a GloMax 20/20 luminometer (Promega, USA). Finally, we calculated the ratios of LUC to REN. For each experiment, we included five biological replications.
Data availability
All datasets and analyzing scripts are available through GitHub ( [URL]) and methylome data has been submitted to the NCBI SRA. Fig. S1. Comparison of mapping rates (A) and number of methylated cytosine (mC) sites (B) with and without using pseudoreference genome in different populations. B73 reference genome (AGPv4) was used in the analyses. Red asterisks indicated the statistical significances with one asterisk denoting P-value < 0.05 and two asterisks denoting P-value < 0.01. Hyper-and hypomethylation were defined based on maize. In the upper panel, the schematic diagram shows the genes that involved in the 1st, 2nd, and 3rd level interactions with maize hypomethylated DMRs located in intergenic regions. Red asterisks indicated the statistically significant differences (P-value < 0.01). (B) Gene expression level of Zm00001d018036 in teosinte and modern maize (P-value = 4.6e −141 according to [30]). approach for forward epimutation rate (µ the left panels), backward epimutation rate (ν, the middle panels), and selection coefficient (s, the right panels) under CG (A) and CHG (B) contexts. In each plot, the top panels are the MCMC tracing plots with 25% burnin, the middle panels are the prior (grey) and posterior (colored) parameter distributions and the bottom panels are the enlarged posterior distributions.
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Domain: Biology
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Modeling nonsegmented negative-strand RNA virus (NNSV) transcription with ejective polymerase collisions and biased diffusion
Infections by non-segmented negative-strand RNA viruses (NNSV) are widely thought to entail gradient gene expression from the well-established existence of a single promoter at the 3’ end of the viral genome and the assumption of constant transcriptional attenuation between genes. But multiple recent studies show viral mRNA levels in infections by respiratory syncytial virus (RSV), a major human pathogen and member of NNSV, that are inconsistent with a simple gradient. Here we integrate known and newly predicted phenomena into a biophysically reasonable model of NNSV transcription. Our model succeeds in capturing published observations of respiratory syncytial virus and vesicular stomatitis virus (VSV) mRNA levels. We therefore propose a novel understanding of NNSV transcription based on the possibility of ejective polymerase-polymerase collisions and, in the case of RSV, biased polymerase diffusion.
Introduction
Viruses with nonsegmented negative-strand RNA genomes (NNSV) (all viruses of the order Mononegavirales) contain major pathogens such as Ebola, rabies, measles virus, respiratory syncytial virus (RSV), and vesicular stomatitis virus (VSV)-the latter is a highly studied bovine pathogen of the same family, Rhabdoviridae, as rabies virus.
The RNA genomes of NNSV are coated in nucleoprotein and support both whole genome replication and the transcription of subgenomic mRNAs by viral RNA-dependent RNA polymerases in the cytosol of infected cells. These genomes have a single promoter located at the 3' end that is essential for both processes, presumably by facilitating the transient dissociation of terminal genomic RNA from nucleoprotein and the entry of viral polymerases, hitherto bound only to the nucleoprotein of the ribonucleoprotein (RNP) complex, into the RNA genome.
Every NNSV gene contains essential and highly conserved gene start (GS) and less highly conserved gene end (GE) signal sequences flanking the open reading frame (ORF).
Transcription is initiated at the GS signal which also serves as a capping signal on the 5' end of nascent mRNA (Barik, 1993;Liuzzi et al., 2005;Noton and Fearns, 2015). The polymerase then enters elongation mode until it reaches a GE signal, where it either continues translocating and transcribing (i.e., reads through) or it stops translocating and the mRNA is polyadenylated and released (i.e., terminates transcription) (Kuo et al., 1997;Noton and Fearns, 2015). In RSV, the two genes that are most 5' terminal have overlapping ORFs: the GE signal of matrix 2 (M2) occurs downstream of the GS signal of the last gene, the large polymerase (L) gene. Thus, for full-length L mRNA to be made, a polymerase must translocate 3' from the M2 GE signal (Fearns and Collins, 1999), suggesting that polymerases scan the RSV genome bidirectionally (i.e., diffuse) for a new GS signal after terminating transcription. Indeed, multiple studies suggest that scanning polymerase dynamics, or polymerase diffusion along the genome, may be a universal feature of NNSV transcription (Fearns and Collins, 1999;Kolakofsky et al., 2004;Barr et al., 2008;Noton and Fearns, 2015;Brauburger et al., 2016).
The still widely accepted textbook model of NNSV gene expression predicts a transcription gradient from 1) polymerase entry at the 3' end of the genome; 2) "obligatorily sequential" startstop transcription in response to the conserved GS and GE signal sequences; and 3) transcriptional attenuation via an unknown mechanism between genes (Whelan et al., 2004;Noton and Fearns, 2015). However, multiple published studies show NNSV gene expression patterns-especially from RSV, which is one of its most highly studied members-that are either non-gradient, with one or more downstream genes appearing more highly expressed than upstream genes, or inconsistent with a simple gradient from a constant level of attenuation between genes (Krempl et al., 2002;Pagan et al., 2012;Aljabr et al., 2016;Levitz et al., 2017;Piedra et al., 2020a;Donovan-Banfield et al., 2022;Rajan et al., 2022). Regarding the latter, multiple studies show an abrupt and dramatic decrease in gene expression over the last two genes of the RSV genome (Krempl et al., 2002;Aljabr et al., 2016;Levitz et al., 2017;Donovan-Banfield et al., 2022;Rajan et al., 2022), the sole region of the genome containing overlapping ORFs-the textbook model of NNSV transcription offers no way of explaining this. In addition, the textbook model is devoid of potentially important biophysical phenomena: 1) polymerase (pol) diffusion along the viral genome; 2) potential interactions among pols (both diffusing and transcribing); and 3) stochastic transcription initiation and termination.
Here we implement a coarse-grained, mechanistic and stochastic computational model incorporating known and, ultimately, newly proposed features (ejective pol-pol collisions and 5' biased pol diffusion) of the underlying molecular biophysics to gain a deeper understanding of NNSV transcription and to capture, for the first The model: linear respiratory syncytial virus (RSV) and vesicular stomatitis virus (VSV) genomes support the stochastic initiation and termination of transcription by a diffusing viral RNA-dependent RNA polymerase (pol). (A) The genetic structure of RSV and VSV genomes. The modeled RSV genome is 15,222 nt long and contains 10 ORFs with 8 gene junctions and a single short region (68 nt) of overlapping ORFs between genes M2 and L (see black asterisk). The modeled VSV genome is 11,152 nt long and contains 5 ORFs with 4 gene junctions. The genomes were divided into chunks approximating the size of a pol footprint (28 nts). Most of each genome is coding sequence (represented as cyan beads). (B) Essential model phenomena and parameters. A single RNAdependent RNA polymerase (pol) starts an unbiased random walk at a rate D scan (= 1 genomic chunk per event) at the most 3' chunk (depicted as a burnt orange bead) of the modeled genome. Transcription initiation occurs with a probability P transc when a pol diffuses onto a genomic chunk containing a gene start (GS) signal (depicted as a green bead). If transcription is not initiated, the unbiased random walk (i.e., diffusion) resumes. If transcription is initiated, the modeled pol state changes and the pol starts translocating 5' down the genome at a rate k transc (= x genomic chunks per event). Transcription termination occurs with a probability P term when a transcribing pol translocates onto a genomic chunk containing a gene end (GE) signal (depicted as a red bead). If termination occurs, the pol state changes back to non-transcribing and resumes diffusion along the genome at a rate D scan ; if termination does not occur, the pol 'reads through' the GE signal and continues transcribing into the next ORF. (Cyan beads represent coding sequence).
Frontiers in Molecular Biosciences
frontiersin.org 02 time, experimentally observed non-gradient RSV and gradient VSV gene expression patterns.
The model
Computational models of RSV and VSV transcription were written in the Python programming language using the free and open-source Scientific Python Development Environment (Spyder version 3.3.2). The model code is freely available on GitHub: [URL]_ NNSV_Gene_Expression.
In brief, the models simulate one or more viral RNA-dependent RNA polymerases (pols) entering a linear RSV or VSV genome at the 3' end and taking a random walk at a rate D scan (units = "genomic chunks" per simulated event; D scan = 1 throughout the results presented in this MS). A random walk is a simple model of diffusion where a simulated pol moves either one genomic chunk 5' or 3' along the genome. A parameter D bias is used as a multiplicative factor (D scan *D bias ) to 5' bias (or not) the random walk taken by modeled pols-i.e., D bias > 1 biases pol movement 5'; D bias = 1 results in an unbiased random walk. Each genome is divided into chunks of a size thought to reasonably approximate the footprint of a single RSV or VSV pol (28, 14, or 7 nt). Diffusing non-transcribing pols cannot "hop" over other pols and a single genomic chunk can only be occupied by a single pol at any one time.
Gene start (GS) and gene end (GE) signal sequences are modeled as separate genomic chunks positioned along the modeled genomes according to their known positions from sequencing data ( Figure 1A). Transcription is initiated with a data-constrained probability (see Table 1) when a non-transcribing pol (pol_state = 0) diffuses onto a GS signal; termination of transcription or transcriptional readthrough occurs with a probability derived from published sequencing data when a transcribing pol (pol_state = 1) moving 5' at a rate k transc (units = "genomic chunks" per simulated event) translocates onto a GE signal ( Figure 1B). Initiations of transcription and transcriptional readthrough events are counted as gene expression events for the genes where they occur. For simulations incorporating multiple pols on a single genome, ejections of a non- (Kuo et al., 1997). The G gene GS signal contains a single mutation (relative to the most common GS signal sequence) at position 10 that reduced gene expression by~35%. Kuo et al. reported that the L gene GS signal gave rise to a magnitude of gene expression equal to that of the most common GS signal. It is therefore reasonable to model RSV transcription with a single probability of transcription initiation at all GS signals except for G, where the probability should be multiplied by 0.65. Effect on P transc --0.65 X 1 X (no change)
FIGURE 2
Single pol simulations produce flat patterns of gene expression across P transc values tested. (A) Simulated RSV transcription. Histograms of mRNA # for each RSV gene divided by the total mRNA # show uniform gene expression across the 10 genes for all three sets of P transc tested (max 0.1, max 0.5, and max 0.9). For each set of P transc, the max value equals the probability of transcription at every GS signal except for that of the G gene, which equals 0.65*max. Blue bars depict results from simulations; black horizontal bars depict average published experimentally observed values (Rajan et al., 2022). Each data point is the average of three 100,000 event simulations; error bars show the standard deviation. The number in parentheses and red above each histogram is the rootmean-square deviation (RMSD) of the simulated gene expression pattern from the experimental observations. (B) Simulated VSV transcription. Histograms of mRNA # for each VSV gene divided by the total mRNA # show uniform gene expression across the 5 genes for all three sets of P transc tested (0.1, 0.5, and 0.9). For each set of P transc, the probability of transcription is the same at every GS signal. Lavender bars depict results from simulations; black horizontal bars depict average published experimentally observed values (Iverson and Rose, 1981). Each data point is the average of three 100,000 event simulations; error bars show the standard deviation. The number in parentheses and red above each histogram is the root-mean-square deviation (RMSD) of the simulated gene expression pattern from the experimental observations.
Frontiers in Molecular Biosciences frontiersin.org transcribing pol occur when a transcribing pol passes it. When a pol reaches the extreme 5' end of a modeled genome, it either diffuses 3' or dissociates from the genome. The simulations occur one event at a time (i.e., time is modeled implicitly) whereby the positions and states (non-transcribing or transcribing) of the one or more modeled pols is stochastically updated according to the rules outlined above before proceeding to the next event. After simulating 10 s of thousands of events, each gene's mRNA level divided by the total mRNA level is outputted. These data are plotted to visualize a gene expression pattern.
Results and discussion
Determining the effects of stochastic transcription using a range of initiation probabilities We took a heuristic approach to fitting actual observations of RSV and VSV gene expression and started by modeling a single pol taking an unbiased random walk down either genome and stochastically initiating and terminating transcription ( Figures 1A,B).
In this simple case, the parameters to explore are probabilities of transcription initiation and termination. The termination probabilities can be derived directly from published sequencing data for RSV, as these are simply the complement of the published readthrough rates (Rajan et al., 2022). For VSV, we made use of estimates suggesting a very high probability of termination (0.99) for the GE signals modeled here (Barr et al., 1997). In contrast with termination probabilities, probabilities of transcription initiation are completely unknown. However, the three GS signals of the RSV genome modeled here have been tested in minigenomes for their relative strength of gene expression (Kuo et al., 1997). These relative strengths were used to constrain the ten transcription initiation probabilities of RSV (Table 1). The five GS signals of the VSV genome modeled here were all assumed to support an equal probability of transcription initiation.
Simulated patterns of RSV and VSV gene expression were essentially flat for all three sets of transcription probabilities ( Figure 2). Standard deviations of individual mRNA levels were, as expected, highest for the lowest transcription probabilities tested Multiple pols on a single genome undergoing ejective collisions between transcribing and non-transcribing pols produce gene expression gradients of increasing steepness with increasing 5' translocation rate (k transc ) and increasing maximum pol number (max pol #). (A) The M2/L overlap in ORFs. The final two genes of the RSV genome, M2 (which encodes both a transcription processivity factor and a regulatory factor that enhances replication) and L (which encodes the polymerase), share a 68 nt stretch (approximately two genomic chunks of 28 nts each-depicted as magenta beads) of ORF. This ORF overlap should be a hotspot for collisions between transcribing pols and non-transcribing pols diffusing in the neighborhood of the M2 GE signal (shown as red bead). The L gene GS signal is depicted as a green bead. (B) RSV gene expression patterns over a range of k transc and max pol #. The parameter k transc sets the rate at which transcribing pols move 5' down the genome (units = genomic chunks per simulated event) and the parameter max pol # sets the maximum number of pols allowed on the genome at one time. Simulations of RSV transcription were performed at three different values of k transc x three different values of max pol #. Histograms of mRNA # for each RSV gene divided by the total mRNA # depict results from the simulations (blue bars) and average published experimentally observed values (black horizontal bars) (Rajan et al., 2022). Each data point is the average of three 100,000 event simulations; error bars show the standard deviation. The number in parentheses and red above each histogram is the root-mean-square deviation (RMSD) of the simulated gene expression pattern from the experimental observations.
Frontiers in Molecular Biosciences
frontiersin.org ( Figure 2). In the case of RSV, a slight bump in gene expression occurs for the SH gene and becomes most visible at the highest transcription probabilities tested (Figure 2A). This is because of the lower rate of transcription initiation at the G gene GS signal (0.65x), which is directly downstream of the SH gene: the modeled pol occasionally fails to initiate transcription at the G gene before diffusing to the nearest GS signal, SH, where it is~1.5x more likely to initiate transcription. We also calculated a root-mean-square deviation (RMSD) for each simulated gene expression pattern to quantify how well the model fit the observed in vitro gene expression patterns ( Figures 2B,D).
Incorporating multiple polymerases into our model of NNSV transcription
Modeling a single pol diffusing along an RSV or VSV genome and stochastically starting and stopping transcription with the sequence-based probabilities used here cannot capture experimentally observed gene expression patterns. It is also well established that VSV virions contain 10 s of pols per genome (Thomas et al., 1985), making it very likely that both VSV replication and transcription involve multiple pols interacting with a single genome.
Thus, we decided to model multiple pols interacting with and transcribing single RSV and VSV genomes. This required conceiving of rules to govern interactions between the pols interacting with a single genome. We decided to implement one-by-one pol entry at the 3' end of the genome, a variable maximum number of pols interacting with the genome at any one time, "soft" collisions between nontranscribing pols that prevent one pol from "hopping over" another, and hard collisions between 5' translocating transcribing pols and diffusing non-transcribing pols resulting in the latter's ejection from the genome.
The latter rule was partly inspired by observing that the steepest drop in RSV gene expression, a dramatic decrease reported by multiple Simulations of at most 50 pols and collision-based pol ejections fit benchmark observations of VSV gene expression best at the highest k transc tested. VSV gene expression patterns over a range of k transc and a single max pol #. The parameter k transc sets the rate at which transcribing pols move 5' down the genome (units = genomic chunks per simulated event) and the parameter max pol # sets the maximum number of pols allowed on the genome at one time. Simulations of VSV transcription were performed at three different values of k transc . Histograms of mRNA # for each VSV gene divided by the total mRNA # depict results from the simulations (lavender bars) and average published experimentally observed values (black horizontal bars) (Iverson and Rose, 1981). Each data point is the average of three 100,000 event simulations; error bars show the standard deviation. The number in parentheses and red above each histogram is the root-mean-square deviation (RMSD) of the simulated gene expression pattern from the experimental observations.
Frontiers in Molecular Biosciences
frontiersin.org independent groups (Krempl et al., 2002;Aljabr et al., 2016;Levitz et al., 2017;Donovan-Banfield et al., 2022;Rajan et al., 2022), occurs over what should be a hot-spot for collisions between transcribing and non-transcribing pols: the overlap in the M2 and L gene ORFs ( Figure 3A). We also took inspiration from work by Tang et al. (2014) reporting a very high affinity of VSV pols for the VSV ribonucleoprotein (RNP) complex and suggesting, through computational modeling, the importance of a class of ejective polpol collisions somewhat different from the class modeled here.
Specifically, here we model two pol states-non-transcribing, which diffuse bidirectionally; and transcribing, which move only 5'-for pols that have gained access to the RNA genome through the 3' promoter; in contrast, Tang et al. modeled ejective collisions between pols that have accessed the RNA genome via the 3' promoter and pols "scanning" the VSV RNP complex via interactions between polbound P protein and N protein for the 3' promoter. We make no attempt to model the "scanning" pols that have yet to access the RNA genome of (Tang et al., 2014). Because our model was modified to include multiple pols undergoing ejective collisions between transcribing and nontranscribing pols, it was necessary to explore another parameter, k transc , setting the 5' translocation speed of a transcribing pol. We simulated RSV transcription under three different values each of k transc and maximum pol number ( Figure 3B), and VSV transcription under three different values of k transc and a single maximum pol number (Figure 4). A single maximum pol number was used for VSV transcription because of published work suggesting approximately 50 VSV pols per VSV genome (Thomas et al., 1985); to our knowledge, this ratio is not known for RSV.
Simulated RSV gene expression patterns display a 3' to 5' gradient of increasing steepness with increasing maximum pol number and, for simulations with a maximum of 5 and 10 pols, with increasing k transc ( Figure 3B). The transcription gradient in our model is a consequence of a gradient in pol concentration emerging from ejective pol-pol collisions and obligatory pol reentry at the 3' end of the genome. In the case of simulations of at most 50 pols, the gene expression gradient is steepest at the middle value of k transc because the higher value supports such a high frequency of ejective pol collisions that the actual number of modeled pols occupying a genome at steady-state tends to~10, while the middle value leads to one of~20 pols, which leads to a sharper pol concentration gradient along the genome and a steeper gene expression gradient. It is clear from both the calculated RMSD values and visually inspecting the fits that simulations incorporating a high maximum number of RSV pols per genome produce a gene expression pattern that is too steeply gradient; in contrast, simulations of at most 5 RSV pols per genome yield much better fits of the published data across the 20-fold range of k transc values tested ( Figure 3B).
We simulated VSV gene expression across the same 20-fold range of k transc values and only one value of maximum pol number (Figure 4). At the highest value of k transc tested, the model captures benchmark observations of VSV transcription fairly well. It is interesting that the middle value of k transc results in the worst fit of the data; this results from the phenomenon described above for RSV transcription under the same maximum pol number: the highest value of k transc tested leads to such a high frequency of pol collisions that the actual number of pols occupying the genome at steady-state is much lower than the maximum possible; because the lower value of k transc leads to less frequent collisions and a concomitant increase in the number of pols occupying the genome, a steeper gene expression gradient results (Figure 4).
Further exploring the effects of collisionbased pol ejections on RSV transcription
Thus, our simple model incorporating multiple pols undergoing random diffusion along the genome when not transcribing and ejective collisions when a transcribing and non-transcribing pol meet captures benchmark observations of VSV gene expression (Iverson and Rose, 1981) well while poorly fitting our published observations of RSV gene expression (Rajan et al., 2022). Furthermore, the model most poorly fits data coming from the last two genes of the RSV genome, where multiple groups report a dramatic decrease in gene expression. This is the sole region of the modeled genomes where two ORFs overlap; and this overlap helped inspire the addition of ejective pol collisions into our model. We decided to further investigate the effect of the modeled pol collisions on gene expression over the M2-L region of RSV by analyzing the relationships between 1) the number of pol ejections per run of our simulation and values of the maximum pol number and k transc ; and 2) Figure 5A). However, at the higher values of k transc (1 and 5 genomic chunks per event), the average number of pol ejections starts to plateau beyond a max pol # of 10. This again shows that the steady-state number of pols bound to a genome in the model depends on k transc and that this number is close to 10 at the highest value of k transc tested (assuming a pol footprint of 28 nt). In addition, curves for the higher values of k transc start to converge, suggesting that the model is reaching its maximum pol ejection frequency.
Consistent with the expected effect of the modeled pol collisions, ratios of L:M2 generally decrease with increasing max pol # and increasing k transc ( Figure 5B). However, L:M2 plateaus sharply for the higher values of k transc tested beyond a max pol # of 10. In addition, curves for higher values of k transc start to converge, suggesting that the model is reaching its minimum L:M2 which remains much higher than the average experimentally observed value of~0.09 (Rajan et al., 2022). This suggested that the RSV model was missing something of fundamental importance.
Modifying the model to include 5' biased diffusion of non-transcribing pols
We therefore decided to test the effects of including biased pol diffusion in our model, specifically a 5' bias, which might help explain increased P transc (= max of 0.9). As predicted, an increased P transc resulted in a further decreased L:M2. Simulations with D bias = 2 (results highlighted in pale yellow) were chosen for subsequent simulations. Right panel: histograms show simulated (blue bar) and experimentally observed (horizontal black bar) L:M2 values for two different values of k transc x two different pol footprint sizes (14 and 7 nt) and D bias = 2. A decreased pol footprint size increases the effective distance between the M2 GE and L GS signals, and results in simulated levels of L:M2 that closely match experimental observations. Each data point is the average of three 100,000 event simulations. (C) Global fits of the RSV gene expression data improve with the introduction of D bias and reduced pol footprint size. Simulations of RSV transcription were performed at two different values of k transc x two different values of pol footprint and D bias = 2. Histograms of mRNA # for each RSV gene divided by the total mRNA # depict results from the simulations (blue bars) and average published experimentally observed values (black horizontal bars) (Rajan et al., 2022). Each data point is the average of three 100,000 event simulations; error bars show the standard deviation. The number in parentheses and red above each histogram is the rootmean-square deviation (RMSD) of the simulated gene expression pattern from the experimental observations.
Frontiers in Molecular Biosciences
frontiersin.org 07 why gene expression is possible but falls off steeply when a pol must diffuse 3' to reach the nearest GS signal after terminating transcription (as occurs in RSV from the M2-L overlap) ( Figure 6A). The biased diffusion of proteins has been shown before (Ricchetti et al., 1988;Kwok et al., 2006;Powers et al., 2009), making this change to the model biophysically reasonable.
A 5' pol diffusion bias was modeled by including a new parameter in the model, D Bias , with a value used as a multiplicative factor for 5' diffusion only ( Figure 6A). Thus, a D Bias value of 2 would result in a pol moving two steps (genomic chunks) with every 5' movement while moving only one step (assuming D scan = 1) with every 3' movement; the probabilities of moving in either direction remain equal. This change could also be modeled by modifying the probabilities of 5' vs. 3' pol translocation and keeping each step size the same.
In order to test the effects of 5' biased pol diffusion on gene expression in our model, we chose two of the parameter sets yielding fits with lower RMSDs from our first set of RSV transcription simulations involving multiple pols ( Figure 3B), ran these with three different values of D Bias , and looked for a drop in the predicted value of L:M2 mRNA levels ( Figure 6B). The two lower values of D Bias tested produced a greater drop in L:M2 than the highest value tested ( Figure 6B). This is because under a maximum transcription initiation probability of 0.5, a high 5' D Bias leads to frequent "missing" of the M2 GS signal before transcription initiation FIGURE 7 The model captures published observations of RSV and VSV transcription with adjustments to the underlying transcription probabilities (P transc ). (A) High quality fits of experimentally observed RSV gene expression patterns. P transc were manually adjusted to achieve optimized fits at max pol # = 5, k transc = 5, D bias = 2, and pol footprint of 14 and 7 nt. Histograms of mRNA # for each RSV gene divided by the total mRNA # depict results from the simulations (blue bars) and average published experimentally observed values (black horizontal bars) (Rajan et al., 2022). Each data point is the average of three 100,000 event simulations; error bars show the standard deviation. The number in parentheses and red above each histogram is the root-mean-square deviation (RMSD) of the simulated gene expression pattern from the experimental observations. (B) A high quality fit of the benchmark experimentally observed VSV gene expression pattern. P transc were manually adjusted to achieve an optimized fit at max pol # = 50, k transc = 5, D bias = 1 (i.e., NO 5' bias), and pol footprint = 28 nt. The histogram of mRNA # for each VSV gene divided by the total mRNA # depicts results from the simulations (lavender bars) and average published experimentally observed values (black horizontal bars) (Iverson and Rose, 1981). Each data point is the average of three 100,000 event simulations; error bars show the standard deviation. The number in parentheses and red above the histogram is the root-mean-square deviation (RMSD) of the simulated gene expression pattern from the experimental observations. TABLE 2 Major parameter values for high quality fits of different data sets by our model. Our model produces high quality fits of two different RSV data sets (Donovan-Banfield et al., 2022;Rajan et al., 2022) and benchmark observations of VSV gene expression (Iverson and Rose, 1981). Each list of transcription initiation probabilities (P transc ) contains the values used for every RSV or VSV GS signal following their 3' to 5' order along the genome. at the L GS signal. We therefore decided to increase the maximum transcription initiation probability to 0.9 and reran simulations at the lower values of 5' D Bias tested. As expected, this resulted in a further drop in predicted L:M2 mRNA levels. However, simulated L:M2 levels remained much higher than our experimentally observed value ( Figure 6B). Finally, we decided to decrease the pol footprint size by factors of 2 and 4, separately, knowing that this would increase the effective distance between the M2 GE signal and the L GS signal, and predicting a drop in L:M2 levels. The smallest pol footprint size tested, seven nts, is equal to the number of nucleotides bound by a single subunit of RSV nucleoprotein (N protein) and only three nts less than the size of the highly conserved RSV GS signal. Decreasing the pol footprint size yielded predicted L:M2 values that are very close to the experimentally observed value ( Figure 6B); and global fits of the RSV gene expression data quantitatively improved for the higher value of k transc tested and remained roughly the same for the lower value ( Figure 6C).
Optimizing model fits
With the addition of D Bias to our model of RSV transcription, it seemed that both RSV and VSV versions of the model were poised to capture experimentally observed patterns of gene expression. We therefore set about finding RSV and VSV transcription initiation probabilities that would produce optimal fits of the experimental data ( Figure 7A, B). Using a set of transcription probabilities spanning a 10-fold range of values for a maximum pol number of 5, our RSV model yielded high quality fits of our experimental data ( Figure 7A; Table 2). Our VSV model yielded a high quality fit of the experimental data with a set of transcription probabilities spanning a 6-fold range and a maximum pol number of 50 ( Figure 7B; Table 2).
We also decided to fit the recently reported RSV long-read sequencing data of Donovan-Banfield et al., 2022. An increased max pol # and an approximately 2-fold range of P transc were needed to capture their data (Table 2). These changes reflect the more gradient nature of the observed gene expression pattern, while our experimental observations showed much higher levels of G gene mRNA (Rajan et al., 2022).
A 5' diffusion bias was needed to capture both RSV data sets because of a common dramatic decrease in expression between genes M2 and L. In contrast, a 5' diffusion bias was not needed to capture the benchmark observations of VSV gene expression used here; however, including one has minimal effect on the model's output (data not shown). Thus, we simply cannot make a model-supported prediction about whether non-transcribing VSV pols diffuse with a 5' bias. Continuing with VSV, the high quality fit we report involves a 6fold range of P transc , but a quality fit can also be obtained with a 5-fold range of P transc and less variation (= 0.1, 0.5, 0.5, 0.5, 0.5; RMSD = 0.009).
We believe the changes to transcription probabilities needed to produce high quality fits of the experimental data are reasonable. For instance, we have obtained preliminary data using RSV minigenomes encoding luciferase reporter genes showing that a single RSV GS signal sequence can support a 1.5-fold range of gene expression according to its alignment with bound nucleoprotein or N-phase (Piedra et al., 2020b). We do not know whether the reported N-phase-mediated changes to gene expression are exactly proportional to the changes in microscopic probabilities of transcription initiation modeled here because the former come from luciferase activity measurements and therefore reflect the addition of translation. Moreover, sequence changes outside of the highly conserved 10 nt stretch of the RSV GS signal can lead to gene expression changes (Kuo et al., 1997), and the VSV GS signal is less conserved than RSV's. However, we are not aware of minigenome studies exploring the effects of VSV GS signal sequence or N-phase on gene expression. Finally, it is also possible that the shape of the observed RSV and VSV gene expression patterns depends partly on differences in the underlying mRNA stabilities, which we make no attempt to model here; but we have shown previously that any such differences are unlikely to significantly affect experimentally observed RSV gene expression patterns (Piedra et al., 2020a). It is also worth mentioning that we make no attempt to model the potential effects of 1) variable nascent mRNA capping efficiency and 2) mRNA polyadenylation. Both could be modeled as a variable pause time at the start and end of transcription, respectively. However, we do not believe their inclusion would change the major results presented here.
Conclusion and limitations
Our model can capture observed RSV and VSV transcription patterns with biophysically reasonable parameters and parameter values. Our model makes the following major predictions in need of wet lab experimental testing: 1) ejective collisions occur between transcribing and non-transcribing NNSV pols; 2) non-transcribing RSV pols (and perhaps VSV pols) undergo 5' biased diffusion along the viral genome; and 3) an increase in the number of pols bound to and diffusing along an NNSV genome at any one time will lead to more frequent pol-pol collisions and a sharper transcription gradient. Sophisticated single molecule TIRF-based assays are needed to directly test predictions 1-2, while 3 can be tested using established minigenome or recombinant genome assays along with high throughput sequencing.
Data availability statement
The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author.
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Domain: Biology
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What happens after a blood meal? A transcriptome response from the main tissues involved in egg production in Rhodnius prolixus, an insect vector of Chagas disease
The blood-sucking hemipteran Rhodnius prolixus is a vector of Chagas disease, one of the most neglected tropical diseases affecting several million people, mostly in Latin America. The blood meal is an event with a high epidemiological impact since in adult mated females it initiates the production of hundreds of eggs. By means of RNA-Sequencing (RNA-Seq) we have examined how a blood meal influences mRNA expression in the central nervous system (CNS), fat body and ovaries in order to promote egg production, focusing on tissue-specific responses under controlled nutritional conditions. We illustrate the cross talk between reproduction and a) lipids, proteins and trehalose metabolism, b) neuropeptide and neurohormonal signaling, and c) the immune system. Overall, our molecular evaluation confirms and supports previous studies and provides an invaluable molecular resource for future investigations on different tissues involved in successful reproductive events. Analyses like this can be used to increase the chances of developing novel strategies of vector population control by translational research, with less impact on the environment and more specificity for a particular organism. Author summary The blood-sucking hemipteran Rhodnius prolixus is one of the main vectors of Chagas disease. The blood meal is an event with a high epidemiological impact since in adult mated females, blood-gorging leads to the production of hundreds of eggs. This work describes an in-depth central nervous system (CNS), ovary and fat body transcriptome analysis, focusing on transcripts related to blood intake which may be relevant in promoting egg production. To date, the principle focus in Chagas disease prevention is on the elimination of triatomine vectors and their progeny. This work will serve as a starting point for initiating novel investigations on targets identified with a potential for use in vector control; for example using specific genes to generated symbiont-mediated RNAi, a powerful technology which provides a novel means in biocontrol against tropical disease vectors.
Introduction
Insects, which represent more than half of all living organisms on earth, have a close relationship with human beings. To many of them, we can ascribe a negative interaction, for example those that act as carriers of disease. Chagas disease, one of the most neglected tropical diseases, is caused by the protozoan Trypanosoma cruzi, which is transmitted to mammalian hosts primarily by blood-feeding insects, the triatomines [1]. This disease affects 6-7 million people, mostly in Latin America, but because of migration the disease has spread to other continents [2]. To date, treatment of the chronic phase of this disease is limited [3], resulting in 2000 deaths per year [1], although it is known that Chagas disease is an under-reported cause of death [4]. The principle focus in Chagas disease prevention is on the elimination of triatomine vectors from human homes. Currently, the most heavily used option is chemical control, although resistance to these insecticides has been reported in the last decade [5]. Furthermore, the devastating impact of chemical insecticides on the environment and other organisms, such as beneficial insects, can no longer be ignored [6].
Triatomines have developed an integrated control over the reproductive system, whereby different tissues work with extreme precision and coordination to achieve successful production of progeny. There are three tissues that work in concert to promote reproduction; the central nervous system (CNS), fat body and ovaries. The CNS contains neuroendocrine cells that synthesize neuropeptides involved in the coordination of events that promote egg production.
These neuropeptides are produced as large precursors, which are then cleaved and modified to become biologically active neuropeptides [7]. These neuropeptides are secreted as neuromodulators or neurohormones to act via specific receptors [8]. With regard to reproduction, In addition to being a main vector of Chagas disease, with high epidemiological relevance for easily colonizing domestic habitats [14], R. prolixus has been the subject of intense investigations over the past century, which have contributed to our understanding of important aspects of metabolism and physiology in insects [15]. It is important to highlight that the complete genome of R. prolixus has been published [16] and, therefore, many new questions can be asked and answered with regard to insect physiology/endocrinology. Next-generation sequencing allows us to study biological systems at the genomic level to link mRNA sequences with specific biological functions of specific tissues during a particular stage or state. Recently, by transcriptome analysis we reported an up-regulation of transcripts involved in insulin-like peptide/target of rapamycin (ILP/ToR) signaling in unfed insects. However, we demonstrated that this signaling pathway is only activated in the fat body and ovaries of fed insects. Thus, we demonstrated that unfed females are in a sensitized state to respond to an increase of ILP levels by rapidly activating ILP/ToR signaling after a blood meal [17]. Here, we examine how a blood meal influences CNS, fat body and ovary gene expression to promote egg production; focusing on details associated with tissue-specific responses in particular nutritional states. Our data opens up avenues of translational research that could generate novel strategies of vector population control, with less impact in the environment and with more specificity for a particular organism, such as using specific genes to generated symbiont-mediated RNAi; a powerful technology which provides a potential means in biocontrol against tropical disease vectors [18]. Insects were maintain in incubators at 25°C under high humidity (~50%). Newlyemerged adult females (10 days post-ecdysis) were segregated, and placed together with a recently fed male to copulate. Then, female insects were fed on defibrinated rabbit blood (Cedarlane, Burlington, CA) to initiate egg growth. Only insects that fed 2.5 to 3 times their initial body weight were used for the experiments. CNS, fat body (FB) and ovaries (OV) from adult mated females were dissected at 10 days post-ecdysis for the unfed condition (UFC) and 3 days post-feeding as the fed condition (FC), according to Leyria et al. [17]. Insects in the fed condition will have begun vitellogenesis and egg growth.
Transcriptomic data analysis
Read sequences were obtained from Leyria et al. [17]. This study reported transcriptomes of CNS, FB and OV from fed and unfed females. The raw sequence dataset of this project is registered with the National Center for Biotechnology Information (NCBI) under PRJNA624187 and PRJNA624904 bioprojects. A detailed description of our bioinformatic pipeline can be found in Leyria et al. [17]. Briefly, CNS, OV and ventral and dorsal FB of R. prolixus females were dissected in cold autoclaved phosphate buffered saline (PBS, 6.6 mM Na 2 HPO 4 /KH 2 platforms (HiSeq 2500) at the Novogene sequencing facility (California, USA). Raw data were recorded in a FASTQ file, which contains sequence (reads) and corresponding sequencing quality information. Fastq format were first processed through in-house perl scripts, where clean data (clean reads) were obtained by removing reads containing the adapter, reads containing ploy-N and low quality reads from raw data. Also, Q20, Q30 and GC content from the clean data were calculated. All the downstream analyses were based on the clean data [17].
Differential expression analysis
First, clean reads were aligned to the reference genome using HISAT2 software. After that, HTSeq v0.6.1 was used to count the number of reads mapped to each gene. FPKM (expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene were calculated based on the length of the gene and numbere of reads mapped to the gene. In general, an FPKM value of 0.1 was set as the threshold for determining whether the gene is expressed or not. Differential expression analysis of two nutritional conditions were performed using the DESeq R package (1.18.0). DESeq provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate. Genes with an adjusted P-value < 0.05 found by DESeq were assigned as differentially expressed. Gene Ontology (GO) enrichment analysis of differentially expressed genes was implemented by the GOseq R package, in which gene length bias was corrected. GO terms with corrected P-value less than 0.05 were considered significantly enriched by differential expressed genes.
Validation of quantitative gene expression
To validate Illumina sequencing results, 7 genes were chosen at random and their expressions were analyzed by quantitative polymerase-chain reaction (RT-qPCR) [17]. Briefly total RNA was extracted as described above, followed by cDNA synthesis using the High Capacity cDNA Reverse Transcription Kit (Applied-Biosystems, by Fisher Scientific, ON, Canada). RT-qPCR was performed using an advanced master mix with super green low rox reagent (WISENT Bioproducts Inc, QC, Canada). Three independent experiment were performed (n=3) with each n composed of a pool of 5 tissues. Each reaction contained 3 technical replicates and were carried out using a CFX384 TouchTM Real-Time PCR Detection System (BioRad Laboratories Ltd., Mississauga, ON, Canada). The primers used (by Sigma-Aldrich, ON, Canada) for amplification are shown in S1 Table. Quantitative validation was analyzed by the 2^-ΔΔCt method. All reactions showed an amplification efficiency higher than 95 %. β-actin, which was previously validated for transcript expression in FB and OV from R.
prolixus at different nutritional conditions [17], was used as the reference gene. For each pair of primers a dissociation curve with a single peak was seen, indicating that a single cDNA product was amplified. Specific target amplification was confirmed by automatic sequencing (Macrogen, NY, USA). The correlation coefficient between Illumina RNA sequencing and RT-qPCR data was analyzed by the Pearson test. Ovaries and ventral and dorsal FB were dissected from insects during UFC and FC under cold R. prolixus saline (NaCl 150 mM, KCl 8.6 mM, CaCl 2 2.0 mM, MgCl 2 8.5 mM, NaHCO 3 4.0 mM, HEPES 5.0 mM, pH 7.0). Total lipids and carbohydrates from tissues were measured by colorimetric assays as previously described [19]. Briefly, the tissues were placed in either 500 μl of isopropanol (for lipid quantification) or 500 μl 10 % cold trichloroacetic acid (TCA, for carbohydrate quantification), homogenized and then centrifuged for 10 min at 20 °C and 8000 g.
Lipid and carbohydrate measurements
For lipid quantification, 400 μl of the supernatants were transferred to 1.5 ml tubes containing 100 μL of 1 M KOH. Then, the tubes were incubated at 60°C for 10 min and once they were cool, 100 μl of sodium periodate solution (11.6 mM sodium periodate in 2 N glacial acetic acid) was added. After 10 min of incubation at room temperature, 600 μl of chromogenic solution (40 ml of 2 M ammonium acetate, 40 ml isopropanol, 150 ml acetyl acetone) were added to the samples and incubated for 30 min at 60 °C. The resultant colour was measured at 410 nm using a plate reader spectrophotometer (Cytation 3 Imaging Reader, BioTek, Winooski, VT, USA). A standard curve of triglycerides ranging from 0 to 60 μg was run independently and in parallel with the experimental samples. FB and OV carbohydrate content was measured using the anthrone colorimetric assay. Briefly, 50 μl of the supernatants after TCA precipitation were mixed with 500 μl of anthrone solution (26 mM anthrone, 1.31 mM thiourea, 66 % sulfuric acid) and incubated for 20 min at 100 °C. The samples were allowed to cool in the dark for 15 min and then quantified at 620 nm using a plate reader spectrophotometer described. A standard curve was run in parallel with the experimental samples using a 0 -40 μg range of trehalose. Proteins were measured according to Bradford [20] processing the tissue as described previously [17].
Three independent experiments were analyzed (n=3) for each measurement with each n composed of a pool of 5 tissues.
Results and Discussion
We were surprised to observe no major gene differences in the CNS between the UFC and FC. None of the GO functional terms were enriched in the CNS under these different nutritional states. We chose 3 days post-blood meal as the fed condition because of the morphological changes observed in the FB and OV (Leyria et al., 2020). The days chosen to monitor transcriptional regulation are appropriate for FB and OV but apparently not for CNS.
Possibly for the CNS, transcriptional regulation begins early after a blood meal to control remotely molecular, biochemical and physiological changes that we then observed in the FB and OV during the FC. Using R. prolixus adult insects, Sterkel et al. [21] reported a quantitative proteomic analysis of the post-feeding response from CNS in 3 different conditions: unfed, 4 h and 24 h after blood intake. These researchers found only 4 neuropeptides (NVP-like, ITG-like, kinin-precursor peptide and neuropeptide-like precursor 1 (NPLP1)) that were significantly upregulated in response to the blood meal. Taken together, this appears to indicate that the changes in the transcriptional and protein levels in the CNS of R. prolixus adult insects occur quickly or more slowly so that it is difficult to find any changes. For this reason, below, we focused our attention on the FB and OV and reflect on the CNS transcriptome analysis when making reference to peptide/hormone signaling.
To validate Illumina sequencing, 7 mRNAs were chosen and their relative transcript abundance in FB and OV in both nutritional states monitored by RT-qPCR. A good correlation was found between RNAseq and RT-qPCR data; the Pearson tests were 0.9311 (to FB) and 0.9109 (to OV), with a statistical significance of p<0.01. Multiples genes from these transcriptomes were also validated using RT-qPCR by Leyria et al. [17].
GO enrichment analysis
Nutrients are essential for energy homeostasis of any organism and important changes in nutrient stores occur between feeding and non-feeding periods and also more remarkably in adult insects during reproductive processes [9,22]. GO enrichment was used to assign a functional classification to differentially expressed genes (DEGs). All DEGs categorize into two main groups: cellular components and biological processes. In cellular components, they are divided into 21 terms which are significantly up-regulated in FB_FC with respect to FB_UFC (Fig 1A).
The most represented cellular components terms are cell parts involved in protein synthesis, as is to be expected since the FB is the main synthesis and secretory organ responsible for the production of virtually all haemolymph proteins. With regard to biological processes, the main terms in the FB are involved in biosynthesis and lipid and carbohydrate metabolism ( Fig 1B).
Recently, by examining KEGG enrichment we reported that the "ABC transporters pathway", transporters which use energy to translocate substrates (e.g., sugar, lipid and peptides) across cell membranes, is up-regulated in FB_FC, which shows that the synthesized nutrients are released, in this case likely to support vitellogenesis [17]. In the OV, the main terms of cellular components and biological processes which are significantly up-regulated in OV_FC with respect to OV_UFC are related to lipid, carbohydrate and protein metabolism, insect hormone biosynthesis, and yolk granule formation (specialized structures which stores all nutrients which are used as substrates for embryogenesis and maintenance of the newly hatched nymph) (Fig 2A and B). These nutrients are mostly proteins, lipids and carbohydrates, produced by the FB, released into the hemolymph and subsequently taken up by the oocytes [23]. As we anticipated in light of the results of the GO enrichment, lipid, protein and carbohydrate levels in the FB and OV are increased in fed females (Fig 3A and B), as reported in other vectors of Chagas' disease [22,[24][25]. In addition, it is clear that stored proteins are always the major component in both tissues, followed by lipids and then carbohydrate stores. and quantified as described in Materials and Methods. The results for total lipid, carbohydrate and protein content were graphed as the mean ± Standard Error of the Mean (SEM) from three independent experiments. Graphs and statistical tests were performed using GraphPad Prism 7 (GraphPad Software, CA, USA, www.graphpad.com). All datasets passed normality and homoscedasticity tests. The statistical significance of the data was calculated using Student's ttest. A P value < 0.05 was considered statistically significant.
Protein and hormone analysis
Vitellogenins (Vgs), the main yolk protein precursors (YPPs), are large molecules synthesized predominantly by the FB, secreted into the hemolymph and then transported to the OVs. The number of genes encoding insect Vgs varies from one to several depending on the species [26]. Our results show that the mRNA levels for Vgs are considerable higher in the FB with respect to the OV, which is not surprising (Fig 4A). In the FB transcripts for Vg1 and Vg2 are up-regulated during vitellogenesis (FB_FC), with Vg1 having the highest expression (Fig 4A and S2 Table). In Triatoma infestans, a triatomine related to R. prolixus, Vg1 and Vg2 genes are expressed at relatively low levels during the UFC and both Vg transcripts are up-regulated after blood-feeding [27]. Recently, by KEGG enrichment we reported "amino sugar and nucleotide sugar metabolism" and "N-Glycan biosynthesis" are pathways up-regulated in the FB of fed females [17]. Glycosylation is a critical post-translational modification to obtain the proper protein structure for adequate protein function and for Vgs glycosylation is a step necessary for folding, processing and transport to the oocyte [28]. As previously reported in R. prolixus [29], our results suggest that Vg synthesis also occurs in the OV, with Vg transcripts up-regulated after a blood meal and Vg1 levels higher than Vg2 (Fig 4A and S2 Table). Interestingly, in T. infestans the Vg2 transcript is quantitatively more important that Vg1 in OVs after feeding [27]. The vitellogenin receptor (VgR) mRNA expression, the endocytic receptor responsible for Vg uptake by oocytes, is up-regulated in OV from unfed insects (Fig 4A and S2 Table), contrary to expectation since Vg uptake occurs after a blood meal. However, as expected the main KEGG enrichment pathways involved in receptor-mediated endocytosis signaling (endocytosis, lysosome and phagosome pathways) are enriched in OV_FC of R. prolixus [17]. This result indicates that even when the OV expresses high endocytic receptor transcript levels in the UFC, only after a blood meal does the endocytic process occur. Similarly, in cockroaches VgR mRNA levels remain low during the vitellogenic phase [30][31]. A similar pattern of high VgR mRNA levels in non-reproductive stages and low levels during vitellogenesis is found not only in insects but also in oviparous vertebrates [32][33]. It can interpreted as a recycling of VgR protein during the vitellogenetic period. In contrast to these observations, in the mosquito A. aegypti, VgR mRNA starts to rise one day after the adult moult, continues to increase dramatically during the vitellogenic period, and then peaks one day after the blood meal [34]. On the other hand, using R. prolixus females, Oliveira et al. [35] described another YPP, a 15-kDa protein called Rhodnius heme binding protein (RHBP), which works as an antioxidant agent in hemolymph.
After the blood meal, a large amount of heme is released from hemoglobin, crosses the digestive barrier and reaches the hemolymph, where it is sequestered by RHBP. Here, we show that in the FB, RHBP mRNA levels are up-regulated in females 3 days after feeding (Fig 4A and S2 Table).
The increase of synthesis of YPPs in FB_FC coincides with the KEGG analysis reported recently, where we show an enrichment of "biosynthesis of amino acids pathway" [17].
The Wnt signaling pathway was first discovered as a key event in D. melanogaster development [36]. The Wnt (glycoprotein ligand) and Frizzled (Fz, transmembrane Wnt receptor) proteins interact with structural components at the cell surface to initiate the signaling cascades that result in transcriptional regulation of gene expression. In A. aegypti, a fundamental role of Frizzled 2 (Fz2) was reported in egg production [37]. Here, we find that Wnt and Fz2 mRNA levels are up-regulated in OV_FC (Fig 4A and S2 Table). Additionally, Wnt and ToR signaling interact synergistically in the vitellogenic process [37] and supporting this finding, we showed mToR signaling is active in OV_FC [17]. Also, the non-canonical Wnt pathway indicates that Wnt/Fz signaling leads to the release of intracellular calcium through trimeric G proteins [37]. The calcium release and intracellular accumulation activates several Ca 2+ -sensitive proteins, including protein kinase C (PKC), calcineurin and calcium/calmodulin-dependent kinase II (CamKII). In A. aegypti it was found that juvenile hormone (JH) activates the phospholipase C (PLC) pathway and quickly increases the levels of Ca 2+ for the activation and autophosphorylation of CaMKII, which is involved in patency development [38]. On the other hand, in many animals, a rise in intracellular Ca 2+ levels is the trigger for egg activation, the process by which an arrested mature oocyte transitions to prepare for embryogenesis. Genetic studies have uncovered essential roles for the calcium-dependent enzyme calcineurin in Drosophila egg activation [39]. By DEG analysis, we demonstrate an up-regulation of PKC and calcineurin in OV from fed insects (Fig 4A and S2 Table). In R. prolixus, earlier studies by Ilenchuk et al. [40] suggested that a PKC might be involved in patency and Vg uptake but until now the receptors or molecular mechanisms responsible for this cascade are unknown. The results we observe in vitellogenic oocytes of R. prolixus could be indicative of a relationship between patency and Wnt/Fz2/Ca 2+ signaling. Methoprene-tolerant (Met), which encodes a transcription factor of the bHLH-PAS family, was reported to be a JH receptor [41]. Krüppel homolog 1 (Kr-h1), identified as the main JH primary-response gene activated by Met [41], is up-regulated in OV_FC (Fig 4A and S2 Table), which supports the hypothesis that in R. prolixus, JH is working directly on OVs to stimulate egg formation. Heat shock proteins represent different protein families based on their sequence homology and molecular masses. Among them, Heat shock protein 70 family (Hsp70) is highly conserved. The expression of Hsp70 is considered a good marker for the inducible stress response in an organism [42]. In T. infestans Hsp70 is strongly expressed in unfed insects [43].
Similarly, in R. prolixus, we find that Hsp70 is up-regulated in the FB from unfed females (Fig 4A and S2 Table), a condition inherently associated with a stressful situation. Glucose-regulated protein of 78 kDa (Grp78) is a member of the Hsp70 family which acts as a chaperone to facilitate protein folding and to inhibit protein aggregation of new peptides. Interestingly, in Locusta migratoria, Grp78 was reported as a regulatory factor of Vg synthesis and cell homeostasis in the FB via JH signaling [44]. In R. prolixus, we show a significant up-regulation of Grp78-like protein in both FB and OV from fed insects (Fig 4A and S2 Table). This result suggests a novel regulatory mechanism involved in the vitellogenic process of R. prolixus. Notch is a receptor that directly translates information of cell-cell contact to gene expression in the nucleus [45]. By KEGG analysis, it was demonstrated that Notch signaling is up-regulated in the OV from fed females [17]. Here, we find that transcripts involved in Notch developmental functions, such as Fridge, presenilin enhancer 2 (PEN-2) and presenilin-1, are upregulated in OV_FC (Fig 4B and S2 Table). Mastermind is an essential nuclear factor that supports the activity of Notch [46]. In OV_FC of R. prolixus, mastermind transcriptional factor is up-regulated, as well as Bx42 (Fig 4B and S2 Table), an essential factor which through Notch is involved in the formation of different tissues during embryogenesis [47]. In Blattella germanica, it was demonstrated that Notch is important in maintaining the proliferative and nonapoptotic state of follicular cells, as well as in differentiation of the posterior follicular cell population [48]. It is likely that the up-regulation of this signaling in R. prolixus after a blood meal is related to follicular cell metabolism during egg growth.
During vitellogenesis, JH titres are expected to increase, since JH is one of the main hormones involved in Vg synthesis. In insects, the corpora allata (CA), a pair of endocrine glands associated with the brain, are responsible for the synthesis of this sesquiterpenoid hormone [41]. By KEGG analysis, two pathways related to JH, "Insect hormone biosynthesis" and "Terpenoid backbone biosynthesis", are up-regulated in the FB and OV during the FC [17].
Here, we find that the levels of enzymes responsible for the synthesis of JH, in general, display an up-regulation in the OV and a non-statistically significant increase in the FC with respect to UFC (Fig 5A and S2 Table). It is important to highlight that R. prolixus allatectomized immediately after emergence as adults, continue to make a few eggs [49]. This finding suggests an alternate source for JH apart from the CA, but so far, this source has not been identified. Our results suggest that vitellogenic FB and OVs could be capable of synthesizing JH. In addition, insect cytochrome P450s include a group of different enzymes involved in detoxication and biosynthesis of ecdysteroids and JH [50][51]. Previously, by KEGG analysis, we reported an upregulation of metabolism of xenobiotics by cytochrome P450 in FB_FC, possibly because of an increase in hormone synthesis or/and a detoxification after a blood meal [17]. Allatostatin-C (ASTC) is a family of peptides originally associated with the control of CA activity but now known to be pleiotropic. ASTC and its paralog, ASTCC, are very similar peptides, likely generated by gene duplication, and their receptors possibly have a common ancestor as well [52].
We find a significant up-regulation of ASTCC mRNA expression in OV_UFC (Fig 5A and S2 Table), but so far, there is no evidence about the specific role of this peptide on OVs. [53]. The takeout genes (To) were discovered as a circadian-regulated gene and belong to the JHBPs family [54]. The To genes modulate various physiological processes, such as behavioral plasticity in the migratory locust L. migratoria and feeding in D. melanogaster [55][56]. In the brown planthopper Nilaparvata lugens, the To family of genes were reported to be regulated by JH signaling [57]. Fifteen such genes were identified in the antenna of R. prolixus [58]. Here, we find that To genes have a unique pattern of expression according to the tissue analyzed and feeding condition (Fig 5B and S2 Table). To1, To2, To4 and To7 mRNA expression is highly expressed in the CNS of unfed insects, suggesting that starvation could induce the expression of these genes. To9, To11, To12 and To15 mRNA expression is significantly increased in the FB from females after a blood meal, To5, T12 and T13 transcripts show a significantly increased expression in OV_FC (Fig 5B and S2 Table). This is the first report of an analysis of To genes in different tissues involved in reproduction in R. prolixus, providing new insights into the mechanisms involved in egg formation.
Ecdysteroids are also critical developmental hormones involved in the regulation of molting and metamorphosis. The prothoracic glands (PGs) are the major source of these ecdysteroids in larvae, but PGs are absent from adult insects, where alternative sites of ecdysteroid production have been described. Cardinal-Aucoin et al. [59] reported that in R.
prolixus, between days 3 and 4 after a blood meal, ovarian ecdysteroid content increased 4-5 fold to a level that was sustained for the duration of egg development. This pattern is similar to that seen in the hemolymph ecdysteroid titer. Two interpretations were proposed a) the ovary passively absorbs hemolymph ecdysteroids or b) the ovary produces the ecdysteroids found in the hemolymph. After a blood meal, we find up-regulation of 3 enzymes involved in ecdysteroid synthesis in the OV, Shade, Phantom and 26-hydroxilase, supporting the second hypothesis ( Fig 5C and S2 Table). Similarly, in Tribolium castaneum, an insect with the same type of ovaries growth, oocyte maturation and follicle cell differentiation [60]. Overall, this work suggests that the OVs in R. prolixus females are an alternate source for ecdysteroid synthesis.
Carbohydrate analysis
The main blood sugar in insects is trehalose, a sugar that consists of two glycosidically linked glucose units. Trehalose homeostasis is controlled by trehalose-6-phosphate synthase, the main enzyme involved in trehalose synthesis by the FB; trehalose transporter (TRET), which has a particular direction of transport depending on the trehalose gradient, and trehalases, specifically two isoforms, soluble (TRE-1) and membrane-bound (TRE-2), involved in the conversion of trehalose to glucose to generate energy [61][62]. DEG analysis reveals that trehalose-6phosphatase synthase and TRET are up-regulated in the FB during the FC (Fig 6 and S2 Table).
It is widely accepted that the vitellogenic process is an event with high energy demands. Thus, trehalose synthesis and release by TRET after a blood meal are steps necessary to provide energy to support successful vitellogenesis as well as trehalose to be taken up by developing oocytes, which accumulate carbohydrates as a resource for embryogenesis [63]. Supporting this finding, specific phospholipase A2-like mRNA (RPRC008617) is up-regulated in FB_FC (Fig 7 and S2 Table). This belongs to a group of enzymes that are involved in either the formation or release of trehalose from FB cells [64]. In addition, after a blood meal, we find that trehalose-6phosphatase synthase is down-regulated in OV (Fig 6 and S2 Table). Therefore, the trehalose that is stored in the OV to promote glycogen synthesis must be incorporated from extra-ovarian sources. In R. prolixus, it has been suggested that TRE-2 in ovaries could react directly with trehalose in the haemolymph supporting the idea that hydrolysis of trehalose at the cellular surface could be an obligatory step to provide glucose for carbohydrate accumulation by oocytes [65]. The researchers found that trehalase activity seemed not to be regulated at the transcriptional level after a blood meal. In addition, here we find that TRE-2 is up-regulated in OVs but in unfed females (Fig 6 and S2 Table). We hypothesize that glucose obtained by the breakdown of trehalose could participate in the regulation of the energy necessary (contributed by different tissues, including OVs) to maintain overall metabolism of the insect until physiological conditions improve. An interesting finding from our results is that TRET is more than 6-fold up-regulated in OVs of fed insects (Fig 6 and S2 Table), supporting the hypothesis that a direct trehalose uptake from the hemolymph by TRET is the most important process involved in the storage of carbohydrates in ovaries.
Lipid analysis
In insects the majority of lipid reserves are found in the FB as triacylglycerol (TAG).
Lipids are critical to support situations of high metabolic demand, such as vitellogenesis [9]. TAG storage is mainly the result of 2 mechanisms: a) the transfer of dietary fat from the midgut to the FB by lipophorin (Lp), the main lipoprotein of insects, during feeding, and b) the synthesis of lipids from carbohydrates reserves. The participation of lipids in oocytes growth is mainly to supply energy for the developing embryo [23]. As the ability of insect oocytes to obtain fatty acids by de novo synthesis is very small, most of the lipids in the oocyte come from the FB via the hemolymph using Lp as transport [66]. In vitellogenesis, lipid accumulation by OVs is associated with a considerable reduction in the lipid content of the FB [9]. However, after a large blood meal, the triatomines must store a vast amount of TAG to support a possible period of fasting. This reality promotes a fine balance between lipid mobilization for egg growth and lipid storage to survive starvation. Here, we demonstrate that there are different types and subtypes of enzymes involved in lipid metabolism, as reported by Gondim et al. [67], and each one seems to have a particular role according to the specific tissue and physiological condition. TAG can be synthesized by 4 different pathways: a) the monoacylglycerol (MG)-pathway; b) the glycerol-3 phosphate (G3P) pathway; (c) degradation of phospholipids; or (d) deacylation of triglyceride catalyzed by lipases [9]. In R. prolixus, only the G3P pathway has been reported [67]. This pathway starts with acylation of G3P, catalyzed by G3P acyl transferases (GPAT). Two GPAT, GPAT1 and GPAT4, have been described in R. prolixus [68]. Here, we find that the mRNA expression of GPA1 and GPA4 is predominantly increased in the OV with respect to the FB and only GPAT4 is up-regulated in the OV of unfed insects (Fig 7 and S2 Table). We had expected these enzyme to be increased in the FB after a blood meal but it is important to highlight that Alvez-Bezerra et al. [68] indicated that GPAT activity is regulated by a post-translational mechanisms and not at the mRNA levels. However, other transcripts for enzymes involved with the synthesis, elongation and lipid storage, such as insect microsomal and cytosolic fatty acid synthases (FAS1 and FAS2), lipid elongases and sterol regulatory element-binding protein (SREBP) are up-regulated in the FB after a blood meal (Fig 7 and S2 Table). These finding coincide with our previous report, where we show that both, "fatty acid biosynthesis" and "fatty acid elongation", are KEGG pathways enriched in FB_FC [17]. Fatty acid desaturases (FAD) are essentials for de novo FA synthesis. In R. prolixus we show that 2 transcripts encoding for FAD are up-regulated in both FB and OV of fed insects (Fig 7 and S2 Table). These results suggest that after a blood meal, FA synthesis increases and confirms that, besides incorporation of lipids from hemolymph, de novo synthesis of FAs by the FB of R. prolixus occurs, as was suggested by Pontes et al. [69]. Therefore, FAs stored in tissues could be used to synthesize TAG, phospholipids or be oxidized for ATP production. For any of these pathways, FAs need to be activated and that is the role of acetyl CoA synthetases (ACS). In R. prolixus, we find different ACS transcripts that encode short-chain ACS, regular ACS, long-chain ACS and very long chain ACS. All these enzymes are present in both the FB and OV, but their expression patterns depend on the nutritional condition (Fig 7 and S2 Table). In general, ACS could be considered more important during the unfed condition, suggesting that β-oxidation is an essential pathway in unfed insects to promote the synthesis of ATP as an energy source (Fig 7 and S2 Table). For FA mobilization, lipases play a critical role to catalyze the hydrolysis of TAG molecules [9]. In this sense, transcripts related to lipid breakdown (lipases) or lipid transfer (lipophorin receptor, LpR) in general are increased in the FB of unfed insects (Fig 7 and S2 Table). Among others, we also find an increase (not statistically significant) of Brummer lipase-like and Hormone-sensitive lipase-like mRNA expression in FB_UFC. Interestingly, in D. melanogaster, Brummer lipase is induced in the FB during starvation by FoxO-signaling [70]. Recently we showed that Foxo signaling is up-regulated in FB_UFC [17]. Hormone-sensitive lipase is present in the lipid droplet of D. melanogaster and is involved in FB lipid mobilization during starvation [71]. Also, it was reported that in insects, the activation of lipolysis is accompanied by hydrolysis of phospholipids from lipid droplets, which suggests that the phospholipase enzyme could be required to allow access of lipases to TAGs contained in the core of the lipid droplets [72]. In this context, Brummer lipase belongs to the calcium-independent phospholipase A2 (iPLA2) family [9]. In N. lugens, a deficiency of this enzyme during vitellogenesis impairs lipid mobilization, negatively affecting egg production [73]. The reality that Brummer lipase mRNA expression show only a small increase during UFC (statistically non-significant, S2 Table) could be due to the fact that in R. prolixus this enzyme is working the same as in N. lugens, being necessary in both nutritional conditions, due to a pleiotropic effect. In addition, the lipase maturation factor 1 is a protein involved in the post-translational maturation of secreted homodimeric lipases [74]. In times of high energy demand, such as starvation, insects use TAG stores via the coordinated action of lipases. In our experiment, lipase maturation factor transcript expression is up-regulated in OV_UFC, as is the expression of Hydr2 (lipase activity enzyme), among other lipases (Fig 7 and S2 Table). These findings are another indication of the fine crosstalk between lipid synthesis and mobilization in both nutritional conditions. Table). Recently, we reported via KEGG analysis an up-regulation of "fatty acid biosynthesis pathway" in OV_UFC [17]. In mosquitoes, FAS is more highly expressed in diapause-destined females than in non-diapausing individuals [75]. This finding suggests that in R. prolixus, FAS could be working to convert carbohydrate reserves to lipid stores for use as an energy source to maintain OVs under optimal physiological conditions for successful reproduction when nutritional conditions are adequate, such as after feeding. Massive endocytosis of YPPs in oocyte and intense VgR, LpR and heavy-chain clathrin synthesis are all energy-dependent processes [76]; for that reason, lipid reserves in pre-vitellogenic oocytes (UFC) could play a critical role in supporting the energetic demands of the growing oocyte at the beginning of vitellogenesis.
In the triatomine, Panstrongylus megistus, lipid transfer to the developing oocyte during vitellogenesis is accomplished by endocytosis of Lp (through LpR) and by the classic extracellular lipophorin shuttle mechanism [23]. However, Machado et al. [77] suggested that in R. prolixus, endocytosis is not a pathway involved in lipid transfer to oocytes. Conversely, our results demonstrate that LpR transcript is up-regulated in OV_FC, probably to maximize lipid delivery to oocytes. Moreover, in mammals, it is known that once lipid levels drop, SREBP induces the expression of many genes involved in lipid synthesis and uptake, including the LDL receptor [78]. It has been reported that SREBP controls lipid uptake and accumulation in oocytes from D. melanogaster by regulation of LpR expression [79]. In our data we find up-regulation of SREBP mRNA in OV_FC (Fig 7 and S2 Table), suggesting that this transcription factor could be involved in lipid accumulation by the oocytes during vitellogenesis. The difference found in R.
prolixus [77] Diacylglycerol kinase (DGK) is a family of enzymes that catalyzes the conversion of diacylglycerol (DAG) to phosphatidic acid (PA). We find that DGK transcript expression is upregulated in OV_FC (Fig 7 and S2 Table). PA is a component of the membrane phospholipids and at this stage there is a high demand for membrane synthesis, which is used for oocyte growth and/or for organelles formation, such as yolk granules and lipid droplets. On the other hand, PA affects numerous intracellular signaling pathways, including those regulating cell growth, differentiation, and membrane trafficking. Indeed, PA can bind to mToR and promote ToR signaling [80]. This finding further supports mToR signaling activation after a blood meal in OVs of R. prolixus [17]. Also in insects, the requirement of an acyl-CoA synthetase long chain (ACSL1) for oviposition and egg viability has been reported [81]. In our work, we find upregulation of ACSL1 mRNA in OVs during FC (Fig 7 and S2 Table). Acyl-CoA-binding protein (ACBP) are small proteins that binds acyl-CoA esters with very high affinity to protect them from hydrolysis.
Although ACPB-2, ACPB-3, ACPB-4 and ACBP-5 transcripts are present in both tissues, only ACPB-3 is up-regulated in FB_FC meanwhile ACPB-4 is up-regulated in OV_FC (Fig 7 and S2 Table), indicating that the involvement in TAG mobilization by ACPB is specific and unique, depending on the tissue and physiological condition.
Neuropeptides and neurohormonal signaling, and serotonin
A variety of neuropeptides and neurohormones have been identified in the CNS of R.
prolixus [82]. FB and OV development and function are largely regulated by several hormonal and nutritional signals, i.e. ILP/ToR signaling [17]. Our transcriptome analysis showed no significant change in mRNA expression after blood intake in CNS. However, we made a deep analysis in CNS, FB and OV to explore the relative expression of transcripts related to hormonal signaling in both nutritional conditions. Here, we discuss neuropeptides, in addition to the amine serotonin, and their receptors, which show high expression in some of the tissues analyzed (for more details see S2 Table). All neuropeptides are synthesized as part of a larger precursor molecule. The selective processing of those precursors determines which peptides are finally released by the specific cells [83]. Here, we find 7 enzymes involved in neuropeptide processing and all of them are expressed in the CNS, FB and OV in both nutritional conditions (Fig 8A and S1 Table). The results support the contribution of FB and OV for neuropeptide production in both nutritional condition. The presence of the AKH precursor and receptor [84] in OVs suggests a role in egg production and/or egg-laying behaviour as has been shown in other insects [85], possibly by an autocrine pathway. Here, we find that AKH transcript expression is detected in CNS but is upregulated in OV_FC (Fig 8B and S2 Table). In insects, buriscon is a heterodimeric glycoprotein hormone which plays a key role in melanization and cuticle hardening during development of insects [86]. Recently, a novel function of bursicon was reported in the stimulation of Vg expression in the black tiger shrimp, Penaeus monodon [87]. In R. prolixus, we find higher expression of the bursicon receptor in OVs with respect to the CNS and FB (Fig 8B and S2 Table), suggesting a novel role for this hormone in reproductive physiology in an insect. Human genome screening reveals the presence of another glycoprotein hormone, consisting of the novel alpha (GPA2) and beta (GPB5) subunits (GPA2/GPB5) [88]. In A. aegypti, GPA2/GPB5 signaling has been implicated in controlling ionic balance [89]. In addition, this signaling pathway could play a role in spermatogenesis and oogenesis in male and female mosquitoes, respectively [90]. We find an up-regulation of GPA2/GPB5 receptor mRNA expression in OV and FB during UFC, suggesting an involvement of this signaling pathway in the stage prior to vitellogenesis (Fig 8B and S2 Table). Also, in rats, it has been reported that GPA2/GPB5 in the ovary may act as a paracrine regulator in reproductive processes [91]. Our results show upregulation of GPA2 mRNA in OV_UFC and conversely, up-regulation of this transcript in FB_FC (Fig 8B and S2 Table). Future experiments will determine the involvement of this new signaling pathway in insects and its interplay with reproductive processes. Calcitonin-like diuretic hormones (CT/DHs) are related to the mammalian calcitonin and calcitonin gene-related peptide hormonal system. Here, in addition to the expression in CNS, we show a high mRNA expression level of CT/DH-Rs in OV with moderate levels in the FB (Fig 8B and S2 Table).
Previously, in R. prolixus, it was suggested that CT/DH-Rs signaling may have a critical, but unknown, role in reproductive physiology [92].
R. prolixus genome has two paralogue genes encoding capability (CAPA) peptides, named RhoprCAPA-α and RhoprCAPA-β [93][94]. These genes are mainly expressed in the CNS, supporting our transcriptome results (Fig 8B and S2 Table). RhoprCAPA-α expression was also detected in testes from 5th instar nymphs but not from adults, suggesting a role in the maturation of male gonads [93]. Here, we find CAPA-β transcript expression is up-regulated in OV_FC.
Future experiments using gene silencing strategies will be performed to analyse the possible involvement of CAPA-β peptides on oocyte maturation or egg formation.
Pleiotropic effects of crustacean cardioactive peptide (CCAP) in insects and crustaceans have been described. Previously, it was reported that CCAP is involved in the fertilization process in L. migratoria since it increases the basal tonus and frequency of spontaneous spermathecal contractions [95]. Our results show an up-regulation of CCAP mRNA expression in OV_FC (Fig 8B and S2 Table), suggesting an autocrine regulation but future experiments are required to determine the specific involvement of this signaling in R. prolixus reproduction.
Ion transport peptides (ITPs) in locusts (Schistocerca gregaria and L. migratoria) were identified based on their antidiuretic activity on the ileum [96][97]. Later, in T. castaneum it was suggested that ITP signaling participates in ovarian maturation and female fecundity regulation [98]. However, its specific role in reproductive physiology in R. prolixus has not yet been reported. Here, we found an up-regulation of ITP receptor mRNA in both FB and OV from fed insects (Fig 8B and S2 Table).
In insects, long neuropeptide F (LNPF) has been reported as a main player in feeding behaviour, metabolism and stress responses [99]. Previously, in R. prolixus, it was reported that pre-follicular cells within the germarium express the NPF receptor, as do cells located between developing oocytes [100]. Taking into account both findings, our results suggest that LNPF signaling could have a critical role in oocyte maturation more than in egg production, since we find an up-regulation of LNPF receptor mRNA in OV_UFC with respect to FC (Fig 8B and S2 Table). Neuropeptide-like precursor 1 (NPLP1) was first identified in D. melanogaster. In R.
prolixus NPLP1 peptides are involved in the feeding response, providing the first clues in the elucidation of their function [21]. We find an up-regulation of NPLP1 transcript expression in OV_UFC (Fig 8B and S2 Table). The physiological role of NLPL1 signaling in reproduction is currently unknown.
By quantitative peptidomic assays, it was reported that in R. prolixus, NVP-like (NVPL) signaling is involved in the regulation of rapid events, such as diuresis/antidiuresis, and in delayed events such as mating and reproduction [21]. In our transcriptome analysis, we show an up-regulation of NVPL mRNA in OV_UFC (Fig 8B and S2 Table). Gene silencing techniques could be implemented to evaluate the role of this peptide in reproduction.
Myosuppressin is a neuropeptide only found in insects and crustaceans. It has been demonstrated to have anti-feeding activity and to inhibit gut and oviduct contraction and neuropeptide secretion [101]. In the Australian crayfish Cherax quadricarinatus, myosuppressin was detected in ovaries from mature females, suggesting a potential link between myosuppressin and reproduction [102]. Here, we also report myosuppressin mRNA expression in OVs of R. prolixus (Fig 8B and S2 Table).
A corticotropin-releasing factor-like peptide acts as a diuretic hormone in R. prolixus (Rhopr-CRF/DH) [103]; however, its distribution throughout the CNS and the expression of its receptor in feeding-related tissues as well as the female reproductive system suggests a multifaceted role for the neuropeptide. Adult female R. prolixus, injected with Rhopr-CRF/DH produce and lay significantly fewer eggs [104]. In addition, in locusts, CRF/DH inhibits oocyte receptor mRNA in OV and FB from unfed insects (Fig 8B and S2 Table), where vitellogenesis is inhibited, supporting its effects as a negative regulator of reproduction.
By bioinformatic predictions, Ons et al. [105] showed for the first time the existence of RYamide in R. prolixus. However, the functions of this signaling in insects is currently unclear.
We find a high expression of RYamide mRNA in OVs during both nutritional condition (Fig 8B and S2 Table).
Proctolin was the first insect neuropeptide to be sequenced and synthesized and is found in a variety of arthropods, including R. prolixus [107], where it plays a myostimulatory role on anterior midgut, hindgut, heart, and reproductive tissue [108]. In the cockroach Blaberus craniifer, nanomolar quantities of proctolin induce Vg uptake [109]. Here, we find for first time a high expression of proctolin receptor mRNA in OVs, encouraging further studies to analyze the role of this signaling in the reproductive organs (Fig 8B and S2 Table).
Serotonin
(5-hydroxytryptamine or 5-HT) is an ancient monoamine neurotransmitter/neurohormone. 5-HT receptors are classified based on sequence similarities with their counterparts in vertebrates [110]. In R. prolixus, we find that mRNA expression to 5-HT receptors is higher in the CNS but also expressed in the OV and FB (Fig 8B and S2 Table).
In mosquitos, 5-HT2B was reported to be a critical player in the fat body-specific serotonin signaling system, governing antagonistic ILP actions [111]. It would be interesting to analyse 5-HTs functional role in reproductive tissues of R. prolixus.
The transcriptome data highlights directions for future research in examining the role of particular neuropeptides/amines on specific responses to processes such as ovarian maturation or egg formation, extending the temporal range of transcript/protein expression of these neuropeptides/amines capitalizing on gene silencing assays.
A brief analysis of genes related to immunity
The overall achievement of insects in maintaining a stable population of individuals is due, in part, to their ability to recognize pathogens and eliminate them successfully using the immune system. The immunity of insects comprises multiple elements that work in concert and, in general, includes physical barriers as well as innate immune responses, which lead to a combination of cellular and humoral immunity [112]. In recent years, it has been shown that reproduction and immunity can be mutually constraining since both responses are energetically costly, and therefore need to be traded off. In this context, increased reproductive activity reduces constitutive and induced immunity across a diversity of female insects [113]. However, metabolic changes that occur after the acquisition of a blood meal, include the induction of oxidative stress [114]. Increased metabolic activity during the process of blood digestion has been shown to alter levels of different detoxification enzymes in mosquito, which are the same as these implicated in insecticide detoxification; indeed blood feeding status in mosquitos confers increased tolerance to insecticides [115]. Thus, it is clear that the immune system is working in both nutritional conditions, before a blood meal, due to the stress that is generated by starvation, and after a blood meal, due to the potential toxicity of the molecules ingested with the blood.
Along with all the roles described above for FB in reproduction, the FB also responds to microbial infection. One important humoral response is the production of inducible antimicrobial peptides (AMPs), which are rapidly synthesized after microorganism invasion [116]. In D.
melanogaster, the Toll pathway (activated by fungi and gram-positive bacteria) and the Imd pathway (activated by gram-negative bacteria) lead to the synthesis of AMPs, not only by a pathogenic challenge, but also by aging, circadian rhythms, and mating [118][119][120]. Interestingly, in R. prolixus, we find an up-regulation of AMPs in OV_FC (Fig 9A and S3 Table), suggesting a role of the vitellogenic oocytes in humoral immunity, an event that has not yet been studied in insects. In addition, we find different mRNAs involved with both Toll and Imd pathways which are up-and down-regulated in FB and OV, without revealing a characteristic expression pattern in any of the nutritional conditions analyzed (Fig 9B, C and S3 Table). This finding clearly suggests that the immune system is responding to both stimuli: to detoxification of compounds which enter with blood intake and/or to avoid tissue damage due to stress caused by lack of food.
In addition, Foxo transcriptional factor could promote activation of the stress-responsive Jun-Nterminal kinase (JNK) pathway, which antagonizes ILP signaling in D. melanogaster, causing nuclear localization of FoxO and inducing its targets, including growth control and stress defense genes [120]. Recently, we demonstrated that in unfed females, FoxO factor is translocated to the nucleus, stimulating the insulin-sensitive pathway and modulating longevity signaling in R.
prolixus [17]. In the current work, we find up-regulation of most of the genes involved with JNK signaling, mainly in OV_UFC (Fig 9D and S3 Table) possibly to overcome effects of stress and low nutrition. In R. prolixus, Duox is the enzyme that generates H 2 O 2 in ovarian follicles is used as a fuel for hardening of eggshell proteins, a process essential for the acquisition of resistance to water loss [121]. In accordance with those finding, we show an up-regulation of Duox mRNA expression in OV_FC (Fig 10A and S3 Table). In addition, melanization and the production of nitric oxide (NO) and reactive oxygen species (ROS) are effector mechanisms also activated as a first line of defense. Upon infection, pattern recognition receptors activate downstream serine protease cascades that culminate in the activation of prophenoloxidase (PPO), a precursor activated by proteolytic cascades to phenoloxidase for de novo synthesis of melanin. NO is highly toxic for a wide variety of pathogens and is produced by nitric oxide synthase (NOS).
ROS are produced by conserved nicotinamide adenine dinucleotide phosphate (NADPH) enzymes. Here, we find up-regulation of PPO, NOS mRNA levels and increases of NADPH transcript in OV_UFC (Fig 10A and S3 Table). Also, it is interesting to see that enzymes which performs as antioxidant elements, such catalases, thioredoxin peroxidases and glutathione peroxidase have their mRNA levels up-regulated in OV and FB from fed insects (Fig 10B and S3 Table RNAi signaling in OV_UFC (Fig 10C and S3 Table). These results suggest immunological signaling in OV of unfed insects, possibly to prevent damage during unfavorable metabolic conditions. female adults in different nutritional condition. The input data is the readcount value from the gene expression level analysis after normalization and is presented by means of a colour scale, in which green/yellow/red represent lowest/moderate/highest expression. DESeq was used to perform the analysis.
Overall, the information on immunity in hemipterans, including Triatominae vectors remains incomplete and fractionated [123]. The data presented here on immunity and reproduction in triatomine females encouraging the development of future studies to shed light on the relative contribution of the immune system in successful reproductive events.
Conclusions
We present here a comprehensive analysis of mRNA expression of components of biological processes related with feeding and reproduction. Broadly, using high-throughput sequencing and a comparative expression analysis we find that a blood meal taken by R. prolixus potential for biocontrol against tropical disease vectors. In R. prolixus, the ability to constitutively deliver dsRNA by supplying with recombinant symbiotic bacteria generated against specific target genes involved in the reproductive success (Vg), have already been tested in laboratory trials and is effective in dramatically reducing the fitness of R. prolixus [18].
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Domain: Biology
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THE MORPHOGENESIS OF CIRCUMVALLATE PAPILLAE AND THE DIFFERENTIATION OF TASTE BUDS IN THE PIG AT 41 TO M DAYS OF PRENATAL DEVELOPMENT
Tichy, F.: The Morphogenesis of CircumtJallate PapiUae and the Differentiation of Taste Buds in the Pig at 41 to 64 Days of Prenatal DefJe/opment. Acta vet. Bmo, 60, 1991: 99-1()()' Samples of tongues collected from porcine foetuses at 41, 44, 50, 53, 57 and ~ days after fc:rtilization were examined for the appearance and development of surface lingual structures. At each of the stages studied, the morphogenesis of circumvallate papillae and the differentiation of taste buds were recorded. Attention was paid to the formation of gustatory glands· and their ducts and to the development of the furrow encircling the circumvallate papilla. The differentiation of the other types of lingual papillae was also studied. The formation of taste buds in the epithelium coincided with the shape differentiation of circumvallate papillae. The buds appeared first in the dorsal surface epithelium and later in the wall epithelium. The number of differentiating buds within one papilla increased with foetal age. Different cell types composing the taste bud could be distinguished soon after the bud was formed. The circUmvallate papilla was either formed as a unified structure of typical appearance or had first a composite base later giving rise, by fusion of its segments, to the typical papilla. The other, lingual papillae developed at later stages than the circumvallate papillae. Tongue, circumtJallate papilla, taste bud, differentiation, pig The structure, function and distribution of taste buds were studied as early as in the second half of the last century. The first observations were made in fish (Schulze 1863), then in mammal' (Loven 1868; Schwalbe 1868). The general information on the structure of the taste organ in some species of laboratory mammals and in man were obtained by means of light microscopy at the beginning of this century (Kolmer 1910; Retzius 1912; Heidenhain 1914). Pro~s in understanding the mechanism of taste perception occurred with the advent of techniques ailowing observations at the ultrastructural level. Many of data on the submicroscopic structure of taste buds together with characterization of their cell types (Beidler and Smallman 1965; De Lorenzo 1958; Engstrom and Rytzner 1956; Fahrmannand Schuchard 1967; Farbman 1965a; Farbman and Vonkers 1971; Fujimoto and Murray 1970; Murray 1969,1971, 1973; Murray and Murray 1960, 1967, 1970; Murray et aI. 1969; Takeda and Hoshino 1975; Trujillo-Cenoz 1957; Uga 1966,1969) have provided the grounds for the development of biochemical and physiological methods for investigation of taste perception (Beidler 1970; De Han and Graziadei 1971,1973; Desgranges 1966; Hirata and Nada 1975; Spoendlin 1970; Takeda 1976; Beauchamp and Cowart 1986). All these studies have contributed significantly to the understandiDg of processing and transport of taste stimuli within the bud. The most important information and methods of investigation have been reviewed and preSented in textbooks (Cormack 1984; Banks 1981; Smolich and Michael 1985; Rooss and Reith 1985).
The structure, function and distribution of taste buds were studied as early as in the second half of the last century. The first observations were made in fish (Schulze 1863), then in mammal' (Loven 1868; Schwalbe 1868). The general information on the structure of the taste organ in some species of laboratory mammals and in man were obtained by means of light microscopy at the beginning of this century (Kolmer 1910;Retzius 1912;Heidenhain 1914).
Pro~s in understanding the mechanism of taste perception occurred with the advent of techniques ailowing observations at the ultrastructural level. Many of data on the submicroscopic structure of taste buds together with characterization of their cell types (Beidler and Smallman 1965;De Lorenzo 1958;Engstrom and Rytzner 1956;Fahrmannand Schuchard 1967;Farbman 1965a;Farbman and Vonkers 1971;Fujimoto and Murray 1970;Murray 1969Murray ,1971Murray , 1973;;Murray and Murray 1960, 1967, 1970;Murray et aI. 1969;Takeda and Hoshino 1975;Trujillo-Cenoz 1957;Uga 1966Uga ,1969) ) have provided the grounds for the development of biochemical and physiological methods for investigation of taste perception (Beidler 1970;De Han andGraziadei 1971,1973;Desgranges 1966;Hirata and Nada 1975;Spoendlin 1970;Takeda 1976;Beauchamp and Cowart 1986). All these studies have contributed significantly to the understandiDg of processing and transport of taste stimuli within the bud. The most important information and methods of investigation have been reviewed and preSented in textbooks (Cormack 1984;Banks 1981;Smolich and Michael 1985;Rooss and Reith 1985).
The. de,telopment of the' taste organ• in' the lingual epithelium is well recorded in both man and various mammalian species (Beidler and Smallman 1965;Farbman 1965bFarbman , 1971;;Takeda 1972). None of the relevant studies, however, has been concerned with the stages of ontogenic development or has paid attention to the changes occurring during development of the gustatory receptor. Our earlier study (Tichy and Cerny 1987) described the differentiation of taste buds ~t selected stages of ontogeny in sheep. This paper will deal with the formation, development and distribution of the taste buds and their functionally and topographically related structures in the prenatal pig~
Materials and Methods
Samples were collected from the tongues of porcine foetuses at 41, 44, 50, 53, 57 and 64 days after fertilization. The age of foetuses was estimated by their crown-rump lenght according to Evans and Sack (1973).
. .' Three pigs were sampled in each age category. The material Was excised from the tongues in the following areas: junction between thebody:,~d J;oot, lateral part of the root, apical dorsum linguae.
The samples were fixed immediately in 10 % neutral formaldehyde. To prevent artifacts ensuing from a sudden dehydration of embryonic tissues rich in water, the graded alcohol series, starting with 10 % alcohol, increased by 10 % in each subsequent bath.
The sections were routinely stained with haematoxylin and eosin. To intensify the visualization of selected structures, the impregnation technique according to Gomori and nuclear red staining were used. Some of the seetions were stained with the greerttrichrome reagent.
Results'
Linguiumucosa at 41 days offoetaLage (Fig. 1, Plate I, at the end of the volume) The mucosal surface of the dorsum linguae was uneven. Frequent dome-shaped protrusions:,lthe anlagen.(rudiments) of papillae, could be seen .along•.the lateral margin,s of the tongue. J'hey were more prominent in the caudal regions, particularly at the junction, of the body and root of the tongue.
The mucosa was covered with markedly stratified epithelium .. It.consisted of 2 to 3 lower layers, in: which cells .with rounded hyperchromatic nuclei were arranged in a palisad~-like manner, and.an upper layer of poorly-stained cells irregular in shape and size .. The,10wer layer cells, distin(:tly smaller than the upper layer ones, constituted the lower half of the epithelium,.while the other half was made of only' one layer of light, large cells. The upper layer was markedly thinner above the dome-like protrusions; in some instances it was even missing. The linguiu epithelium had a uniform appearance allover the mucosal surface wi~out any changes indicating the onset of taste bud formation. :'A distinct basement membrane separated ,the epitQ.eliumfrom the layer of mucosal connective tissue containing numerous bloc)d.~essels. This extended against the epithelium.forming the stromata of ptimitive papillae. Below these, connectiv~ tissue increased in density and began to organize which was a:,:commencem,ent of the aponeurosis linguae.
, .' Linguiu mucosa at 44 days of foetal age (Fig. 2, Plate I) 1"'he: dome-like protrusions evident on the linguiu surface in the previous period were masked completely w~th proliferating epithelial cells; which.gave the mucosal.surface a mildly undulating appearance.
' . Cross-sections through the linguiu mucosa showed distinct anlagen of circumvallate papillae which were marke4, with compact cell bands preceding the formation ofa furrow around each papill~.•No anlagen of the other types of linguiu papillae were laid at this stage.
' ,., The epithelium of tllelingual surface, similarly to the previoll.. C1.period, had two parts: the lower layer consisted of several strata of well-stained cells, the upper layer was made up of one or two strata of light, large, irregular cells. These poorly-stained cells produced the cell bands marking the extent and localization of the future encircling furrow.
In the epithelium of the dorsum linguae in the close vicinity of the cell bands, the lower epithelial layer included cells differing by their elongated shape and low intensity of staining. These cells extended down to the basement membrane and, in cross-sections, were bordered by pairs of cells with dark elongated nuclei.
The structure of the musocal connective tissue was markedly denser and more vascularized than in the previous period. The rudiment of the aponeurosis linguae was also more distinct.
Lingual mucosa at 50 days of foetal age (Figs 3,4,Plate II) At the lateral margins of the dorsum linguae, the lingual mucosa showed large disc-shaped elevations markedly protruding above the surface at the junction between the body and root of the tongue.
It could be seen in cross-sections that the discs were the anlagen of circumvallate papillae; they had already attained a typical shape but had not yet developed the encircling furrows. Their underlying cell bands, however, were found to run deeply into the connective tissue of the lamina propria mucosae. With their bottom part they penetrated under the base of the circumvallate papilla, while their terminal part produced fork-like extensions later developing into ducts of the gustatory glands. The other types of lingual papillae were observed only as mild protrusions of the lingual mucosa.
The lingual epithelium did not differ in structure from that of the previous age stzge. However, its thickness was greater due to an increased number of layers of poorly-stained polyedric surface cells.
The dorsal surface of the anlagen of circumvallate papillae showed the epithelium with taste buds at different stages of differentiation. These presented most frequently as cell clusters slightly exceeding in height the basal epithelial layer and easily discernible by less intense staining. All the cells involved in the commencement of taste buds at this stage were uniform in appearance. Cells of the basal epithelial layer attached to the anlagen of the taste buds attained a spindle--like shape; this was accompanied by similar morphological changes in their nuclei.
The rudiment of the aponeurosis linguae was more marked than in the previous age category, but under the bases of circumvallate papillae the thickening of connective tissue could not be seen.
Lingual mucosa at 53 days of foetal age (Fig. 5, 6, Plate III) The epithelium of the dorsum linguae at this stage was characterized by numerous protuberances, varying in size and shape, and shallow and narrow depressions. The protuberances were the future papillae not yet differentiated in shape, the depressions indicated the separation of the papillae from the surrounding tissue. The epitheliaum did not differ in structure from that seen in the previous period.
A cross section through the dorsum linguae demonstrated more frequent and larger anlagen of papillae than in the 50-day-old foetus. The picture, however, did not yet allow us to distinguish between different types of lingual papillae with the exception of the circumvallate papillae.
These papillae had a typical configuration and, because of their size, they became the most conspicuous formations on the lingual epithelium. Although the encircling furrow was not completed, its basis in the form of a cell band ex-• tended deeply into the mucosal connective tissue. In the cell band, a narrow groove began to form from th~ epithelial surface down. At this stage it ran down one third of the total cell band height. Its lower part divided into thin projections directed towards the aponeurosis linguae rudiment. These would later .constitute the gustatory gland ducts.
Taste buds in various stages of differentiation were observed in the epithelium of the dorsal surface and walls of the circumvallate papilla as well as in the epithelial basis of the encircling furrow. Most frequently they presented as round cell clusters which did not reach as far as the epithelial surface but were covered with one or two layers of cells. The taste bud cells included cells with dark nuclei and those with light nuclei. A clear cut between the sensory.and supporting cells could not be made by light microscopic observations at this stage.
The connective tissue of lamina propria mucosae was' rich in blood vessels. The aponeurosis linguae rudiment had the same appearance as in the previous period.
Lingual mucosa at 57 days of foetal age (Figs 7 to 11, Plates IV, V and VI) The tongue surface differed markedly from that seen in the lower age category. The mucosa within the dorsum linguae appeared to consist of numerous big,.'Separated papillae still undifferentiated in shape. Occasionally the separation was only suggested. The lateral surface of the tongue had a mildly undulating appearance.
. The most conspicuous structures seen in cross-sections were the rapidly developing circumvallate papillae. They had a typical shape and were bordered by deeply extending epithelial bands. These contained round hollow spaces of different volumes seen both near the surface and in the ba$al Iarts of the cell band. The spaces near the surface were elongated md separated with one or two long epithelial cells. The basal spaces were narrow and markedly smaller. The bases of the epithelial bands were divided into many developing ducts of the gustatQry gland which ran deep into the differentiating muscular tongue base. Their terminal portions separated rounded formations, later presenting as secretory components of the gland. In cross-sections the ducts at this stage showed lumina.
At this foetal age, another form of the circumvallate papilla was regularly observed. These atypical papillae comp osed of several segments were surrounded with solid cell bands whose lower parts branched into ducts of the gustatory gland. The bigger segments were always found in the centre while their size decreased close to the periphery of th is composite anlagen of the circumvallate papilla. These composite anlagen were observed on the tongue of one and the same foetus together with the usual anlagen of circumvallate papillae described above.
Taste buds were generally found on the circumvallate papillae showing the typical differentiation. They occurred in the epithelium of the dorsal surface and of the walls. They had a characteristic appearance and were never found reaching the surface of the epithelium. T hey included nuclei of two types: hyperchromatic nuclei, smaller in size and oval in shape, and nuclei with diffuse chromatin, larger in size and also val in shape.
The anlagen of the atypical papillae, particularly in the epithelium of the dorsal surfaces of the segments, also showed differentiating taste buds. Their appearance was reminiscent of that observed in the previous age category, where it was not possible to differentiate various cell types on the basis of their nucleus characteristics.
Taste buds at the initial stage of differentiation were also recorded on the primitive anlagen of foliate papillae situated on the lateral surface of the tongue. They presented as cell clusters on the top of the papillary anlage. They did not extend beyond the basal epithelial layer and from the connective tissue of the lamina propria mucosae were• separated with a fold of the basement membrane.
The cells of the basal epithelial layer which were in the close vicinity of the taste bud were elongated, with rod-shaped nuclei. Changes in shape found in cells attached to the taste bud were more marked with increasing bud differentiation.
Anlagen of secretory compartments of the gIl.gustatoriae were found in the layer of mucosal connective tissue and also deep !llllong fasciculi of the developing tongue wasculature. They presented as oval cell aggregations of different size.
At the border of the lamina propria mucosae and the muscle tissue, a thick dense layer of the aponeurosis linguae was observed. It was not seen under the bases of the circumvallate papillae.
The mucosal surface of the dorsum linguae bore numerous papillae much bigger than those seen in the previous period. Apart from circumvallate papillae, however, they were not differentiated in terms of shape. The lateral surface of the lingual mucosa showed elevations indicating the formation of foliate papillae.
TJ:;te circumvallate papillae presented as large formations separated from the surrounding tissue by well defined furrows. Their dorsal surface was seen as a pattern of depressions and elevations. A cross-section through a depression showed that a thin cell band,.involving a lumen, extended from its bottom downward into the lingual musculature to be connected with secretory regions of the gustatory gland. These ducts opening onto the dorsal surface of the circumvallate papillae had lumina of varying width but never as wide as the true orifices of the gustatory gland.
The lingual epitheliuni on the dorsal surface of the circumvallate papillae had the appearance of a mature papilla with one or two surface layers of flat cells showing signs of keratinization. The epithelium lining the furrow around the circumvallate papilla grew thinner toward the furrow bottom. The epithelium on the rest of lingual mucosa was characterized by a distinct surface layer of poorly--staining large polyedric cells, as at the previous stage.
The anlagen of foliate papillae present on the lateral surfaces of the tongue were not separated distinctly from the surrounding tissue. When sectioned transversally, they presented as cylinders of connective tissue covered with a thick stratified epithelium composed of light polyedric cells. The germinative part of the epithelium was limited to one or two layers of small but well-stained cells with oval hyperchromatic nuclei.
Taste buds were observed in the epithelium on the dorsal surface and in the walls of circumvallate papillae. Taste buds in the process of differentiation were also present on the dorsal surface of the anlagen of foliate papillae.
The taste buds observed in the epithelium of circumvallate papillae had a ty-pical shape and consisted of two well differentiated cell types. Cells with nuclei containing less chromatin were more numerous than those with hyperchromatic nuclei. Epithelial cells adjoining the taste bud were markedly increased in length and made the outer boundary of the bud. The apical end of the bud was overlaid with one or two layers of flat cells preventing communication with the surface. The taste buds on foliate papillae, still in the process of differentiation, appeared as cell clusters with the bases extending beyond the lower epithelial margin into the lamina propria mucosae, from which they were separated with basement membrane folds. At this stage of differentiation it was possible to distinguish cells with hyperchromatic nuclei from those with light nuclei.
The lamina propria mucosae and the aponeurosis linguae had similar structures to those found in the previous age group.
Discussion
The picture of the lingual mucosa with its structures and components is well documented by both light and electron microscopic studies. Similarly, a lot of data have been accumulated on taste buds, giving, comprehensive information on their shape, size, ultrastructure, the mechanism of development and principles of taste perception. The cell types constituting the taste bud, and their ultrastructure and function have also received attention.
Considerably less information has been reported on the processes related to the• commencement of various mucosal structures of the tongue and their further development in the foetuses of domestic animals. Particularly incomplete are data concerning the sequence of events leading to the development, differentiation and distribution of taste buds and their relation to the development of lingual papillae.
In the porcine foetus, the most significant period for morphological changes in the lingual mucosa seems to be from day 50 to day 64. This stage is marked by a rapid development of various components of the lingual mucosa; the anlagen of papillae are formed and taste buds are iniciated.
In the surface lingual epithelium of the porcine foetus, the first signs of differentiation are apparent between day 41 and day 44. It is 10 days earlier than in ovine foetuses (Tichy and Cerny 1987). Epithelial cells proliferate at a high rate, which makes the uneven appearance of the dorsal surface at day 41 become smooth at day 44. At the same time growing epithelial bands penetrate into the mucosal connective tissue forming the first, not too distinct outlines of the future circumvallate papillae. At 44 days the basal epithelial layer of the lingual mucosa within the anlages of papillae contains cells with poorly-stained cytoplasm; these are not seen in the ovine foetus of the same age (Tichy and Cerny 1987). The cells are found single or in small clusters and are thought to be implemented in the initial stages of taste bud formation. Their adjoining cells change in morphology; they became rod-shaped and so do their nuclei. The slender cells encompass the light cells; they will presumably give rise to one of the cell types constituting the taste bud. Some authors (Beidler and Smallmann 1965;Farbman 1965a) regard them as a stock of cellular material which serves to supply cells to the developing taste bud. Similar arrangements of epithelial cells adjoining the developing taste bud were, in more pronounced forms, seen at the other stages of prenatal development, which implies that all cell types are continually supplied (Farbmann 1971;Whiteside 1927). At 50 days the anlagen of taste buds are distinctly laid. They are formed by cluster of light cells which do not extend beyond the basal epithelial layer. In the light microscope they have a uniform appearance. At this stage it was not possible to distinguish cell types reported in electron--microscopic studies (De Lorenzo 195~;Engstrom and Rytzner 1956;Fahrman and Schuchard 1967;Farbman 1965Farbman , 1971;;Graziadei and De Han 1971;Murray and Murray 1960, 1967, 1970 and others). The buds at this stage are localized to the dorsal surface of the anlagen of circumvallate papillae. It is suggested that this localization is also typical for the early stages of development of the lingual mucosa in other domestic animals (Tichy and Cerny 1987). In the wall epithelium of the circumvallate papillae, the taste buds appear later, at 53 days of foetal age, but at greater numbers. As suggested by some authors, the taste bud differentiation is initiated by a contact of epithelial cells with a nerve fibre (Desgranges 1966;Farbman 1965a, b;Fujimoto and Murray 1970;Kurosumi and Kurosumi 1969;Munger 1965;Spoendlin 1970;Takeda and Hoshino 1975). The site of contact determines the location of the future bud (Takeda 1976). The presence of taste buds in the epithelium is, in our opinion, directly related to t1!e development and growth of the anlagen of circumvallate papillae (Tichy and Cerny 1987). From day 57 on, the circumvallate papilla anlagen undergo marked differentiation, while the occurrence of taste buds on their dorsal surface is being reduced, so that, from day 64 on, they occur only occasionally. It is of interest that taste buds begin to from in the lingual epithelium at the time when circumvallate papillae start to differentiate in shape. In contrast to the findings published earlier (Tichy and Cerny 1987) in porcine foetuses the commencement of differentiation of both the papilla and the bud occur at the same time.
Taste buds on the anlagen of foliate papillae differentiate later than those on circumvallate papillae, i. e. from day 57. They appear on the dorsal surface of the foliate papilla at the time of its commencement. If the view that the differentiation of taste buds is initiated by contact of epithelial cells with a nerve fibre (Farbman 1965;Murray and Murray 1967;Takeda 1976) is correct then the area of developing circumvallate papillae is supplied with nerves earlier than the lateral parts bearing the anlagen of foliate papillae. This is also evidenced by our finding that in each period the stage of differentiation of the epithelium was higher on the dorsum linguae than on the lateral surfaces.
The differentiation of cell types inside the taste bud can be seen from day 53, when the occurrence of two cell types differing in the appearance of nuclei was first recorded. This seems to be the early period of development of sensory and supportive cells. The ultrastructure of this and other types of cells composing the taste bud was documented by many electron microscopic studies (D e L 0renzo 1958;Farbman 1965a, b;Gray and Watkins 1965;Graziadei 1969;Murray 1971Murray , 1973;;Murray and Murray 1969;Takeda 1972;Takeda and Hoshino 1975;Trujillo-Cenoz 1957;Uga 1966Uga , 1969)).
In the period between the 50th and 64th days of prenatal development, the epithelium of the foundations of circumvallate papillae shows taste buds at various stages of differentiation in terms of shape, size and structure. First they look like cell clusters located within the basal epithelial layer. In some instances they penetrate below the epithelial base into the layer of mucosal connective tissue, from which they are separated by a fold of the basement membrane.
On day 57 the developing taste buds reach as far as the surface layer of epithelium but on day 64 they still do not communicate with the epithelial surface. This implies that in this period the bud differentiates only in structure while maturation leading to the definite size and localization occurs at the following stages.
An unexpected finding was the detection of composite anlagen of circumvallate papillae on day 57. Their occurrence was regular along with that of typical circumvallate papillae. At 64 days of prenatal development, however, the anlagen of circumvallate papillae presented as typical uniform structures. It can be speculated whether the composite anlagen will have any effect on the appearance of circumvallate papillae in the adult animal whether they occur only as an insignificant anomaly in the course of development. We favour the concept of involvement of composite papillary anlagen in the final appearance of the circumvallate papillae because in the pig these papillae are know to have a variety of shapes and sizes. In the spaces between segments of the composite anlage of the circumvallate papilla, the gll.gustatoriae ducts are formed; they persist even after the segments fuse into a unified anlage of the papilla at 64 days. Our observations show that the ducts open at the bottom of depressions on the dorsal surface of the circumvallate papilla. These cannot be considered the definite ducts because their lumina are noticcebly smaller and they, themselves, disappear ot the later stages.
The finding of hollow spaces among the cells of the epithelial band, later the furrow encircling the papilla, was also unusual. They are first seen on day 57 and are accounted for by changes in the compact band, which leads to the formation of the encircling furrow.
From day 50 on, it was observed that the continuity of the aponeurosis linguae under the anlagen of circumvallate papillae was broken. The absence of the aponeurosis linguae in these regions may be explained as enabling the developing ducts of the gustatory gland to grow downward.
Since no similar report on the development and maturation of the lingual mucosa of the prenatal period reported in this paper has been found in the literature, the observations described here can be considered an original contribution to this field investigation.
Conclusions
This paper describes the differentiation of certain structures of the lingual mucosa in the pig at 41 to 64 days of foetal age. Attention was focused on the morphogenesis of circumvallate papillae, the differentiation and localization of taste buds in the lingual epithelium and the development of some other related structures. The following conclusions have been drawn: .1. Taste buds appear in the lingual epithelium first on the dorsal surface of developing circumvallate papillae at 50 days of prenatal development. However, the first signs of their differentiation can be recognized as early as on day 44.
2. Taste buds begin to form on the anlagen of foliate papillae on day 57.3. A considerable increase in the amount of taste buds in the epithelium of circumvallate papillae is recorded from day 53.
4. Taste buds are at first limited to the dorsal epithelial surface of the circumvallate papillae. At 53 days they appear in the wall epithelium involved in the basis of the encircling furrow. From day 64 on, the occurrence of taste buds on the dorsal surface of circumvallate papillae is a rare finding.
5. The differentiation of ceU types inside the taste bud can be recorded from the 53rd day of foetal development.
6. Taste buds do not reach up to the surface of epithelium during the period described in this paper.
7. The anlagen of circumvallate papillae are laid down at 44 days of foetal age and attain their appearance at 64 days. The composite anlagen of circumvallate papillae are first seen on day 57~ but by day 64 the circumvallate papillae is a unified structure.
S. The ducts of gustatory glands are commenced at 53 days and the first lumina can be observed on day 57. At that time the lamina propria mucosae and lingual musculature show the first signs of secretory regions of the glands. The ducts are formed between segments of the composite anlagen of circumvallate papillae at 57 days and persist even after the segments disappear in the unified bases of the papillae at 64 days.9. The furrow encircling the circumvallate papillae is started when cells of the epithelial band begin to move apart at 53 days. On day 57 ~ spaces among cells appear and the cleft is completed at 64 days.
10. Foliate papillae begin to differentiate at 57 days.11. The rudiment of the aponeurosis linguae is indicated at 41 days and at 50 days it presents as a compact layer of connective tissue reduced under the bases of circumvallate papillae.
Ostatni typy jazykovych papil vznikaji pozdeji nez papila hrazena. Fig. 3: A forming circumvallate papilla on the tongue of a porcine foetus at 50 days (2). A taste bud at the early stage of differentiation on the dorsal surface of the papillary anlage (1). The anlagen of ducts of the gIl.gustatoriae (4). The developing aponeurosis linguae (3). HE, magnification: X 100. Plate VII. Fig. 13: A part of the circumvallate papilla of the 64-day-old porcine foetus. The epithelial surface is covered with a layer of keratinized cells (1). The epithelial bands extend into the connective stroma of the papilla (2) as remnants of the composite anlage. An inconspicuous dense connective tissue layer under the base of the papilla (3). HE. magnification: x 250.
Fig.1: A part of the lingual surface of a porcine foetus at 41 days. Simple elevations on the dorsum linguae(1). The surface layer of poorly-stained cells of the epithelium (2). Thickening connective tissue as a rudiment of the aponeurosis linguae (3). Haematoxylin-eosin (HE); magnification: x 250.
Fig. 2 :
Fig. 2: A part of the lingual sun ace of a porcine foet1ls at 44 days. A developing cell band (1) as the basis of the fUrtow encirclm.gthe clrcumvalliite papilla. A light cell in the germinative layer of the epithelium (2), bordered by well-stained cells with elongated nuclei. A capillary in the layeI of mucosal connective tissue (3). The rudiment of the aponeurosis linguae (4). HE, magnification: x 250.-
Fig. 4 :
Fig.4: A detail of a differentiating taste bud (1) in the epithelium of the dorsal surface of the anlage of a circumvallate papilla in a 50-day-old porcine foetus. HE, magnification: x 400.
Fig. 5 :
Fig.5: The anlage of a circumvallate papilla on the tongue of a porcine foetus at 53 days. Taste buds forming on both the dorsal and lateral surfaces of the papilla (1). The rudiment of the aponeurosis linguae in the layer of mucosal connective tissue (2). Differentiating ducts of the gIl.gustatoriae (3) HE, magnification: x 250.
Fig. 6 :
Fig. 6: A part of the anlage of a circumvallate papilla with a developing taste (1) bud in the lingual epithelium of a 53-day-old porcine foetus. An indication of the cleft in the compact cell band (2) in the process of encircling furrow formation. HE, magnification: x 400.
Fig
Fig.-i:The lingual surface of a porcine foetus at 57 days. A differentiating circumvallate papilla with spaces of different sizes in the epithelium (1), surrounded with undifferentiated anlagen of other lingual papillae (2). Cross-sectioned lumina of gustatory gland ducts (3) and the indication of secretory regions (4). The aponeurosis linguae (5) is missing under the base of the circumvallate papilla. HE, magnification: x 100.
Fig. 8 :
Fig.8: A developing circumvallate papilla with the spaces in. the epithelial basis of the encircling furrow (1) and differentiating taste buds in the wall epithelium (2) in a 57-day-old porcine foetus. In the connective tissue and lingual musculature, regions of the developing gustatory gland (3) can be seen. The aponeurosis linguae (4). HE, magnification: x 100.
Fig. 10 :Fig. 11 :
Fig. 10: The composite anlage of a circumvallate papilla in a porcine foetus at 57 days. Sections through the lumina of gustatory gland ducts (1) in the connective tissue. HE, magnification: x 100.
Fig. 16 :
Fig. 16: The surface of the lateral part of the tongue at 64 days. A developing foliate papilla with a taste bud (1) in the dorsal epithelium. Fasciculi in the process of organization (2). HE, 'magnification: x 250.
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Domain: Biology
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Unraveling the Genetic Basis for the Rapid Diversification of Male Genitalia between Drosophila Species
Abstract In the last 240,000 years, males of the Drosophila simulans species clade have evolved striking differences in the morphology of their epandrial posterior lobes and claspers (surstyli). These appendages are used for grasping the female during mating and so their divergence is most likely driven by sexual selection. Mapping studies indicate a highly polygenic and generally additive genetic basis for these morphological differences. However, we have limited understanding of the gene regulatory networks that control the development of genital structures and how they evolved to result in this rapid phenotypic diversification. Here, we used new D. simulans/D. mauritiana introgression lines on chromosome arm 3L to generate higher resolution maps of posterior lobe and clasper differences between these species. We then carried out RNA-seq on the developing genitalia of both species to identify the expressed genes and those that are differentially expressed between the two species. This allowed us to test the function of expressed positional candidates during genital development in D. melanogaster. We identified several new genes involved in the development and possibly the evolution of these genital structures, including the transcription factors Hairy and Grunge. Furthermore, we discovered that during clasper development Hairy negatively regulates tartan (trn), a gene known to contribute to divergence in clasper morphology. Taken together, our results provide new insights into the regulation of genital development and how this has evolved between species.
Introduction
To understand the evolution of animal morphology, we need to better link genotypic and phenotypic changes. This requires identifying the causative genes, how they are integrated into gene regulatory networks, and how changes in these interactions alter developmental processes and consequently the phenotype (Stern 2011;Nunes et al. 2013;Kittelmann et al. 2018). There has been great progress in identifying genes that cause changes in animal morphology (reviewed in Martin and Orgogozo [2013]). However, we still lack information on the genes that contribute to changes in quantitative traits, such as organ size, and how they combine to achieve this.
The size and shape of male genital organs evolve rapidly among species, driven by sexual selection (Eberhard 1985(Eberhard , 2010Hosken and Stockley 2004;House et al. 2013;Simmons 2014). For example, the epandrial posterior lobes and claspers (surstyli) have changed dramatically in size in the Drosophila simulans species clade in the last 240,000 years (Garrigan et al. 2012) (fig. 1A). Both the claspers and posterior lobes play important roles during copulation. The claspers open the female oviscapt through interdigitization of bristles, and help achieve correct copulatory positioning (Robertson 1988;Acebes et al. 2003;Jagadeeshan and Singh 2006;Kamimura and Mitsumoto 2011;Yassin and Orgogozo 2013;Masly and Kamimura 2014;Mattei et al. 2015), whereas the posterior lobes also contribute to stability during mating by inserting into grooves on the female tergites (Robertson 1988;Kamimura and Mitsumoto 2011;Yassin and Orgogozo 2013).
The posterior lobes are a novelty of the D. melanogaster species subgroup (Kopp and True 2002;Jagadeeshan and Singh 2006;Glassford et al. 2015). In D. mauritiana, they are small, thin, finger-like projections in comparison to the much larger, helmet-shaped lobes of D. simulans ( fig. 1A). D. . 1A), whereas the D. sechellia lobes are also intermediate in size and resemble "boots." It is important to note that there is some variation within species but the extremes of intraspecific variation do not overlap with the differences observed between species (McNeil et al. 2011;Hackett et al. 2016).
The claspers lie beneath the posterior lobes, and about twice as large in D. mauritiana compared with D. simulans, with a third more bristles Tanaka et al. 2015) ( fig. 1A). The morphology of these bristles also differs between the species, with the D. mauritiana bristles being generally shorter and thicker than those of D. simulans Tanaka et al. 2015). D. sechellia male claspers have very similar morphology to those of D. simulans, whereas the claspers of D. melanogaster appear to be intermediate between D. mauritiana, and D. simulans/D. sechellia ( fig. 1A).
Genetic mapping of changes to posterior lobe and clasper morphology among D. melanogaster subgroup species have shown that these differences are polygenic and generally additive (Coyne et al. 1991;Liu et al. 1996;Laurie et al. 1997;True et al. 1997;Macdonald and Goldstein 1999;Zeng et al. 2000;Tanaka et al. 2015Tanaka et al. , 2018. For example, up to 19 QTL have been identified for the difference in posterior lobe size between D. mauritiana and D. simulans, and QTL have been mapped to all major autosomal arms for the differences in clasper size between these species True et al. 1997;Zeng et al. 2000;Tanaka et al. 2015Tanaka et al. , 2018. Therefore, it appears that many loci contribute to these differences in genital organ size. We previously used an introgression-based approach to fine-scale map QTL on chromosome arm 3L underlying posterior lobe and clasper size differences between D. mauritiana and D. simulans Zeng et al. 2000;Tanaka et al. 2015;Hagen et al. 2019). The genomes of these lines were D. simulans, apart from introgressed regions of D. mauritiana DNA on 3L (Tanaka et al. 2015;Hagen et al. 2019). The regions that we found to contribute to posterior lobe and clasper size differences were mutually exclusive; suggesting that different genes underlie divergence in these two structures (Tanaka et al. 2015). Furthermore, this approach revealed that sequence divergence in tartan (trn), which encodes a leucinerich repeat transmembrane protein, contributes to the larger claspers of D. mauritiana compared with D. simulans (Hagen et al. 2019). This is likely due to more extensive and persistent expression of trn in the developing claspers in D. mauritiana (Hagen et al. 2019). However, since trn does not appear to contribute to posterior lobe size differences and explains only 16% of the clasper size difference between the species (Hagen et al. 2019), there must be additional loci involved in posterior lobe and clasper size differences on chromosome arm 3L.
To try to identify other causative genes on 3L, we generated new introgression lines to further refine existing candidate regions (Tanaka et al. 2015). We complemented this approach with RNA-seq on the developing genitalia of both species to identify genes expressed and differentially expressed both genome-wide and in the mapped regions. Subsequent functional testing of positional and expression candidate genes in D. melanogaster identified novel players involved in genital development, including the transcription factors (TFs) Grunge (Gug) and Hairy (H), which appear to positively and negatively regulate clasper size, respectively. Furthermore, we found that H represses trn expression in the developing claspers suggesting that changes in this regulatory interaction may contribute to interspecific differences in this structure. Taken together our findings provide new insights into the genetic interactions that underlie genital development, as well as the divergence of genital morphology between Drosophila species.
Results
Mapping Genomic Regions Underlying Male Genital Divergence between D. simulans and D. mauritiana Previously, we resolved the C2 candidate region for clasper size divergence between D. simulans and D. mauritiana by successfully identifying trn as the causative gene in this region (Hagen et al. 2019). In order to increase the resolution of other candidate regions contributing to male genitalia divergence (Tanaka et al. 2015
Analysis of Genes Expressed in Developing Male Genitalia
We next carried out RNA-seq on stages 2 and 4.5 of male genital development in D. mauritiana strain D1 (Dmau D1) and D. simulans strain w 501 (Dsim w 501 ) (Hagen et al. 2019). This allowed us to assay the genes expressed in the developing genitalia and those differentially expressed between these two species genome-wide and in our mapped regions.
We detected expression of 8,984 and 8,458 genes above the threshold value of 1 transcripts per million (TPM) in all biological replicates in the developing genital arches of Dsim w 501 and Dmau D1, respectively (supplementary file 4A, Supplementary Material online). A total of 760 genes are only expressed in Dsim w 501 , whereas 264 genes are only expressed in Dmau D1. However, many of these genes (114 and 121 genes, respectively) have low expression in the species where they are detected (<2 TPM on an average Diversification of Male Genitalia between Drosophila Species . doi:10.1093/molbev/msaa232 MBE between replicates) and therefore are less likely to underlie functional expression differences between species. Gene ontology (GO) analysis of the remaining 676 detected genes in Dsim w 501 indicated the most significant enrichment is in genes involved in heme binding (supplementary file 4B, Supplementary Material online) such as Cyp4d14, Cyp9b2, Cyp6d5, Cyp6t1, Cyp4g1, Cyp12a5, Cyt-c-d, Cyp6d2, glob2, Cyp6a20, and Cyp4aa1. The remaining 143 detected genes exclusive to Dmau D1 were enriched for ion transmembrane transporters (supplementary file 4B, Supplementary Material online), the majority of which were ionotropic receptors (IRs) (IR76b, IR7g, IR60b, IR7f, IR25a) as well as the ionotropic glutamate receptor eye-enriched kainate receptor (Ekar).
Of the 8,194 genes detected in both species, 1,169 were significantly differentially expressed between Dsim w 501 and Dmau D1, with 547 upregulated in the former and 622 in the latter, respectively (P adj < 0.05, supplementary file 4C, Supplementary Material online). Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we determined that 14/547 and 16/622 of these differentially expressed genes encode proteins in signaling pathways (supplementary file 4D, Supplementary Material online). This includes components of the mTOR, Notch, Hippo, Toll, and Imd pathways that are upregulated in Dsim w 501 , and members of the mTOR, MAPK, Wnt, FOX0, Toll, and Imd pathways that are upregulated in Dmau D1 (supplementary file 4D, Supplementary Material online). However, note that none of these genes is located in the introgressed regions we have analyzed.
To further explore divergence in gene regulation in the developing male genitalia of these species, we next assessed the expression of TF-encoding genes. We found 802 out of 994 genes-encoding TFs and cofactors are expressed in the developing genitalia of Dmau D1 and Dsim w 501 according to our RNA-seq data set (supplementary file 4E, Supplementary Material online). We identified eight TF genes that appear to be exclusively expressed in the developing male genitalia of Dmau D1, whereas 16 appear to be exclusive to Dsim w 501 . However, three and ten of these TFs, respectively, were detected at relatively low levels (TPM < 2) and are therefore not likely to contribute to functional regulatory differences in genital development between species. Of the 778 TF genes expressed in both species (supplementary file 4E, Supplementary Material online), 49 are differentially expressed with 33 upregulated in Dmau D1, and 16 upregulated in Dsim w 501 (supplementary fig. 1, Supplementary Material online). This includes five of the TF genes whose spatial expression in the developing genitalia of D. melanogaster was recently characterized (hinge3, Myb oncogene-like, single stranded-binding protein c31A, Sox21b and enhancer of split m3, helix-loop-helix) (Vincent et al. 2019).
We then focused on which of the genes in our mapped introgression regions are expressed in the developing genitalia. We found that 260 of the 380 protein-coding genes in the introgression-mapped regions could be detected in our RNAseq data, including 31 TFs (supplementary file 5, Supplementary Material online). 49 of the expressed candidate genes are differentially expressed between Dsim w 501 and Dmau D1, with about half the genes being upregulated in each (table 1). This includes one TF that is upregulated in Dsim w 501 (mirror [P4]) and four in Dmau D1 (meiotic central spindle, Sox21b, CG17359 [all P5], and CG10147 [C0/P1]).
Identifying Developmental Candidate Genes
We next sought to test if the positional candidate genes that are expressed in the genitalia according to our RNA-seq data have a role in the development of either the posterior lobes or the claspers. To do this, we performed RNAi in D. melanogaster to knockdown candidate genes in the smallest posterior lobe (P2) and clasper (C1) candidate regions, as well as a selection of promising genes from the other regions based on their expression profiles (table 1 and In combination with our previous study (Tanaka et al. 2015), we have now carried out RNAi for all expressed C1 candidate genes with available UAS lines (32 out of 35, supplementary file 6, Supplementary Material online). We previously observed that RNAi knockdown of cuticular protein 66D (Cpr66D) and minichromosome maintenance 7 (Mcm7) results in larger and smaller claspers, respectively (Tanaka et al. 2015). In addition to these two genes, we have now found that knocking down hairy ( 2I). Therefore, the C1 region contains five promising clasper developmental candidate genes. However, none of these genes is differentially expressed between Dsim w 501 and Dmau D1 (upregulated in the latter, supplementary file 5, Supplementary Material online). Analysis of the spatial expression of Cpr66D during genital development revealed that this gene is expressed in a wider domain along the inner clasper edge in D. melanogaster and D. mauritiana compared with D. simulans, and in bands extending from this region toward the lateral edge of the anal plates ( fig. 2J).
Region C0/P1 encompasses 99 genes (table 1). About 69 of these genes are expressed in the developing genitalia according to our RNA-seq data, with 14 exhibiting significantly differential expression between Dmau D1 and Dsim w 501 (table 1 and supplementary file 5, Supplementary Material online). Together with our previous study (Tanaka et al. 2015), we have now carried out RNAi against two of these differentially expressed genes (SP1173 and CG9953), and five other nondifferentially expressed genes (sugarless [sgl], CG32388, ventral veins lacking [vvl], CG10064, and lactate dehydrogenase [ImpL3]) in addition to sugarless [sgl] which we had previously knocked down (Tanaka et al. 2015). Only RNAi against sgl produced a phenotype (Tanaka et al. 2015). sgl appears to have a role in clasper development because RNAi knockdown of this gene led to significantly smaller claspers, but had no effect on the posterior lobes ( fig. 2A, D, and D 0 ; supplementary file 6, Supplementary Material online) (as shown previously in Tanaka et al. 2015).
Interactions between Genes Underlying Clasper Divergence
trn is the only gene identified so far that has been shown to contribute to clasper differences between D. simulans and D. mauritiana. The D. mauritiana allele of trn generates larger claspers with more bristles than the D. simulans allele (Hagen et al. 2019). This is likely achieved through the expanded and/ or more enduring expression of trn in the developing claspers in D. mauritiana compared with D. simulans (Hagen et al. 2019). It was previously shown that the transcriptional corepressor h, represses trn expression during embryogenesis in D. melanogaster (Chang et al. 1993;Kok et al. 2015) and in Drosophila Kc cells (Bianchi-Frias et al. 2004). Since we found that h RNAi in D. melanogaster results in significantly larger claspers with more bristles (fig. 2), we hypothesized that this gene might negatively regulate clasper size through repression of trn.
Consistent with a previous study (Vincent et
Discussion
Regions on Chromosome Arm 3L Contributing to Inter-and Intraspecific Variation in Posterior Lobe and Clasper Size As found previously, all regions identified through our introgression approach affect the claspers and/or posterior lobes consistently in the direction of their differences between the two species: Dmau D1 DNA resulted in larger claspers and smaller posterior lobes than Dsim w 501 and vice versa Tanaka et al. 2015;Hagen et al. 2019).
Also consistent with previous studies, we have shown clasper area and clasper bristle number map to the same genomic locations, which suggests that the same genes may influence both traits (Tanaka et al. 2015;Hagen et al. 2019). This could at least in part be explained by the process of bristle formation through lateral inhibition (Heitzler and Simpson 1991) and consequently large claspers developing more bristles than small claspers. It is not clear, therefore, whether selection drove changes in clasper bristle number, and clasper size changed as a by-product, or vice versa. However, the interdigitization of clasper bristles with those of the female oviscapt would perhaps argue for the former scenario (Mattei et al. 2015). (Tanaka et al. 2015) and C1 candidate gene Gug resulted in significantly fewer clasper bristles compared with both the UAS-Gug-hp (black asterisks, P < 0.001) and NP6333 driver controls (orange asterisks, P < 0.001). In contrast, knocking down C1 candidate gene h resulted in significantly more clasper bristles compared with the NP6333 driver (P < 0.001, orange asterisks) and UAS-h-hp controls (P < 0.001, black lines and black asterisks). (B) In addition, knocking down C1 candidate gene Gug resulted in the development of significantly smaller posterior lobes compared with the UAS-Gughp (orange asterisks) and NP6333 driver controls (P < 0.001, black asterisks). Asterisks indicate significant differences detected with Tukey's pairwise comparisons, where P < 0.001*** and P > 0.05 ¼ "ns" (supplementary file 6, Supplementary Material online). Orange indicates comparisons between the NP6333 driver control and UAS-gene-hp controls/gene knockdowns, whereas comparisons between UAS controls and knockdowns are indicated by black lines and black asterisks. Boxes indicate the range, upper and lower quartiles, and median for each sample. hp, hairpin; KD, knockdown. (C-I) Morphology of claspers and posterior lobes in NP6333 driver controls, UAS controls, and gene knockdowns (D-F 0 and H, H 0 ). (J) An illustration of stage 5 male genitalia (excluding the posterior lobes) and in situ hybridizations of Cpr66D in Dsim w 501 , Dmau D1, and Dmel w 1118 . Cpr66D transcripts were detected in a wider domain along the clasper inner edge (small arrowheads) and in bands extending toward the anal plates (large arrowheads) in the two species with larger clasper. Crpr66D is also expressed in the aedeagus of all three species. CL, clasper primordia; A, aedeagus (internal genitalia). Note that sgl RNAi knockdown data were generated in Tanaka et al. (2015) and reanalysed here.
Apart from C0/P1, all regions identified only affected either the claspers or the posterior lobes, which suggests different genes underlie the diversification in size of these two structures between D. simulans and D. mauritiana ( fig. 1). The effects observed for C0/P1 could be explained by a single evolved locus that is able to affect growth of the claspers and posterior lobes in opposite directions with D. mauritiana C0/P1 alleles generating smaller posterior lobes and larger claspers ( fig. 1B and C). Alternatively, since C0/P1 is still a relatively large region, it is possible that further mapping would resolve this region into distinct clasper and posterior lobe loci.
Interestingly, genes within region C0/P1 may underlie intraspecific variation as well as interspecific differences in posterior lobe size. This region overlaps with the 3L QTL peak observed in other interspecific mapping studies of differences in posterior lobe size between D. simulans and D. mauritiana or D. sechellia (Liu et al. 1996;Macdonald and Goldstein 1999;Zeng et al. 2000;Masly et al. 2011), as well as QTL peaks found in studies that mapped genetic variation underlying differences in posterior lobe size between D. melanogaster strains (McNeil et al. 2011;Takahara and Takahashi 2015;Hackett et al. 2016). Several other studies have also found cases where intraspecific variation maps to the same genomic region as interspecific variation (Nuzhdin and Reiwitch 2000;Gleason et al. 2002;Tatsuta and Takano-Shimizu 2006). Therefore, P1 genes represent excellent candidates for contributing to variation in the size of this structure both within and between species.
Genome-Wide Gene Expression During Genital Development in D. mauritiana and D. simulans
We carried out RNA-seq to identify and compare genes expressed in the developing genitalia between D. mauritiana and D. simulans. We were able to filter out positional candidates and also obtain a genome-wide perspective of gene activity during genital development as well as inferring differential expression between species.
We found that a small proportion of genes (<10%) are exclusively expressed in the developing genitalia of either Dsim w 501 or Dmau D1. The Dsim w 501 male genital-specific genes are enriched for iron ion binding proteins, whereas the Dmau D1 genes are enriched for multiple IRs. IRs are a conserved family of chemosensory receptors best known for their role in olfaction (Benton et al. 2009;Grosjean et al. 2011;Silbering et al. 2011;Min et al. 2013;Ziegler et al. 2013). Interestingly, some IRs, for example, IR52c and IR52d, are candidate taste and pheromone receptors (Koh et al. 2014) expressed in a sexually dimorphic manner on the sensilla of the D. melanogaster male foreleg, which makes contact with the female during courtship (Koh et al. 2014). The neurons in which these IRs are expressed in D. melanogaster males are only activated upon contact with females of the same species (Koh et al. 2014). Therefore, the striking differences in IR expression between male Drosophila species' genitalia may be an evolved mechanism to prevent conspecific mating.
Of the genes that are expressed in both Dsim w 501 and Dmau D1, we found that 1,169 were differentially expressed. This includes 30 signaling pathway components and 49 TFencoding genes. This suggests that the regulatory landscape of developing genitalia is generally conserved between D. mauritiana and D. simulans. However, the differentially expressed TFs will help to better understand the gene regulatory networks involved in genital development and evolution, and represent excellent candidate genes for further investigation.
Functional Analysis of Expressed Positional Candidates on Chromosome 3L During Genital Development
We have now analyzed the function of 57 of the expressed genes by RNAi knockdown in D. melanogaster, including 32 out of the 35 genes expressed in C1 (including those we Diversification of Male Genitalia between Drosophila Species . doi:10.1093/molbev/msaa232 MBE studied previously in Tanaka et al. [2015]), as well as all expressed genes in P2 (table 1). Note that we did not just focus on differentially expressed genes because genes can exhibit localized differences in expression during genital development that may contribute to morphological differences (Hagen et al. 2019).
RNAi against the expressed P2 genes did not have any significant effect on the posterior lobes (supplementary file 6, Supplementary Material online). RNAi against some of these genes simply may not have worked for various reasons, including when partial knock-down of the gene may not be sufficient to result in a phenotype (although numerous P2 genes were tested with multiple RNAi constructs, supplementary file 6, Supplementary Material online). Given this caveat of potential false negatives from RNAi, this approach allows us to prioritize genes for downstream analysis rather than completely exclude them as candidates. It remains possible that a nonprotein coding element in region P2 may explain the phenotypic effect of this region on posterior lobe size. Indeed, P2 encompasses a microRNA, mir-4940, as well as a long noncoding RNA CR45408 (Thurmond et al. 2019). Therefore, the causative element in P2 could be either of these factors, or a long-range enhancer responsible for the differential regulation of a gene outside P2 between these two species.
Our functional analysis of region C0/P1 identified one excellent candidate gene, sgl. sgl has been implicated in boundary formation and may interact with Wnt signaling (Hacker et al. 1997). RNAi against sgl resulted in smaller claspers ( fig. 2A and D 0 ; Tanaka et al. 2015), but this gene is not differentially expressed between D. mauritiana and D. simulans. However, since C0/P1 is a large region that is likely to contain many other developmental candidates, higher resolution mapping, and functional analysis of genes in C0/P1 is needed.
RNAi against C1 genes revealed five interesting genes for clasper development and evolution: Gug, foi, Mcm7, Cpr66D, and h (this study; Tanaka et al. 2015). Cpr66D expression is more extensive along the inner edge of the claspers and in bands extending toward the anal plates in Dmau D1 compared with Dsim w 501 ( fig. 2K). Cpr66D encodes a structural protein that forms chitin-based cuticle (Ren et al. 2005;Chandran et al. 2014;Stahl et al. 2017) and its role in genital development merits further study.
We also found evidence for potential interactions between other genes in mapped regions during genital development. Repression of trn by H has been predicted (Bianchi-Frias et al. 2004;Kok et al. 2015) or shown (Chang et al. 1993) in different developmental contexts. We found that H also negatively regulates trn expression in the developing claspers of D. melanogaster; with larger claspers generated by h RNAi likely being caused by expansion of the trn expression domain ( figs. 2A and F 0 and 3D; supplementary fig. 2, Supplementary Material online). H also negatively regulates trn expression during embryogenesis to help define compartmental boundaries (Chang et al. 1993;Pare et al. 2019). Therefore, this regulatory interaction could represent a more general mechanism for coordinating the correct positioning of cells during development. However, h is not differentially expressed between Dsim w 501 and Dmau D1 and appears to be ubiquitously expressed in the developing genitalia of D. melanogaster ( fig. 3B) (Vincent et al. 2019). Although it is possible that there could also be localized differences in h expression in the developing genitalia, these observations suggest that the differences in trn expression between Dsim w 501 and Dmau D1 could be the result of protein-coding changes that affect the DNA-binding efficiency of H, or variation in the number and/or sensitivity of H binding sites in trn regulatory elements. Indeed, there are several predicted H binding sites across the trn locus, but identification of trn genital enhancers and further analyses of H binding sites between D. mauritiana and D. simulans is needed to test this further.
In addition to trn, H may regulate multiple genes during clasper development including candidates revealed by our mapping and functional analyses. For example, H is also predicted to negatively regulate the C1 candidate gene, Gug (Yeung et al. 2017). Indeed, Gug itself is predicted to regulate the C0 candidate gene sgl, as this gene contains a Gug binding site in its intron (Yeung et al. 2017). However, since Gug acts as a transcriptional corepressor, and RNAi against both Gug and sgl reduces clasper size, it is unclear at this stage if there is a regulatory interaction between these genes in the developing claspers. It will be interesting to test these predictions in the future to learn more about the architecture of the gene regulatory network for clasper development and how this evolved during the rapid diversification of these structures.
Introgression Line Generation and Phenotyping
We generated new recombinants in our candidate regions by backcrossing virgin IL D11.01/Dsim w 501 heterozygous females, and virgin IL D08.04/Dsim w 501 heterozygous females to Dsim w 501 males. IL D1101 is an introgression line with D. mauritiana D1 DNA in the genomic location 3L: 7527144.15084689 Mb and encompasses the candidate regions C1, P2, and P3 (Tanaka et al. 2015). IL D08.04 is an introgression line with D. mauritiana w À DNA on 3L: 5911371.9167745 Mb (R2.02 D. simulans) and includes candidate regions P1 and C1 (Tanaka et al. 2015). New recombinants were detected by selecting for the loss of the visible marker D1 Tanaka et al. 2015) (fig. 1D), restriction fragment length polymorphisms, and sequencing markers (see supplementary file 7, Supplementary Material online for primer list). New introgression lines (supplementary file 1, Supplementary Material online) were all maintained as homozygous stocks.
Male genitalia were phenotyped from flies cultured under controlled growth conditions. All males used were progeny of ten females and five males that were transferred every 2 days, and allowed to develop at 25 C in a 12-h light-12-h dark cycle incubator unless otherwise stated. All adult males were maintained on a standard cornmeal diet at 25 C for at least 3 days before collection and storage in 70% EtOH.
Where possible, two or three replicates of ILs were phenotyped. Replicates are defined as introgression lines derived from the same recombination event and therefore containing Hagen et al. . doi:10.1093/molbev/msaa232 MBE the same introgressed region of D. mauritiana DNA. The abdominal tip and T1 leg were dissected for each fly in 70% EtOH, and transferred to Hoyer's medium. Using entomological pins, the posterior lobes were then dissected away from the claspers and anal plates. The claspers, posterior lobes, and T1 tibia were mounted in Hoyer's medium for imaging.
Images were taken using a Zeiss Axioplan light microscope at 250Â magnification for the claspers and lobes and 160Â for the T1 tibia, using a DFC300 camera. Clasper area, posterior lobe size, and tibia length were measured manually using ImageJ (Schneider et al. 2012), and bristle number was counted for each clasper (supplementary file 2A, Supplementary Material online). T1 tibia length was used as a proxy for body size, in order to assess consistency in rearing conditions and to ensure genital differences were not a result of general differences in size. Most introgression lines showed no significant difference in T1 tibia length compared with Dsim w 501 (supplementary file 2G, Supplementary Material online), and since genitalia are hypoallometric (Coyne et al. 1991;Liu et al. 1996;Macdonald and Goldstein 1999;Eberhard 2009;Shingleton et al. 2009;Masly et al. 2011), the phenotypic data were not corrected for body size. A detailed description of statistical methods and the comparisons used to map candidate regions based on these data can be found in the supplementary supportive text, Supplementary Material online.
RNA Sequencing and Differential Expression Analysis
Three independent RNA-seq library replicates were generated for Dsim w 501 and Dmau D1 developing male genitalia. Flies were reared under the above conditions, and white prepupae collected. Males were selected using gonad size and allowed to develop in a humid container at 25 C until either stage 2 or stage 4.5 (Hagen et al. 2019). Between these stages, the claspers develop from a ridge structure to a distinct appendage separate from the surrounding tissue, and the posterior lobe has begun to extend outward from the lateral plate primordia (Hagen et al. 2019). The anterior of pupae were impaled with a needle onto a charcoal agar plate and submerged in 1ÂPBS. Dissection scissors were used to remove the distal tip of the pupal case and the outer membrane, and pressure applied to the abdomen to allow the developing genitalia to be quickly expelled from the pupal case and dissected away from the abdomen. Note that the entire genital arch, including internal genital organs (but not including abdominal tissue), was isolated for RNA extraction. The genitalia from 15 males from each stage were collected and then combined in TRIzol (ThermoScientific). RNA was then extracted using standard procedures. Quality and quantity of RNA were verified using a Qubit fluorometer. Samples were sequenced by the NERC Biomolecular Analysis Facility (NBAF) at the Centre for Genomic Research, University of Liverpool, where dual-indexed, strand-specific RNA-seq libraries were prepared using NEBNext polyA selection and Ultra Directional RNA preparation kits. Samples were then sequenced using Illumina HiSeq 4000 (paired-end, 2Â150-bp sequencing). These RNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/ arrayexpress) under accession number E-MTAB-9465 ( [URL]-9465). Ribosomal reads were filtered out using default settings in SortMeRNA version 4.2.0 (Kopylova et al. 2012), and Trimmomatic version 0.38 (Bolger et al. 2014) was used to trim low-quality reads using default parameters. The remaining D. simulans reads were mapped against the reannotated transcriptomes of D. simulans, "GSE76252_ReanDsim_with_ ReanDmau_GeneSet_1to1orth," and the remaining D. mauritiana reads were mapped against the reannotated transcriptome of D. mauritiana, "GSE76252_PubDmau_ with_ReanDsim_exoutput_1to1orth" (Torres-Oliva et al. 2016), using Bowtie2 version 2.3.5 with the -very-sensitivelocal option (Langmead and Salzberg 2012). Reads mapped to reannotated genomes with an overall alignment rate of 68-69%. The SAM files were then converted to BAM files, sorted by coordinate, and index files created using Samtools version 1.10 (Li et al. 2009). Duplicate reads were marked but left in the data set. These data were then used with HTSeqcount version 0.11.1 in order to generate raw read counts for each gene (Anders et al. 2015). TPM was calculated using these counts in order to quantify gene expression, and the DEseq2 R package version 1.28.1 was used to determine differential expression between species using the default parameters (Love et al. 2014). Genes were considered to be expressed if TPM > 1 in all three biological replicates. Genes were only considered differentially expressed in comparisons where P adj (FDR) < 0.05.
Gene Ontology Analysis
In order to investigate the nature of the expressed, not expressed, and differentially expressed genes in our RNAseq data set, we determined their ontology using PANTHER version 15.0 (Thomas et al. 2003). We conducted overrepresentation tests (released 09/11/2019) of GO (released December 9, 2019) for the positional genes against the D. melanogaster reference list using the Fisher test (Thomas et al. 2006). Genes were considered significantly overrepresented when P adj (FDR) < 0.05.
Pathway Database Analysis
To identify potential differences in signaling pathway gene expression between Dsim w 501 and Dmau D1 developing male genitalia, we searched for differentially expressed genes in the KEGG pathway database (Kanehisa and Goto 2000). Those annotated as signaling pathway components are reported in supplementary file 4D, Supplementary Material online.
Annotation of TFs Present in RNA-Seq Data
In order to extract the genes encoding TFs from the RNA-seq data set, we used the databases of genes from Flymine ( [URL]; Lyne et al. 2007), amiGO ( [URL]/; Carbon et al. 2009), and Flybase (Thurmond et al. 2019), and bioinformatic analysis and manual curation from Hens et al. (2011). We filtered the genes in our data set corresponding to TFs by their GO Diversification of Male Genitalia between Drosophila Species . doi:10.1093/molbev/msaa232 MBE terms and gene groups in molecular function using the previously mentioned sources. The GO terms used were the following: "FlyTF_putativeTFs" from Flymine (Lyne et al. 2007), "transcription factor regulator activity" and "transcription factor coregulator activity" from amiGO (Carbon et al. 2009), "transcription factor gene group" and "transcription coregulator activity" from Flybase (Thurmond et al. 2019) and the data set of TFs from Hens et al. (2011). Genes that were annotated with these terms in any of the four resources were considered TF genes and used for downstream analysis.
RNAi Knockdown of Candidate Genes
The developmental role of genes was tested using RNAi in D. melanogaster. UAS-RNAi lines for these genes were provided by the Vienna Drosophila RNAi Center and the Bloomington Drosophila Stock Center (see supplementary file 6, Supplementary Material online for stock numbers). UAS males of candidate genes were crossed to NP6333-Gal4 ("NP6333") driver virgins (P[GawB]PenNP6333) (Chatterjee et al. 2011) carrying UAS-Dicer-2 P[UAS-Dcr-2. D] (Stieper et al. 2008). RNAi knockdown was conducted at either 25 or 28 C (supplementary file 6, Supplementary Material online) (Tanaka et al. 2015), under identical rearing conditions, and dissection, imaging, and analysis were carried out as described above (supplementary file 6, Supplementary Material online). To assess the role of a gene during genitalia development, we compared the phenotype of genital structures of gene knockdowns against the respective NP6333 driver controls using a Dunnett's test (supplementary file 6, Supplementary Material online). If the gene knockdown phenotype differed significantly from the NP6333 driver control, we then assessed whether or not this significant effect is a result of genetic background (e.g., an effect of the UASparental phenotype), or reflects a role of the gene in genital development. To do this, we compared all three experimental groups of males using an ANOVA (supplementary file 6, Supplementary Material online). If this was significant, we then analyzed where these differences arise from using a Tukey's test, and only concluded genes have a developmental role in the genitalia if the RNAi knockdown males were significantly different in phenotype compared with both parental controls.
In Situ Hybridization
Sample collection, RNA extraction, cDNA synthesis, and probe synthesis were conducted as described in Hagen et al. (2019). We performed in situ hybridization to detect expression of Cpr66D in D. mauritiana, D. simulans, and D. melanogaster, h in Dmel w 1118 and trn in UASh Bloomington TRiP 27738, NP6333-Gal4; UAS-Dicer x UASh Bloomington TRiP 27738 using species-specific probes. Probes were generated using the following oligos (forward followed by reverse) with the addition of T7 linker sequences added to the 5 0 end of each primer; trn (514 bp) ATCGAGGAGCTGAATCTGGG and TCCAGGTTACCATTG TCGCT (Hagen et al. 2019), Cpr66D (314 bp) CTCCTCG TATCAGTTTGGCTTC and CTGGTGGTACT GTGGCTGCT.
Antisense h probes were generated by amplification using T7 primers from a BLUESCRIBE plasmid that contained sequences for all three h coding exons (a gift from B. Jennings, Oxford Brookes University). In situ hybridizations were based on the Carroll lab "Drosophila abdominal in situ" protocol ( [URL]:// carroll.molbio.wisc.edu/methods.html) with minor modifications. All in situ hybridizations were conducted at least twice, with n ¼ 5-10 in each experiment.
Supplementary Material
Supplementary data are available at Molecular Biology and Evolution online.
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Domain: Biology
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Orientopius Fischer (Hymenoptera, Braconidae, Opiinae) new for Europe, with first notes on its biology and description of a new species
The genus Orientopius Fischer, 1966 (Hymenoptera: Braconidae: Opiinae) is reported for the first time from Europe and the West Palaearctic region, its biology (parasitoids of Phytobia spp.) is given for the first time and a new species (O. europaeus sp. n.) is described from France and Bulgaria.
Introduction
The large subfamily Opiinae (Braconidae), with 1,975 valid species according to Yu et al. (2009), is a common group containing generally small (2-5 mm) parasitoid wasps of mainly mining or fruit-infesting dipterous larvae. It has a worldwide distribution and the world fauna has been reviewed by Fischer (1972Fischer ( , 1977Fischer ( , 1986Fischer ( , 1987)). Currently about 35 genera are used, but the number of genera and the limits of several genera are still matters of discussion. The genus Orientopius Fischer, 1966, is a small genus with 13 described species from the East Palaearctic, Oriental and Australian (New Guinea) regions. Up to now, its biology was unknown; the metasomal carapace may indicate that oviposition is through e.g. a woody substrate or an egg-shell, both difficult to penetrate.
The second and third authors reared a new species of Orientopius from two species of the genus Phytobia Lioy, 1864 (Diptera: Agromyzidae) mining in the cambium of trees and shrubs of Crataegus monogyna Linnaeus and Prunus spinosa Linnaeus in northern France. The same species was collected in Central Bulgaria by the first author. Opiinae are solitary koinobiont endoparasitoids of larvae of cyclorrhaphous Diptera, but oviposition may take place into the egg of the host (ovo-larval parasitoids). They may play an important role in the control of dipterous pests such as fruit-infesting Tephritidae and mining Agromyzidae. The parasitoid larva has its final development when the host larva has made its puparium, and the adult parasitoid emerges from the host puparium.
Material and methods
The larvae and pupae of Phytobia were collected by J. L. Gumez in North France (Aisne, Bouconville-Vauclair, forêt domaniale de Vauclair and forêt domaniale de Samoussy). Some puparia were collected directly from the soil under the host plants (Fig. 30), others were obtained from young seedlings of Crataegus monogyna and Prunus spinosa with larvae at the ends of their branches. The infested branches were cut and placed in containers to obtain the puparia. These puparia were kept indoors under semi-natural conditions by the second and third authors to obtain adults of the Phytobia miner and its parasitoids. The identifications of the larvae and adults of Phytobia were made by M. Martinez and of the parasitoids by the first and last authors. Beside the fairly large series of Braconidae (10-20 specimens per species) some Ichneumonidae were reared, but these (together with more recently reared Phytobia parasitoids) will be treated in a second paper dealing with all reared material of Phytobia spp. in North France.
The specimens are deposited in the collection INRA (CBGP) at Montpellier and in the NCB Naturalis collection (RMNH) at Leiden. For identification of the subfamily Opiinae, see van Achterberg (1990Achterberg ( , 1993Achterberg ( , 1997)), for identification of the genus, see Fischer (1966), Tobias (1998) and Chen and Weng (2005), for references to the Opiinae, see Yu et al. (2009) and for the terminology used in this paper, see van Achterberg (1988Achterberg ( , 1993)). Measurements are taken as indicated by van Achterberg (1988).
Notes. Orientopius Fischer, 1966, is closely related to Coleopius Fischer, 1964; both have the female metasomal carapace covering the fourth and following tergites, the second metasomal tergite distinctly (1.3-1.9 times) longer than the third tergite, the third tergite with a sharp lateral crease and the second submarginal cell of the fore wing short. They can be separated as follows: 21); antenna yellowish-brown; pterostigma dark brown; second tergite 1.7-1.9times as long as third tergite; third tergite 0.5 times longer than its basal width; third metasomal tergite subparallel-sided, subrectangular and densely reticulate-rugose (Figs 18,33); fourth tergite of female smooth and retracted (Figs 1, 2, 32); setose part of ovipositor sheath 0.6-0.7 times as long as combined first-third metasomal tergites, 0.3 times as long as fore wing and 1.0-1.1 times as long as hind tibia (Fig. 32).
Description. Holotype, ♀, length of body 2.8 mm, of fore wing 2.5 mm. Head. Antenna with 28 segments and 1.2 times as long as fore wing; third segment 1.3 times as long as fourth segment, length of third, fourth and penultimate segments 3.1, 2.5 and 1.5 times their width, respectively; length of maxillary palp 0.9 times height of head; labial palp segments slender; occipital carina widely removed from hypostomal carina and dorsally absent; hypostomal carina narrow; length of eye in dorsal view 7.3 times temple; temples directly narrowed (Fig. 21) and largely sparsely punctulate; frons slightly depressed behind antennal sockets and with some rugulae, remainder slightly convex and setose, largely coarsely punctate, with interspaces mostly somewhat wider than punctures; face medio-dorsally elevated, coarsely punctate, with interspaces slightly wider than punctures and some rugae latero-dorsally; width of clypeus 2.7 times its maximum height and 0.55 times width of face; clypeus flat, smooth and its ventral margin rather thin and medially straight; hypoclypeal depression wide and deep (Fig. 20); labrum flat but with upcurved rim; malar suture complete; with punctures between malar suture and clypeus; length of malar space 1.3 times basal width of mandible (Fig. 22); mandible strongly constricted and twisted apically, without distinct ventral carina (Fig. 22), second tooth medium-sized.
Legs. Length of femur, tibia and basitarsus of hind leg 3.0, 5.9 and 3.4 times as long as wide, respectively (Fig. 25); hind femur with medium-sized setae and tibia densely short setose; third and fourth segments of fore tarsus about as long as wide.
Metasoma. Length of first tergite 0.8 times its apical width, its surface punctate in front of dorsal carinae and longitudinally reticulate behind carinae, convex and dorsal carinae united and with median carina posteriorly (Fig. 18); second suture crenulate, nearly straight and moderately impressed; second and third tergites longitudinally reticulate-rugose; median length of second tergite 1.7 times median length of third tergite; following tergites smooth and largely retracted below carapace; length of setose part of ovipositor sheath 0.32 times fore wing, 0.6 times first-third tergites combined and 1.1 times longer than hind tibia; hypopygium far retracted, truncate apically and about 0.3 times as long as metasomal carapace.
Etymology. Name derived from "Europa", because it is the first species of this genus known from Europe.
Notes. The species can be separated from the other (all East) Palaearctic species as follows:
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Domain: Biology
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Gene conversion limits the cost of asexuality in somatically immortal worms
Most multicellular organisms reproduce sexually despite the costs associated with sexuality. This has been explained as the result of selection favouring the ability to recombine the genome. The lack of recombination in asexual species constrains their adaptability and leads to the accumulation of deleterious mutations, ultimately increasing their risk of extinction. Nonetheless, successful asexual life histories persist among multicellular organisms, and explanatory mechanisms which may help limit the cost of asexuality remain enigmatic. In search of these mechanisms, we looked at that the molecular evolutionary changes in the sexual and obligately asexual strains of the planarian flatworm, Schmidtea mediterranea. We find that the accumulation of deleterious mutations in highly conserved genes is largely avoided in the asexual strain. We find evidence that this is achieved by somatic gene conversion in stem cells allowing for the restoration of beneficial alleles and purification of deleterious mutations. Taken together, our analysis identifies gene conversion as a mechanism which may contribute to the maintenance of asexuality in an obligately fissiparous metazoan.
Introduction
Most multicellular life reproduces sexually, while obligate asexuality is exceedingly rare (Judson and Normark, 1996). This pattern persists despite the high costs of sex, including the "two-fold" cost of sex (Gibson et al., 2017), the breaking up of beneficial haplotypes (Charlesworth and Barton, 1996), and the broad costs associated with securing a mate (Lehtonen et al., 2012). The maintenance of sexuality in the face of such costs suggests that asexuality, the alternative reproductive strategy, is somehow fundamentally limited and is outcompeted by sexual strategies in the long term (Lynch et al., 1993). However, some ancient asexual organisms exist, which raises the question of how they avoid the limitations of asexuality (Bast et al., 2018;Butlin et al., 1998;Judson and Normark, 1996;Lázaro et al., 2011;Wilson et al., 2018).
The potential disadvantages of asexual reproduction are driven by the tight linkage between alleles in an asexual genome (Box. 1) (Comeron et al., 2008;Felsenstein, 1974;Hill and Robertson, 1966). This linkage means that selection cannot act on individual alleles or allele combinations, but instead must act on the genomes of individuals as a whole -a problem known as "The Hill-Robertson effect" (Comeron et al., 2008;de Visser and Elena, 2007;Hill and Robertson, 1966). This reduces the adaptability of asexual populations, which has been shown to be particularly problematic in the context of changing environments and biotic competition (Hamilton et al., 1990;Lively and Morran, 2014;Vergara et al., 2014).
Additionally, the inability to remove deleterious mutations from the next generation through recombination results in the accumulation of deleterious mutations over time in a process called "Muller's ratchet" (Lynch et al., 1993;Muller). This occurs since even the fittest individual in an asexual population contains some deleterious mutations, and as this fittest genome reaches fixation, so do its deleterious mutations, leading to a ratchet-like process of mutation accumulation (Lynch et al., 1993;Muller). Taken together, the reduced ability to unlink alleles in asexual genomes is thought to drive Muller's ratchet and the Hill-Robertson effect, both suggested as explanations for why obligate asexual reproduction is rare (Felsenstein, 1974).
Box. 1: The limitations of obligate asexual reproduction stem from the tight linkage of alleles in the genomes that lack recombination. Muller's ratchet describes how this lack of recombination leads to the accumulation of deleterious mutations over time. This is because even the fittest asexual genome will have some deleterious mutations that will be passed on to the next generation, while in sexual reproduction, genomes that only contain beneficial alleles can theoretically be generated and selected for. Hill-Robertson interference is a broad term describing the inefficient selection that occurs when alleles are tightly linked due to the lack of recombination. This means that deleterious mutations can interfere with the selection for beneficial mutations (and vice versa), while in sexual populations these mutations can be selected upon independently. Clonal interference describes the inefficacy resulting from the fact that two beneficial mutations in different lineages will compete with one another. Since asexual lineages don't recombine with one another, only the fittest variant will persist, while in sexual populations a genome containing both variants can be formed.
However, some obligate asexual species persist over evolutionary time (Barraclough et al., 2007;Bast et al., 2018;Lázaro et al., 2011;Schön et al., 2009), suggesting that they may have evolved mechanisms to limit the impact of long-term asexuality. Such ancient asexuals include bdelloid rotifers, oribatid mites, darwinulid ostracods and more, all of which have likely been asexual for millions of generations (Brandt et al., 2017;Brandt et al., 2021;Flot et al., 2013;Mark Welch and Meselson, 2000;Schön et al., 2003;Schön et al., 2009). To date, there is no consensus regarding the methods by which they avoid the costs associated with asexuality. One suggested mechanism is gene conversion (Box.2): a form of mitotic recombination in which one allele overwrites another (Butlin et al., 1998;Flot et al., 2013;Schön et al., 2009). While deleterious and beneficial alleles are equally likely to get overwritten in the process of gene conversion, selection may be able to act to remove the deleterious gene conversion events and drive the spread of beneficial alleles (Box.2).
Ancient asexuals such as bdelloid rotifers (Flot et al., 2013) and darwinulid ostracods (Schön et al., 2009) exhibit excess homozygosity, which has been proposed to be the result of gene conversion, leading the authors to suggest a role for gene conversion in the long term survival of these asexuals. However, evidence from other species suggests that gene conversion may not be sufficient to limit mutation accumulation, since young asexual lineages of Daphnia show relatively high rates of gene conversion while still showing a significant accumulation of deleterious mutations (Omilian et al., 2006;Tucker et al., 2013). In neither case has it been shown that selection acts upon gene conversion events to remove deleterious mutations and fix beneficial alleles in the population. Taken together, there is currently no consensus about the mechanisms that limit the costs of asexuality in ancient asexuals, and we need to assess more groups of long-term asexuals with diverse modes of asexual reproduction to better understand how ancient asexuals might overcome the limitations of asexuality.
Box. 2: Gene conversion as a method to remove deleterious mutations. Gene conversion can limit the progression of Muller's ratchet by removing deleterious mutations from some of the progeny. A heterozygous diploid is depicted by homologous regions of red and blue chromosomes. The red chromosome contains a region with a deleterious mutation. The progeny containing a gene conversion event which removes the deleterious mutation in the red chromosome has the highest fitness (left) and can be selected for, while gene conversion events leading to a homozygous deleterious mutation (right) will be selected against. The most deleterious genome (a/a) is indicated by high transparency, followed by a/A, and A/A respectively.
The planarian flatworm S. mediterranea is a highly regenerative animal that has both a sexual and an asexual strain (Tan et al., 2012). The sexual strain is a cross fertilizing hermaphrodite and the obligate asexual strain lacks reproductive organs, reproducing solely by fission followed by whole body regeneration (Newmark et al., 2008;Saberi et al., 2016) (Fig. 1).
The asexual strain is evidently able to thrive, despite the limitations of asexual reproduction, providing us with an opportunity to examine potential mechanisms for dealing with the limitations of asexuality (Lázaro et al., 2011;Sahu et al., 2017;Tan et al., 2012). The fissiparous nature of asexual planarian reproduction is based on whole body regeneration that is mediated by somatic, immortal, pluripotent adult stem cells called neoblasts (Wagner et al., 2011). Continuous BrdU labelling suggests that these neoblasts have a generation time of 24-72 hours as they are constantly in the cell cycle, suggesting that since the origin of the asexual lineage there have been at least tens to hundreds of millions of asexual neoblast generations (Fields and Levin, 2018;Newmark and Sánchez Alvarado, 2000), dependent upon when the asexual strain arose (Lázaro et al., 2011). Such a high number of asexual generations is equivalent to some of the previously studied ancient asexuals but represents an understudied mode of asexual reproduction in metazoans. This fissiparous lifestyle and the presence of a closely related sexual strain which allows for the direct genetic comparison of sexual and asexual strains, makes S. mediterranea a valuable species for understanding how such asexual lineages might persist over evolutionary time.
In this study, we investigate how asexual planarians may limit the costs of asexuality. Specifically, we test whether planarian flatworms are able to escape the costs of asexuality through gene conversion. We find that asexual planarians limit the impact of Muller's ratchet, and that they show GC-biased excess homozygosity indicative of gene conversion. Furthermore, we find that mutations that are predicted to be more deleterious are less homozygous, suggesting that gene conversion events can be acted upon by selection to fix the beneficial allele in the population and remove deleterious mutations. Thus, we find evidence that gene conversion mediated removal of deleterious mutations may allow asexual planarians to limit Muller's ratchet and persist despite their obligate asexuality.
Population structure of laboratory S. mediterranea
To better understand how laboratory populations of S. mediterranea are related to one another, we used 169 asexual RNAseq libraries from 7 studies and 46 RNAseq libraries from 3 studies and evaluated their population structure (SI1) (Currie and Pearson, 2013;Davies et al., 2017;Duncan et al., 2015;Khan and Newmark, 2022;Scimone et al., 2014;Tu et al., 2015;van Wolfswinkel et al., 2014;Vogg et al., 2014;Zhu et al., 2015). We found that all asexual samples clustered together independently from the sexual samples (Fig. 1A). Asexual populations also largely clustered by BioProject, suggesting that populations are evolving independently to a certain extent in each laboratory (Fig. 1B). To ensure that no one population would be biasing the results, for example by being abnormally homozygous, we checked for outliers in mean allele frequency and proportion of homozygosity across all BioProjects (Fig. S1). We found no outliers in the asexual population (Fig. S1A-C). One sexual population (Davies et al., 2017), had a higher proportion of rare alleles compared to the other sexual populations (Fig. S1D). To ensure that our results are not biased by this population, all further analyses comparing asexual to sexual allele frequencies were also done separately against each sexual population. Most of the variants isolated were specific to the asexual lineage (806,712 variants, which amounts to 67.1% of the variants found in this study), and only 7.7% were shared between the sexual and asexual populations (Fig. 1C, Fig. S1E). This abundance of asexual variants is expected since variants were called against the available sexual genome (Grohme et al., 2018). In both sexual and asexual populations, the majority of variants were non-coding, followed by synonymous mutations, non-synonymous mutations, and indels respectively (Fig. 1D, Fig. S1F). In summary, our analysis suggests that asexual populations form subpopulations across different laboratories, and are distinct from the sexual population, with over 800,000 variants that are unique to the asexual population.
Limited impact of Muller's ratchet in asexual planarians
We began by looking for evidence of deleterious mutation accumulation in the obligate asexual strain by using dN/dS analyses (SI2-3). We found that across the entire set of protein coding genes (Neiro et al., 2022) (SI4), the median asexual dN/dS value is 3.6% higher than that found in the sexual strain (Wilcoxon rank sum; NAsexual = 16,445, NSexual = 10,700, W = 91,647,379, P < 0.001, Fig. 2A). High dN/dS values can indicate both deleterious mutation accumulation and positive selection, thus we assessed dN/dS values in genes that are unlikely to be under positive selection. Specifically, we compared the dN/dS values of exons belonging to highly conserved single copy BUSCO genes (SI5) (Manni et al., 2021), and found no significant difference between the strains (Wilcoxon signed rank; Npairs = 160, V = 7,069, P = 0.284, Fig. 2B). However, when we compared the dN/dS values of exons under significant negative selection in both strains (SI6), we found that the asexual dN/dS values were 27% higher than in the sexual strain (Wilcoxon signed rank; Npairs = 41, V = 665, pvalue = 0.002, Fig. 2C). This is in contrast to some previous studies that found higher impacts of Muller's ratchet in asexual lineages (dN/dS values ~200%-~3,000% higher than in their sexual counterparts) (Barraclough et al., 2007;Bast et al., 2018;Henry et al., 2012;Johnson and Howard, 2007;Neiman et al., 2010;Paland and Lynch, 2006). Additionally, we isolated genes that contain exons under positive selection in the asexual population (Fig. 2D, SI7-8).
These included multiple genes related to GLIPR1-like genes (see methods), which may suggest that these genes evolved new functions in the asexual population. Taken together, while asexual planarians may be subject to Muller's ratchet, the accumulation of deleterious mutations is somehow limited, and is not present in highly conserved single copy genes. This requires a mechanistic explanation in the absence of sexual recombination.
Loss of heterozygosity mechanisms in asexuals may limit Muller's ratchet
Mechanisms limiting Muller's ratchet in asexual planarians may involve somatic recombination mechanisms, such as gene conversion, that in combination with selection can remove deleterious mutations (Box.2). A signature of such a process would be homogenisation of the genome, leading to low polymorphism and high homozygosity. We compared allele frequencies of synonymous variants in asexual and sexual populations and found that asexual allele frequency is significantly higher than in the sexual population for both shared ancestral variants and lineage specific variants suggesting that there is lower polymorphism in the asexual population (Wilcoxon signed rankancestral; Npairs = 9,206, V = 40,369,398, Pancestral < 0.001, Fig. 3A; Wilcoxon rank sumlineage-specific, Nasexual = 30,463, Nsexual = 13,428, W = 338,332,851, Plineage-specific < 0.001, Fig. 3B). Additionally, in asexuals, the allele frequencies of ancestral variants were 45% higher than lineage specific variants, while in the sexual population the allele frequencies of ancestral variants were only 4% higher than lineage specific variants (Wilcoxon rank sum; Nasexual = 309, Nsexual = 305, W = 82,102, P < 0.001, Fig. 3C). The increased allele frequency in ancestral variants suggests a process of homogenisation over time, which is more effective in the asexual genome.
High allele frequencies in asexuals may be explained by the clonal nature of asexual planarians, so we tested whether they also show excess homozygosity which would be indicative of a homogenising process that cannot be accounted for by clonality. In the absence of recombination, half of the ancestral variants shared between the sexual and asexual strains should be homozygous in the asexual strain (see methods). However, we find that the mean proportion of homozygosity in asexual ancestral variants is 0.82, suggesting that some homogenising process is taking place over time (Wilcoxon signed rank; Nasexualancestral = 9,206, V = 34,205,206, P < 0.001, Fig. 3D).
We also tested whether there is excess homozygosity in the uniquely asexual variants. For asexual specific variants, the expected proportion of homozygosity depends on the proportion of variants that formed by mutation after the sexual-asexual split, versus those that were inherited from their sexual ancestors and subsequently lost from the sexual population. At the extremes, if all of them formed after the sexual-asexual split we would expect almost none of them to be homozygous, and if all of them were inherited from their sexual ancestors we would expect a third of them to be homozygous (see methods). We find that the mean proportion of homozygosity in asexual specific variants is 0.32 (Fig. 3D), which in the absence of recombination would suggest that the vast majority (97%) of asexual specific variants were inherited from sexual ancestors and then lost from the sexual population. This would correspond to only 914 de-novo synonymous mutations in the asexual population since the sexual-asexual split, which is unlikely given that the asexual population split from the sexuals hundreds of thousands to millions of years ago (Lázaro et al., 2011). Thus, we conclude that some form of recombination acts as a homogenising force.
However, we wanted to explicitly test whether variant inheritance from a sexual ancestor followed by subsequent loss in the sexual population can explain the homozygosity levels in uniquely asexual variants. To this end we assessed the proportion of homozygosity in uniquely asexual variants that are fixed within the asexual population. These represent the ancestral asexual variants of our population, and so are likely to be older than other variants that are only found in a subset of samples. We found that the mean proportion of homozygosity in ancestral asexual specific variants was 0.58, suggesting that there is excess homozygosity even if all these asexual specific ancestral variants were originally inherited from sexual ancestors rather than forming de-novo in the asexual lineage (Wilcoxon signed rank; Nancestral asexual specific = 2,275, V= 2,121,916, P < 0.001, Fig. 3E). Additionally, ancestral variants shared between the sexual and asexual population are more homozygous than asexual specific variants (Wilcoxon rank sum; Nasexual-ancestral = 9,206, NAsexual specific = 30,463, W = 211,577,670, P < 0.001, Fig. 3D), suggesting that more ancient variants are more likely to be homozygous. We also found that within asexual specific variants, those found in more samples were more likely to be homozygous (Binomial GLM, N = 30,463, P < 0.001, Fig3E).
Taken together, this suggests that some homogenising process might be taking place in the asexual genome, and that its impact is larger on the more ancient variants compared to those with a more recent origin. These results indicate a genome wide mechanism in driving the loss of heterozygosity in asexual planarians that could potentially limit mutation accumulation in this asexual lineage.
GC-biased gene conversion in asexual planarians
We hypothesised that since gene conversion has a GC bias (Chen et al., 2007), homozygous variants should display this signature if they were indeed generated by gene conversion. We find that homozygous variants are GC enriched compared to heterozygous variants, with 53% of homozygous variants being composed of GC nucleotides compared to 35% in heterozygous variants (Chi-sqr = 1,328.6,df = 1, NHOM = 17,487, NHET = 22,517, P < 0.001, Fig. 4A), suggesting a GC bias in the mechanism driving high asexual homozygosity.
Additionally, we find that homozygosity is a significant predictor of GC-AT identity, with more homozygous variants being more likely to have a GC identity (Binomial GLM, N = 44,669, P < 0.001, Fig. 4B). Furthermore, we show that AT to GC mutations are 1.5 times more homozygous than GC to AT mutations, with mean homozygosity proportions of 0.57 and 0.38 respectively (Wilcoxon rank sum, NAT->GC = 17,984, NGC->AT = 21,483 W = 232,985,919, P < 0.001, Fig. 4C). This GC bias is not driven by AT to GC mutations across the transcriptome generally (Fig. S3). Finally, we find that variants with higher allele frequency are more likely to have a GC identity, and that this bias is stronger in the asexual strain compared to the sexual (binomial GLM, P < 0.001, Fig. 4D). Taken together, this suggests that GC biased gene conversion takes place in the asexual planarian, leading to a loss of heterozygosity which could limit the impact of Muller's ratchet (Box.2).
Figure 4 -GC bias in homozygous variants. A) Bar plot showing that the proportion of GC is higher in homozygous variants compared to heterozygous variants. Black line indicates the 50% line; B) Binomial GLM showing that variants with a higher proportion of homozygosity are more likely to be of GC identity in asexual planarians; C) Violin plot showing that AT to GC mutations are more homozygous than GC to AT mutations. Black dots indicate medians and black cross bars indicate means; D) Binomial GLM showing that variants with higher allele frequencies are more likely to be of GC identity in both sexuals and asexual, but in asexuals the effect is larger.
Selection on gene conversion events removes deleterious mutations
Since gene conversion has the potential to generate homozygotes for both beneficial and deleterious variants, selection must be able to remove deleterious gene conversion events for gene conversion to be a viable solution to Muller's ratchet. We find that mutations that are likely to be more deleterious due to their effect on predicted proteins (see methods), are less likely to be homozygous, suggesting that deleterious gene conversion events are selected against. We find that coding SNPs have a mean homozygosity proportion of 0.41 and are more homozygous than coding indels which have a mean homozygosity proportion of 0.25 (Wilcoxon rank sum; Nindels = 883, NSNPs = 64,220, W = 23,118,081, P < 0.001, Fig. 5A).
Additionally, coding SNPs have higher allele frequencies than coding indels, with mean allele frequencies of 0.69 and 0.36 respectively (Wilcoxon rank sum; Nindels = 883, NSNPs = 64,220, W = 11,652,733, P < 0.001, Fig. 5B). These patterns could be driven by other factors such as error rates in each category of mutation, so we also compared the homozygosity and allele frequencies of mutations within each category. We found that the homozygosity and allele frequencies were higher in synonymous vs nonsynonymous SNPs (Wilcoxon rank sum; NSynonymous = 39,669, NNonsynonymous 24,471=, WHom = 462,855,666, PHom < 0.001, WAF = 452,432,400, PAF < 0.001, Fig5C-D), non-framshifting indels vs framshiting indels (Wilcoxon rank sum; NNon-frameshifting = 154, NFrameshifting = 664, WHom = 44,439, PHom < 0.001, WAF = 23,168, PAF < 0.001, Fig5C-D), and Nonsynonymous SNPs vs nonsense SNPs (Wilcoxon rank sum; NNonsynonymous = 24,471, NNonsense = 65, WHom = 613,538, PHom < 0.001, WAF = 494097, PAF < 0.001 Fig. 5C-D). This suggests that mutations more likely to be deleterious are less likely to be homozygous and are rarer in the population. However, in contrast to this pattern, mutations in noncoding regions were altogether more heterozygous and had lower allele frequencies than those in coding regions (Wilcoxon rank sum; NNoncoding = 21,908, NCoding = 74,724, WHom = 891,850,288, PHom < 0.001, WAF = 860,143,632, PAF < 0.001, Fig. S4) That being said, the majority of noncoding mutations are found within repetitive regions (Fig. S4) which were shown in previous studies to have high mutation and erroneous mapping rates that may also explain their abnormally high levels of heterozygosity (Nishant et al., 2009;Patil and Vijay, 2021). We propose that when gene conversion events lead to a homozygous deleterious mutation in a cell, the homozygous deleterious clone is eliminated by intra-organismal and inter-organismal selection, leading to the lower observed homozygosity in variants that are predicted to be more deleterious. The separation of alleles via gene conversion followed by subsequent selection against unfit stem cell clones may help limit the impact of both Muller's ratchet and the Hill-Robertson effect. Frameshifting indels have lower allele frequencies than non-frameshifting indels (mean allele frequencies of 0.3 and 0.57 respectively). Nonsense variants have lower allele frequencies than nonsynonymous variants (mean allele frequencies of 0.46 and 0.66 respectively).
Nonsynonymous variants have lower allele frequencies than synonymous variants (mean allele frequencies of 0.66 and 0.7 respectively).
Discussion
Mechanisms explaining how some asexual species persist despite the potential costs of asexuality have remained elusive. Our results support a role for gene conversion in unlinking loci in the asexual genome of S. mediteranea. Combined with selection against deleterious homozygotes this allows these asexual planarians to limit the impact of Muller's ratchet and Hill-Robertson effects, potentially explaining how this obligate asexual can persist over evolutionary time.
Our results suggest that the impact of Muller's ratchet is relatively limited in asexual planarians. This is surprising since it has been shown both empirically and by simulation that Muller's ratchet should drive the rapid accumulation of deleterious mutations even in recent asexual lineages (Loewe and Lamatsch, 2008;Lynch and Gabriel, 1990;Neiman et al., 2010;Tucker et al., 2013). For example, Daphnia pulex, an asexual lineage that is estimated to be only around 1,000 years old, is already showing signs of deleterious mutation accumulation (Tucker et al., 2013). Additionally, Potamopyrgus antipodarum, a freshwater snail in which the sexual form coexists with a recent obligate asexual form, shows higher rates of deleterious mutations in the asexual strain (Johnson and Howard, 2007;Neiman et al., 2010). This suggests that Muller's ratchet can act rapidly to drive mutation accumulation in asexuals. Thus, one might expect asexual planarians to show clear signs of Muller's ratchet, given that the asexual stem cells that make up their soma have been asexual for at least tens of millions of generations. However, they seem to only show an 27% increase of dN/dS in exons under negative selection, and this may be driven by the higher number of variants in the asexual dataset, increasing the statistical power for calling exons under negative selection and including those with higher dN/dS ratios. Furthermore, there is no evidence for elevated dN/dS ratios in highly conserved BUSCO genes, where nonsynonymous mutations are likely to be deleterious. This lack of high mutation accumulation in asexual planarians could explain how they have persisted as asexuals and is similar to the patterns seen in some other long term asexual lineages (Brandt et al., 2017;Swanstrom et al., 2011). For example, asexual oribatid mites have been shown to have lower dN/dS ratios compared to their sexual counterparts, suggesting that, in this case, purifying selection might be stronger in the asexual species (Brandt et al., 2017). In bdelloid rotifers nuclear genes do not show signs of Mullers ratchet (Birky et al., 2005;Welch and Meselson, 2001). Others have shown an accumulation of deleterious mutations in bdelloid mitochondrial genes (Barraclough et al., 2007), but have later suggested that this may be due to other factors which when taken into account suggest no signs of Muller's ratchet in the mitochondrial genes of bdelloid rotifers (Swanstrom et al., 2011). The lack of strong impacts of Muller's ratchet on some long term asexuals suggests that they may possess mechanisms to limit the accumulation of deleterious mutations. Some authors have previously suggested gene conversion as a mechanism to limit Muller's ratchet in asexual lineages (Flot et al., 2013;Loewe and Lamatsch, 2008;Schön et al., 2009).
The idea is that deleterious alleles can be overwritten by fit alleles, and that selection will favour these events, ultimately removing deleterious mutations from the population (Box.2). However, beneficial alleles can equally be overwritten by deleterious alleles in the gene conversion process, and so selection must be able to act against these events to effectively counteract Muller's ratchet. This might explain why some asexuals show high rates of gene conversion without limiting Mullers ratchet (Omilian et al., 2006;Tucker et al., 2013).
Furthermore, for gene conversion to stop the progression of Mullers ratchet, it may need to occur at a rate higher than the rate of point mutations. Otherwise, mutations may accumulate at a rate faster than gene conversion can remove them. This might be the case in some asexuals such as Timema stick insects, which show lower rates of gene conversion compared to their sexual relatives, and dN/dS values 3.6-13.4times higher compared to their sexual counterparts (Bast et al., 2018;Henry et al., 2012). Despite these caveats, gene conversion remains a potential mechanism for limiting the cost of asexuality. This is in part because it is a byproduct of DNA damage repair mechanisms that are available to diploid (or higher ploidy) organisms (Chen et al., 2007) and so, it does not need to evolve de novo as a solution to asexuality. Supporting this are the shared signatures of gene conversion between bdelloid rotifers (Flot et al., 2013), darwinulid ostracods (Butlin et al., 1998), and now
S.mediterranea.
While gene conversion may be important in both fissiparous and parthenogenic asexuality, we hypothesise that it might be significantly more effective in fissiparous asexuality. This is due to the variation within the soma being heritable in fissiparous organisms, increasing the amount of variation subject to selection (Howe et al., 2022). This is particularly relevant in S.mediterranea whose immortal stem cells are extremely abundant and represent a considerable proportion of the soma (van Wolfswinkel et al., 2014). These high numbers of stem cells are important since each stem cell will experience unique gene conversion events, and with a large enough population of stem cells it is likely that some of them will overwrite deleterious alleles with their beneficial counterparts. These stem cells may then outcompete other stem cells within the worm and spread, potentially reaching fixation in the worm.
Selection may then act between individual worms, selecting for those that have purged deleterious mutations, ultimately removing deleterious mutations from the population. Thus, fissiparous organisms may be more efficient than parthenogenetic asexuals at removing deleterious mutations from the population since gene conversion within their soma can also be selected upon to remove deleterious mutations.
However, it is important to point out that there is always the possibility that asexual planarians have rare cryptic sex that we are simply not aware of, a fundamental difficulty when researching the impacts of asexuality. For example, the brine shrimp, Artemia parthenogenetica, was once thought to be asexual but was later shown to undergo rare sex in a mass crossing experiment (Boyer et al., 2021). Additionally, even the presumed ancient asexual bdelloid rotifer Macrotrachella quadricornifera has had its asexuality contested with evidence supporting allele sharing between different isolates (Laine et al., 2022). However, similar patterns can potentially be explained without sex by ancestral duplications followed by independent allele losses (Wilson et al., 2024). Either way, it is entirely possible that in the case of S. mediterrannea rare cryptic sex might occur and might explain how they avoid the accumulation of deleterious mutations. However, it would not explain the strong signatures of gene conversion, which is not as clear in the genome of sexual planarians (Fig. 3C, Fig. 4D).
Finally, even if rare sex occurs, gene conversion may still be useful as a mechanism to deal with the cost of highly reduced levels of sexual recombination.
In summary, obligate asexual reproduction in multicellular organisms is rare and thought to be an evolutionarily ephemeral phenomenon due to the fundamental limitations of asexuality.
When we examine S. mediterrannea, which adopts this strategy successfully, we are able to assess how it limits the costs of obligate asexuality. Our results suggest that it likely uses asexual recombination in the form of gene conversion, to unlink loci and thus limit the impact of Muller's ratchet and Hill-Robertson interference. While such mechanisms may be available to most asexual lineages, the opportunity for selection to act on a population of somatic stem cells may be key in allowing asexual planarians to persist.
When choosing the studies, we only used those that used illumina sequencing and had clear annotations of the strains used. The Oxford sexual S. mediterranea population data was acquired by extracting RNA from whole, mature sexual worms using a standard Trizol extraction (Liu and Rink, 2018). Extracted RNA was then sequenced with Ilumina sequencing of 150bp paired end reads. Raw reads were deposited at the SRA repository (XXXX/XXXX).
Variant calling and filtration
RNAseq data was then trimmed and quality filtered with trimmomatic and FASTQC using default settings (Bolger et al., 2014). STAR was then used to map the RNAseq reads to the dd_Smes_g4 genome (Dobin et al., 2013;Grohme et al., 2018). For variant calling we used the GATK 4.2.2.0 software following the best practice (Van der Auwera et al., 2013). We used the suggested parameters for variant filtration (SI9), except that we allowed for variants that had no reads supporting the reference (these are usually filtered out since some of the quality measures require a comparison of reads supporting the reference and alternative alleles). This provided us with a list of variants in each population (SI10).
Defining Orthologues
We ran Orthofinder using a proteome from the Neiro et al annotation to isolate the orthologues of each predicted gene (Emms and Kelly, 2019;Neiro et al., 2022). To reduce redundancy, we first ran CD-hits to collapse all peptides with 99% identity to a single transcript ID.
dN/dS analysis
We used the dNdScv R package for dN/dS analysis and annotation of mutation impact in coding regions (Martincorena et al., 2017).dNdScv uses an expansion on the Poisson framework to obtain dN/dS values and a full tri-nucleotide model to avoid systematic bias due to sequence context (Martincorena et al., 2017). To get the list of genes for dN/dS analysis we used the list included in the OrthoFinder output, and further filtered for the longest transcript per gene. We then ran dNdScv with no covariates and no limits on the number of variants per CDS. When comparing dN/dS values between the strains in genes under purifying selection, we used the local model from dNdScv, since the overdispersion value for the sexual dataset was lower than 1. To compare dN/dS values of BUSCO gene exons between the strains, we isolated single copy BUSCO genes in planarians using BUSCO (metazoan gene list). We then isolated BUSCO exons that had both synonymous and nonsynonymous mutations within them (to allow for meaningful dN/dS assessment using the dNdScv local model) and compared their dNdS values provided by dNdScv. We also compared dN/dS ratios in exons that were under negative selection in both strains. These were defined by having a qmis_loc < 0.05 and wmis_loc < 1 in the dN/dS output (SI3-SI4, SI6). Finally, we used the global model to isolate genes under positive selection in asexual planarians (SI7-SI8), and top blast hits were taken from planmine (Rozanski et al., 2019).
Analysis of zygosity and allele frequency
To assess allele frequency and homozygosity levels accurately, we only used information about variants from samples that had a minimum depth of 10 at the site (SI11). To achieve this, we split the VCF into individual VCFs (one per sample), and then filtered out any variants that were not covered by at least 10 reads. Allele frequency in each sample was calculated using VCFtools (0.1.16)freq function (Danecek et al., 2011). This resulted in each variant obtaining a 0, 0.5 or 1 value for each sample, with the numbers corresponding to homozygous reference, heterozygous, and homozygous alternative respectively. We could then use this information to calculate allele frequency in the population for each variant (total number of alternative alleles in the population / total number of alleles in the population). We also calculated the proportion of libraries that were homozygous for each variant (number of libraries homozygous for alternative variant / number of libraries containing the variant).
Note that assuming a complete lack of recombination in asexuals, the measure of homozygosity from a group of individuals should be very similar to the measure of homozygosity within a single individual.
Expected values for mean proportion of homozygosity
To test whether there is excess homozygosity in the asexual strain, we compared the observed and expected proportions of homozygosity in the asexual population. When testing for excess homozygosity, or GC bias in homozygous variants (Fig. 4), only synonymous mutations were used to limit the impact of selection. Assuming there is no recombination, the expected mean proportion of homozygosity for ancestral variants (those shared between sexual and asexual strains) is 0.5. This is because for a variant to be ancestral, it must have been inherited from a sexual worm as at least one of the alleles in the founding asexual population. Thus, whether an ancestral variant is homozygous depends only on whether the variants was also inherited as the second allele, meaning that half of the variants are expected to be homozygous. The expected proportion of homozygous asexual specific variants cannot be precisely known because it depends on how many of them were formed by mutation after the sexual-asexual split, and how many of them were inherited from a sexual ancestor and subsequently lost from the sexual population. However, we can place lower and upper limits for the expected homozygosity in the two extremes. The first case, which defines the lower limit, is a scenario in which all asexual specific variants arose by mutation after the sexual-asexual split. In the absence of recombination, we would expect this to lead to no homozygous variants (since the de-novo mutations should be heterozygous). In the second case, which sets the upper limit, all asexual specific variants were inherited from a sexual ancestor and so the proportion of homozygous variants depends on Mendelian principles. This means that there are 3 possible ways that the alternative alleles could have been inherited, alt/alt, alt/ref, and ref/alt, meaning that if all 3 options are equally likely, we would expect one third of all asexual specific variants to be homozygous (Fig. S4).
Assessing evidence for effective selection on gene conversion events
To assess whether there is effective selection to remove deleterious gene conversion events, we wanted to test whether more harmful mutations are less likely to be homozygous. Since we cannot know the precise impact of each mutation on fitness, we broadly ranked variants based on the amount of change the mutation is likely to have on the protein sequence. In our analysis we assumed that coding indels are more deleterious on average than coding SNPs, frameshifting indels are more deleterious than non-frameshifting indels, nonsynonymous mutations are more deleterious than synonymous mutations, and nonsense mutations are more deleterious than nonsynonymous mutations.
It would also be reasonable to assume that that, on average, noncoding mutations are less deleterious than coding mutations. However, noncoding regions are often rich in repeats which can complicate the assessment of homozygosity and allele frequency. We wanted to test if this was indeed the case in our data, so we ran RepeatMasker 4.1.2-p1on the dd_Smes_g4 genome which gave us a gff3 file containing all the repetitive regions in the genome. We then used bedtools (Quinlan and Hall, 2010) intersect to test the proportion of coding vs noncoding variants that are found within repetitive regions.
Supplementary Data:
SI1 -List of all libraries used in study (Sexual RNAseq from Oxford is unpublished).
Figure 1 -
Figure 1 -Description of population structure and variants. A) PCA of all libraries in study;
Figure 2
Figure 2 -dN/dS analysis in sexual and asexual populations. A) Violin plot showing the
Figure 3 -
Figure 3 -Excess homozygosity in the asexual population. Red dots indicate means. A)
Figure 5 -
Figure 5 -Predicted deleterious mutations are less likely to be homozygous. Dots indicate SI2 -Asexual dN/dS local model. SI3 -Sexual dN/dS local model. SI4 -List of all exons used in study. SI5 -List of all BUSCO exons with synonymous and nonsynonymous mutations in both sexual and asexual strains. SI6 -List of all exons under negative selection in both sexual and asexual strains SI7 -Asexual dN/dS global model. SI8 -List of exons under positive selection in asexual strain using the global dNdScv model. SI9variant filtration criteria. S10 -List of variants S11 -List of variants where homozygosity and allele frequency information only comes from libraries with at least a depth of 10 at the variant site.
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Domain: Biology
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Short Communication Strand Analysis, a free online program for the computational identification of the best RNA interference (RNAi) targets based on Gibbs free energy
The RNA interference (RNAi) technique is a recent technology that uses double-stranded RNA molecules to promote potent and specific gene silencing. The application of this technique to molecular biology has increased considerably, from gene function identification to disease treatment. However, not all small interfering RNAs (siRNAs) are equally efficient, making target selection an essential procedure. Here we present Strand Analysis (SA), a free online software tool able to identify and classify the best RNAi targets based on Gibbs free energy (ΔG). Furthermore, particular features of the software, such as the free energy landscape and ΔG gradient, may be used to shed light on RNA-induced silencing complex (RISC) activity and RNAi mechanisms, which makes the SA software a distinct and innovative tool.
The RNA interference (RNAi) gene silencing technique is a recently developed technology that allows potent and specific gene silencing through the use of doublestranded RNA molecules (dsRNAs; Fire et al., 1998). The RNAi technique is widely used for identification of gene function (reverse genetics), functional genomics (Fraser et al., 2000), to combat pathogens (Gitlin et al., 2002;Mohmmed et al., 2003), as a therapeutic tool in cancer (Brummelkamp et al., 2002) and some specific genetic disorders (Xia et al., 2004), in the generation of biotechnological products (Ogita et al., 2003) and for the construction of model animals (Fedoriw et al., 2004).
In mammals, however, dsRNAs trigger antiviral responses and cell death ensues, so in these models small interfering RNAs (siRNAs) are the molecules of choice for RNAi studies because they are too small to trigger such responses. Molecules of siRNA possess a well defined structure i.e. a 21-mer duplex, two-nucleotide 3' overhang and a 5' phosphate. It is interesting to note that siRNAs directed to different regions of a specific transcript display widely different silencing efficiencies (Holen et al., 2002), possibly in part due to the fact that, intracellularly, siRNAs are incorporated into an RNA-induced silencing complex (RISC) containing slicer endonuclease activity. Slicer cleaves one strand of siRNA while keeping the other strand (the guide strand) to direct target RNA cleavage (supplementary data, Figure S1: Rand et al., 2005). If the antisense strand remains in the RISC, efficient silencing occurs but if the sense strand remains in the RISC silencing is reduced or even compromised (Khvorova et al., 2003;Schwarz et al., 2003). Two independent research groups (Khvorova et al., 2003;Schwarz et al., 2003) have shown that the thermodynamic features of the siRNA termini, defined in terms of Gibbs free energy (ΔG, kcal mol -1 ), determines the guide strand choice. Thus, Tuschl's rules, the well known protocol for siRNAs design (Elbashir et al., 2001) now including DG via computational and systematic calculations, would reduce the time and costs involved in RNAi experiments.
In this paper we present Strand Analysis (SA), a free online program (see internet resources section) for the identification of the best RNAi targets based on thermodynamic features (Khvorova et al., 2003, Table 1). The SA program computes ΔG in kcal mol -1 , the higher the ΔG value then the more preferentially will the antisense strand be kept within the RISC slicer domain thus resulting in better efficiency. As shown in Figure 1, the SA program has two different entry modes "Oligo Analysis" (OA mode) and "Sequence analysis" (SA mode). The OA mode can be used to compute single pre-selected 23-mer targets derived from messenger RNA (DNA or RNA format) and presents the results as ΔG values, with positive ΔG values for the "active guide strand" and null or negative ΔG values for the "non-active guide strand". The SA mode scans all the query sequence and calculates the ΔG values for all the 23-mer targets along the sequence to produce a list of ΔG values which may be set as a function of target position along the transcript or as the best values in decreasing order. Alternative outputs are the identification of only active or non-active strands. The SA ΔG gradient varies from +9.3 kcal mol -1 to -9.3 kcal mol -1 and may be used for special purposes in molecular analysis, as for example when a haploinsufficiency (50% silencing) would be more interesting than a knockdown (99.9% silencing). More exact molecular analysis may now be possible using this gradient principle, uncovering new phenotypes resulting from partial silencing. The correlation between ΔG values and silencing efficiency has been well-characterized (Khvorova et al., 2003;Schwarz et al., 2003) and was reproduced in our laboratory during the experimental validation of the SA program (supplementary data, Figure S2).
When working with H1/U6-based vectors for the production of short-hairpins it is important to avoid four thymines (Ts) or adenines (As) in a row and, likewise, four guanines (Gs) or cytosines (Cs) in a row should also be avoided when chemical synthesis is the choice. The SA program takes these factors into consideration and presents a warning message when such motifs are found during analyses.
The input file for the SA program is a.txt file, with the first line format as "> name of the gene" and its coding sequence (CDS, with or without numbers or spaces, DNA or RNA sequence) in the lines below. The .txt format output file is automatically generated in the same folder as the input file and presents i) position of the first siRNA nucleotide along the input sequence, ii) the ΔG value, iii) the siRNA structure (anti-parallel misaligned duplex for didactic visualization) and iv) the resulting siRNA oligos both on 5'-3' orientation (for ordering).
The SA program calculates the ΔG values of specific 23-mer targets or performs a complete scanning of the query sequence, listing the best targets by position or by the best ΔG values. Given that optimal siRNAs are selected, we believe that the SA program will improve knockdown efficiencies in RNAi experiments. When using RNAi to combat viral replication for example, targeted genomes may extend to tens of kilobases. The SA program can scan such large sequences presenting the few excellent targets (ΔG value greater than 6.0), which would not be identified by random choice. For example, a SA scan of the HIV genome (Genbank AF033819) indicated that the best target is located in position 1940, within the "pol" gene (ΔG = 8.5). Furthermore, the ΔG gradient may also be used to shed some light on RISC activity and the mechanism of RNAi.
The SA program also displays a ΔG-based landscape along the gene sequence in a graphic format, which facilitates visualization of gene (or genomic) "ΔG hotspots" where many siRNAs may be used (supplementary data, Figure S3). Furthermore, since RNAi acts as an antiviral Pereira et al. 1207 Table 1 -Standard Gibbs energy of activation ΔG values (kcal mol -1 ) used for calculating the internal stability of RNA duplexes. The equation used was ΔG = S Gas -S Gs , where S Gas = Sum of the ΔG values for the first four nucleotides in the anti-sense 5' region, S Gs = Sum of the ΔG values for the first four nucleotides in the sense 5' region. Note that ΔG ≤ for the non-functional strand and ΔG > 0 for the functional strand. Table modified from Khvorova et al., 2003).
First nucleotide base pair
Second nucleotide ΔG values (kcal mol -1 ) system, such landscapes may provide insight into changes in viral genomes and adaptations which occur over time under such pressure, aspects which are currently under investigation in our laboratory.
The SA program was implemented on the Linux platform, is web based and written in the Perl programming language, which is widely used in bioinformatics. With a small source code of only 7.9 kb the SA program shows good performance, taking only 2.3 s to run a sequence of 20,000 bases, and can be used along with other bioinformatics tools developed in our laboratory. The SA program is freely available, but is not open source.
It is important to note that the SA program must be used in combination with other computational tools for the design of siRNAs (Tuschl's rules) and not alone. For example, it is important to exclude 23-mer targets with strong secondary structures, a task that may be performed using Gene Runner (see internet resources section). Strand Analysis has already been registered at the Brazillian Patent Office (Instituto Nacional de Propriedade Industrial, INPI) under number 00068371.
Although there are other web-based programs used for siRNA design (Pei et al., 2006), some of them are very slow, not user friendly or do not even consider thermodynamic features in their calculations. Those which do include thermodynamic parameters compute them along with many other factors, generating a raking that is not a function of ΔG alone, thus making selection based on free energy difficult. Our Strand Analysis (SA) program distinguishes itself from its counterparts by providing the following advantages: i) the results are displayed in RNA format for both strands in the 5' to 3' orientation; ii) the ability to view positive or negative values alone or altogether; iii) the fact that the list of standard Gibbs energy values (ΔG) result may be set as a function of target position along the transcript or as ΔG values; and iv) the ΔG landscape may be analyzed along the gene sequence, thus providing a distinct and innovative tool.
Figure 1 -
Figure 1 -Strand Analysis (SA) flowchart. The SA software has two different modes: 'oligo analysis' for 23-mer pre-selected targets (continuous lines only) and 'sequence analysis' for complete transcript scanning (all lines).
Figure S1 .
Figure S1. Role of thermodynamic stability features of small interfering RNAs (siRNAs) termini in RNA-induced silencing complex (RISC) activity. The more stable 5´ strand is cleaved while the more unstable 5´ strand is kept within the RISC for target RNA cleavage. Strand Analysis displays RNAi targets for which the antisense strands remains in RISC, i.e. which have a positive Gibbs free energy (∆G) value.
Figure S2 .
Figure S2. Gibbs free energy (∆G) values clearly correlate with silencing efficiencies. As a practical example of such correlation, two small interfering RNAs (siRNAs) directed against the MeCP2 gene were identified in our laboratory using the Strand Analysis (SA) program and evaluated in vivo in mice using a hydrodynamic transfection protocol*. Western blot followed by densiometric analysis of the gel clearly confirmed that the greater the ∆G value, the more efficient was the siRNA (below). As a negative control, mice were injected with phosphate buffered saline (PBS) only.*McCaffrey AP et al. (2002) RNA interference in adult mice. Nature.418:38-9.
Figure S3 .
Figure S3. The Gibbs free energy (∆G) landscape of a gene sequence. The Strand Analysis (SA) program calculates the ∆G value of each small interfering RNA (siRNA) along a specific DNA sequence and displays them in a comprehensive fashion, see graphic below using the human MeCP2 sequence (AF158180), with ∆G in the Y axis and the gene position in the X axis. This analysis indicates possible ∆G hotspots along the sequence and may also provide information regarding the ∆G profile of viral genomes, thus shedding light on viral adaptation to host RNAi responses.\===
Domain: Biology. The above document has 4 sentences that start with 'The SA program', 2 paragraphs that start with 'The RNA interference (RNAi)', 3 paragraphs that start with 'The SA program', 2 paragraphs that end with 'a distinct and innovative tool'. It has approximately 1857 words, 71 sentences, and 22 paragraph(s).
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Ochna kirkii Oliv: Pharmacognostical Evaluation, Phytochemical Screening, and Total Phenolic Content
Ochna kirkii is an ornamental perennial shrub or small tree, one species among 86 species of evergreen trees in the genus of Ochna, belongs to the Ochnaceae family. Its species has sinonym Ochna carvalhi, Polythecium carvalhi, Polythecium kirkii, Ochna thomasiana Engl. & Gilg. Unique flower shaped like a cartoon character of Mickey Mouse is a specific feature of this plant. So, they are generally referred to as ‘Mickey-Mouse plants’ or ‘Ochnas’. Several members of this family are cultivated as decorative plants due to their colorful flowers and unusual fruits.1 This plant is originally from tropical Africa, but now widely cultivated in tropic countries such Asia and Madagascar. Traditional remedies of Ocnaceae have been recorded in several Asian and Africans countries. For example, the stem bark of O. lanceolata Spreng., a semi-evergreen tree found widely in Central and Peninsular India, is used by the Palliyar tribes as an abortifacient and for treatment of gastric complaints and menstrual disorders.1 In Tanzania, Washambaas used O. macrocalyx Oliv. bark for treatment of dysmenorrhoea, diarrhea, hemorrhoids, and stomach pain.2
INTRODUCTION
Ochna kirkii is an ornamental perennial shrub or small tree, one species among 86 species of evergreen trees in the genus of Ochna, belongs to the Ochnaceae family. Its species has sinonym Ochna carvalhi, Polythecium carvalhi, Polythecium kirkii, Ochna thomasiana Engl. & Gilg. Unique flower shaped like a cartoon character of Mickey Mouse is a specific feature of this plant. So, they are generally referred to as 'Mickey-Mouse plants' or 'Ochnas' . Several members of this family are cultivated as decorative plants due to their colorful flowers and unusual fruits. 1 This plant is originally from tropical Africa, but now widely cultivated in tropic countries such Asia and Madagascar. Traditional remedies of Ocnaceae have been recorded in several Asian and Africans countries. For example, the stem bark of O. lanceolata Spreng., a semi-evergreen tree found widely in Central and Peninsular India, is used by the Palliyar tribes as an abortifacient and for treatment of gastric complaints and menstrual disorders. 1 In Tanzania, Washambaas used O. macrocalyx Oliv. bark for treatment of dysmenorrhoea, diarrhea, hemorrhoids, and stomach pain. 2 Previous phytochemical studies have revealed that the Ochnaceae is a prolific source of complex flavonoids and related phenolic compounds. Reported chemical constituents from the genus Ochna, so far in total ca. 111, include flavonoids, anthranoids, triterpenes, steroids, fatty acids, and a few other compounds and biflavonoids are the predominant constituents within the genus Ochna. 1 Vitexin (apigenin-8-C-glucoside) and isovitexin (apigenin-6-C-glucoside), flavonoid compound that has been proved to give multiple pharmacological effect including anti-cancer 3,4 , anti-oxidant 5,6 , antiinflammatory 7,8 , anti-nociceptive. 9 Nair et al. isolated vitexin from the acetone-insoluble fraction of the EtOH extract of O. jabotapita leaves. while isovitexin was obtained from the leaves of O. squarrosa. 1 On the other hand, research data on the chemical compounds and the traditional used of Ochna kirkii have not been found. Recently, the pharmacognostical studies of Ochna kirkii was carried out. In addition, phenolic level as one of essential compound also explore. This study expected to be useful in the initial data for standardization of its species.
Plant collection and authentication
The leaves, flower and fruits were collected on June 2019 from Duren Sawit area, located in the eastern of Jakarta, Indonesia. The plant was characterized by the Research Centre for Biology, Indonesian Institute of Sciences, (LIPI), Cibinong Bogor Indonesia and voucher specimen was kept in the Pharmacognosy Laboratory, Faculty of Pharmacy and Sciences, UHAMKA University, Jakarta, Indonesia. Plant material were dried at room temperature for 2 weeks in a dry and airy environment. The dried samples were powdered using an electric grinder, then stored in the dark bottle at a dry place until further use.
Chemical reagent
Folin-Ciocalteu, gallic acid, were purchased from Sigma Aldrich. Sodium carbonate, n-Hexane, dichloromethane, ethyl acetate, ethanol was obtained from Merck Chemical Supplies. All chemicals used were analytical grade.
Macroscopic evaluation
The macroscopic study is the morphological characters/description of the plant parts (leaf, flower, fruit etc.) were observed by naked eye or magnifying lens. Various organoleptic features of O. kirkii parts like color, shape, size, odor, taste, surface characteristic and fracture were evaluated.
Microscopic study
Microscopic of transverse section of fresh leaf, stem and seed was perform. Preparing for powder microscopic of the plants part were observed under the microscope and photographed. The procedure for the microscopic study was described in Indonesian Herb Pharmacopoeia 10 and Hanani et al. 11
Extract preparation
The powdered leaves were extracted using maceration method to determine the phytochemical parameter and total phenolic content. A 500 g of sample was soaked in 5 L of 70% ethanol for 24 hr at room temperature. The mixture was filtered and the residue was extracted again in 3 L the same solvents and repeated the same procedure. All filtrate combined and concentrated under vacuum rotary evaporator N-1200 BS series (EYELA, Shanghai, China) at 40 -50 O C to a yield of 21.25% of plant extract. This extract stored in air-and water proof containers kept in a refrigerator at 4 o C. From this stock, fresh preparation was made whenever required.
Physicochemical evaluation
Physicochemical parameters, of the extract was conducted on the determination of moisture content, ash content, acid insoluble ash content, Water-, ethanol-and ether-soluble extractive value. The extract characterization method was performed by standard procedures in Indonesian Herb Pharmacopoeia and WHO guidelines. 10,12,13 Test samples were run in triplicate.
Phytochemical screening analysis
Phytochemical analysis of this extract was conducted by screening chemical substances and determination of major compounds. Identification of the presence of phenols, alkaloids, flavonoids, tannins, saponins glycosides, terpenoids were carried out according to the procedures in WHO guidelines 6 , Indonesian Herb Pharmacopoeia 10 , and Dixon and Jena. 14
Fluorescence character analysis
Ethanol extract of Ochna kirkii leaves powder was fractionated further with n-hexane, dichloromethane (DCM), ethyl acetate and 70% ethanol. All extracts were concentrated using a rotary evaporator. Leaves powder and all fractions are treated with various chemical reagents. Strong acids, alkalis and organic solvents were added and observed under the daylight and ultraviolet light at 254 nm and 366 nm. 11,15 Thin layer chromatography profile All fractions were analyzed for the chromatographic profile using TLC (silica gel 60 GF 254 ) with different solvents. Each fraction was solved with suitable solvents and spotted using the micro-pipette in the silica plate surface. The plates were developed using a different mobile phase: n-hexane-ethyl acetate (9:1), chloroform-methanol (9.5:0.5), methanol -ethyl acetate (1:9), and 100% ethyl acetate. The dried plate was sprayed with 10% sulfuric acid/methanol solution, followed by heating at 105 o C for 5 minutes in an oven, and observed under visible light 16 . The retention factor value was measured and the color was observed.
Total phenolic content
Total phenol content (TPC) in the ethanol extract of Ochna kirkii leaves was determined using the Folin-Ciocalteu method described by Stankovic (2011) with minor modifications. 17 The extract was dissolved in distilled water to a concentration of 50 µg/mL. The calibration curve was established using gallic acid (15; 25; 35; 45; 55 µg/mL). The Dilute extract or gallic acid (1.6 mL) was added to 0.2 mL FC reagent (5fold diluted with distilled water) and mixed thoroughly for 3 minutes. Sodium carbonate (0.2 mL, 10% w/v) was added to the mixture and allowed to stand for 30 minutes at room temperature. The absorbance of the mixture at 765 nm was measured by spectrophotometer UV-Vis UV-1601 series (Shimadzu, Kyoto, Japan). The concentration of total phenolic compound was determined as mg of gallic acid equivalents/ g of the extract by using an equation obtained from the gallic acid calibration curve. The samples were prepared in triplicate and the mean value of absorbance was obtained.
Macroscopic
Ochna kirkii is a big shrub or a small tree, with a height of 3-5 meters tall (Figure 1). The leaves are dark green color, glossy, alternate, oblongelliptic to narrow-obovate, heart-shape, rounded at the tip, up to 12 cm long (Figure 2A). Flowers are bright yellow color and vibrant red flower-like calyxes ( Figure 2B, 2C, 2D, 2E), as well as green immature fruits and ripe black berries ( Figure 2F, 2G, 2H). Each flower is about 4 -5.3 cm in diameter, has 5-petaled single in axillary and terminal clusters and has a cluster of yellow stamens with orange anthers in the center.
Physicochemical characteristics
The physicochemical characteristics such as loss on drying, ash values, water, alcohol and ether soluble extractive, were given in Table 1.
Phytochemical screening
The preliminary phytochemical screening of the ethanol extract of the leaves of O. kirkii were analyzed. Flavonoids, phenols, tannins, saponins, and terpenoids were present in the extract, except the alkaloids.
Fluorescence analysis
The fluorescence of leaves powder and all fraction were analyzed under daylight, ultraviolet light (254 and 366 nm) by treatment with different chemical reagents. The results were summarized in Table 2.
TLC chromatography
The TLC chromatography results (Table 3) of the n-hexane, DCM, ethyl acetate and, 70% ethanol extract showed the presence of 8, 6, 7, and 10 spots respectively, in different mobile phase system.
Determination of total phenolic content
The total phenolic content of the extract was determined by Folin-Ciocalteu methods, and gallic acid was used as the standard. The amount of phenolic content in the extract was presented in Table 4.
DISCUSSION
The macroscopic and microscopic studies of the plant part will enable to identify the crude drug. The microscopic evaluation is one of the simplest and cheapest methods for establishing the correct identification of the sources of the drug material. The moisture content (loss on drying and water content) were not so high, it means gives to suitable condition and eliminated the proliferation of microorganism. 10 The extractive value of ethanol was found to be highest followed by water and ether. It also helpful in estimation of specific chemical constituents which are soluble in particular solvents. 18 Ash value determination the inorganic substance and other impurities present in the drug. Ash and extractive value can be used as reliable aid for detecting adulteration. Fluorescence is one of the important methods exhibited by various chemical reagents which show different color in daylight and fluorescence in the UV light. Thus, the fluorescence is used for qualitative assessment of crude drug. 19 Fluorescence analysis showed the characteristic of extract reacted to reagent used. Chemical compound in plant extract may be often converted into fluorescent derivatives by using different chemical reagents though they are not fluorescent so that this can be used as one of the important qualitative test parameters in plants. 20 Identification results of secondary metabolite of the extract showed the presence of flavonoids, phenols, tannin, saponins, triterpenoids, while the results could not show any positive indication for alkaloids.
The thin layer chromatographic profile of hexane, DCM and 70% ethanol extracts were presented with different mobile phase system to determine how many compounds in the three kinds of extract, and each compound has a different Rf value and color.
Phenols are one class of secondary metabolites in plants. They are known to have antioxidant activity. 21 Phenolic compounds are known as high-level antioxidants because of their ability to scavenge free radicals and active oxygen species, such as singlet oxygen, superoxide free radicals and hydroxyl radicals. 22 The total phenolic compounds are one of an important factor in the consideration of antioxidant activity. 23 The results in this study have shown in the 70% ethanol extract exist 252.08 mg GAE/g extract of phenolic compound, it can be assumed that the extract have antioxidant activity.
CONCLUSION
Specific characteristics of a plant especially those that are beneficial in medicine are very important to be observed so that the falsification of this plant can be prevented. The pharmacognostical evaluation for the Ochna kirkii are laid down for the first time in this study. The present research are helps in setting the standards for proper identification, authentication and also for the standardization of crude drugs material. Herbal manufactures can utilize them for selection and identification of the raw material for drug production. The recent study showed that Ochna kirkii Oliv. has a significant phenolic level, it is necessary to evaluate the antioxidant and the specific pharmacological activity for further study.
CONFLICTS OF INTEREST
All authors declare that they have no conflicts of interest.
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Domain: Biology
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Genome editing of the vermilion locus generates a visible eye color marker for Oncopeltus fasciatus
Insects display a vast array of eye and body colors. Genes encoding products involved in biosynthesis and deposition of pigments are ideal genetic markers, contributing, for example, to the power of Drosophila genetics. Oncopeltus fasciatus is an emerging model for hemimetabolous insects, a member of the piercing-sucking feeding order Hemiptera, that includes pests and disease vectors. To identify candidate visible markers for O. fasciatus, we used parental and nymphal RNAi to identify genes that altered eye or body color while having no deleterious effects on viability. We selected Of-vermilion for CRISPR/Cas9 genome editing, generating three independent loss-of-function mutant lines. These studies mapped Of-vermilion to the X-chromosome, the first assignment of a gene to a chromosome in this species. Of-vermilion homozygotes have bright red, rather than black, eyes and are fully viable and fertile. We used these mutants to verify a role for Of-xdh1, ortholog of Drosophila rosy, in contributing to red pigmentation using RNAi. Rather than wild-type-like red bodies, bugs lacking both vermilion and xdh1 have bright yellow bodies, suggesting that ommochromes and pteridines contribute to O. fasciatus body color. Our studies generated the first gene-based visible marker for O. fasciatus and expanded the genetic toolkit for this model system.
Results
RNAi targeting ok paralogs fails to reveal clear role in pigmentation. We first investigated two components of the pteridine pigment pathway: two previously identified ok orthologs in the O. fasciatus genome 9 .
Since the ok subfamily of ABC transporter-encoding genes is most closely related to the brown subfamily 35 , which is required for pteridine pigment transport in many insects ("Introduction"), the two O. fasciatus ok paralogs are good candidates for pteridine transport in this species. To evaluate the roles of these paralogs in O. fasciatus pigmentation, we designed two non-overlapping dsRNAs per gene. Following Of-ok1 pRNAi, no difference in eye or body color in 1st or 2nd instar nymphs was observed compared to gfp controls (Fig. 1A,B). Following Of-ok2 pRNAi, very few embryos hatched despite appearing fully developed. Only 0.87% of embryos hatched following pRNAi with dsRNA-A (n = 344), and 4.96% hatched after pRNAi using dsRNA-B (n = 262), compared with an average hatch rate of 91.52% (n = 544 across four replicates) for gfp controls (Table 1). Co-injection of both Of-ok1 and Of-ok2 dsRNA produced intermediate hatch rates (Table 1), but no change in coloration of hatched nymphs was observed. Furthermore, nRNAi knockdown of Of-ok1 in 4th instar nymphs yielded no discernible change in eye or body coloration in either 5th instar nymphs or adults (Fig. 1G) compared to gfp dsRNA-injected controls (Fig. 1F). In sum, despite being strong candidates for involvement in pteridine pigment transport in O. fasciatus, we found no evidence that either would serve as a useful visible marker gene for this species.
xdh1 contributes to body pigmentation in O. fasciatus. In Drosophila, rosy (ry) mutants display dark red eyes 26 resulting from mutation of the xanthine dehydrogenase gene, a component of the pteridine pathway. To further investigate potential components of the O. fasciatus pteridine pathway, we used pRNAi to assess the functions of the Of-xanthine dehydrogenase (xdh) ortholog. BLAST searches of the O. fasciatus official gene set (OGS) followed by phylogenetic analysis (Fig. S1) revealed two orthologs of Dmel-ry, which we refer to as Of-xdh1 (OFAS027123) and Of-xdh2 (OFAS013815) (Figs. S1, S2). However, as we were unable to amplify the region encompassing both dsRNA templates of Of-xdh2 from cDNA, it is possible the OFAS013815 gene model encodes more than one transcript (see "Methods" for more detail). Knockdown of Of-xdh1 by pRNAi produced nymphs with slightly lighter body coloration than gfp pRNAi nymphs, appearing yellow-orange rather than red (Fig. 1A,C). This difference was most discernible by the 2nd instar ( Fig. 1Cii), likely because the com- www.nature.com/scientificreports/ paction of the body in newly hatched 1st instar nymphs obscures slight differences in pigmentation (Fig. 1Ci).
The body color change persisted through the third and fourth nymphal instars (Fig. 1Ciii,iv). Some Of-xdh1 dsRNA-treated nymphs appeared wild type by the fourth instar, indicating that the RNAi effect waned over time in some individuals. However, following Of-xdh2 pRNAi, no clear change in pigmentation relative to gfp controls was observed at any instar, although a marginal decrease in red body color intensity cannot be ruled out (Fig. 1D). Simultaneous knockdown of both Of-xdh1 and Of-xdh2 resulted in nymphs with yellow-orange coloration indistinguishable from the Of-xdh1 single knockdown (Fig. 1E).
Since we have noticed variation in body coloration within our O. fasciatus lab population and wanted to ensure that the lighter body coloration observed after xdh1 pRNAi was indeed due to specific knockdown of this gene and not lighter variants already present in the population, we performed another round of Of-xdh1 pRNAi to be scored according to a single-blind protocol. In this experiment, adults were injected with either gfp or Of-xdh1 dsRNA by one researcher, and second instar offspring from both treatments were individually placed in tubes labeled with a random number to be analyzed by a second researcher, described in more detail in the "Methods". The second researcher scored each individual using a four-point scale (yellow-orange, orange, orange-red, or red). Of 188 gfp pRNAi offspring scored, 97.8% were scored as orange-red or red; just 4 (2%) were scored as orange. Of 193 Of-xdh1 pRNAi bugs scored, none were scored as red, just one (0.5%) was scored as orange-red, and 99.4% were scored as yellow-orange or orange. Thus, Of-xdh1 knockdown produced a definitive and consistent reduction in body pigmentation outside the range of variation seen in wild type populations. Overall, these results demonstrate that Of-xdh1 is required for dark red pigments in the O. fasciatus body, consistent with a role in the pteridine synthesis pathway, and does not appear to be required for viability, making this a potentially useful marker in this species. Interestingly, we did not observe any effect of Of-xdh1 on eye color.
Of-v and Of-st are required for brown pigmentation in adult compound eyes. We next investigated roles of genes expected to function in ommochrome pigmentation pathways. The tryptophan 2,3-dioxygenase-coding gene vermilion (v) and ABC transporter-encoding gene scarlet (st) are involved in ommochrome pigment synthesis and transport in Drosophila and other insects (see "Introduction"). Furthermore, previous work showed that RNAi targeting either Of-v or Of-st results in loss of dark brown pigmentation in the eyes 47,48 . We employed both pRNAi and nRNAi to assess the function of v and st in O. fasciatus. Following pRNAi targeting either Of-v or Of-st, no change in eye or body coloration was observed relative to gfp controls in 1st or 2nd instar nymphs ( Fig. 2A-C). However, nRNAi targeting Of-v or Of-st in 4th instar nymphs resulted in a loss of brown pigments at the medial edge of the compound eyes in 5th instar nymphs (Fig. 2Ei,Fi). The effect was even more pronounced after individuals molted to adulthood (Fig. 2Eii,Fii). This partial change in eye color is attributed to the gradual addition of newly formed ommatidia after each molt. No change in body coloration following knockdown of Of-v or Of-st was observed. In short, Of-v and Of-st both appear to play roles in ommochrome synthesis or transport, respectively, with loss of these transcripts resulting in clear phenotypic differences late in development. In addition, no impact on viability was observed after RNAi knockdown for either gene (Table 1), making these genes good candidates for use as eye color markers in this species. (Fig. S3). While we cannot be sure of the cause, the symptoms we observed closely matched those described for a disease of O. fasciatus populations infected with the bacterium Pseudomonas aeruginosa 51 , namely: paralysis, darkening of the body, bad smell, and very quick death following the onset of symptoms. The disease caused by P. aeruginosa was likewise associated with populations raised at high temperature (30 °C) and humidity. The eyes of healthy G0s, sequestered from the diseased population, were inspected for the expected red-eyed mosaic phenotype, which would result from biallelic hits in the injected animals. Mosaicism was observed in many individuals. In our efforts to isolate potentially infected bugs from the rest of our O. fasciatus lab population, we were unable to tabulate the frequencies of G0 phenotypes. Rather, all G0s with a red-eyed mosaic phenotype were allowed to mate to each other, producing many red-eyed G1s, which were selected and maintained as a heteroallelic Of-v mutant population.
Of-vermilion X-linkage and establishment of homozygous lines. Having established a population
of red-eyed mutant bugs with many different v mutant alleles, we sought to establish single-allele homozygous lines. Five G2 males, offspring of the red-eyed G1 heteroallelic Of-v stock described above, were individually outcrossed to wild type virgins ("founder crosses", Fig. 3Bi). When founder offspring reached the 5th instar or early adulthood, a single leg was removed for genomic DNA isolation, allowing us to genotype individuals before crossing them. We found that samples from all female offspring of crosses 3-5 showed the presence of a mutant allele, while no male samples produced heteroduplex bands, consistent with Of-v X-linkage (Fig. 3Bii). To test for X-linkage, a single red-eyed virgin female from the heteroallelic Of-v population was outcrossed to a wild-type male. When all progeny of this cross were in their final nymphal instar or adults, their eye color was examined. All female offspring (37/37) were found to have wild-type black eyes, and all male offspring (30/30) were found to have red eyes, consistent with X-linkage (data not shown).
Accordingly, to generate homozygous lines, a single virgin presumably heterozygous G3 female offspring from each of the original five founder crosses was outcrossed to one or more wild-type males (Fig. 3Biii). All of these crosses produced both red-and black-eyed G4 male progeny (Fig. 3Biv). Red-eyed males and all females from each cross were selected and crossed to each other. Finally, the G5 red-eyed progeny were selected to produce homozygous lines (Fig. 3Bv).
From the original five founder crosses, three homozygous Of-v mutant lines were established: v 1 , v 3 , and v 4 . Sequencing of Of-v exon 2 from each line showed all three alleles result in premature stop codons within exon Of-v exon 2 was amplified from genomic DNA isolated from the progeny of these crosses and a heteroduplex mobility assay performed; all females gave rise to heteroduplex bands, while no heteroduplex bands were found in male samples, consistent with Of-v X linkage; (iii) One G3 female offspring from each founder cross was mated with one or more wild type males; (iv) Male G4 offspring had either red or black eyes, while all female G4s had black eyes. All red-eyed males were selected to mate with female siblings; (v) In the G5, all red-eyed males and red-eyed virgin females were selected to establish homozygous lines. www.nature.com/scientificreports/ 2 (Fig. 3Aii, red dots; Fig. 3C). The wild type Of-v allele has a 1167 bp long coding DNA sequence (CDS). Allele v 1 has a single guanine insertion at the expected double-stranded break (DSB) site, causing a frameshift and introduction of a stop codon at position 87 of the CDS (Fig. 3Ci). Allele v 3 has a 10 bp deletion, removing bases from both sides of the expected DSB, as well as introducing a stop codon at CDS position 98 (Fig. 3Cii). We also observed a single substitution in the intron preceding exon 2 in which a guanine was present where a thymine was observed in 8/8 wild-type samples. As this is 61 bp away from the expected DSB site, the difference most likely represents a polymorphism present in the G0 individual before Cas9 cleavage. Allele v 4 has a 6 bp (TC|TCC|T) insertion immediately before AA (Fig. 3Ciii), resulting in a TAA stop codon at that site (Fig. 3Ciii, red box).
Scoring Of-v mutants for use as genetic tool. All three Of-v mutant lines are viable and fertile, mak- ing Of-v useful as an eye color marker for this species. To determine at which instars the Of-v mutant phenotype can reliably be scored, we photographed individuals from each of our three Of-v mutant lines at every nymphal instar and as adults to document and compare the phenotype to wild-type bugs over the lifespan. During the first instar (Fig. 4A), the Of-v lines are almost indistinguishable from wild type, though the body color of Of-v mutants is slightly lighter (Fig. 4Ai, dorsal view; ii, lateral view). Consistent with pRNAi results, the eye color of wild type first instar nymphs is not clearly different from that of the Of-v lines (Fig. 4Aiii-vi), perhaps because ommochromes have not yet been deposited in the eye. By the second instar, there is a clear difference in eye color between wild type (Fig. 4Biii) and Of-v mutant lines (Fig. 4Biv-vi). Any differences in body coloration become less apparent as development proceeds, while differences in eye pigmentation become much more pronounced as the compound eyes enlarge in subsequent instars (Fig. 4C-E). In adulthood, male and female Of-v mutants have bright red compound eyes in stark contrast to the black eyes of wild type adults (Fig. 4F,G). Overall, we noticed no differences in phenotype between the three Of-v strains. We have maintained each Of-v line for multiple generations and have noticed no difference in their developmental rate or overall health compared to our wild type population. Thus, these mutants provide a reliable visible marker for the O. fasciatus research community for transgenesis and other genetic manipulations.
Because of issues with disease described above, we were not able to determine the frequency of mutagenesis in our initial Of-v CRISPR experiment. To determine basic statistics of CRISPR/Cas9 efficiency and germline mutation rates, we conducted a second round of Of-v CRISPR injections, making use of our new Of-v mutant lines for crosses (Fig. S4). Wild type embryos were injected with Cas9 protein and an Of-v gRNA as described above. We characterized the phenotype of mosaic G0s, and crossed these to v 4 virgins. G1 offspring from each cross were then analyzed for red or black eyes. We observed a progressive increase in germline mutation rates with the severity of somatic knockout seen in the mosaic G0s. G0s that appeared wild type produced a germline mutation rate of 66.18 ± 9.12%, while G0s with one red eye had a germline mutation rate of 74.56 ± 9.37%, and those with two red eyes had a rate of 90.41 ± 4.33% (Table 2). Importantly, these results suggest that Of-v co-mutation may be a helpful strategy to concentrate screening efforts when trying to isolate non-visible mutations at other loci. xdh1 knockdown in v 3 mutant produces bright yellow body color. Given that we observed a lighter body color after Of-xdh1 pRNAi (Fig. 1C) and after Of-v mutation (Fig. 4A-C), we wondered if both genes might contribute to body pigmentation in an additive manner. To test this, we performed Of-xdh1 pRNAi in an Of-v mutant background. Of-v 3 females were injected with either Of-xdh1 dsRNA or gfp dsRNA as a negative control. Interestingly, v 3 ; Of-xdh1 pRNAi offspring had bright yellow bodies (Fig. 5B), lighter than the v 3 ; gfp pRNAi controls (Fig. 5A), and even lighter than the yellow-orange pigmentation observed after xdh1 pRNAi knockdown in a wild type background (compare to Fig. 1C). Since Of-v mutants do not synthesize ommochromes and thus have red eyes throughout the lifespan (Fig. 4), any effect of Of-xdh1 knockdown on eye color that may have been masked by the presence of ommochromes in a wild type background should be more apparent in the Ofv 3 background. However, no distinguishable effect of the knockdown on Of-v 3 eye pigmentation was observed, consistent with our previous observation that Of-xdh1 knockdown in a wildtype background had no effect on eye pigmentation. The yellow body phenotype was marginally detectable in 1st instar nymphs (Fig. 5Bi) but was clear in 2nd instars (Fig. 5Bii) and lasted through the 4th instar (Fig. 5Biv). These results suggest that both Of-v and Of-xdh1 contribute independently to O. fasciatus body coloration and that Of-xdh1 would be useful as a visible body color marker for O. fasciatus.
Discussion
Here we have expanded the repertoire of genetic tools available for O. fasciatus, an emerging model system for hemimetabolous insects. Among pigmentation mutants, those that display loss of eye pigmentation are particularly valuable, given the established efficacy of the 3XP3 synthetic enhancer-promoter construct, which can direct gene expression in eyes across a range of phylogenetically diverse insects 52 . Combined with a fluorescent protein-encoding gene or rescue allele, this becomes a powerful tool for screening low-frequency genetic events, including gene replacement using CRISPR/Cas9 or transposon-mediated transgenesis. Even in the absence of validated transgenes, pigmentation mutants are useful for tracking germline mutation in the case of CRISPR/ Cas9 co-conversion, allowing alleles that may not produce visible phenotypes to be more easily detected.
In investigating genes that would be useful as visible markers, we identified functions for both ommochrome and pteridine synthesis pathways in eye and body color of these bugs. This contrasts with holometabolous insects such as the beetle Tribolium castaneum, where mutations in ommochrome pathway genes generate white-eyed animals, suggesting no role for pteridines in eye color in that species 53,54 . Based on our previous experience that loss-of-function mutation of Of-w results in lethality in homozygotes 9 , we pre-screened candidate genes by RNAi before performing CRISPR/Cas9 mutagenesis. Similar to previous work in O. fasciatus 47 and R. prolixus 40 , we did not observe roles for the Of-ok paralogs in eye pigmentation, but also observed no change in body www.nature.com/scientificreports/ pigmentation following Of-ok1 knockdown, in contrast to the functions of these genes in R. prolixus. However, we did observe an unexpected role for Of-ok2 in embryonic viability. In Bombyx mori, where ok is involved in transporting uric acid to the larval epidermis, both ok and white mutants display translucent larval skin 55 , leading Wang et al. (2013) to hypothesize that Bmor-White and Bmor-Ok form a heterodimer responsible for uric acid transport in this species. A similar heterodimer forming between Ofas-White and Ofas-Ok2 could explain the lethality seen here after Of-ok2 RNAi and after Of-w mutation 9 , perhaps resulting from the inability to transport waste products like uric acid. Knockdown of Of-xdh1-ortholog of rosy, a gene required for pteridine pigment synthesis in Drosophila-revealed a subtle but consistent change in body color, with loss of red pigmentation, while no role was identified for Of-xdh2 (Fig. 1). Within the ommochrome pathway, our studies confirmed roles of Of-st, encoding a putative ommochrome transporter, and Of-v, encoding tryptophan 2,3-dioxygenase, in eye color (Fig. 2). Neither parental nor nymphal RNAi appeared to impact viability, making both of these genes potentially useful eye color markers. Accordingly, we used CRISPR/Cas9 genome editing to generate three independent loss-of-function mutations via non-homologous end joining in Of-v (Figs. 3, 4), each of which has been maintained as a viable colony of bright red-eyed homozygotes for multiple generations without any apparent impact on viability or fertility. As in Drosophila, this gene is located on the X-chromosome of O. fasciatus (Fig. 3). Thus, although the phenotype appears identical to that of Lawrence's re mutants, they are not allelic as re behaved as an autosomal recessive 44 . We further made use of Of-v mutants to test the efficiency of CRISPR/Cas9 mutagenesis in this species, showing that G0 individuals exhibiting somatic mutation displayed germline transmission rates as high as 90%, making Of-v an excellent marker for co-CRISPR 56 . Finally, using RNAi to knock down Of-xdh1 in Of-v homozygotes, we revealed a role for the ommochrome pathway in body coloration, as the body color of these double "mutants" was bright yellow (Fig. 5), compared to the yellow-orange color of Of-xdh1 RNAi animals (Fig. 1). Of-xdh1 does not appear to be required for viability, making this another potentially useful O. fasciatus marker. In contrast to our findings, studies in water striders suggested that ommochromes play no role in body pigmentation but function only for eye color 41 . Conversely, rosy plays roles in body and eye pigmentation in water striders 41 , but we found it to function only in body coloration in O. fasciatus. These differences may reflect species variation or stage-specific utilization of the different pathways, which remain to be examined.
Methods
O. fasciatus rearing. An O. fasciatus wild type population was maintained at room temperature (23-24 °C) in clear plastic storage containers (15.25 × 11 × 11 in.) and exposed to a 16 h:8 h light:dark cycle. All RNAi experiments were performed at 25 °C. As described in the "Results", subpopulations were briefly maintained at 29 °C www.nature.com/scientificreports/ and 75% relative humidity, which appeared to promote disease (Fig. S3). A paper towel and cotton ball wick placed atop a 32 oz. plastic container full of water provided a constant source of water and raw organic sunflower seeds were provided as food. Single crosses were performed either using Drosophila vials as described 9 or using one gallon plastic storage containers.
Gene isolation.
To identify the O. fasciatus ortholog of v, a BLAST search against the O. fasciatus official gene set (OGS) 8 was conducted using the Dmel-v amino acid sequence as a query. Only one hit had an e-value less than 1.4, that of OFAS025214 (e-value = 2e−129), suggesting that there is only one Of-v ortholog. To confirm the identity of OFAS025214, a phylogenetic tree was constructed from an alignment of this gene model to amino acid sequences of several insect v orthologs. The OFAS025214 gene model clustered with Lhes-v, the only other heteropteran v ortholog included in this analysis, suggesting that OFAS025214 is indeed the Of-v ortholog. To identify the Of-xdh ortholog, the O. fasciatus OGS was searched using BLAST and the Dmel-ry amino acid sequence as query. Several hits had significant e-values, so sequences for all gene models with e-values below 8e−71 were collected and subjected to phylogenetic analysis. The resulting gene tree showed two gene models-OFAS027123 and OFAS013815-clustering with other insect xdh/ry orthologs, whereas the remaining O. fasciatus gene models clustered with Dmel-aox, which was included in the analysis as an outgroup, suggesting that there are two Of-xdh paralogs. The O. fasciatus orthologs of st, ok1, and ok2 were analyzed previously 9 . Primers for all sequences were designed based on the O. fasciatus genome sequence (see Table S1). For Of-v, we amplified the coding DNA sequence using primers 30 and 31, and the PCR product was blunt-end ligated into SmaI-cut pUC19, and subsequently Sanger sequenced. We Sanger sequenced fragments from all other genes spanning the two dsRNA templates (Fig. S2), except for Of-xdh2. For this gene model (OFAS013815), we were able to amplify two non-overlapping fragments, but we were not able to amplify a fragment uniting these two regions. This may suggest that the two regions are not part of the same transcript; more work is needed to determine if the OFAS013815 gene model represents a single gene.
Double-stranded RNA (dsRNA) synthesis. All dsRNA templates were PCR-amplified using Q5 highfidelity DNA polymerase (NEB) from O. fasciatus first instar complementary DNA (cDNA). In some cases, this PCR product was inserted into the pGEM T-easy vector before dsRNA synthesis, while in others the PCR product was used directly as template for dsRNA synthesis. A dsRNA template targeting turbo gfp (tGFP) was amplified from plasmid pBac[3xP3-DsRed; UAS-Tc'hsp_p-tGFP-SV40] (Addgene 86453) for use as a negative control. All primers used to amplify dsRNA templates have T7 RNA polymerase promoter sequences at the 5' end allowing synthesis of both RNA strands in a single reaction. PCR products were used as templates in an RNA transcription reaction using the MEGAscript T7 Transcription kit (Invitrogen) at 37 °C overnight. 1 ul kit-provided TURBO DNase was then added and the reaction was incubated at 37 °C for 15 min. The RNA was denatured by heating to 95 °C for 3 min in a heat block; the heat block was then turned off, and the RNA strands were allowed to anneal as the heat block temperature slowly decreased. The dsRNA was precipitated with ethanol and lithium chloride, and resuspended in water. For each gene except Of-v and tGFP, two non-overlapping dsRNAs were made (denoted dsRNA A and B, Fig. S2). All primer sequences are listed in Table S1.
Parental RNA interference (pRNAi). For pRNAi, each dsRNA was diluted to 5 uM in injection buffer (5 mM KCl, 0.1 mM phosphate buffer pH 6.8) with green food coloring (McCormick) diluted 1:50. Each adult female was injected with 3 μl injection mix between the 4th and 5th abdominal sternites using a glass needle pulled from borosilicate glass capillary tubes (World Precision Instruments). Each injected female was paired with a male and kept individually in a Drosophila vial supplied with raw sunflower seeds, damp cotton as a source of water, and dry cotton for egg laying. Vials were laid on their sides to keep seeds from contacting the wet cotton. Embryos were collected daily over 7 days after injection and incubated at 25 °C for approximately one week until hatching. Offspring were checked every day, and photographed within 24 h of hatching or molting to the next instar, after melanin deposition. For Of-xdh1 + xdh2 and Of-ok1 + ok2 double injections, either 2.5 μM of each dsRNA (dsRNA A, Fig. S2) or 5 μM of each dsRNA (dsRNA B, Fig. S2) were used. After an initial xdh1 knockdown experiment produced a subtle phenotype, we carried out a blind experiment to ensure that this phenotype was indeed due to xdh1 knockdown and not to lighter colored variants in our population. In this experiment, phenotypes of offspring of Of-xdh1 or gfp dsRNA-injected females were scored by a researcher who had no knowledge of the treatment each bug had received. Two researchers independently injected five adult females each with Of-xdh1-B dsRNA or gfp dsRNA (as described above). The first four egg clutches laid by each female were discarded as we have often observed that the first several clutches fail to display knockdown phenotypes, likely due to their position within the ovariole at the time of injection. Each additional clutch of embryos was placed in a Drosophila vial provided with sunflower seeds and damp cotton and incubated at 25 °C for approximately 7 days until hatching. Nymphs were allowed to molt into 2 nd instar as we had noticed the lighter coloration seen after xdh1 knockdown is more apparent at this stage. Each researcher selected 100 gfp pRNAi and 100 Of-xdh1 pRNAi nymphs derived from their independent injections and placed each nymph in a tube labeled with a random number from 1 to 200. Each researcher gave the other researcher (the "analyzer") the 200 nymphs to score on a 4-point scale from light orange to dark red. Any individuals found by the analyzer to be dead or at the wrong instar were excluded from analysis. The analyzer had no information about any of the www.nature.com/scientificreports/ nymphs aside from their random labels. Finally, the score sheets of the two analyzers were combined, treatment groups were unblinded, and scores assigned to individuals from each RNAi treatment group were tabulated and compared.
Nymphal RNA interference (nRNAi). For nRNAi, 4th instar nymph injections were performed as described above for pRNAi, except that only 1 μl of the 5 μM dsRNA injection mix was injected into each nymph and individuals were injected between the 5th and 6th abdominal sternites. After injection, nymphs were kept in small cages provided with damp cotton and sunflower seeds, incubated at 25 °C, and were photographed after molting to 5th instar and/or adulthood.
CRISPR/Cas9 mutagenesis of Of-vermilion.
The Of-v exon 2 sequence was submitted to CHOPCHOP 61 to identify potential gRNA target sites. Each of the suggested gRNAs was aligned to the O. fasciatus genome assembly using blastn -task blastn-short, and the resulting alignments were used to evaluate the potential of off-target Cas9 cleavage. A gRNA was selected that produced no alignments in the PAM plus seed region for any genomic region other than Of-v exon 2. This gRNA was synthesized in vitro following the recommendations of [URL]:// www. crisp rflyd esign. org/ grnat ransc ripti on/. Briefly, the gRNA sequence was introduced using primer 24, which was used with primer 23 to amplify a gRNA scaffold sequence in plasmid pCFD3 61 Heteroduplex mobility assay. When sampling 5th instar or adult bugs, genomic DNA was prepared as described in 62 , except that a single mesothoracic leg was used instead of whole individuals and only 25 μl of squishing buffer was used, which was then diluted 1:2 with water after DNA extraction. When sampling embryos, a single embryo was squished in 6 μl squishing buffer. Primers 25 and 26 were used to amplify a 581 bp fragment around Of-v exon 2, and the heteroduplex mobility assay was performed following standard methods 9,63 .
Crosses to establish homozygous Of-v lines. All G0s were inspected as young adults or last instar nymphs to identify mosaic animals, reflecting biallelic gene targeting events. As described in the "Results", G0s with at least one red eye were selected and allowed to mate with each other. All G1s with two red eyes-presumably homozygous for Of-v mutation-were selected to establish a heteroallelic Of-v mutant population. Five G2 red-eyed males drawn from this population were each crossed to three wild-type virgins (Fig. 3B). A heteroduplex mobility assay was performed on embryos resulting from these crosses. Since this assay is not sensitive enough to detect very small indels, it was expected that not all samples would give rise to heteroduplex bands; indeed no heteroduplex bands were observed in samples of offspring from two of the five crosses. Offspring from three of the five founder crosses produced heteroduplex bands in all female fifth instar or adult samples and in none of the male samples, suggesting that Of-v is X-linked. Crosses proceeded as described in the Results and illustrated in Fig. 3B. To characterize each line's mutation, genomic DNA was isolated as described above from two individuals per line, Of-v exon 2 was independently PCR-amplified from each sample using primers 25 and 26, and PCR products were Sanger sequenced.
Second Of-v CRISPR experiment. Wild type embryos were injected with 300 ng/μl Cas9 protein (PNA Bio), 500 ng/μl pBac[3xP3-EGFP;Tc'hsp5'-Gal4Delta-3'UTR] (Addgene #86449), 80 ng/μl of the Of-v gRNA described above (see "CRISPR/Cas9 mutagenesis of Of-vermilion"), and 80 ng/μl of a gRNA designed to target the ampicillin resistance gene of the injected plasmid for Cas9-mediated linearization. This AmpR gRNA was synthesized in vitro as described above for the Of-v gRNA, except that primers 23 and 29 (Table S1) were used to amplify the gRNA template. The AmpR gRNA was injected to allow whole plasmid integration through nonhomologous end joining at the Of-v locus, potentially tagging an Of-v mutation with a 3xP3 > egfp fluorescent eye marker. The phenotypes of adult G0s were recorded, before being crossed to v 4 virgins. 99 G0 crosses were set up; 30 G0s either died before mating or were infertile. G1s were CO 2 -anaesthetized and the eye phenotype of each was characterized as wild-type or v under a brightfield microscope; the presence of GFP in the eyes was assessed using a fluorescent microscope equipped with a GFP filter. Unfortunately, we observed no GFP + G1 offspring, suggesting that either no plasmid incorporation occurred or that the reduction in eye pigmentation in Of-v mutants is not sufficient to allow GFP visualization. Germline mutation rate was differentially calculated for G0 males and G0 females, since male v G1 offspring of male G0s obtained their mutant v allele from their v mothers, and thus are not informative of v mutations that may have occurred in the paternal germline. Germline www.nature.com/scientificreports/ mutation rate for male G0s was calculated as the number of v female G1s/total female G1s; for G0 females the calculation was v G1s/total G1s.
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Domain: Biology
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Aphasmaphleps, a New Genus of Long-Legged Flies from Senegal, with a Key to the Genera of Afrotropical Diaphorinae (Diptera: Dolichopodidae)
ABSTRACT The genus Aphasmaphleps Grichanov, gen. n. is described from Senegal to accommodate a new species, A. bandia sp. n. The new genus has been placed in the subfamily Diaphorinae and is considered close to the genus Phasmaphleps Bickel, 2005. A key to Afrotropical diaphorine genera of long-legged flies is compiled, and characters of the new genus are discussed.
INTRODUCTION
The subfamily Diaphorinae has a mainly stable wing venation throughout the subfamily, with completely developed major veins. However, two genera, Asyndetus Loew, 1869, and Cryptophleps Lichtwardt, 1898, are characterized by vein M being distinctly weakened or broken, and in the latter genus usually also having crossvein dm-cu absent. Monotypic Phasmaphleps Bickel, 2005, which is widespread across the western dm-cu crossvein reduced to a faint trace. A new diaphorine genus from Senegal, Aphasmaphleps, having Phasmaphleps-like venation is described here. The World Catalog of Dolichopodidae lists 18 genera in the subfamily Diaphorinae, and many of them are found in the Afrotropics.
A key to regional genera of Diaphorinae is also presented.
MATERIAL AND METHODS
Aphasmaphleps bandia sp. n. was studied and illustrated with a Zeiss Discovery V-12 stereomicroscope and an AxioCam MRc5 camera. The holotype Falbouria acorensis (Parent, 1933) was photographed by Vladimir Blagoderov in the Sackler Biological Imaging Lab at the Natural History Museum, London (NHML) using a Zeiss Stemi V11 stereomicroscope with a Canon EOS 450D camera attached, the resulting images The relative lengths of the podomeres should be regarded as representative ratios and not measurements. Body length is measured from the base of the antenna to the tip of view are oriented as they appear on the intact specimen, with the morphologically ventral surface of the genitalia facing up, dorsal surface down, anterior end facing right and posterior end facing left.
The holotype of the new species is housed at the Museum of Natural History, Paris (MNHN). Abdomen hypopygial foramen left lateral; epandrium circular with phallus following curvature of epandrium; epandrial lobe with 2 apical setae; surstylus digitiform; postgonite present; cercus short, rounded.
Phasmaphleps by a complex of characters, most of which are met in either Asyndetus or Cryptophleps. The following characters of his new genus have not been observed in the latter two genera: postpedicel with apical arista-like stylus; vein R 4+5 ending near wing apex; vein M beyond crossvein dm-cu becoming a trace, and continuing as a faint fold to just behind wing apex; vein R 4+5 and trace vein M bowed with respect to each other; male cercus elongate with strong distal setae. Aphasmaphleps has dorsoapical arista-like stylus and rounded male cercus (the characters being not rare in Asyndetus and Cryptophleps), strongly differing from these genera in wing venation. The latter in the new genus is most similar to the venation of Phasmaphleps, differing in costal vein reaching half distance between R 4+5 and M 1+2 (at R 4+5 in Phasmaphleps); R 4+5 ending before wing apex; vein M ending at wing apex; distal parts of R 4+5 and M 1+2 slightly diverging and slightly convex anteriorly, parallel at apex. In addition, Aphasmaphleps differs from Phasmaphleps in the presence of one proepisternal seta and biseriate acrostichals (absent in Phasmaphleps), elongate vs short vs simple male fore and mid tarsi, two projecting setae on male segment 8 (absent in Phasmaphleps), and median (Diaphorus-like) position of antennae (at the upper third of head in Phasmaphleps, Asyndetus and Cryptophleps). It is worth noting that the genus Falbouria Dyte, 1980 (nom. n. for Balfouria Parent, 1933) was described from Azores by a male with antennae positioned at middle of head right above Falbouria has normal wing venation, quite bristly legs including strong anterior preapicals on mid and hind femora, short antenna and simple tarsi (Parent 1933). The very long antenna (4/5 the body length) of A. bandia sp. n. is a quite unusual apomorphy in the Diaphorinae (a male secondary sexual character?). The complex of characters supports the generic status of the Aphasmaphleps.
sp. n.
Etymology: The species is named after the type locality. Description: Male. Head verticals, one pair of strong vertical and one pair of strong ocellar setae present; face present as narrow silvery white triangle beneath antennae; eyes (shrunken) joined across lower face with anteroventral facets enlarged; antenna mainly brown, positioned at middle of head; scape mostly pale brownish except dorsal surface, slightly swollen; pedicel with circlet of short setae, with one longish dorsal seta; postpedicel pale at base, A. bandia scape to pedicel to postpedicel to stylus (1 st and 2 nd palpus and proboscis small; palpus black with short black apical seta.
Thorax: Mesonotum and pleura entirely dark metallic bluish green with little pruinosity; setae black; acrostichals in two irregular rows; 5 pairs of dorsocentrals present of approximately equal length except 4 th pair shortened, with posteriormost pair slightly offset laterally; upper part of proepisternum with 1 weak white seta; lower part of proepisternum with pale seta just above coxa, subtended dorsally by shorter seta; one pair of scutellar setae strong, lateral scutellars absent.
Abdomen: Dull metallic with black vestiture; terga 1 and 2 dull green with copper resternum 8 ovate, with 2 strong diverging bristles which project posteriad, and covering left lateral hypopygial foramen; epandrium black with brown surstylus and cercus, circular with thin phallus following curvature of epandrium; hypandrium forming hood over phallus; epandrial lobe small, with 2 strong apical setae and 1 short seta at base; surstylus digitiform, with broader ventral arm and narrow dorsal arm, each with 3 or 4 dorsal setae decreasing in length distad; postgonite swollen at base, with 2 long narrow hooks; cercus short rounded, with strong setae.
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Domain: Biology
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Transition to Self-compatibility Associated With Dominant S-allele in a Diploid Siberian Progenitor of Allotetraploid Arabidopsis kamchatica Revealed by Arabidopsis lyrata Genomes
Abstract A transition to selfing can be beneficial when mating partners are scarce, for example, due to ploidy changes or at species range edges. Here, we explain how self-compatibility evolved in diploid Siberian Arabidopsis lyrata, and how it contributed to the establishment of allotetraploid Arabidopsis kamchatica. First, we provide chromosome-level genome assemblies for two self-fertilizing diploid A. lyrata accessions, one from North America and one from Siberia, including a fully assembled S-locus for the latter. We then propose a sequence of events leading to the loss of self-incompatibility in Siberian A. lyrata, date this independent transition to ∼90 Kya, and infer evolutionary relationships between Siberian and North American A. lyrata, showing an independent transition to selfing in Siberia. Finally, we provide evidence that this selfing Siberian A. lyrata lineage contributed to the formation of the allotetraploid A. kamchatica and propose that the selfing of the latter is mediated by the loss-of-function mutation in a dominant S-allele inherited from A. lyrata.
Introduction
Most angiosperms are hermaphroditic, with bisexual flowers producing both female and male gametes, and can thus potentially self-fertilize. Diverse self-recognition systems based on pollen-pistil interactions evolved repeatedly Zhao et al. 2022), preventing inbreeding, and subsequently, several independent transitions from outcrossing to self-pollination have occurred through degradation of these recognition systems (Shimizu and Tsuchimatsu 2015). A transition to selfing provides an immediate advantage in the face of low population density, often at the edges of the species distribution (Levin 2012). Pinpointing the genetic changes undermining self-rejection in nature not only improves our understanding of self-incompatibility mechanisms but also provides a more complete evolutionary history of the self-compatible species, providing essential context to understand their genome evolution (Guo et al. 2009;Slotte et al. 2013;Vekemans et al. 2014;Durvasula et al. 2017;Mable et al. 2017;Fulgione et al. 2018;Mattila et al. 2020).
In Brassicaceae, the sporophytic self-incompatibility (SI) system involves a self-pollen recognition mechanism determined by the S-locus, where two main genes are linked: The male SCR gene is expressed in tapetum cells of anthers, the protein is embedded into the pollen coat and serves as a ligand for the receptor kinase coded by the female SRK gene, which is expressed on the surface of the stigma (Stein et al. 1991;Schopfer et al. 1999;Takayama et al. 2000Takayama et al. , 2001Takayama and Isogai 2005;Nasrallah 2019). A breakdown of SI and transition to self-compatibility occurs when recognition between SCR and SRK (or downstream signaling) leading to pollen rejection is impaired (Uyenoyama et al. 2001;Shimizu and Tsuchimatsu 2015;Mable et al. 2017). In outcrossing Arabidopsis species (e.g., Arabidopsis lyrata, Arabidopsis halleri, and Arabidopsis arenosa), more than ten different S-haplotypes can segregate in a population (Castric and Vekemans 2004;Castric et al. 2008). This haplotypic diversity is essential for an SI system to function and has been maintained by frequency-dependent balancing selection for over 8 My (Mable et al. 2003;Castric and Vekemans 2004;Mable et al. 2004;Castric et al. 2008;Llaurens et al. 2008;Le Veve et al. 2022). A diploid outcrossing individual can possess two different S-alleles but often only one of them is expressed due to dominance, thus increasing the chances of reproduction (Hatakeyama et al. 2001;Kusaba et al. 2002;Prigoda et al. 2005;Okamoto et al. 2007), although codominance has also been reported (Prigoda et al. 2005;Llaurens et al. 2008). The expression of only one S-allele increases the chances for successful mating in heterozygous outcrossers, however, which of the S-alleles will be expressed can differ in pollen and stigma (Bateman 1954). Pollen-driven dominance is more thoroughly described and is conditioned by different trans-acting microRNA precursors and their targets on recessive S-alleles. MicroRNAs produced by dominant S-alleles silence the expression of the SCR gene on recessive S-allele through methylation of a 5′ promoter of SCR (Kusaba et al. 2002;Shiba et al. 2006); (Tarutani et al. 2010;Durand et al. 2014;Fujii and Takayama 2018). As dominance is uncoupled from self-recognition in this system, a dominant loss-of-function mutation is possible and would yield a self-compatible phenotype in a heterozygous individual.
The ancestral state in the genus Arabidopsis is outcrossing due to self-incompatibility. However, self-compatible species have evolved multiple times: in the model species Arabidopsis thaliana, and allotetraploids Arabidopsis suecica and Arabidopsis kamchatica. One of the early challenges for a new polyploid is the scarcity of compatible karyotypes for mating, and competition with established nearby diploids (Levin 1975). Selfing alleviates such challenges. In A. suecica, the transition to self-compatibility was likely immediate following the cross between an A. thaliana with a nonfunctional dominant S-haplotype (Tsuchimatsu et al. 2010) and an outcrossing A. arenosa (Novikova et al. 2017). However, the origin of self-compatibility in A. kamchatica is less clear, as the species originated from multiple crosses between A. lyrata and A. halleri in East Asia (Shimizu et al. 2005;Shimizu-Inatsugi et al. 2009;Tsuchimatsu et al. 2012;Paape et al. 2018). Whereas A. halleri is an obligate outcrosser, A. lyrata is predominantly self-incompatible with described self-compatible populations restricted to the Great Lakes region of North America (Mable et (Willi et al. 2022). A selfing individual of A. lyrata collected in Yakutia has been reported as genetically closest to the A. lyrata subgenome of A. kamchatica (Shimizu-Inatsugi et al. 2009;Paape et al. 2018), but the evolutionary history of this selfing lineage and S-locus genotype has not been described.
Here, we ask 1) how and when self-compatibility evolved and spread in Siberian A. lyrata; 2) is it plausible that A. lyrata was already self-compatible when it contributed to allopolyploid A. kamchatica? and 3) could a loss of self-incompatibility in only one of the diploid ancestors (A. lyrata) be sufficient to render A. kamchatica selfcompatible? Broad sampling combining live and herbarium collections allowed us to describe the selfing lineage of A. lyrata in Siberia ranging between Lake Taymyr and Chukotka, across north-central and eastern Russia. We first present chromosome-level assemblies of a Siberian selfing A. lyrata and the reference North American selfing accession (Hu et al. 2011), characterize the genomic and structural differences between them, and describe the S-locus structure and the likely mechanism of the failure of selfincompatibility in the Siberian selfing populations. Using demographic modeling, we date the transition to selfing in Siberian A. lyrata and suggest that it happened prior or concurrent with the formation of allopolyploid A. kamchatica. We confirm that the Siberian selfing A. lyrata was likely one of the progenitors of the allotetraploid A. kamchatica using overall genetic relatedness assessment and the phylogeny of the SRK gene in the S-locus. Together, our results suggest that one of the allopolyploid A. kamchatica origins and its transition to selfing was facilitated by the loss-of-function in the dominant S-allele inherited from Siberian A. lyrata.
Genome Assembly of the Selfing Siberian NT1 Accession
We grew seeds of A. lyrata collected from three populations in Yakutia (supplementary table S1 suggesting self-compatibility. We observed that flowers of the selfing NT1 accession appeared to be smaller compared with flowers of outcrossing plants and another selfing accession MN47 from North America (supplementary fig. S1C-E, Supplementary Material online). We confirmed that selfpollen successfully germinated in a selfed NT1 accession and made pollen tubes, whereas self-pollination of an outcrossing plant from the NT8 population did not result in pollen tube growth (supplementary fig. S2, Supplementary Material online).
Various papers (Long et al. 2013;Slotte et al. 2013;Henry et al. 2014;Burns et al. 2021;Dukić and Bomblies 2022) have reported potential artifacts in the reference A. lyrata MN47 (version 1 or v1) genome assembly (Hu et al. 2011 (Long et al. 2013;Slotte et al. 2013;Burns et al. 2021;Dukić and Bomblies 2022). We confirmed the existence of such artifacts and corrected them through long-read DNA sequencing (supplementary table S2, Supplementary Material online). Specifically, we obtained 868,563 HiFi reads of the MN47 accession with N50 length of 20,206 bp (total length of raw read sequences is ∼17,6 Gbp; ∼80× coverage). In total, we assembled ∼244 Mb in 820 contigs with an N50 of 23.506 Mb, indicating that full chromosome arms of MN47 were assembled as single contigs. Contigs were scaffolded into eight chromosomes using the genomes of MN47 v1 and NT1 as guides. The scaffolded contigs amount to ∼209 Mb. Completeness of the new MN47 v2 A. lyrata genome assembly by BUSCO was 4,544 complete and single-copy (97.1%), 83 complete and duplicated (1.8%), 8 fragmented (0.2%), and 44 missing genes (0.9%) from the Brassicales_odb10 set. The placement and orientation of contigs in the scaffolds were corrected using previously published Hi-C data Transition to Self-compatibility Associated with Dominant S-allele · [URL]122 MBE (NORs) of A. lyrata is located at the end of chromosome 3 (Lysak et al. 2006). We confirmed that chromosome 3 contains a partially assembled NOR using Basic Local Alignment Search Tool (Blast). Overall, we have assembled high-quality chromosome-level genomes for two A. lyrata accessions and through pairwise genome alignment, we identified several inversions up to 2.4 Mb long segregating in the species.
Breakdown of the SI System in Siberian A. lyrata NT1 Both genes flanking the S-locus (U-box and ARK3) were assembled in a single contig in the HiFi assembly before any scaffolding, indicating that the entire ∼44.5 kb S-locus of the NT1 accession was fully assembled. We further confirmed the completeness of the S-locus by mapping PacBio reads back to the assembly and found even coverage spanning the S-locus with no gaps (supplementary fig. S8, Supplementary Material online). Blast analysis of SRK and SCR sequences from the known S-haplotypes (supplementary Data 1 and 2, Supplementary Material online) (Boggs et al. 2009a, b;Tsuchimatsu et al. 2010;Guo et al. 2011;Goubet et al. 2012;Tsuchimatsu et al. 2012) revealed no hits for SRK, and one hit for SCR from the A. halleri S12 haplogroup ( fig. 2B). Due to long-term frequency-dependent balancing selection on the S-locus in Brassicaceae, relatedness among S-haplotypes is not consistent with species relatedness, such that the closest sequences to A. halleri S12 (AhS12) are not other A. halleri S-haplotypes but rather specific S-haplotypes from A. lyrata S42 (AlS42) and A. kamchatica D (Ak-D) (Wright 1939;Vekemans and Slatkin 1994;Mable et al. 2003;Castric and Vekemans 2004;Kamau and Charlesworth 2005;Castric et al. 2008;Llaurens et al. 2008;Tsuchimatsu et al. 2012;Roux et al. 2013). We estimated a phylogeny of the known SCR protein sequences (Guo et al. 2011;Goubet et al. 2012) and the manually annotated NT1 A. lyrata SCR sequence from the Blast results ( fig. 2A). As expected, the SCR phylogeny has a different topology than the species phylogeny, as S-haplotypes are trans-specifically shared across Arabidopsis. The SCR phylogeny confirms that the closest haplotype to the NT1 A. lyrata S-locus is the S12 haplotype from A. halleri (AhS12).
We compared the structures of the AhS12 and NT1 S-loci ( fig. 2B) and confirmed the absence of SRK (i.e., the female component of the self-incompatibility system), which is sufficient to explain the selfing nature of the NT1 accession. We also mapped short reads from NT1 to the NT1 genome assembly plus the intact AhS12 sequence from A. halleri containing SRK, and found no reads mapped to SRK (supplementary fig. S9C, Supplementary Material online). This provides additional confirmation of a complete loss of SRK from the NT1 S-locus. Analyzing the SCR protein sequences more closely, we also observed a loss of one of the eight conserved cysteines in the NT1 SCR sequence, which are important in proteinfolding and the recognition of the SCR ligand by the SRK receptor (Kusaba et al. 2001;Mishima et al. 2003;Tsuchimatsu et al. 2010) (supplementary fig. S10A, Supplementary Material online). This suggests that the SCR protein is nonfunctional in the NT1 A. lyrata accession. We tested for expression of the SCR gene in the Comparison of the S-locus region of the A. lyrata NT1 genome assembly with the Arabidopsis halleri S12 haplotype (Durand et al. 2014). Links between S-loci are colored according to the Blast scores from highest (blue) to lowest (gray). SCR, SRK, and flanking U-box and ARK3 genes have green, orange, and purple borders, respectively. SRK gene appears to be completely absent from the S-locus of the NT1 A. lyrata selfing accession. The only Blast hit to SRK is a spurious hit to ARK3 as they both encode receptor-like serine/threonine kinases. (C) Protein sequence alignment of S-locus SCR genes from A. halleri and A. lyrata, including NT1. One of the eight conserved cysteines important for structural integrity has been lost from the NT1 SCR protein.
Kolesnikova et al. · [URL]122 MBE flowers of NT1 using RNAseq and did not detect any transcript of the AhS12 SCR (supplementary fig. S9A and B, Supplementary Material online), though this may be due to the timing of floral development as expression of SCR is transient (Burghgraeve et al. 2020). Sequence comparison of the SCR region between AhS12 and NT1 showed high similarity in the promoter region (supplementary fig. S9D, Supplementary Material online) indicating that structural rearrangements did not cause loss of expression-but nucleotide substitutions at critical sites cannot be excluded. To verify whether SCR is indeed nonfunctional and/or not expressed in NT1, we performed controlled crosses, fertilizing an outcrossing A. lyrata accession (NT8.4-24, which has a functional AhS12 haplogroup) with NT1 pollen, resulting in successful pollen tube growth (supplementary fig. S11, Supplementary Material online). This outcome is possible if 1) the SCR protein from the NT1 accession could not be recognized by SRK receptors from the same AhS12 haplogroup or 2) the SCR gene was not expressed at all. Both of these scenarios lead to the conclusion that the SCR gene is nonfunctional in the NT1 selfing Siberian A. lyrata accession. There is, however unlikely, an additional possibility: 3) A self-compatible reaction could be possible with a functional SCR in NT1 if the SRK gene from haplogroup AhS12 was not expressed in the outcrossing maternal plant (NT8.4-24). We describe scenario 3 as improbable because outcrossing maternal plant NT8.4-24 is heterozygous at the S-locus, possessing two S-alleles: AhS12 and AlS25. The latter is known to be either codominant or recessive to AhS12 as it belongs to a lower dominance class (Llaurens et al. 2008;Durand et al. 2014), therefore, AhSRK12 is most likely expressed in NT8.4-24.
According to the classification of S-haplotypes, AhS12 belongs to dominance class IV (the most dominant class), and it is documented that it has an sRNA precursor, which can silence the expression of SCR genes from S-haplotypes belonging to classes I, II, and III (Durand et al. 2014;Burghgraeve et al. 2020). Indeed, by Blast analysis, we identified an sRNA precursor sequence in the NT1 S-locus assembly similar to the mirS3 precursor of A. halleri S12 haplotype (Durand et al. 2014), suggesting a conserved dominance mechanism of A. lyrata S12. fig. 3). This heterozygositybased assignment is supported by our observations of individuals growing in the greenhouse: NT1 populations produced seeds without crosses, whereas NT8 and NT12 populations did not. Allotetraploid A. kamchatica cooccurring in the same geographical region is also selfcompatible. To ensure that none of our A. lyrata samples were misclassified, we first mapped allotetraploid A. kamchatica samples in the same way to the NT1 A. lyrata reference without separating subgenomes. The majority of the single nucleotide polymorphisms (SNPs) in A. kamchatica represent divergent sites between the two subgenomes, which explains its high heterozygosity levels, clearly distinct from selfing A. lyrata samples (supplementary figs. S12, Supplementary Material online and 3).
Genotyping S-alleles in Outcrossers
We genotyped S-alleles of all the short-read sequenced accessions in our data set by using a genotyping pipeline for de novo discovery of divergent alleles with both SCR and SRK sequences as the reference allele databases (Schierup et al. 2001;Mable et al. 2003;Bechsgaard et al. 2004;Castric and Vekemans 2007;Castric et al. 2008;Boggs et al. 2009a, b;Guo et al. 2009;Castric et al. 2010;Guo et al. 2011;Goubet et al. 2012;Dwyer et al. 2013;Durand et al. 2014;Mable et al. 2017;Tsuchimatsu et al. 2017;Mable et al. 2018;Takou et al. 2020;Kodera et al. 2021) (supplementary Data 1 and 2, Supplementary Material online and supplementary table S1, Supplementary Material online). For each outcrossing individual, we find two different SRK alleles and at most one SCR allele ( fig. 3C). Identifying SCR alleles is more difficult than SRK, likely due to an incomplete SCR database rather than these genes being absent in outcrossing individuals. Transition to Self-compatibility Associated with Dominant S-allele · [URL]122 This may be because the founder was a heterozygous outcrosser, and a certain amount of gene flow does occur between lineages, as self-compatibility does not prevent plants from mating with outcrossers.
To further investigate the relationships between selfing and outcrossing populations and to date the selfincompatibility breakdown, we implemented a series of demographic models in fastsimcoal26 (Excoffier et al. 2013). The best-fit model is shown ( fig. 3D, table 1), which includes divergence between selfers and outcrossers with a subsequent bottleneck in the selfing lineage, with asymmetric introgression between populations. The estimate of divergence time (T DIV ) in this model is ∼90 ka (87,756), though we suggest caution when interpreting such estimates. All tested models can be viewed in supplementary figure S15, Supplementary Material online, with corresponding parameters in supplementary table S4, (Durand et al. 2014), and AhS63 belongs to class III of dominance (corresponding to AlS41 in Mable et al. 2018), which is expected to be recessive to the class IV AhS12 allele in A. lyrata (Prigoda et al. 2005 Paape et al. 2018). This is also apparent in the strong population structure of the S-allele combinations inherited from different parental lineages ( fig. 4C). The most common S-allele in A. kamchatica on the A. lyrata subgenome is AhS12 (AkS-D), which is also fixed in the selfcompatible Siberian A. lyrata lineage. Moreover, F1 crosses ( fig. 4A and B) show that the pollendominance mechanism is retained in self-compatible Siberian We, therefore, hypothesize that A. kamchatica with AhS1 (AkS-C)/AhS12 (AkS-D) combination of S-alleles was selfcompatible in the first generation due to dominance of the AhS12 S-allele inherited from self-compatible Siberian A. lyrata over AhS1 inherited from A. halleri.
Discussion
Full A. lyrata Genomes Selfing accessions can be considered natural inbred lines, which are especially useful in genomics, as the assembly of their genomes is not complicated by long heterozygous stretches. So far, only one selfing accession (MN47) of A. lyrata from North America has been fully assembled and serves as a reference for this species (Hu et al. 2011). An additional draft assembly of A. lyrata subsp. petraea has also been released (Paape et al. 2018), though its utility is hindered due to gaps in the assembly (12.75% missing) and lack of contiguity (scaffold N50 of 1.2 Mb). Furthermore, whereas a single reference genome provides a useful resource for short-read re-sequencing-based population genetic studies (Novikova et al. 2016; The 1001 Genomes Consortium 2016), reference bias is an increasingly recognized problem. Using long and proximityligation reads we assembled high-quality genomes of the Siberian selfing A. lyrata accession NT1 and reassembled North American A. lyrata MN47 accession. We found five inversions ranging from 0.3 to 2.4 Mb in length in between these independently evolved selfing accessions ( fig. 1 and supplementary table S3, Supplementary Material online). Large genomic structural rearrangements, especially inversions, can prevent chromosomal pairing and drive reproductive isolation and speciation (Rieseberg 2001;Stevison et al. 2011;McGaugh and Noor 2012;Ayala et al. 2013;Jeffares et al. 2017). In these circumstances, selfing probably increases tolerance to such rearrangements and can even promote their fixation. For example, karyotypic changes from 8 to 5 chromosomes in A. thaliana are linked to a transition to self-compatibility at about 500 Kya (Durvasula et al. 2017). A. lyrata transitions to selfing are more recent but are consistent with this observation. Interestingly, the inversions found within A. thaliana (Jiao and Schneeberger 2020; Goel and Schneeberger 2022) and within A. lyrata (this study) are comparable in size: up to Table 2. The Genotypes and Phenotypes of Outcrossing Mother Plants (TE10.3-2 and TE11.1-2) and F1 Progeny From Their Pollination by NT1 Self-compatible A. lyrata Accession With AhS12 S-allele (SCR Present and SRK Lost- fig. 2B). Mating types are abbreviated with SC for self-compatibility and SI for self-incompatibility. fig. S1C-E, Supplementary Material online). The lack of so-called "selfing syndrome" in the latter 4. (A) Self-pollinated F1 progeny (F1.1-1) resulting from a cross between a self-incompatible (shown in B) ♀ TE10.3-2 Arabidopsis lyrata accession and ♂ NT1 self-compatible A. lyrata accession shows pollen tube growth (yellow arrow) and dominance of self-compatibility in the F1 generation. (B) Self-pollinated self-incompatible A. lyrata accession TE10.3-2 (used as the maternal plant in A shows no pollen tube growth, demonstrating its self-incompatibility. (C ) The geographical distribution of Arabidopsis kamchatica S-haplotypes shows a strong population structure across the species range. Circles are individual accessions, with S-haplogroups indicated by colors of pie slices. Arabidopsis halleri orthologous S-haplogroups are mentioned in the parenthesis next to the A. kamchatica S-haplogroups (AkS-A-E). Circle outline indicates either previously published data (grey) or newly reported accessions (black). A. kamchatica occurrences from the Global Biodiversity Information Facility (GBIF) are indicated by transparent grey dots. Transition to Self-compatibility Associated with Dominant S-allele · [URL]122 MBE was described previously (Carleial et al. 2017). Similarly, in the outcrossing species Leavenworthia alabamica, two independent selfing lineages have been described, with the older (∼150 Kya) showing an obvious selfing syndrome whereas the younger selfing lineage (∼48 Kya) did not (Busch et al. 2011). Although further investigation is required to quantify this difference in flower size, such observations in A. lyrata may also be explained by differences in transition to selfing: The North American A. lyrata likely transitioned to selfing during or after colonization of the area, around ∼10 Kya (Carleial et al. 2017), which is much more recent than our estimates of the Siberian selfer originating ∼90 Kya. Transition to Self-compatibility in Siberian A. lyrata Is Associated with S-locus All selfing Siberian accessions spanning the massive geographical area between Lake Taymyr and Chukotka share the same S-haplotype (AhS12), suggesting the breakdown of self-incompatibility in Siberia is linked to the S-locus. This also suggests a single breakdown of selfincompatibility in the Siberian selfing lineage, as it is unlikely that this transition to self-compatibility occurred independently in multiple individuals with the same AhS12 allele. More than one origin of self-compatibility in the studied Siberian populations is improbable for two reasons: First, the S-locus is highly diverse, as tens of divergent S-alleles typically segregate within outcrossing populations to facilitate reproductive success (Schierup et al. 2001;Castric and Vekemans 2004;Castric and Vekemans 2007), so one would expect more diversity of S-alleles if there were multiple origins; and second, because dominant alleles, including AhS12, are more rare compared with recessive ones (Schierup et al. 1997;Billiard et al. 2007;Genete et al. 2020). The probability of independent loss-of-function on two of the same rare alleles is low (multiplied probabilities of drawing the same rare allele by chance). However, the breakdown of self-incompatibility in North American A. lyrata is not associated with a specific S-allele or S-locus mutation, but rather with another genetic factor, likely in a downstream cascade of reactions preventing pollen-tube growth Foxe et al. 2010;Mable et al. 2017). Therefore, despite strong evidence supporting an S-locus-driven loss of selfincompatibility in Siberian A. lyrata, it is possible that a mutation in a downstream cascade caused the initial mating system switch (Goring et al. 2014;Jany et al. 2019), followed by fixation of a single S-allele due to drift, and further degeneration of the S-allele sequence, reinforcing self-compatibility. Another scenario could involve a modifier mutation specific to AhS12 S-allele, which arose prior to loss-of-function in the S-locus. The existence of allelespecific modifiers has been proposed based on observed segregation patterns in offspring (Nasrallah et al. 2004 Whereas these alternative explanations are plausible, based on the strong association between a specific S-haplotype (AhS12) and self-compatibility, we conclude that inactivation of AhS12 is the most likely scenario.
Self-compatibility in Siberian A. lyrata Is Likely Male-driven
Our long-read-based genome assembly of A. lyrata NT1 contains a fully-assembled S-locus ( fig. 2), in which we manually annotated SCR by Blast analysis of all known SCR sequences in Arabidopsis. The SRK gene was absent from our assembly. Mapping of the short reads from the A. lyrata NT1 accession to A. halleri AhS12 sequence of the same haplotype also did not yield any coverage of the SRK gene, so we conclude that SRK was lost from the NT1 genome. However, this does not mean that the loss of SRK is the causal mutation leading to selfing, as the SCR protein of NT1 A. lyrata also appears to be nonfunctional: 1) it lacks one of the eight cysteine residues ( fig. 2C) that were shown to be functionally important (Kusaba et al. 2001;Mishima et al. 2003;Tsuchimatsu et al. 2010) (fig. 2C), and 2) its expression was not detected in flowers (supplementary fig. S9, Supplementary Material online). Genotyping of the S-locus in other selfing A. lyrata accessions reveals that all of them share the same S-haplotype AhS12 ( fig. 3C and supplementary table S1, Supplementary Material online), which suggests their shared origin. Moreover, one of the selfing A. lyrata accessions has SRK, but seems to lack SCR (accession number MW0079456, fig. 3). Different reciprocal gene loss mutations of SCR or SRK across accessions ( fig. 3B) exclude the possibility of gene loss being a causal mutation and rather suggest that gene loss happened after a common causal mutation.
In controlled crossing experiments (Tsuchimatsu et al. 2012), haplogroup-D SRK in the A. lyrata subgenome of A. kamchatica (AkSRK-D, orthologous to AhSRK12) was shown to be functional. This suggests that SRK in the ancestors of both A. kamchatica and selfing A. lyrata was also functional. We discuss the role of selfing A. lyrata in the origin of A. kamchatica in the next section. If the breakdown of self-incompatibility is indeed S-locus driven (and not caused by an unlinked S-allele specific modifier), it most likely occurred on SCR rather than SRK in this lineage. Our results show that indeed, SCR from NT1 is not recognized by a functional SRK of the same haplogroup (from accession NT8.4-24; supplementary fig. S11, Supplementary Material online). Whether the initial loss-of-function in the SCR protein was due to a loss of a structurally important cysteine residue ( fig. 2C) or a loss of expression (supplementary fig. S9A and B, Supplementary Material online) is unclear. Transitioning to selfing through degradation of male specificity gene would be consistent with the recurrent pattern in the evolution of self-compatibility (reviewed in Shimizu and Tsuchimatsu (2015)). According to Bateman's principle, Kolesnikova et al. · [URL]122 MBE an S-haplotype with nonfunctional SCR and functional SRK will produce pollen of higher fitness, as it will be compatible with all other S-haplotypes including itself. In contrast, an S-haplotype with a functional SCR and a nonfunctional SRK will produce pollen that will be selfcompatible but incompatible with the fraction of the population carrying the same, albeit fully functional, S-haplotype. Pistils with a nonfunctional SRK do not have a higher fitness unless pollen availability is very limited, making fixation of the male-driven selfing more likely (Bateman 1954;Tsuchimatsu and Shimizu 2013). The most likely scenario suggested by our results, where selfcompatibility in Siberian A. lyrata is SCR-driven, is therefore consistent with Bateman's principle.
Self-compatible Siberian A. lyrata Is Ancestral to A. kamchatica
A previous study showed a Siberian A. lyrata accession (lyr-pet4) to be genetically closest to A. kamchatica, however this was limited to sampling in a single locality and did not include assessment of S-alleles (Shimizu-Inatsugi et al. 2009;Paape et al. 2018). In addition to the previously reported selfing individual, our field and herbarium collections yielded seven more self-compatible accessions, spanning a wide geographical range across Siberia (supplementary table S1, Supplementary Material online). We explored the relationships among all Siberian A. lyrata accessions with A. kamchatica using network analysis and hierarchical clustering. Genetic network of Nei's D ( fig. 3B) shows that A. kamchatica clusters closely to selfcompatible Siberian A. lyrata, which is consistent with the sister relationship between A. kamchatica and selfcompatible A. lyrata in a well-supported ML phylogeny (supplementary fig. S13B, Supplementary Material online). Moreover, we identified a fixed S-allele (AhS12) associated with self-compatibility in Siberian A. lyrata.
Allopolyploid A. kamchatica has three S-alleles inherited from A. halleri-AhS26 (AkS-A), AhS47 (AkS-B), and AhS1 (AkS-C) and two S-alleles inherited from A. lyrata -AhS12 (AkS-D) and AhS02 (AkS-E) (Tsuchimatsu et al. 2012). The AhS12 S-allele is the most frequent in the A. lyrata subgenome of A. kamchatica and was inherited from a self-compatible Siberian A. lyrata lineage. A tree of A. lyrata and A. kamchatica accessions, which share the AhS12 haplotype (based on exon 1 of the SRK gene; supplementary fig. S13A, Supplementary Material online), shows that a self-compatible A. lyrata accession is nested within a clade of A. kamchatica accessions, providing further support for their shared origin.
Furthermore, our demographic modeling suggests the Siberian selfing lineage originated approximately 90 Kya. This is in line with estimates by Paape et al. (2018), who dated the divergence times of both A. kamchatica subgenomes. Their estimates for divergence time of the A. halleri subgenome range from ∼60 to 100 Kya and the A. lyrata subgenome between ∼70 Kya and 140 Kya. The authors recommend caution when interpreting these parameters, and we agree that: Mutation rates used in both studies are from A. thaliana rather than A. lyrata, and sample sizes are small in both cases. Still, given the overlap in divergence estimates from both our study and work by Paape et al. (2018), it is plausible that at least one of the multiple polyploid origins of A. kamchatica included this selfing Siberian A. lyrata lineage as a parental genome donor.
Combinations of A. kamchatica S-alleles show a strong population structure ( fig. 4C) consistent with multiple origins of A. kamchatica in different geographical regions (Shimizu et al. 2005;Shimizu-Inatsugi et al. 2009;Tsuchimatsu et al. 2012;Paape et al. 2018). However, the current sampling of A. kamchatica is biased towards Japan and the Kamchatka Peninsula, and this uneven coverage of the species range means that observed frequencies of S-allele combinations may not represent their true distribution. That said, a combination of dominant nonfunctional AhS12 (A. lyrata-derived) and recessive AhS1 (A. halleri-derived) S-alleles is common in A. kamchatica in the eastern Siberian mountains bordering Okhotsk sea in Aldan-Amur interfluve ( fig. 4C).
Interestingly, whereas both progenitors of A. kamchatica coexist in Europe, and interspecific crosses can be created ex situ (Sarret et al. 2009), A. lyrata and A. halleri do not form other allotetraploids (Clauss and Koch 2006;Schmickl et al. 2010). The variation (or lack thereof) of mating systems in A. lyrata and A. halleri can explain why allopolyploid establishment is limited to Asia: A. halleri is self-incompatible throughout its range (no known selfing accessions have been described to date), and selfing A. lyrata is found only in Siberia and North America. Previous work showed that self-compatibility in A. kamchatica was likely male (SCR)-driven in the more dominant S-haplotype inherited from A. lyrata (Ah12/Al42/Ak-D) (Tsuchimatsu et al. 2012). We argue that self-compatibility is ancestral to A. kamchatica, and inherited from Siberian A. lyrata. We also show that dominance between nonfunctional AhS12 and functional AhS01 is retained in selfcompatible A. lyrata ( fig. 4A and B) and therefore argue that the transition to selfing in A. kamchatica with this combination of S-alleles was likely immediate upon allopolyploid formation. Our results show that Siberian selfing diploid A. lyrata is ancestral to allotetraploid A. kamchatica, and contributed the most widely observed A. lyrataderived S-allele (AhS12) in A. kamchatica. Furthermore, the nonfunctional AhS12 S-allele is still dominant over the recessive AhS01 S-allele in A. lyrata. This dominance of the nonfunctional S-allele likely explains the transition to self-compatibility in A. kamchatica with the same combination of S-alleles (AhS12/AkS-D and AhS01/AkS-C), rather than self-compatibility evolving de novo in A.
kamchatica.
Similar examples where a loss-of-function mutation on a dominant S-haplotype in one progenitor facilitated transition to selfing in allotetraploids have been recently reviewed (Novikova et al. 2022) and include A. suecica (Novikova et al. 2017), Capsella bursa-pastoris (Bachmann et al. 2019(Bachmann et al. , 2021Duan et al. 2023), and Transition to Self-compatibility Associated with Dominant S-allele · [URL]122 MBE Brassica napus (Okamoto et al. 2007;Kitashiba and Nasrallah 2014). Allopolyploid establishment may be facilitated by a transition to self-compatibility, ensuring reproductive success in the face of limited mating partners.
Plant Collection and Growth
We collected seeds from three A. lyrata populations (NT1, NT8, and NT12) during an expedition to the Yakutia region in Russia in the summer of 2019 (supplementary table S1, Supplementary Material online). Multiple individual plants were collected from those three populations: three individuals from NT1 (NT1_1, NT1_2, and NT1_3), four from NT8 (NT8_1, NT8_2, NT8_3, and NT8_4) and two from NT12 (NT12_1 and NT12_2). Collected seeds were grown in the greenhouse at 21 °C, under 16 h of light per day until a full rosette was formed, after which plants were moved to open frames outside on the grounds of the Max Planck Institute for Plant Breeding Research in Cologne, Germany. We grew several seeds per collected bag of seeds from individual plants, each was given an additional number extension (e.g., NT1_1_1, NT1_1_2, etc.). In this work, we only used the plants with last extension 1. All the individuals grown from NT1 population formed long fruits and appeared to be selfing.
Pollen Tube Staining to Characterize Mating Type
Almost mature flower buds were opened and after removing the anthers, they manually pollinated. Pistils were collected 2-3 h after pollination, fixed for 1.5 h in 10% acetic acid in ethanol, and softened in 1 M NaOH overnight. Before staining, the tissue was washed three times in KPO 4 buffer (pH 7.5). For staining, we submerged the tissue in 0.01% aniline blue for 10-20 min. After that, pistils were transferred to slides into mounting media and observed under UV light (Lu 2011). A self-compatible reaction was called if we counted more than ten pollen tubes.
Long-read Sequencing for de novo Genome Assembly DNA extraction, library preparation, and long-read sequencing of the NT1 and MN47 A. lyrata accessions were performed by the Max Planck-Genome-centre Cologne, Germany ( [URL]/). High molecular weight DNA was isolated from 1.5 g material with a NucleoBond HMW DNA kit (Macherey Nagel). Quality was assessed with a FEMTOpulse device (Agilent), and quantity was measured by a Quantus fluorometer (Promega). HiFi libraries were then prepared according to the manual "Procedure & Checklist-Preparing HiFi SMRTbell® Libraries using SMRTbell Express Template Prep Kit 2.0" with an initial DNA fragmentation by g-Tubes (Covaris) and final library size selection on BluePippin (Sage Science). Size distribution was again controlled by FEMTOpulse (Agilent). Size-selected libraries were then sequenced on a Sequel II device with Binding Kit 2.0 and Sequel II Sequencing Kit 2.0 for 30 h (Pacific Biosciences).
Short-read Sequencing for Population Analyses
Plant material was processed in two different ways, indicated by types I and II in supplementary table S1, Supplementary Material online.
Type I: Herbarium material was extracted in a dedicated clean-room facility (Ancient DNA Laboratory, Department of Archaeology, University of Cambridge). The lab has strict entry and surface decontamination protocols, and no nucleic acids are amplified in the lab. For each accession, leaf and/ or stem tissue was placed in a 2 ml tube with two tungsten carbide beads and ground to a fine powder using a Qiagen Tissue Lyser. Each batch of extractions included a negative extraction control (identical but without tissue). DNA was extracted using the DNeasy Plant Mini Kit (Qiagen). Library preparation and sequencing were performed by Novogene Ltd (UK). Sequencing libraries were generated using NEBNext® DNA Library Prep Kit following manufacturer's recommendations, and indices were added to each sample. The genomic DNA is randomly fragmented to a size of 350 bp by shearing, then DNA fragments were end polished, A-tailed, and ligated with the NEBNext adapter for Illumina sequencing, and further enriched by polymerase chain reaction (PCR) on P5 and indexed P7 oligos. The PCR products were purified (AMPure XP system), and resulting libraries were analyzed for size distribution by Agilent 2100 Bioanalyzer and quantified using real-time PCR.
Type II: Genomic DNA was isolated with the "NucleoMag© Plant" kit from Macherey and Nagel (Düren, Germany) on the KingFisher 96Plex device (Thermo) with programs provided by Macherey and Nagel. Random samples were selected for a quality control to ensure intact DNA as a starting point for library preparation. TPase-based libraries were prepared as outlined by (Rowan et al. 2019) on a Sciclone (PerkinElmer) robotic device. Short-read (PE 150 bp) sequencing was performed by Novogene Ltd (UK), using a NovaSeq 6000 S4 flow cell Illumina system.
Transcriptome Sequencing for S-locus Gene Expression Assessment
We used three flash-frozen open flowers of the A. lyrata NT1 accession as input material for RNA sequencing, which we used to assess the expression of the S-locus genes. RNA was extracted by the RNeasy Plant Kit (Qiagen) including an on-column DNase I treatment. Quality was assessed by Agilent Bioanalyser and the amount was calculated by an RNA-specific kit for Quantus (Promega). An Illumina-compatible library was prepared with the NEBNext® Ultra™ II RNA Library Prep Kit for Illumina ® and finally sequenced on a HiSeq 3000 at the Max Planck-Genome-centre Cologne, Germany.
Kolesnikova et al. · [URL]122 MBE PacBio de novo Assembly and Annotation of NT1 and MN47 A. lyrata Accessions Raw PacBio reads of NT1 were assembled using Hifiasm assembler (Cheng et al. 2021) in the default mode, choosing the primary contig graph as our resulting assembly. The completeness of our assembly was assessed using BUSCO (Seppey et al. 2019) with Brassicales_odb10 set. Repeated sequences were masked using RepeatMasker (Smit et al. 2013(Smit et al. -2015 with the merged libraries of RepBase A. thaliana repeats and NT1 A. lyrata repeats, which we modeled with RepeatModeler (Smit andHubley 2008-2015). Then, annotation from the reference MN47 genome (Rawat et al. 2015) was transferred to our NT1 repeat-masked assembly by using Liftoff (Shumate and Salzberg 2020). Contigs were reordered according to their alignment to the reference chromosomes and updated gene and repeat annotations using RagTag (Alonge et al. 2019) in the scaffolding mode without correction. Assembly of MN47 PacBio reads was done using the Hifiasm assembler with the same parameters.
Synteny Analysis of A. lyrata, A. suecica, and C. rubella Genomes Synteny analysis was done by performing an all-against-all BlastP search using the coding sequences of both genomes. We used SynMap (Haug-Baltzell et al. 2017), a tool from the online platform CoGe, with the default parameters for DAGChainer. The Quota Align algorithm was used to decide on the syntenic depth, employing the default parameters. Syntenic blocks were not merged. The results were visualized using the R (version 4.1.2) library "circlize" (version 0.4.13), as well as using plotsr (version 0.5.3) (Goel and Schneeberger 2022) for the supplementary figures, Supplementary Material online.
HiC Sequencing of NT1 A. lyrata Accession to Validate Structural Variants
A chromatin-capture library of the NT1 A. lyrata accession was prepared by the Max Planck-Genome-centre Cologne, Germany and was used for validation of the large inversions in whole-genome comparisons. We followed the Dovetail® Omni-C® Kit starting with 0.5 g of fresh weight as input. Libraries were quantified and quality assessed by capillary electrophoresis (Agilent Tapestation) and then sequenced at the Novogene Ltd (UK), using a NovaSeq Illumina system.
Mapping of Hi-C Reads for the A. lyrata Accessions NT1 and MN47
To validate the assembled scaffolds of A. lyrata, we used proximity-ligation short read Hi-C data. For NT1, Hi-C reads were mapped to the repeat-masked NT1 genome assembly, using the mapping pipeline proposed by the manufacturer ( [URL]/ index.html). The Dovetail Omni-C processing pipeline is based on BWA (Li and Durbin 2009), pairtools ( [URL]:// github.com/mirnylab/pairtools), and Juicertools (Durand et al. 2016). We mapped the Hi-C reads for MN47 (released previously ) to a repeat masked MN47 genome (Hu et al. 2011) and to a repeat masked version of the newly assembled MN47 genome (in this paper) using HiCUP (version 0.6.1) (Wingett et al. 2015). The assemblies were manually examined using Juicebox (Robinson et al. 2018). Plots of the HiC contact matrix were made using the function hicPlotMatrix from HiCExplorer (Wolff et al. 2020) (version 3.7.2).
Validation of Structural Variants Between NT1 and MN47 A. lyrata Accessions
To validate the inversions (supplementary table S2, Supplementary Material online), we used PacBio, Hi-C data, and synteny analysis results. Guided by synteny analyses, we first identified inversion breakpoints. Then, we investigated the long-read map at these regions and either confirmed their contiguity or manually flipped the genomic region, followed by another round or long-read map investigation (supplementary figs. S3-S8, Supplementary Material online). To map the PacBio HiFi reads we used Winnowmap (Jain et al. 2020). As the last step, we analyzed the Hi-C contact maps in the same regions to show that there is no evidence for alternative genome assembly configurations (supplementary figs. S3-S7, Supplementary Material online).
A. lyrata NT1 S-locus Genotyping and Manual Annotation
We manually annotated the S-locus in our initial assembly before the reference-guided reordering and scaffolding. In the transferred annotation resulting from Liftoff (Shumate and Salzberg 2020), we found both of the flanking genes (U-box and ARK3) in the same contig. The final coordinates of the S-locus in the NT1 assembly on scaffold 7 are 9,291,658 bp to 9,336,246 bp. The length of the assembled NT1 A. lyrata S-locus including both flanking genes is about 44.5 Kbp. We mapped PacBio long reads back to the assembled NT1 genome using minimap2 (Li 2018) with default parameters in order to make sure that there are no obvious gaps in coverage or breakpoints (supplementary fig. S8, Supplementary Material online). Similar to Zhang et al. (2019), we blasted the SRK and SCR sequences from all the known S-haplotypes across Arabidopsis and Capsella to the A. lyrata NT1 S-locus, finding a single hit at the SCR gene from the AhS12 haplogroup. We constructed a comparative structure plot of A. lyrata NT1 and A. halleri S12 (GenBank accession KJ772374) S-loci ( fig. 2B) using the R library genoPlotR (Guy et al. 2010). We aligned SCR protein sequences using MAFFT with default parameters and estimated a phylogenetic tree with RaxML (Stamatakis 2014) using the BLOSUM62 substitution model and visualized the alignment ( fig. 2C) using Jalview2 (Waterhouse et al. 2009). The phylogenetic tree was visualized using R package "ape" (Paradis et al. 2004). Transition to Self-compatibility Associated with Dominant S-allele · [URL]122 MBE A. lyrata S-allele Genotyping From Short-read Sequencing Data The S-alleles from all the re-sequenced samples used in the population analysis (supplementary table S1, Supplementary Material online) and crosses (NT8.4-24, submitted to ENA under ERS12276051) were genotyped using the S-locus genotyping pipeline NGSgenotyp . The list of SRK and SCR alleles used as a reference data set is provided in the supplementary table S3, Supplementary Material online, and the corresponding sequences for SRK and SCR alleles are provided in the supplementary Data 1 and 2, Supplementary Material online. Using the NGSgenotyp pipeline, we could not identify any S-haplotypes for DRR124344 (lyrpet4), for either SRK or SCR databases. However, we found a partial SCR gene sequence matching the AhS12 haplotype by blasting the SCR database to the DRR124344 assembly. We translated the SCR nucleotide sequence and aligned the resulting protein sequence with SCR proteins from other accessions using MAFFT (Katoh and Standley 2013) using default parameters. The resulting alignment shows that SCR from DRR124344 is shorter compared with NT1 or AhS12. To confirm that SCR from DRR124344 belongs to the AhS12 haplotype, we estimated a maximum likelihood tree using IQ-tree web service ( [URL]/) with default parameters (supplementary fig. S10B, Supplementary Material online).
Separation of Subgenomes From A. kamchatica Accessions
To isolate the A. lyrata subgenome of A. kamchatica, we used a combined reference, containing A. lyrata NT1 and A. halleri ssp. gemmifera reference genomes (Briskine et al. 2016). We mapped A. kamchatica short reads to the combined reference with bwa mem (0.7.17) and filtered for reads mapped uniquely to A. lyrata NT1 using samtools ). We then genotyped the resulting A. lyrata-subgenome bam files for each A. kamchatica accession as described above for diploid samples.
Tree and Network Estimation
Genome-wide SNP Tree We filtered the vcf generated above to include only biallelic SNPs without missing data, which resulted in 2,261,679 SNPs. These data were read into R (version 4.1.1) and from them, we estimated a neighbor-joining tree using the nj function from package ape (Paradis and Schliep 2019). We then visualized the neighbor-joining tree as a cladogram using ggtree (Yu et al. 2017(Yu et al. , 2018Yu 2020) and annotated the tips with associated data (supplementary fig. S13B, Supplementary Material online). We then further filtered this data set to include only Siberian A. lyrata and an outgroup (excluding A. kamchatica from this portion) to generate the lyrata-only tree ( fig. 3C).
Network Based on Nei's D and Phylogenetic Inference
We filtered a vcf of biallelic SNPs shared by the lyrata subgenome of A. kamchatica and all A. lyrata accessions down to just four-fold degenerate sites, with maximum 10% missing data across individuals, resulting in 4,141 SNPs. We read the vcf with both Siberian A. lyrata and A. kamchatica into R using vcfR (Knaus and Grünwald 2017), then calculated Nei's D (Nei 1972) between individuals using StAMPP (Pembleton et al. 2013). We visualized the resulting matrix in SplitsTree4 (Huson and Bryant 2006) and in R using the pheatmap package (Kolde 2019). To further explore the evolutionary relationships among accessions, we generated a nexus file from the vcf using vcf2phylip (Ortiz 2019), which served as input for phylogenetic inference with IQTree ( [URL] Tree
We assembled partial SRK sequences from Siberian A. lyrata and A. kamchatica accessions based on short-read sequencing data using the assembly step of the S-locus genotyping pipeline NGSgenotyp ) and aligned sequences with MAFFT (Katoh and Standley 2013). From this alignment estimated 1,000 bootstrap replicates of a ML phylogeny using RaXML (Stamatakis 2014) with substitution model GTR+Γ then visualized the best-scoring ML phylogeny using R package ape 5.0 (Paradis and Schliep 2019). The input alignment is available in supplementary Data 3, Supplementary Material online.
PCR Identification of AhS12 Haplotype
For DNA extraction, 1 cm of leaf material was frozen in liquid nitrogen and ground to a powder. We added 400 μl UltraFastPrep Buffer to the powdered tissue, then mixed, vortexed, and finally spun for 5 min at 5000 revolutions per minute (rpm). We then took 300 μl of the supernatant, added 300 μl isopropanol, and mixed by inversion. We again spun for 5 min at 5000 rpm, then discarded the supernatant and dried 10-30 min at 37 °C. The pellet was resuspended in 200 μl 1xTE and stored at 4 °C. We amplified the AhSRK12 allele by PCR using 1.5 μl of DNA solution and previously published primers (forward ATCATGGCAGTGGAACAC AG, reverse CAAATCAGACAACCCGACCC) (Ruggiero et al. 2008). We ran 35 cycles consisting of 30 s at 94 °C, 30 s annealing at 56.8 °C, and 40 s extension at 72 °C. We visualized PCR products via gel electrophoresis using 1.5% agarose gel with GelGreen® nucleic acid stain (
Demographic Modeling of Divergence Between Selfing and Outcrossing Siberian A. lyrata Lineages
We calculated nucleotide diversity using all biallelic and non-variant sites in 10 kb windows with custom script uploaded to github ( [URL]_ Alyrata). CIs for the median of the distribution were calculated using the basic bootstrap method in the R package "boot" (Davison and Hinkley 1997;Canty and Ripley 2022).
To prepare a joint allele frequency spectrum of the seven self-compatible accessions and the ten selfincompatible accessions, we first filtered the SNP-only vcf to remove centromeric, pericentromeric, and exonic regions. We subsequently filtered out sites with missing data to yield our final vcf for demographic inference. Following Nordborg and Donnelly (1997), we excluded sites heterozygous in the selfing population and treated selfers as haploid. We then generated the joint allele frequency spectrum using easySFS ( [URL]/ isaacovercast/easySFS). EasySFS produces output ready for use in fastsimcoal2 (fsc26) (Excoffier et al. 2013(Excoffier et al. , 2021, which we then used for demographic modeling. We tested five models for the origin of self-compatibility in Siberian A. lyrata as follows: 1) simple divergence, 2) divergence with symmetrical introgression (migration), 3) divergence with asymmetrical introgression, 4) simple divergence model as in Model 1 plus bottleneck in selfing population; and 5) Model 3 (asymmetric gene flow) plus bottleneck in selfing population.
For each model, we initiated 100 fastsimcoal2 runs. We then chose the best run for each model (the run with the best likelihood scores) and from that best run, we calculated the Aikake Information Criterion for the model. After selecting the model with the best AIC score, we used the maximum likelihood parameter file to generate 200 pseudo-observations of joint SFS for bootstrapping. For each of the 200 pseudo-observations, we initiated 100 fastsimcoal2 runs, then selected the best run for each model based on likelihood scores as above. The resulting parameter estimates from the 200 replicate pseudo-observations were used to calculate the 95% CIs in R. Site frequency spectra and other fastsimcoal2 input files (.tpl and .est) are on GitHub ( [URL]/ novikovalab/selfing_Alyrata). Because fastsimcoal2 reports haploid effective population sizes, we divided them by two to report numbers of diploid individuals (table 1). These parameters can be interpreted as the inverse of the coalescent rate estimated from our accessions.
Supplementary Material
Supplementary data are available at Molecular Biology and Evolution online.
Data Availability
The whole genome raw Illumina short reads for the samples used in this study were submitted to the ENA database under the project number PRJEB50329 (ERP134897). Individual accession names are listed in the supplementary table S1, Supplementary Material online. Raw PacBio HiFi reads of NT1 and MN47, Hi-C reads of NT1, RNAseq reads of NT1, and the genome assembly and annotation of A. lyrata NT1 (GCA_945152055) and MN47 (GCA_944990045) have been submitted to ENA Transition to Self-compatibility Associated with Dominant S-allele · [URL]122 MBE database under the same project number PRJEB50329 (ERP134897) and to [URL]/ Arabidopsis_lyrata_genome_assemblies/162343. Scripts associated with the project are at [URL]/ novikovalab/selfing_Alyrata.
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Domain: Biology
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A genetically encoded fluorescent biosensor for extracellular l-lactate
l-Lactate, traditionally considered a metabolic waste product, is increasingly recognized as an important intercellular energy currency in mammals. To enable investigations of the emerging roles of intercellular shuttling of l-lactate, we now report an intensiometric green fluorescent genetically encoded biosensor for extracellular l-lactate. This biosensor, designated eLACCO1.1, enables cellular resolution imaging of extracellular l-lactate in cultured mammalian cells and brain tissue.
T raditionally, the conjugate acid-base pair of L-lactic acid and L-lactate have been considered a "waste" by-product of glucose metabolism 1 . However, growing evidence suggests that L-lactate is better considered a crucial biological "fuel currency", that is shuttled from cell-to-cell 1 . For example, the astrocyte-to-neuron lactate shuttle (ANLS) hypothesis states that astrocytes metabolize glucose to produce L-lactate which is then released to the extracellular environment and taken up by neurons. In neurons, L-lactate is converted to pyruvate which enters the citric acid cycle to produce the energy necessary to sustain heightened neural activity 2 . The ANLS hypothesis remains controversial, with recent reports of evidence both for 2 and against 3 it.
Investigations of such cell-to-cell L-lactate shuttles would be facilitated by a genetically encoded fluorescent biosensor that would enable high resolution spatially and temporally resolved imaging of the extracellular L-lactate concentration. Genetically encoded fluorescent biosensors are powerful tools for cell-based and in vivo imaging of molecules, ions, and protein activities, and many examples have been reported to date 4,5 . However, despite remarkable progress in the development of such biosensors, including ones for intracellular L-lactate 6,7 , no genetically encoded biosensors for extracellular L-lactate have yet been reported.
Here, we report the development of a genetically encoded fluorescent biosensor for extracellular L-lactate. This biosensor, designated eLACCO1.1, is the end-product of extensive directed evolution and structure-based mutagenesis followed by optimization of biosensor expression and localization on the cell surface. We confirm that eLACCO1.1 enables cellular resolution imaging of extracellular L-lactate in cultured mammalian cells and brain tissue.
Results
Development and characterization of a genetically encoded Llactate biosensor, eLACCO1. Periplasmic binding proteins (PBPs) derived from prokaryotic organisms have proven to be particularly effective sensing domain for extracellular single fluorescent protein-based biosensors 5 , with key examples that include glutamate 8 , GABA 9 , acetylcholine 10 , and serotonin 11 . To construct a prototype L-lactate biosensor, we inserted circularly permuted green fluorescent protein (cpGFP) into Thermus thermophilis TTHA0766 L-lactate binding periplasmic protein at 70 different positions ( Supplementary Fig. 1). Positions of insertion sites in TTHA0766 were chosen by manual inspection of the protein crystal structure to identify loop regions that were solvent exposed and likely to undergo L-lactate-dependent conformational changes 5,12 . The variant (designated eLACCO0.1) with the largest change in fluorescence intensity (ΔF/F = (F max − F min )/ F min ) upon L-lactate treatment had cpGFP inserted at position 191, and exhibited an inverse response (fluorescence decrease upon binding) with ΔF/F = 0.3. Efforts to create prototype biosensors by inserting cpGFP into the analogous regions of TTHA0766 homologs produced no variants with larger L-lactatedependent changes in fluorescence intensity ( Supplementary Fig. 2). Accordingly, we focused our efforts on further development of eLACCO0.1.
To develop variants of eLACCO0.1 with larger ΔF/F, we performed two rounds of linker optimization, followed by seven rounds of directed evolution by random mutagenesis of the whole gene, with screening for L-lactate-dependent change in fluorescence intensity (Fig. 1a). This effort ultimately produced the direct response variant eLACCO1 with ΔF/F of 6 ( Fig. 1b-d and Supplementary Fig. 3) and a strict requirement for Ca 2+ at concentrations greater than 0.6 μM ( Supplementary Fig. 4a, b). The apparent dissociation constant (K d ) of eLACCO1 is 4.1 μM and 120 μM for L-lactate and D-lactate, respectively (Supplementary Fig. 4c). eLACCO1 showed a pH dependence similar to that of the GCaMP6f Ca 2+ biosensor, with pK a values of 6.0 and 8.7 in the presence and absence of L-lactate, respectively (Supplementary Fig. 4d) 13 . Relative to L-lactate, eLACCO1 was~320,~460, and~1320 times less sensitive to the structurally similar molecules β-hydroxybutyrate, pyruvate and oxaloacetate, respectively ( Supplementary Fig. 4c).
The X-ray crystal structure of eLACCO1. In an effort to obtain molecular insight into the structure and mechanism of eLACCO1, we determined the crystal structure of eLACCO1 in the L-lactate-bound state at a resolution of 2.25 Å (Fig. 1e and Supplementary Table 1). The overall structure reveals that the cpGFP-derived and TTHA0766-derived domains are closely associated via an extensive interaction surface that contains numerous molecular contacts. The TTHA0766-derived domain of eLACCO1 retains the same dimeric structure as TTHA0766 itself ( Supplementary Fig. 5) 12 . The Hill coefficient of eLACCO1 is close to one, suggesting that the protomers in the dimer do not interact cooperatively ( Supplementary Fig. 4c). Trp509 in the dimer interface forms a hydrophobic interaction with its symmetry-related self via π-π stacking. Mutagenesis of this residue abrogated L-lactate-dependent fluorescence presumably due to disruption of the dimeric structure, providing support for the conclusion that eLACCO1 functions as a dimer ( Supplementary Fig. 5a).
Development and characterization of affinity-tuned eLACCO1.1. As the physiological concentration of extracellular L-lactate at rest is 0.2 mM in the brain 14 and 1 mM in serum 15 , we expected that the affinity of eLACCO1 for L-lactate (apparent K d~4 .1 μM) would be too high to respond to physiologically relevant concentration changes. To decrease the affinity for L-lactate, we introduced the structure-guided Tyr80Phe mutation to remove a hydrogen bond between L-lactate and eLACCO1. This produced the low-affinity eLACCO1.1 variant with apparent K d of 3.9 mM ( Supplementary Fig. 6a, b). A non-responsive control biosensor, designated deLACCO, was engineered by incorporating the Asp444Asn mutation to abolish L-lactate binding ( Supplementary Fig. 6c, d).
With the affinity-optimized eLACCO1.1 variant in hand, we undertook a detailed characterization of its spectral properties. eLACCO1.1 has two absorbance peaks at 397 and 496 nm, indicative of the neutral (protonated) and the anionic (deprotonated) chromophore, respectively (Fig. 2a). Consistent with the absorbance spectrum, eLACCO1.1 displays excitation peaks at 398 and 493 nm, and excitation at either peak produces an emission peak at 510 nm (Fig. 2b). To investigate the effect of Ca 2+ on the biosensor functionality, we determined the dependence of the fluorescence intensity of eLACCO1.1 on Llactate and Ca 2+ . These experiments revealed that the biosensor only functions as an L-lactate biosensor at concentrations of Ca 2+ greater than 9 μM (Fig. 2c). In vitro characterization of eLACCO1.1 revealed the brightness of the L-lactate-bound state to be~80% and 42% of EGFP under one-photon and two-photon illumination, respectively (Table 1) 16,17 . eLACCO1.1 under onephoton excitation yields L-lactate-dependent decrease in excitation at the 398 nm peak and increase at the 493 nm peak (Fig. 2b). Under two-photon excitation (Fig. 2d), eLACCO1.1 also exhibited L-lactate-dependent ratiometric changes in excitation with an increase of the 924 nm peak and decrease of the 804 nm peak, corresponding to a shift from the neutral to the anionic state of the chromophore. The intensiometric L-lactate-induced two-photon-excited fluorescence change at 940-1000 nm (ΔF 2 / F 2 = 8-10) is about twice as large as the one-photon-excited fluorescence change (ΔF/F = 4, at 480-510 nm) (Fig. 2b, d). A similar trend has been observed for some red fluorescent genetically encoded Ca 2+ biosensors 18 . The fluorescence of eLACCO1.1 is pH dependent, exhibiting pK a values of 7.4 and 9.4 in the presence and absence of L-lactate, respectively (Fig. 2e). The control biosensor deLACCO showed no response to L-lactate, and pH dependence that was similar to the lactate-free state of eLACCO1.1 ( Supplementary Fig. 7). Investigation of the molecular specificity of eLACCO1.1 revealed that the decreased binding affinity extended to structurally similar molecules, with pyruvate and oxaloacetate causing negligible fluorescent response even at 100 mM (Fig. 2f). eLACCO1.1 responds to D-lactate with an apparent K d of 21 mM, a concentration that is far greater than the~11-70 nM concentration in serum 19 . Overall, these results indicate that eLACCO1.1 is likely to respond only to changes in Llactate concentration and pH under physiological conditions.
Targeting of eLACCO1.1 to the extracellular environment. To test eLACCO1.1 as a genetically encoded fluorescent biosensor for extracellular L-lactate in a cellular milieu, we targeted it to the surface of mammalian cells by fusing it to various N-terminal leader sequences and C-terminal anchor domains (Fig. 3a). The widely used combination of the immunoglobulin κ-chain (Igκ) leader sequence and the platelet-derived growth factor receptor (PDGFR) transmembrane domain resulted in only intracellular expression with localization reminiscent of the nuclear membrane (Fig. 3b). We screened a range of leader sequences in combination with the PDGFR anchor, but did not discover any that led to robust membrane localization of PDGFR-anchored eLACCO1.1 (Supplementary Fig. 8). Hence, we turned to the combination of an N-terminal leader sequence and a glycosylphosphatidylinositol (GPI) anchor, both of which are derived from CD59. Ultimately, we found that the combination of a CD59-derived N-terminal leader sequence, and a CD59-derived GPI anchor, provided the desired targeting of eLACCO1.1 to the cell surface (Fig. 3b). To further optimize the biosensor functionality, we screened a number of linkers between eLACCO1.1 and GPI anchor. This experiment revealed that the 18 amino acid linker (GSTSGSGKPGSGEGSTKG) provides the best ΔF/F upon treatment with L-lactate (Fig. 3c, d). Bath application of 10 mM L-lactate resulted in no significant difference in ΔF/F in the presence versus absence of monocarboxylic transporter inhibitor AR-C155858, though the variability in ΔF/F was greatly decreased by this treatment (Fig. 3e). This result suggests that only the fraction of eLACCO1.1 on the cell surface contributes to the fluorescence response associated with changes in the extracellular L-lactate concentration. The decreased variability associated with AR-C155858 treatment is attributed greater consistency in extracellular lactate concentration due to blockage of lactate uptake into cells.
Characterization of eLACCO1.1 in live mammalian cells. We characterized cell-surface-targeted eLACCO1.1 in terms of several important parameters. eLACCO1.1, with an optimized linker between eLACCO1.1 and GPI anchor, robustly increased fluorescence intensity (ΔF/F of 3.3 ± 0.3, mean ± s.e.m., n = 26 cells) upon treatment with L-lactate (Fig. 4a, b). The control biosensor Specific sites (i.e., the linkers) or the entire gene of template L-lactate biosensor genes were randomly mutated and the resulting mutant library was used to transform E. coli. Bright colonies were picked and cultured, and then proteins were extracted and ΔF/F upon addition of 10 mM L-lactate was determined. The variant with the highest ΔF/F was used as the template for the next round. b ΔF/F rank plot representing all proteins tested during the directed evolution. For each round, tested variants are ranked from lowest (negative responses indicate inverse response) to highest ΔF/F value from left to right. The first round of evolution used a library in which residues of the C-terminal linker were randomized. Screening of this library led to the discovery of eLACCO0.2 with a direct response (fluorescence increase upon binding) to L-lactate (ΔF/F = 0.3) while the template eLACCO0.1 decreased the fluorescence intensity (inverse response) in response to L-lactate. Nine rounds of the evolution led to eLACCO1 indicated with a magenta circle. c Excitation and emission spectra of eLACCO1 in the presence and absence of 10 mM L-lactate. Excitation and emission peak is at 494 and 512 nm, respectively. d Schematic representation of eLACCO1 and its mechanism of response to L-lactate. Linker regions are shown in black and the two "gate post" residues 5 in cpGFP are highlighted in dark orange (His195) and purple (Phe437). e Crystal structure of eLACCO1. Right panel represents a zoom-in view around the chromophore. Source data of b, c are provided as a Source Data file.
NATURE COMMUNICATIONS | [URL]41467-021-27332-2 ARTICLE deLACCO had similar membrane localization and, as expected, did not respond to L-lactate (Fig. 4a, b). eLACCO1.1 also exhibited an L-lactate-dependent change in the ratio of excitation at 365 and 470 nm, suggesting eLACCO1.1 could be applicable as both an intensiometric and a ratiometric biosensor (Supplementary Fig. 9). To test photostability, we continuously illuminated eLACCO1.1-expressing cells using one-photon wide-field microscopy (Fig. 4c). eLACCO1.1 showed photostability that is comparable to EGFP and cpGFP. To examine the on-rate kinetics of eLACCO1.1, we bathed eLACCO1.1-expressing HeLa cells in a solution containing 10 mM L-lactate. L-Lactate application induced the robust increase in the fluorescence with the on rate (τ on ) of 1.2 min (Fig. 4d). Cell-surface-targeted eLACCO1.1 has an in situ apparent K d of 1.6 mM (Fig. 4e) and displays Ca 2+ and pH dependent fluorescence as shown in Supplementary Figs. 10 and 11. Stopped-flow analysis of purified eLACCO1.1 protein revealed that the off-rate kinetics are faster than the 1.1 ms dead time of the instrument used ( Supplementary Fig. 12). To characterize the performance of eLACCO1.1 in neurons, we expressed eLACCO1.1 in rat primary cortical neurons. We observed that neurons expressing eLACCO1.1 exhibited bright membranelocalized fluorescence with some puncta apparent (Fig. 4f). On 16 ). b Data from ref. 16 . c Data from ref. 17 . d Two-photon brightness is calculated as F2 = ρA × σ2,A × φA at the peak wavelength (in parentheses), where σ2,A is the peak two-photon absorption cross section.
the surface of rat primary cortical neurons, eLACCO1.1 displayed a ΔF/F of 2.0 ± 0.3 upon bath application of 10 mM L-lactate (mean ± s.e.m., n > 10 neurons from 3 cultures, Fig. 4f). Attempts to use the previously reported Förster resonance energy transfer (FRET)-based biosensor Laconic 6 for imaging of extracellular Llactate were unsuccessful ( Fig. 4b and Supplementary Fig. 13). Taken together, these results indicated that eLACCO1.1 could uniquely be useful for imaging of extracellular L-lactate concentration dynamics.
Two-photon imaging of L-lactate on astrocytes in acute brain slices. The ANLS hypothesis states that glial cells such as astrocytes can release L-lactate into the extracellular space and that this L-lactate is taken up by neurons to serve as an energy source 2 . To determine whether eLACCO1.1 remains functional on the surface of astrocytes of mammalian brain tissue, we used two-photon microscopy to examine cortical acute brain slices prepared from mice injected with an adeno-associated virus (AAV) coding eLACCO1.1 under the control of an astrocyte-specific promoter GFAP (Fig. 5a). Bath application of L-lactate elicited a variable and significant increase in eLACCO1.1 fluorescence at all doses tested: 1 mM (ΔF/F = 0.19 ± 0.04), 2.5 mM (ΔF/F = 0.48 ± 0.15), and 10 mM (ΔF/F = 0.65 ± 0.17) (Fig. 5b-d). Collectively, these ex vivo data indicate that eLACCO1.1 enables detection of extracellular L-lactate in acute brain slice and could potentially be applicable to imaging the release of L-lactate from astrocytes in an ex vivo brain preparation.
Imaging of endogenous L-lactate release from cultured glioblastoma cells. To determine if eLACCO1.1 can enable imaging of the release of endogenous L-lactate from cells, we targeted eLACCO1.1 to the surface of T98G glioma cells. Upon treatment with a high glucose concentration (25 mM), which is expected to stimulate the production of endogenous L-lactate, eLACCO1.1 on the surface of T98G cells underwent an increase in fluorescence consistent with increased secretion of L-lactate ( Fig. 6a-c). In the presence of phloretin or AR-C155858, two inhibitors of the monocarboxylate transporter, the glucose-dependent fluorescence increase was diminished. In both the presence and absence of phloretin or AR-C155858, the control biosensor deLACCO showed no substantial change in fluorescence intensity, indicating that the observed fluorescence changes were due to the L-lactatedependent response of eLACCO1.1. To image the production and export of endogenous L-lactate under a more physiologically relevant condition, we observed eLACCO1.1-expressing T98G cells treated with a physiological plasma concentration of glucose (5.6 mM, Fig. 6d) 20 . We first attempted to inhibit production of endogenous L-lactate by treating the cells with 100 μM iodoacetate, an inhibitor of glyceraldehyde 3-phosphate dehydrogenase. Unexpectedly, this stimulation resulted in apparent aggregation of eLACCO1.1 ( Supplementary Fig. 14). To avoid this iodoacetate-induced aggregation artefact, we turned to using NCI-737, an inhibitor of lactate dehydrogenase (LDH) (Fig. 6e, f). Treatment with NCI-737 caused a slight increase in the fluorescence response of deLACCO. In contrast, eLACCO1.1 showed a decrease in the fluorescence response upon NCI-737 treatment.
The opposite effects of NCI-737 on the responses of eLACCO1.1 and deLACCO are consistent with the observed fluorescence changes being due to changes in the extracellular L-lactate concentration. Overall, these results demonstrate that eLACCO1.1 enables imaging of extracellular L-lactate release from glial cells with cellular resolution.
Discussion
This study describes the development of a genetically encoded extracellular L-lactate biosensor, designated eLACCO1.1. Screening of a library of variants with cpGFP inserted into different positions of Thermus thermophilis TTHA0766 L-lactate binding protein led to the identification of a biosensor prototype in which the L-lactate-dependent conformational change of TTHA0766 allosterically modulates the fluorescence intensity of cpGFP. Extensive directed evolution of the prototype led to the highperformance L-lactate biosensor, eLACCO1. Rational mutagenesis based on the crystal structure of eLACCO1 tuned its L-lactate affinity to be optimal for the physiological concentration range of extracellular L-lactate. An intensive effort to target the affinitytuned variant to the extracellular environment eventually produced eLACCO1.1 that enables cellular resolution imaging of extracellular L-lactate in cultured mammalian cells and brain tissue.
The choice of sensing domain is critically important to the development of any genetically encoded biosensor 5 . Biosensors for extracellular applications (e.g., those with specificity for neurotransmitters like glutamate 8 , GABA 9 , acetylcholine 10,21 , serotonin 11,22,23 , dopamine 24,25 , and norepinephrine 26 ) have generally used microbial PBPs or G-protein-coupled receptors (GPCRs) as the sensing domain. In this work, we chose to use Thermus thermophilis TTHA0766 L-lactate-binding PBP. Relative to other possible sensing domains that are normally found in the reducing environment of the cytoplasm, an advantage of microbial PBPs is that they naturally function in the oxidative environment of the periplasm. This property is likely beneficial to PBP-based biosensors for extracellular targets, since the protein must be trafficked through oxidative organelles such as the endoplasmic reticulum and Golgi apparatus and ultimately exposed to the oxidative extracellular environment. A gluconate operon repressor family transcription factor, LldR, has previously been used as the sensing domain for intracellular L-lactate biosensors 6,7 . Attempts to use the Laconic LldR-based biosensor Two-photon imaging of L-lactate on astrocytes in acute brain slices. a Schematic illustration of AAV injection into the somatosensory cortex for the brain slice experiments. ITR inverted terminal repeat, GFAP human glial fibrillary acidic protein promoter, WPRE woodchuck hepatitis virus posttranslational regulatory element, pA human growth hormone polyA signal. b Representative two-photon images of eLACCO1.1 expressed on astrocytes in brain slice before and after 10 mM L-lactate stimulation. Parallel experiments with deLACCO produced baseline fluorescence that was not significantly higher than background and did not produce a significant change in fluorescence relative to baseline (ΔF/F = 0.85 ± 1.6, P = 0.25, two-tailed paired t-test). Similar results were observed from more than 3 slices. Scale bar represents 50 μm. c Fluorescence traces of eLACCO1.1-expressing astrocytes in response to bath application of L-lactate (mean ± s.e.m.). 1 mM L-lactate (n = 5 slices from 4 mice), 2.5 mM L-lactate (n = 5 slices from 4 mice), or 10 mM L-lactate (n = 6 slices from 4 mice). d ΔF/F plots for eLACCO1.1 at each dose of L-lactate. ΔF/F was calculated by: where F x is the peak fluorescence intensity and F b is the baseline fluorescence intensity. 1 mM L-lactate (n = 5 slices from 4 mice, P = 0.01, two-tailed paired t-test), 2.5 mM Llactate (n = 6 slices from 4 mice, P = 0.02, two-tailed paired t-test), or 10 mM L-lactate (n = 6 slices from 4 mice, P = 0.01, two-tailed paired t-test). The horizontal line is the median; the top and bottom horizontal lines are the 25th and 75th percentiles for the data; and the whiskers extend one standard deviation range from the mean represented as black filled circle. One slice data in the 2.5 mM group was omitted from the trace in c because it was not a complete time course, but ΔF/F of the slice could be measured and was included in d. Source data of c, d are provided as a Source Data file. for imaging of extracellular L-lactate were unsuccessful (Fig. 4b and Supplementary Fig. 13), consistent with the suggested importance of using sensing domains that are compatible with the extracellular environment.
Relative to GPCR-based biosensors, there are several technical advantages associated with development of PBP-based single FPbased biosensors. Of particular relevance to the methods used in this work, directed evolution with bacterial expression is applicable to PBP-based biosensors which can be expressed in soluble form, but is not applicable to integral membrane proteins such as GPCRs. Furthermore, these soluble biosensors can often be crystallized in order to resolve their structure at atomic resolution, providing insights that guide and accelerate engineering efforts. These insights ultimately help to produce biosensors with fluorescent responses that are substantially larger than those yet demonstrated for GPCR-based biosensors.
The mechanism of eLACCO1.1 must involve changes in the chromophore environment that are induced by conformational changes that accompany binding of L-lactate to the TTHA0766derived domain 5 . Based on the pH dependence, these changes in the chromophore environment are stabilizing the brightly fluorescent deprotonated form of the chromophore. The structure of eLACCO1, which we assume to be representative of eLACCO1.1, reveals that the imidazole side chain of His195 is likely a key moiety for mediating changes in the chromophore protonation state (Fig. 1e). Further insight into the mechanism comes from examining the position of beneficial mutations discovered during biosensor optimization. In the first round of the directed evolution for optimization of the C-terminal linker, the introduction of the Asn439Asp mutation converted the biosensor from having an inverse response to having a direct response (Fig. 1b). Accordingly, we propose that the mechanism of eLACCO1.1 involves a conformational switch between two states: the L-lactate-free state where the protonated (dark) chromophore is stabilized by interaction with carboxylate side chain of Asp439 (and His195 is further away); and the L-lactate-bound state where the deprotonated chromophore (bright) is stabilized by interaction with the imidazole side chain of His195 (and Asp439 is further away). Consistent with this proposed mechanism, in the final round of the evolution, we discovered the Lys142Arg mutation that substantially improved ΔF/F (Fig. 1b). The eLACCO1 crystal structure reveals that Arg142 forms a salt bridge with Asp439 in the Llactate-bound state. This salt-bridge may further limit residual interaction of Asp439 with the chromophore (possibly due to increased distance or more effective charge neutralization) and thereby contribute to increased brightness of the L-lactate-bound state and a higher ΔF/F.
In vitro characterization revealed that lactate-free eLACCO1.1 harbors 97% of the neutral (dark) chromophore and 3% of the anionic (bright) chromophore, and that this changes to 57% neutral and 43% anionic for lactate-bound eLACCO1.1 (Table 1). This chromophore equilibrium indicates that with respect to further improving the ΔF/F, there is relatively little room to engineer decreased brightness of eLACCO1.1 in the lactate-free state. In contrast, the brightness of the lactate-bound state could be further engineered and improved, in principle, to 230% of its current value (i.e., 0% neutral and 100% anionic). Notably, the ΔF/F rank plot of directed evolution did not plateau (Fig. 1b), suggesting that additional rounds of directed evolution could yet improve ΔF/F by increasing the brightness of lactate-bound state. Considering that only 43% of the biosensor is present in the anionic form, the fluorescence brightness per one anionic chromophore is~70% larger than that of EGFP 16 . This enhancement can be explained by the fact that the anionic form of eLACCO1.1 in the L-lactate-bound state has the extinction coefficient~60% higher than EGFP. Excitation of the neutral form at 398 nm results in fluorescence from the anionic excited state (at 510 nm), presumably due to excited-state proton transfer 27 . The intrinsic two-photon brightness (F 2 = σ 2 × φ × ρ, where σ 2 is the twophoton absorption cross section) of L-lactate-bound eLACCO1.1 at 924 nm (F 2 = 16 GM) is 42% that of EGFP (F 2 = 38 GM) 17 . This is because only 43% of eLACCO1.1 is present in the anionic form when bound to L-lactate. Though moderately dimmer than GFP in terms of two-photon brightness, it is notable that eLACCO1.1 in the L-lactate-bound state is 2.4-fold brighter than Citrine (F 2 = 6.7 GM) 28 .
As with many genetically encoded biosensors 8,11 , the maximal fluorescence response of eLACCO1.1 in acute brain slice (ΔF/F 0.7) is smaller than that in HeLa cells (ΔF/F~3) and cultured neurons (ΔF/F~2). A previous study with the extracellular neurotransmitter serotonin biosensor iSeroSnFR similarly reported that its fluorescence response in acute brain slice (ΔF/F~0.8) is smaller than that in HEK293T cells (ΔF/F~8) and cultured neurons (ΔF/F~6) upon treatment with 1 mM serotonin 11 . In addition to the smaller fluorescence response, eLACCO1.1 shows a relatively large variation of fluorescence response in acute brain slice (Fig. 5d). To develop a next generation eLACCO variant with improved performance in brain tissues, it might be necessary to combine bacteria-based high-throughput directed evolution with secondary neuron-or slice-based assessments to identify those variants that best retain their performance in brain tissues.
To date, the concentration and dynamics of extracellular Llactate in brain tissue has typically been assessed using inserted enzyme-mediated electrodes 14,29 . Relative to electrodes, the inherent advantages of eLACCO1.1 are that it can be noninvasively introduced in the form of its corresponding gene, and it enables spatially-resolved imaging of L-lactate concentration dynamics. In addition, the targeted expression of eLACCO1.1 in specific cell types (e.g., astrocytes or neurons), will enable researchers to accurately determine which cell types are importing, and which are exporting, L-lactate. Accordingly, we anticipate that eLACCO1.1, and further improved variants, will play a central role in investigations of L-lactate shuttles, including the controversial ANLS hypothesis.
Methods
General methods and materials. Synthetic DNA encoding the lactate binding bacterial periplasmic protein TTHA0766 was purchased from Integrated DNA Technologies. Phusion high-fidelity DNA polymerase (Thermo Fisher Scientific) was used for routine polymerase chain reaction (PCR) amplifications, and Taq DNA polymerase (New England Biolabs) was used for error-prone PCR. The QuikChange mutagenesis kit (Agilent Technologies) was used for site-directed mutagenesis. Restriction endonucleases, rapid DNA ligation kits and GeneJET miniprep kits were purchased from Thermo Fisher Scientific. PCR products and products of restriction digests were purified using agarose gel electrophoresis and the GeneJET gel extraction kit (Thermo Fisher Scientific). DNA sequences were analyzed by DNA sequence service of the University of Alberta Molecular Biology Service Unit and Fasmac Co., Ltd. Fluorescence excitation and emission spectra were recorded on Safire2 and Spark plate readers (Tecan).
Engineering of eLACCO1.1. The gene encoding cpGFP with N-and C-terminal linkers (LV and NP, respectively) was amplified using iGluSnFR gene as template, followed by insertion into each site of TTHA0766 lactate binding protein in a pBAD vector (Life Technologies) by Gibson assembly (New England Biolabs). Variants were expressed in E. coli strain DH10B (Thermo Fisher Scientific) in LB media supplemented with 100 μg mL −1 ampicillin and 0.02% L-arabinose. Proteins were extracted using B-PER bacterial protein extraction reagent (Thermo Fisher Scientific) and tested for fluorescence brightness and lactate-dependent response. The most promising variant, designated as eLACCO0.1, was subjected to an iterative process of library generation and screening in E. coli. Libraries were generated by site-directed mutagenesis using QuikChange (Agilent Technologies) or error-prone PCR of the whole gene. For each round,~100-200 fluorescent colonies were picked, cultured and tested on 384-well plates under a plate reader. There were 9 rounds of screening before eLACCO1 was identified. Finally, Tyr80Phe mutation was added to eLACCO1 to tune the lactate affinity using Q5 high-fidelity DNA polymerase (New England Biolabs). The resulting mutant was designated as eLACCO1.1.
Protein purification and in vitro characterization. The gene encoding eLACCO1.1, with a poly-histidine tag on the N-terminus, was expressed from the pBAD vector. Bacteria were lysed with a cell disruptor (Branson) and then centrifuged at 15,000 g for 30 min, and proteins were purified by Ni-NTA affinity chromatography (Agarose Bead Technologies). Absorption spectra of the samples were collected with a Lambda950 Spectrophotometer (PerkinElmer). To perform pH titrations, protein solutions were diluted into buffers (pH from 2 to 11) containing 30 mM trisodium citrate, 30 mM sodium borate, 30 mM MOPS, 100 mM KCl, 10 mM CaEGTA, and either no L-lactate or 10 mM L-lactate. Fluorescence intensities as a function of pH were then fitted by a sigmoidal binding function to determine the pK a . For lactate titration, buffers were prepared by mixing an Llactate (−) buffer (30 mM MOPS, 100 mM KCl, 1 mM CaCl 2 , pH 7.2) and an Llactate (+) buffer (30 mM MOPS, 100 mM KCl, 1 mM CaCl 2 , 100 mM L-lactate, pH 7.2) to provide L-lactate concentrations ranging from 0 to 100 mM at 25°C. Fluorescence intensities were plotted against L-lactate concentrations and fitted by a sigmoidal binding function to determine the Hill coefficient and apparent K d . For Ca 2+ titration, buffers were prepared by mixing a Ca 2+ (−) buffer (30 mM MOPS, 100 mM KCl, 10 mM EGTA, 100 mM L-lactate, pH 7.2) and a Ca 2+ (+) buffer (30 mM MOPS, 100 mM KCl, 10 mM CaEGTA, 100 mM L-lactate, pH 7.2) to provide Ca 2+ concentrations ranging from 0 to 39 μM at 25°C.
Rapid kinetic measurements for the interaction of eLACCO1 or eLACCO1.1 with L-lactate were made using an Applied Photophysics SX20 Stopped-flow Reaction Analyzer. Fluorescence was detected by exciting at 488 nm with 2 nm bandwidth and collecting emitted light at 520 nm through a 10-nm path at room temperature. The dead time of the instrument is 1.1 ms. For k off , 2 μM of purified protein sample saturated with 200 mM L-lactate and 1 mM CaCl 2 was dissociated 1:1 with 100 mM EGTA at room temperature. Graphpad Prism was used to fit a single exponential dissociation for k off . For eLACCO1.1, k off was faster than the dead time of the instrument, so a baseline fluorescence in the saturated state was obtained as a negative control. All measurements were done in triplicates, and error ± represents the s.e.m.
To collect the two-photon absorption spectra, the tunable femtosecond laser InSight DeepSee (Spectra-Physics, Santa Clara, CA) was used to excite the fluorescence of the sample contained within a PC1 Spectrofluorometer (ISS, Champaign, IL). The laser was automatically stepped to each wavelength over the spectral range with a custom LabVIEW program (National Instruments, Austin, TX), with 42 s at each wavelength to stabilize 30 . Two samples per laser scan were measured by using both the sample and reference holders and switching between them with the auto-switching mechanism on the PC1. The laser was focused on the sample through a 45-mm NIR achromatic lens, antireflection coating 750-1550 nm (Edmund Optics, Barrington, NJ). Fluorescence was collected from the first 0.7 mm of the sample at 90°w ith the standard PC1 collection optics through both 633/SP and 745/SP filters (Semrock, Rochester, NY) to remove all laser scattered light. To correct wavelengthto-wavelength variations of laser parameters, LDS798 (Exciton, Lockbourne, OH) in 1:2 CHCl 3 :CDCl 3 was used as a reference standard between 912 and 1240 nm (ref. 31 ), and coumarin 540 A (Exciton, Lockbourne, OH) in 1:10 DMSO:deuterated DMSO was used between 700 and 912 nm (ref. 32 ). Adding the deuterated solvents (Millipore Sigma, Darmstadt, Germany) was necessary to decrease near-infrared solvent absorption. All the dye solutions were magnetically stirred throughout the measurements. Quadratic power dependence of fluorescence intensity in the proteins and standards was checked at several wavelengths across the spectrum.
For the L-lactate-bound state of eLACCO1.1, the two-photon cross section (σ 2 ) of the anionic form of the chromophore was measured versus rhodamine 6 G in MeOH at 976 and 960 nm (σ 2 (976 nm) = 12.7 GM, σ 2 (960 nm) = 10.9 GM) 33 . These σ 2 numbers closely agree with other literature data: ref. 34 at 976 nm (considering the correction discussed in ref. 33 ), and ref. 35 at 960 nm. For the Llactate-free state, the σ 2 of the neutral form of the chromophore was measured versus fluorescein (Millipore Sigma, Darmstadt, Germany) in 10 mM NaOH (pH 12) at 820 and 840 nm: σ 2 (820 nm) = 24.2 GM, σ 2 (840 nm) = 12.9 GM 33 . These σ 2 values for fluorescein also match other literature data 36,37 . Power dependence of fluorescence intensity was recorded with the PC1 monochromator at 550 nm (lactate bound) or 512 nm (lactate free) with the emission slits at a spectral width of 16 nm (full width at half maximum) and fitted to a parabola with the curvature coefficient proportional to σ 2 . These coefficients were normalized for the concentration and the differential fluorescence quantum yield at the registration wavelength. The differential quantum yields of the standard and the sample were obtained with an integrating sphere spectrometer (Quantaurus-QY; Hamamatsu Photonics, Hamamatsu City, Japan) by selecting an integral range~8 nm to the left and right of the registration wavelength. Concentrations were determined by Beer's Law. Extinction coefficients were determined by alkaline denaturation as detailed in ref. 38 . The two-photon absorption spectra were normalized to the σ 2 values at the two wavelengths and averaged. To normalize to the total two-photon brightness (F 2 ), the spectra were then multiplied by the quantum yield and the relative fraction of the respective form of the chromophore for which the σ 2 was measured. The data is presented this way because eLACCO1.1 contains a mixture of the neutral and anionic forms of the GFP chromophore. This is described in further detail in refs. 17,38 .
X-ray crystallography. For crystallization, eLACCO1 cloned in pBAD-HisB with N-terminus 6×His tagged was expressed in E. coli DH10B strain. Briefly, a single colony from freshly transformed E. coli was inoculated into 500 mL of modified Terrific Broth (2% Luria-Bertani Broth supplemented with additional 1.4% tryptone, 0.7% yeast extract, 54 mM K 2 PO 4 , 16 mM KH 2 PO 4 , 0.8% glycerol, 0.2 mg mL −1 ampicillin sodium salt) and incubated by shaking at 37°C and 220 rpm for 8 h to reach exponential growth phase. Then, L-arabinose was added to 200 ppm to induce expression for another 48 h shaking at 30°C and 250 rpm. Bacteria were then harvested and lysed using a sonicator (QSonica) for 4 cycles of 150 s sonication with 2 s off between each 1 s of sonication at 50 W power. After centrifugation at 13,000 g, the supernatant was purified with Ni-NTA affinity agarose bead (G-Biosciences) and eluted into PBS containing 250 mM imidazole. The eluted sample was further concentrated and desalted with an Amicon Ultra-15 Centrifugal Filter Device (Merck). The purified eLACCO1 protein was further applied on Superdex200pg (GE healthcare) size exclusion chromatography and the buffer was exchanged to TBS buffer supplemented with 1 mM CaCl 2 . The fractions containing the eLACCO1 protein were pooled and concentrated to~20 mg mL −1 , and then incubated with 2 mM L-lactate for the crystallization trail. Initial crystallization of the eLACCO1 protein was set up using 384-well plate via sitting drop vapor diffusion against commercially available kits at room temperature. The eLACCO1 crystal used for the data collection was grown in 0.2 M ammonium citrate dibasic and 20% w/v PEG 3350 by mixing 0.6 μL of reservoir solution with 0.6 μL of protein sample. The eLACCO1 crystals grew for 3-5 days and were cryoprotected with reservoir supplemented with 25% glycerol, and then frozen in liquid nitrogen. X-ray diffraction data were collected at 100 K at the Advanced Photon Source beamline line 23ID. The X-ray diffraction data were processed and scaled with XDS 39 . Data collection details and statistics were summarized in Table S1. The crystal structure of eLACCO1 was solved by molecular replacement approach implemented in Phaser program embedded in Phenix program package 40 , using the GFP structure (PDB 3SG6) and the lactate binding protein (PDB 2ZZV) as search models 12,41 . One molecules of the eLACCO1 protein were present in the asymmetric unit. Further tracing of the missing residues and the structure were iteratively rebuilt in COOT and refined with Phenix program package 42,43 . The final model included the most of the residues except the N-terminal affinity tag and the glycine-rich linker to connect the original N-and C-termini of GFP. The final eLACCO1 structure bound with one molecule of L-lactate and one Ca 2+ ion was determined to 2.25 Å and refined to a final R work /R free of 0.1484/0.1871 with high quality of stereochemistry. By generating symmetry mates, the eLACCO1 packed as a dimer in the crystal packing and shared a similar dimerization interface with TTHA0766 lactate binding protein (PDB 2ZZV).
Construction of mammalian expression vectors.
For cell surface expression, the genes encoding eLACCO1.1, deLACCO, cpGFP, EGFP, and Laconic 6 (a gift from Luis Felipe Barros, Addgene plasmid #44238; [URL]:44238; RRI-D:Addgene_44238) were amplified by PCR followed by digestion with BglII and EcoRI, and then ligated into pAEMXT vector (Covalys) that contains N-terminal leader sequence and C-terminal GPI anchor from CD59. To construct PDGFRanchored eLACCO1.1 with various leader sequences, the gene encoding eLACCO1.1 including the CD59 leader and anchor sequence in the pAEMXT vector was first amplified by PCR followed by digestion with XhoI and HindIII, and then ligated into pcDNA3.1 vector (Thermo Fisher Scientific). Next, the gene encoding PDGFR transmembrane domain was amplified by PCR using pDisplay vector (Thermo Fisher Scientific) as a template, and then substituted with CD59 GPI domain of the pcDNA3.1 product above by using EcoRI and HindIII. Complementary oligonucleotides (Thermo Fisher Scientific) encoding each leader sequence were digested by XhoI and BglII, and then ligated into a similarlydigested pcDNA3.1 including PDGFR-anchored eLACCO1.1. The gene encoding mCherry was amplified by PCR followed by digestion with BglII and SalI, and then ligated into pDisplay vector that contains N-terminal Igκ leader sequence and C-terminal PDGFR transmembrane domain. To construct eLACCO1.1 plasmid for neural expression, the gene encoding eLACCO1.1 including the CD59 leader and anchor sequence in the pAEMXT vector was first amplified by PCR followed by digestion with NheI and XhoI, and then ligated into a human synapsin promoter vector (a gift from Jonathan Marvin). The gene encoding eLACCO1.1 was subcloned by restriction digests into pAAV plasmid containing the GFAP promoter (a gift from Michael Brenner) for AAV production.
For imaging of lactate-dependent fluorescence, HeLa cells transfected with eLACCO1.1, deLACCO, or eLaconic were washed twice with Hank's balanced salt solution (HBSS; Nakalai Tesque), and then 2 mL of HBSS supplemented with 10 mM HEPES (Nakalai Tesque) and 10 mM 2-deoxyglucose (Wako) was added to start the imaging at 37°C followed by L-lactate stimulation. For photostability test, HeLa cells transfected with pAEMXT-eLACCO1.1, EGFP, or cpGFP were illuminated by excitation light at 100% intensity of LED (~10 mW cm −2 on the objective lens) and their fluorescence images were recorded at 37°C for 5 min with the exposure time of 50 ms and no interval time. Fluorescence intensities on cell membrane were collected after background subtraction. For imaging of L-lactate release, T98G cells transfected with eLACCO1.1 or deLACCO were washed twice with HBSS, and then 1. For in situ pH titration, HeLa cells seeded onto coverslips were co-transfected with pDisplay-pHuji (Addgene plasmid #61556) and pAMEXT-eLACCO1.1 or deLACCO. Forty-eight hours after transfection, the coverslips were transferred into Attofluor™ Cell Chamber with HBSS supplemented with 20 mM HEPES (Gibco, Cat. #15630130) and 10 mM 2-deoxy-D-glucose (Sigma-Aldrich Cat. #D8375-1G) at pH 7.05. Other bath solutions were supplemented with 10 mM L-lactate (Sigma-Aldrich Cat. #71718-10 G) and subsequently adjusted to their respective pH values. Rapid change of bath solutions during the image was performed in a remove-andadd manner using a homemade solution remover 44 . Cells are imaged on a Nikon Eclipse Ti-E epifluorescence microscope equipped with a 488 nm argon laser and a 543 nm He-Ne laser focused on the back aperture of a × 60 oil total internal reflection fluorescence (TIRF) objective lens (NA 1.49, Nikon). TIRF setup was achieved by a TI-TIRF-E Motorized Illuminator Unit (Nikon) to reduce the contribution of fluorophores that are not localized on the plasma membrane. Images were acquired every 10 s by a Photometrics QuantEM 512SC EM-CCD camera at a gain value of 500. To avoid the photoactivation artefacts, pHuji signal was acquired first in each cycle with the 543 nm laser with a TRITC filter cube followed by the acquisition of eLACCO1.1 signal with 488 nm laser and a FITC filter cube. NIS-Elements AR package software was used for automatic instrument control, data recording and measurement. Data was further analyzed and normalized to the intensity at pH 7.99 using a custom R script and plotted in GraphPad Prism software.
Imaging of eLACCO1.1 in neurons. Male and female P0 pups were obtained from a single timed-pregnant Sprague Dawley rat (Charles River Laboratories). Experiments were performed with rat cortical primary cultures from the P0 pups (pooled tissues from males and females), plated in glass-bottom 24-well plates where 0.5 × 10 6 cells were used for three wells. Cultures were nucleofected at time of plating, and imaged 14 days later. Three wells were plated and imaged per nucleofected construct. Culture media was replaced with 1 mL of imaging buffer (145 mM NaCl, 2.5 mM KCl, 10 mM glucose, 10 mM HEPES, 2 mM CaCl 2 , 1 mM MgCl 2 , pH 7.4) for imaging 13 . Wide-field images were taken at the center of each well using a Nikon Eclipse microscope (20 × 0.4 NA, 488 nm excitation, 500-550 nm emission) at room temperature. These were the "APO" images. After a 20-min incubation in the presence of~10 mM L-lactate, the same fields of view were recorded again. These were the "SAT" images. APO and SAT images were aligned by cross-correlation. A pixel classifier (Ilastik 45 ) was trained using manual annotations to label each pixel in the images as background, neurite, soma, or intracellular inclusion. A scalar constant background was subtracted from all images to account for camera offset. Reported values of ΔF/F for pixels classified as neurites were calculated as (sum of neurite pixel intensities SAT)/(sum of neurite pixel intensities APO) − 1.
Surgery and in vivo microinjections of adeno-associated virus (AAV). AAV was packaged in HEK293 cells by triple transfection of pAAV plasmids encoding eLACCO1.1, a rep/cap encoding plasmid (pXR5 or POM2/9) and the helper plasmid pXX680. At 120 h after the transfection, culture medium was collected, filtered and concentrated through a 100 kDa TFF cassette. Viral particles were purified by ultracentrifugation on an iodixanol gradient, washed on a 100 kDa MWCO spin column and stored in PBS containing 10% D-sorbitol and 0.002% F-68 pluronic acid. Viral titers (GC/mL) were determined by ddPCR using universal primers binding the ITRs. Transduction efficiency was validated in primary rat neuron and astrocyte cultures. Male mice (C57bl/6, P45) were anesthetized via isoflurane (5% for induction, 2-3% for maintenance, v/v). Depth of anesthesia was determined by observing breathing rates and toe-pinch ensured proper loss of reflexes. Following deep anesthesia, mice were head fixed on a stereotaxic apparatus (David Kopf Instruments) with a bite bar and ear bars, with ventilated anesthesia administration. The mice were injected with 0.05 μL of buprenorphine subcutaneously (Buprenex, 0.1 mg mL −1 ), and artificial tears were applied to the eyes before beginning surgery. The hair on the scalp was removed prior to surgery, and the incision was washed with 10% povidone iodine and 70% ethanol, 3 times each, alternating. An incision was made on the scalp to expose bregma and the craniotomy site with coordinates are as follows. Somatosensory cortex: −1.58 mm posterior and +3.0 and −3.0 mm lateral (for bilateral injection) from bregma, and 0.7-0.5 mm ventrally from the pial surface. A 2-3 mm craniotomy was made at the injection site using a small burr (Fine Science Tools), powered by a drill (K.1070, Foredom). Saline (0.9%) was applied to keep the skull cool, to maintain skin hydration, and to remove bone debris. AAVs were injected via a beveled borosilicate pipette (World Precision Instruments) by a Nanoject 2 (Drummond Scientific). Five, 69 nL injections were given at each site, totaling 345 nL of virus was infused into each region of somatosensory cortex, and each virus contained the GFAP promoter driving the following constructs at the indicated titer: eLACCO1.1 (1.5 × 10 13 gC mL −1 ), deLACCO (1.5 × 10 13 gC mL −1 ). Following injection, the needle was left in place for 10 min to allow for fluid pressure normalization. Following needle withdraw, scalp was sutured with silk sutures and mice were closely monitored, kept on a heating pad and given buprenorphine twice daily for 48 h post-op (0.05 mL, 0.1 mg mL −1 ), and fed chow with sulfonamide sulfadiazine trimethoprim (32 g kg −1 ) for 1 week post-op.
Two-photon microscopy of eLACCO1.1 in acute cortical brain slice. Brain slices were imaged using a custom built two-photon microscope 46 fed by a Ti:Sapphire laser source (Coherent Ultra II,~4 W average output at 800 nm,~80 MHz). Image data were acquired using MatLab (2013) running the open source scanning microscope control software ScanImage (version 3.81, HHMI/Janelia Research Campus). Imaging was performed at an excitation wavelength of 940 nm. The microscope was equipped with a primary dichroic mirror at 695 nm and green and red fluorescence was split and filtered using a secondary dichroic at 560 nm and two bandpass emission filters: 525-40 nm and 605-70 nm (Chroma Technologies). Time series images were acquired at 0.98 Hz with a pixel density of 512 by 512 and a field of view size of~292 μm. Imaging used a ×40 water dipping objective lens (NA 1.0, WD 2.5 mm, Zeiss). Imaging was performed at room temperature. Brain slices were superfused with L-lactate (Sigma Aldrich) at concentrations of 1, 2.5, and 10 mM. ΔF/F was calculated by: ΔF/F = ((F x − F b )/F b ), where F x is the peak fluorescence intensity and F b is the baseline fluorescence intensity. Regions of interest were selected based on identifying fine processes via SR-101 fluorescence that did not shift focal plane during the duration of imaging.
Animal care. For experiments performed at University of Calgary, all methods for animal care and use were approved by the University of Calgary Animal Care and Use Committee and were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. For experiments at HHMI Janelia Research Campus, all surgical and experimental procedures were in accordance with protocols approved by the HHMI Janelia Research Campus Institutional Animal Care and Use Committee and Institutional Biosafety Committee.
Statistics and reproducibility. All data are expressed as mean ± s.d. or mean ± s.e.m., as specified in figure legends. Box plots with and without notches are used for Figs. 4b, c, 3d, e, and 5d, respectively. In these plots, the horizontal line is the median; the top and bottom of the notch denote the 95% confidence interval of the median; the top and bottom horizontal lines are the 25th and 75th percentiles for the data; and the whiskers extend one standard deviation range from the mean represented as black filled circle. Sample sizes (n) are listed with each experiment.
No samples were excluded from analysis and all experiments were reproducible. In photobleaching experiments, group differences were analyzed using one-way ANOVA (Igor Pro 8). In eLACCO1.1 imaging (Fig. 3e) and brain slice experiments, the significant differences were analyzed using Student's t-test (Graphpad Prism). Microsoft Excel software was used to plot for Figs. 1b, c, 2a, b, d, and 6c, f.
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Code availability
Custom code is available from the corresponding author, and at [URL]/ shucez/eLACCO_manuscript_TIRF_deltaF_F0.
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Domain: Biology
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First records and a new genus of comb-tailed spiders (Araneae: Hahniidae) from Thailand with comments on the six-eyed species of this family
The family Hahniidae is reported from Thailand for the first time. The genus Hexamatia gen. nov. and two new species, Hexamatia seekhaow gen. et sp. nov. and Hahnia ngai sp. nov., are described and illustrated. DNA sequences are provided for all the species reported here. The phylogenetic position of the novel genus Hexamatia gen. nov. and its relation to Hahnia are discussed. Based on these results, a new combination is proposed for Hexamatia senaria (Zhang, Li & Zheng, 2011) gen. et comb. nov. = Hahnia senaria. Known distribution of the species Hahnia saccata Zhang, Li & Zheng, 2011, originally described from China, is expanded. A brief review and notes on the taxonomy of the six-eyed hahniids are included.
Introduction
The family Hahniidae Bertkau, 1878 is relatively easy to identify due to the advanced location of the tracheal spiracle in relation to the spinnerets and the characteristic arrangement of these in more or less one transverse row (at least in the Hahniinae Bertkau, 1878) (Lehtinen 1967;Opell & Beatty 1976). Other members of this family (e.g., Cicurina Menge 1871 and Cybaeolinae Lehtinen, 1967) do not share this transverse arrangement of the spinnerets (Roth 1967;Wang et al. 2019). The Hahniidae currently includes 351 species in 23 genera distributed worldwide (WSC 2020). The family status of Hahniidae has been confi rmed by molecular phylogenies being placed within the RTA clade, closely related to Cybaeide Banks, 1892 and Dictynidae O. Pickard-Cambridge, 1871 (J. A. Wheeler et al. 2017). However, the relations and delimitations of its genera have always been problematic. Only a few local revisions have been done, two for Nearctic species (Gertsch 1934;Opell & Beatty 1976) and one for New Zealand species (Forster 1970). Beside these revisions, Lehtinen (1967) published some comparative tables including diagnostic characters of 17 extant genera (10 currently valid, WSC 2020) and one more from Baltic amber. Presently, two genera, Cicurina and Hahnia C. L. Koch, 1841, have served as 'wastebin taxa' for new species descriptions, having a great morphological heterogeneity and accounting together for almost 70% of all the valid hahniid species (WSC 2020). The great heterogeneity and unclear delimitations in these and other hahniid genera are a recurrent note in new species publications (Forster 1970;Huang et al. 2017).
The Hahniidae have a worldwide distribution, being more diverse in the Americas and Asia but also having a fair number of species described from Europe, Africa and Oceania (WSC 2020). In Asia, eight genera and 93 species have been recorded distributed from the Middle East to Eastern Russia and Japan. In South and Southeast Asia, hahniids have been reported from Hong Kong, Indonesia, Laos, Philippines, Southern China, Sri Lanka, Taiwan and Vietnam (Lehtinen 1967;Bosmans 1992; Barrion & Litsinger 1995;Tang et al. 1996;Zhang et al. 2011Liu et al. 2015;Huang et al. 2017). This is the fi rst time the Hahniidae are reported from Thailand. Here we describe a new genus and two new species in this family based on molecular and morphological data. Additionally, we include a brief literature review on the rare six-eyed hahniids.
Material and methods
The hahniid species reported here were collected in the Chiang Mai Province, Thailand, between July 16 th and 28 th 2018. All the specimens were captured using methods optimized for ground dwelling spiders: leaf litter sifting, Winkler extractors, pitfall traps and direct collecting on ground, among leaf litter and under rocks or logs.
Specimen habitus and other somatic characters were photographed under a Leica MI6SC stereo microscope equipped with a Nikon DS-Ri2 camera. Genitals were photographed using a Leica DM 2500 microscope attached to the same camera. Specimens were observed in ethanol using semi permanent slide preparations (Coddington 1983). Female genitalia were dissected, digested using pancreatine solution (Álvarez-Padilla & Hormiga 2007) and cleared with methyl salicylate.
Four legs were taken from one individual of each species for DNA extraction. Six gene fragments (COI, H3, 12S, 16S, 18S and 28S) were amplifi ed following M. A. and Wheeler et al. (2017) protocols; list of primers is provided in the Supplementary material (Supplementary fi le 1). Sequences were edited in Geneious Prime 2020.0.5. New sequences generated for this study were deposited in GenBank; accession numbers are reported in Table 1. All the specimens used here have been deposited in the collection of the Naturalis Biodiversity Center, Leiden, the Netherlands (RMNH. ARA.18411-RMNH. ARA.18415).
We used sequences from the three species we collected, as well as 15 other species with available sequences in GenBank. We used in total 14 species of Hahniidae, three species of Cybaeidae Banks, 1892, and one species of Agelenidae C. L. Koch, 1837, Agelena labyrinthica Walckenaer, 1805, as an outgroup. The sequences used to test the relationships and position of the new species within the Hahniidae are listed in Table 1. We used MAFFT ver. 7.450 online ( [URL]/) with default parameters to build the alignments. Alignments for 18S were further trimmed manually due to the size difference of some sequences. 16S and 12S were not used due to the low availability of these loci for the Hahniidae in GenBank; Table 1 only reports accession numbers of these markers for our sequences. Hahnia pusilla C. L. Koch, 1841, type species of Hahniidae, as well as two more species of Hahnia and two of Iberina Simon, 1881 had only COI sequences available in GenBank, therefore, they were not used in our fi nal dataset. Matrix was built using COI, H3, 18S and 28S alignments in Sequence Matrix ver. 1.8 ( [URL]/); matrix is available in Supplementary fi le 2. Each locus was treated as a partition and examined with jModelTest2 (Darriba et al. 2012) in CIPRES (M. A. to get the best model fi t for each; GTR + I + G was selected in all the cases. Our datasets were analyzed using MEGA X (Kumar et al. 2018) for maximum parsimony (SPR, default values, bootstrap = 1000), RaXML (Stamatakis 2014) in CIPRES for maximum likelihood (GTR, bootstrap = 1000) and MrBayes ver. 3.2.6 (Ronquist & Huelsenbeck 2003) for windows for the Bayesian inference (GTR + I + G, two independent runs with one cold and three heated chains, mcmc =1 000 000 gen, samplefreq = 1000, burnin = 2500). The program Tracer ver. 1.7.1 (Rambaut et al. 2018) was used to analyze the performance of our BI analyses, and Mega X to estimate the genetic distances (JC model, gamma dist., gamma parameter = 1.00; gaps data treatment = pairwise deletion) for our whole dataset.
Abbreviations (in text and fi gures)
A = epigynal atrium ALE = anterior lateral eyes ALS = anterior lateral spinnerets AME = anterior median eyes BI = Bayesian inference Cd = copulatory duct
Phylogenetic analyses
Topologies inferred by the three different phylogenetic analyses recovered nearly identical topologies ( Fig. 1a-c). The genus Hahnia was homogeneously recovered as diphyletic. The clade Hahnia 1 was formed by six species of Hahnia, and Hahnia 2 by H. ngai sp. nov. and H. saccata, the two species of Hahnia we captured in Thailand. The clade Hahnia 1 showed high support, although the internal relationships are not fully resolved, having moderate to weak support values in the ML and MP analyses. This clade was found as a sister group to the new genus Hexamatia gen. nov. in all our trees. The clade Hahnia 2 appears to be more related to Antistea + Neoantistea. This branch is recovered and highly supported in all the analyses. The cluster formed by Antistea + Neoantistea is strongly supported although its internal relationships are not resolved and show weak to moderate support in the MP and ML. The three cybaeid representatives form a highly supported group that is consistently recovered as a sister to the monophyletic Hahniidae. Our BI showed an average deviation of split frequencies below 0.003 after 1 000 000 generations. None of the Estimated Sample size parameters fell below the commonly used threshold of 200 suggesting that our BI ran for an adequate length (Drummond et al. 2006;Lanfear et al. 2016
Diagnosis
Hexamatia gen. nov. is distinguished from most hahniid genera by the combination of the following characters: presence of only six eyes, small body size close to 1 mm, and body pale yellow to white, lacking abdominal patterns in males and having faint chevron lines in females (Zhang et al. 2011: fi g. 23a-b). It can be separated from other six-eyed hahniids by the following combination of characters:
Etymology
The genus name is formed from two Greek roots: hexa (six) and mati (eye). It refers to the number of eyes present in this genus, one of its diagnostic characters. The gender is feminine.
Etymology
The species epithet is a derivation of the Thai seekhaow (white); refers to the lack of color on the body of the holotype of this species.
Distribution
Known from the type locality, Doi Suthep National Park, Chiang Mai, Thailand (Fig. 8).
Notes
See the Discussion for remarks on six-eyed species.
Etymology
The species epithet is a derivation of the Thai ngai (simple), in reference to the relatively simple vulva without the well-formed secondary spermathecae commonly seen in other species of Hahnia.
Discussion
The Hahniidae, especially the Hahniinae, have traditionally been seen as an easily diagnosable group in part due to the transversal comb-shaped position of the spinnerets. Nevertheless, their position as a family has changed overtime, being initially considered a subfamily of the Agelenidae (Simon 1875; Gertsch 1934;Lehtinen 1967, among others) and Dictynidae (Lehtinen 1967;Paquin & Dupérré 2009;Wang et al. 2019, among others). Currently, the monophyly of the family is largely recognized, and its relations have been indirectly tested as a part of broad scoped phylogenetic studies (J. A. Wheeler et al. 2017). However, the relations between its genera have never been phylogenetically tested. Although our data did not include representatives of all the known hahniid genera, we found some consistent and well supported results with the 14 hahniid species and four loci we analyzed. The position of the new genus Hexamatia gen. nov. as a sister group to the core species of Hahnia in our study is confi dently recovered in all our topologies. We consider that this plus the morphological differences between the new genus and Hahnia (presence of six eyes, small size close to 1 mm and almost complete lack of coloration and abdominal patterns) are suffi cient to consider it outside of the Hahnia 1 group, and as a genus of its own. We also propose a new combination for Hexamatia senaria gen. nov. Although we were not able to test the relationships between Hexamatia gen. nov. and other six-eyed hahniids like Amaloxenops (Lehtinen 1967;Schiapelli & Gerschman de P. 1958), Intihuatana antarctica (Simon, 1902) (Dupérré & Harms 2018) and Scotospilus , clear morphological differences could be observed in somatic and genital characters like body size, coloration, size and shape of RTA and PA, and the presence of MA (see the diagnosis of Hexamatia gen. nov.).
The clade Hahnia 2 formed by H. saccata and H. ngai sp. nov. was found to be closely related to Antistea + Neoantistea in our analyses (Fig. 1a-c), suggesting that these species might be misplaced in Hahnia. However, these and many other Asian hahniids require a broader revision and more comprehensive phylogeny to fully resolve their relations within this family. Therefore, H. ngai sp. nov. and H. saccata remain in Hahnia; in the case of the later, as it was originally described by Zhang et al. (2011).
Eye reduction in the Hahniidae
This phenomenon appears to be rare in hahniid spiders. Most known species of this family have eight eyes; still, some instances of eye reduction have been documented in at least six genera. The modifi cations of eyes range from size reduction of AME and lack of AME, to complete absence of eyes (Lehtinen 1967). The evolution of this phenomenon in this family has never been studied, and the relations of the species with reduced eyes are largely unknown. Even their taxonomy has been constantly a subject of debate (Lehtinen 1967;Schiapelli & Gerschman de P. 1959;Catley 1999;Dupérré & Harms 2018).
Size reduction of the AME (Fig. 7a) is relatively common being observed in several species of the following genera: Alistra Thorell, 1894 (Lehtinen 1967;Forster 1970;Ledoux 2004), Amaloxenops (Schiapelli & Gerschman de P. 1959;Catley 1999;Dupérré & Harms 2018), Hahnia (Lehtinen 1967;Ubick et al. 2005, among others) and Neohahnia Mello-Leitão, 1917(Mello-Leitão 1917Lehtinen 1967;Heimer & Müller 1988). Reduction in number of eyes ( Fig. 7b-d) is much rarer being documented only in a few species: Amaloxenops vianai Schiapelli & Gerschman, 1958(Schiapelli & Gerschman de P. 1958Lehtinen 1967), Hexamatia senaria gen. nov. (Zhang et al. 2011), Hexamatia seekhaow gen. et sp. nov., Intihuatana antarctica (Dupérré & Harms 2018), Scotospilus longus Zhang, Li & Pham, 2013, and two unpublished species documented in a revision of South American hahniids (Catley 1999); a quick examination of the illustrations and descriptions of these species suggest that they are not closely related. Finally, complete lack of eyes ( Fig. 7e-f) has only been reported in the genus Iberina (Fernández-Pérez et al. 2014;Ledoux 2014). This wide range in the degree of eye reduction and broad geographical spread of this phenomenon suggest that eyes are a very plastic character and the loss or reduction might have evolved independently several times within this family. Nevertheless, a more comprehensive phylogeny of the Hahniidae is necessary to test this hypothesis.
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Domain: Biology
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The circadian clock is disrupted in pancreatic cancer
Disruption of the circadian clock is linked to cancer development and progression. Establishing this connection has proven beneficial for understanding cancer pathogenesis, determining prognosis, and uncovering novel therapeutic targets. However, barriers to characterizing the circadian clock in human pancreas and human pancreatic cancer–one of the deadliest malignancies–have hindered an appreciation of its role in this cancer. Here, we employed normalized coefficient of variation (nCV) and clock correlation analysis in human population-level data to determine the functioning of the circadian clock in pancreas cancer and adjacent normal tissue. We found a substantially attenuated clock in the pancreatic cancer tissue. Then we exploited our existing mouse pancreatic transcriptome data to perform an analysis of the human normal and pancreas cancer samples using a machine learning method, cyclic ordering by periodic structure (CYCLOPS). Through CYCLOPS ordering, we confirmed the nCV and clock correlation findings of an intact circadian clock in normal pancreas with robust cycling of several core clock genes. However, in pancreas cancer, there was a loss of rhythmicity of many core clock genes with an inability to effectively order the cancer samples, providing substantive evidence of a dysregulated clock. The implications of clock disruption were further assessed with a Bmal1 knockout pancreas cancer model, which revealed that an arrhythmic clock caused accelerated cancer growth and worse survival, accompanied by chemoresistance and enrichment of key cancer-related pathways. These findings provide strong evidence for clock disruption in human pancreas cancer and demonstrate a link between circadian disruption and pancreas cancer progression.
Introduction
The circadian clock is a conserved molecular feedback loop that regulates many signaling pathways to control metabolism, immunity, apoptosis, and other critical cellular functions in the body [1]. At its core, the positive arm of the clock (i.e., CLOCK and BMAL1 [also known as ARNTL or MOP3]) drives transcription of the negative arm, including PER1-3 and CRY1-2 [2,3]. The negative arm represses the transcriptional activation of CLOCK and BMAL1, and a second interlocked loop involves the nuclear receptors RORα/β/λ and NR1D1-2 (also known as REV-ERBα/β), which activate and suppress BMAL1 expression, respectively. These, along with other core clock components, form a tightly regulated series of transcriptional-translational feedback loops that ensure rhythmic expression over 24 hours and function to maintain cellular and organ homeostasis [2][3][4].
Environmental cues influence the synchronization of circadian rhythms in various organ systems, and misalignment of external cues with the internal clock (e.g., shift work) can cause clock dysfunction with consequent metabolic derangements and pathologic states [4][5][6]. For instance, circadian dysregulation has been strongly linked to obesity and diabetes, both risk factors for cancer [7][8][9][10][11]. Concordantly, landmark studies have shown that disruption of the endogenous clock through mutations in or suppression of the core clock genes is intricately linked to tumor growth in several cancers [12][13][14]. For example, knockout of Bmal1 in Krasand p53-mutant lung cancer causes marked tumor progression in vivo [13], while MYCinduced repression of BMAL1 in human neuroblastoma drives decreased overall survival in a BMAL1-dependent manner [15]. Importantly, targeting dysfunctional clock components in certain cancers has proven an effective treatment strategy [15,16]. Thus, identifying an aberrantly functioning circadian clock in cancer can lead to key advancements such as understanding pathogenesis, prognosis, and uncovering novel therapeutic targets.
Although indeterminate, there is some evidence that the clock may be dysregulated in pancreatic ductal adenocarcinoma (PDAC), leading to a worse prognosis [17,18]; this is alarming for a deadly malignancy where only 11% of patients survive beyond 5 years [19]. To advance our understanding of how clock disruption impacts PDAC pathogenesis, and ultimately foster the identification of therapeutic targets, an essential first step is to establish that clock dysfunction exists in human PDAC. Unfortunately, to date, the cumulative data does not definitively support this assertion and is inconclusive. Prior studies have relied on contrasting expression differences between the core clock genes in tumor compared to normal pancreas as a basis for clock disruption [17,18,20,21]. But differential expression alone is limited and does not provide insight into critical components of clock health such as relative amplitude, rhythmicity, or correlation of expression amongst core clock genes [22]. For example, in the pancreas, phase advancement as a result of chronic jetlag causes differential expression of core clock genes despite maintaining a robust and healthy clock (i.e., nearly identical relative amplitude, rhythmicity, and clock correlation) [23]. Furthermore, in population-level data where sample time acquisition is unknown and the internal clock from individual patient tumors may not be in phase, the interpretation of differential expression for evaluating clock function becomes challenging. Thus, much more substantive data is required to affirm clock disruption in PDAC.
In pre-clinical studies (e.g., mouse or cell-line models), the determinants of clock health can be identified by obtaining longitudinal data under controlled conditions [23,24]. However, in human studies-particularly human pancreas-the requisite periodic data by multiple sampling is not feasible (or ethical). Therefore, alternative means of determining rhythmically expressed genes in humans are necessary. Following the emergence of several bioinformatics tools, the principal aspects of clock health including relative amplitude, core clock gene correlation, and statistical determination of rhythmicity can be resolved when using the appropriate reference data [22,[25][26][27][28]. We recently generated a robust pancreas dataset demonstrating diurnally expressed genes over 48 hours [23]. With this foundation, we were able to apply normalized coefficient of variation (nCV), clock correlation analysis, and cyclic ordering by periodic structure (CYCLOPS) to evaluate the principal aspects of clock function, respectively, and test the hypothesis that the circadian clock is disrupted in human PDAC [22,25,28]. While on the surface this may be construed as a simplistic hypothesis, several limitations have hindered the ability to evaluate the circadian clock in human pancreas, including the lack of human periodic data and an absence of a reference transcriptional dataset.
Here, we employed nCV, clock correlation analysis, and CYCLOPS on publicly available human population-level expression data from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium 3 (CPTAC-3) datasets to determine the health of the circadian clock in PDAC and adjacent normal tissue [29,30]. We identified an intact circadian clock in human normal pancreas with robust cycling of several core clock genes. We also found a markedly weakened clock in the cancer tissue, providing substantive evidence of a dysregulated clock in PDAC. These findings represent significant advancements in evaluating clock function in PDAC, and the potential clinical implications of clock disruption were further assessed with a pre-clinical PDAC model. We used CRISPR/Cas9 technology to selectively target Bmal1 and examined the effects of clock dysfunction in vitro and in vivo. This revealed that loss of clock function caused accelerated cancer growth, diminished survival, enrichment of key cancer-related pathways, and resistance to commonly used cytotoxic chemotherapies for PDAC. These findings provide strong evidence for circadian clock disruption in human PDAC and demonstrate a link between circadian disruption and pancreas cancer progression.
The clock in human pancreatic ductal adenocarcinoma is less robust than in normal pancreatic tissue
To test the hypothesis that the circadian clock is disrupted in human pancreatic ductal adenocarcinoma (PDAC), we used our bioinformatics pipeline that consists of clock correlation, normalized coefficient of variation (nCV), and cyclic ordering by periodic structure (CYCLOPS) (Fig 1). We performed an analysis on publicly available The Cancer Genome Atlas (TCGA) and Clinical Proteomics Tumor Analysis Consortium 3 (CPTAC-3) pancreatic Recently, there have been three separate bioinformatics tools that have substantially improved the ability to determine circadian clock function in population-level transcriptional data [22,[25][26][27][28]. Normalized coefficient of variation (nCV): Clock gene expression produces robust oscillations with the amplitude of the oscillation defined by the difference between peak and trough, and relative amplitude (rAMP) determined by the ratio between amplitude and baseline level of expression (upper left). Clock disruption (such as with clock gene knockout) causes suppressed datasets [29,30]. However, we first needed to ensure the validity of the pipeline in the pancreas since these had not been previously applied to pancreatic tissue. We assessed the clock in our existing murine normal circadian and chronic jetlag pancreas RNA-sequencing (RNA-seq) datasets by examining the clock correlation matrix and nCV [23]. These datasets contained pancreas samples acquired every 4 hours for 48 hours in male (n = 3 at each timepoint for each condition) and female (n = 3 at each timepoint for each condition) mice under standard lighting (normal circadian, n = 72) and chronic jetlag (n = 72) conditions. Chronic jetlag is known to affect the phase of gene expression but not the relative amplitude (rAMP) or the correlation of the core clock genes in the pancreas [23]. Correspondingly, we found that the core clock relationships (as measured by the z-statistic or standard deviations above/below population mean) remained intact on the correlation matrix when compared to the baseline clock correlation matrix for both normal and chronic jetlag conditions (p < 0.001, z-statistic = 54.96 vs. p < 0.001, z-statistic = 54.08) (Fig 2A) [31,32]. Further, the nCV of 11 clock genes remained rAMP, and thus rAMP can be considered a strong determinant of clock function (lower left). Because rAMP is calculated from time-labeled samples, it can only be measured in human population data when sample time acquisition is known (e.g., MetaCycle) or when using programs to predict longitudinal time-course data (e.g., CYCLOPS). To overcome this limitation and assess core clock gene 'robustness' of oscillation in human data (without labeled time), normalized coefficient of variation (nCV) is used. The nCV is the coefficient of variation (CV) of the core clock gene (in the population data) divided by the mean CV of all genes in the dataset. And the CV is determined by the standard deviation of the gene expression divided by its average expression. Thus, when the circadian clock is intact, circadian genes in large population data sets would be expected to have a relatively high nCV due to oscillating gene expression (upper middle). Conversely, with a disrupted clock, the variation of the core clock genes would be diminished (oscillation is suppressed), resulting in a relatively low nCV (lower middle). Importantly, the nCV was found-across multiple cancer types and tissues-to correspond directly to the normalized rAMP (upper right) [25]; it can thus be used confidently as a surrogate for rAMP, which is a key determinant of clock health. Of note, the rAMP (and thus nCV) of core clock genes differs due to differences in amplitude, and so the between tissue difference (e.g., normal vs cancer) for each core clock gene is the value of importance. Clock Correlation: When the circadian clock is intact, there is an expected progression of core clock gene expression, where the positive arm of the clock (e.g., BMAL1, CLOCK) drives transcription of the negative arm of the clock (e.g., PER, CRY). Thus, when BMAL1 expression peaks, CLOCK expression should also be near its peak, and this should be evident in populationlevel data (i.e., can look at the spread of individual expression points across the population). In turn, when BMAL1 expression peaks, negative arm members (e.g., PER1-2) should be at a trough. Concordantly, strong positive correlations (0.5 < ρ < 1, red) should be apparent among transcriptional activators (e.g., BMAL1 and CLOCK) and among transcriptional repressors (e.g., NR1D1 and PER2), and a strong negative correlation (-0.5 > ρ > -1, blue) should be present amongst activators and repressor targets (e.g., BMAL1 and PER2). If this is the case, then the clock is intact; if these correlations are not preserved (i.e., ρ~0), this indicates the clock is disrupted. Using the set of core clock genes and clock-associated genes, the correlation of each gene against the others is determined by Spearman's rho (ρ) and mapped in matrix form. This experimental matrix is then compared to a baseline correlation matrix from the mouse circadian gene atlas using the Mantel test, which compares the correlation between the two matrices to produce a z-statistic (z-stat). A higher z-stat value corresponds to a correlation matrix that is closer to the circadian gene atlas baseline (i.e., highly preserved clock correlation). CYCLOPS: Intrinsic to circadian clock genes is rhythmic expression. CYCLOPS (cyclic ordering by periodic structure) is an algorithm that can identify rhythmic (longitudinal) data from population data where sample time acquisition is unknown. The basis for this type of program is that each sample has a different 'clock time' due to differences in time of sample acquisition and environmental factors such as sleep/wake cycle or shift worker status (e.g., 15-20% of patients are night shift workers). Seed genes that are known to be rhythmic in the tissue of interest (i.e., pancreas) are inputted into CYCLOPS, and the population level data is reduced to two vectors (Eigengene 1 and Eigengene 2) derived from the seed genes. When plotted, the optimal Eigengene pair will demonstrate an ellipse, which indicates that the two Eigengenes are rhythmic and anti-phasic-this pair can then be used for each patient sample to determine the sample 'time', and thus the order of that sample relative to the 24-hr period (phase). By incorporating enough patients, population-level data can be transformed into longitudinal data. Subsequently, with the patient sample dataset ordered by CYCLOPS (by the Eigengene pair), individual genes (e. g., oscillating genes or core clock genes) can be evaluated for rhythmicity. An intact circadian clock is indicated by the ability of CYCLOPS to order the data (statistically) and the identification of rhythmically expressed core clock genes. Meanwhile, a disrupted clock is designated as arrhythmic core clock gene expression or an inability of CYCLOPS to order the data (statistically). Statistically significant rhythmic gene expression is determined by p < 0.05, rAMP > 0.1, fitmean > 16 and goodness of fit (rsq) > 0.1. The fitmean value can be conceptualized as a mean level of expression of that gene across the dataset, and therefore a minimum level of expression (fitmean > 16) is required for rhythmicity cutoff. The goodness of fit of the experimental values by cosinor regression is calculated by R squared (rsq). CYCLOPS reordering is assessed by the Met smooth and Stat err (significant reordering: Met smooth < 1 and Stat err < 0.05). The Met smooth compares the smoothness of the reconstructed circular trajectory versus a linear ordering based on the first principal component, while the Stat err (we designate as p-value) is analogous to the F statistic of a typical nested regression and compares the model fit when a circular rather than linear bottleneck node is used. A full mathematical derivation has been previously outlined by Anafi et al. [28] unchanged (p = 0.76) between the normal circadian (mean nCV (± standard error) = 1.78 (± 0.27)) and chronic jetlag (mean nCV = 1.9 (± 0.28)) conditions (Fig 2B and 2C)-consistent with the murine pancreatic clock being strongly rhythmic and intact, matching precisely what we had found in our prior longitudinal analysis [23].
CYCLOPS was then used to reorder the mouse pancreas samples by their predicted phase, and cluster reordering of samples by CYCLOPS was analyzed for appropriate phase progression compared to the known zeitgeber time (ZT) of sample collection. CYCLOPS reordering can be assessed by the Met smooth and Stat err (significant reordering: Met smooth < 1 and Stat err < 0.05). A full mathematical derivation has been previously outlined by Anafi et al [28]. Notably, CYCLOPS accurately (phase appropriately) reordered both the normal (p < 0.01; Met smooth = 0.90) and the chronic jetlag conditions (p < 0.01; Met smooth = 0.97), validating the pipeline for use in the pancreas (S1A-S1C Fig). Collectively, these results confirmed the ability of nCV, correlation matrix, and CYCLOPS to determine the robustness of the circadian clock in the pancreas.
Considerable data support the tumor suppressor role of the circadian clock and the assertion that circadian disruption is present in human cancer [33]. In human PDAC, the supposition is that the clock may be altered, but supporting data is limited to the relative expression of single genes and is consequently inconclusive [18,21,34]. We therefore applied our pipeline to determine clock health in PDAC using the TCGA and CPTAC-3 pancreatic datasets [22,[26][27][28][29][30]. After processing and batch correction of the matched 50 available normal and 318 available PDAC samples (S2 Fig), we examined the correlation matrix between the core clock genes (CCGs) and found that there was a weaker relationship amongst the CCGs in PDAC compared to normal (z-statistic PDAC = 13.77 vs. z-statistic normal = 22.42) (Fig 2D). Concordantly, we found that there was a clear decrease (p = 0.04) in the nCV between PDAC (mean nCV = 0.69 (± 0.08)) and normal (mean nCV = 1.03 (± 0.13)) (Fig 2E and 2F), indicating a weaker clock in PDAC compared to normal pancreas. We then applied our pancreatic CYCLOPS analysis which significantly reordered the normal samples (p < 0.01; Met smooth = 0.99), indicating an intact clock. This was further reinforced when evaluating the predicted phase of CCGs and clock-controlled genes using CYCLOPS. We found that the phase sequence of normal pancreatic samples was similar to the phase sequence of mouse pancreatic samples (Fig 3A and 3B), with the phase of expression in the human genes equivalent to the mouse genes in 12 out of 17 CCGs and clock-controlled genes and ROR-phased genes largely peaking before E-box phase genes. Based on the CYCLOPS ordering, we analyzed the proportion of genes that were rhythmically expressed in normal pancreas. We found that 4,034/ 18,196 (22.17%) of normal pancreatic genes were rhythmic, (S1 Data). We found that several key CCGs and clock-controlled genes were rhythmically expressed in normal samples on cosinor analysis, including CLOCK, PER1, PER3, NR1D2, RORC, NFIL3, TEF, NR1D1, BHLHE40, and NPAS2 (Fig 4A and S1 Table). The CCGs BMAL1 (p = 0.07, rAMP = 0.8) and CRY1 (p = 0.08, rAMP = 0.48) were nearly rhythmic in the normal samples based on the pre-defined cutoff. Finally, phase set enrichment analysis (PSEA) was used to determine phase-dependent gene enrichment. The top 15 significant pathways were selected for analysis (Fig 4B). Consistent with our prior murine pancreas data, we found that normal pancreas was associated with time-dependent metabolic gene pathway enrichment [23,35]. In aggregate, this data confirms the health of the circadian clock in tumor-adjacent normal pancreatic tissue, which was expected but not previously shown to our knowledge.
Despite the validation steps and success in ordering normal pancreas, CYCLOPS could not reorder PDAC samples (p = 0.43; Met smooth = 0.99), revealing a less robust (or disrupted) clock in PDAC compared to normal. This inability to reorder strongly supports the attenuated circadian clock in PDAC identified with nCV and clock correlation analysis. We selected the best reordering available based on the eigengenes to enable visual representation of the PDAC samples compared to normal pancreas samples. This also enabled us to estimate as best as possible the rAMP of the PDAC samples, to coincide with the visual representation. Consistent with the nCV analysis, we found that rAMP decreased substantially in every core clock gene from normal to tumor (S1 Table), as evidenced by PER1 and PER3 expression (Fig 4A). Collectively, the weaker clock correlation, markedly reduced nCV, and CYCLOPS analysis of human population-level data show that the circadian clock is significantly disrupted in PDAC.
Creation of a circadian dysfunction pancreatic cancer model
After demonstrating that the clock in human PDAC was dysfunctional, we sought to understand the potential clinical implications of clock disruption. Identifying an aberrant clock in other cancers has consistently demonstrated accelerated cancer progression and worse prognosis, while simultaneously revealing novel therapeutic targets [13][14][15][16]. Moreover, there appears to be a putative correlation between suppressed BMAL1 expression and poor prognosis in patients with PDAC [17,18]. Therefore, we hypothesized that disruption of the circadian clock in PDAC would cause accelerated cancer progression. To test this hypothesis, we used a syngeneic Kras-and Trp53-mutant pancreas cancer cell line (KPC) and employed CRISPR/ Cas9 technology to functionally knockout Bmal1 (KPC-BKO) so that we could examine the effects of clock dysfunction in vitro and in vivo [36]. The reason for mutating Bmal1 in the PDAC cells was several-fold: i) KPC cells demonstrated an intact clock (Fig 5D) necessitating core clock gene modulation, ii) BMAL1 is a central transcriptional regulator of the circadian clock machinery and functional knockout of this single gene abolishes circadian function [37,38], iii) suppressed BMAL1 expression has been found in several human cancers, correlating with worse prognosis [13,15,17,18,20,21,39], iv) BMAL1 expression is decreased in tumor compared to normal tissue in human PDAC [18], v) we similarly found a substantially dampened nCV (rAMP) of BMAL1 expression in PDAC compared to normal tissue (PER1, PER3, NR1D1, and BMAL1 displayed the greatest decrease in nCV), and vi) segregating patients with
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PDAC into high and low BMAL1 expression appears to be prognostic for survival outcomes [17,18,21].
Consequently, guide RNAs were designed to target exon 8 of the Bmal1 gene, which resulted in the insertion of adenine on one allele and a 2 base pair deletion on the other between nucleotide 81,074 and 81,075 at amino acid 73 (GRCm38) (Fig 5A). The result of both mutations was a frameshift just upstream of the basic helix loop helix (bHLH) domain known to assist the PAS A domain in heterodimerization with its binding partner CLOCK (Fig 5B) [40,41]. Confirmatory western blotting revealed the presence of protein in wild-type (WT) cells and an absence of protein in Bmal1 knockout (BKO) KPC cells (Fig 5C). To evaluate clock functionality, mRNA was isolated at 4-hour intervals (n = 3 per condition) for 24 hours after cell synchronization, and we performed RT-qPCR for Per1, a core downstream repressor of the positive arm of the molecular clock and a gene that demonstrated rhythmicity in our human normal CYCLOPS analysis (Fig 5D). Rhythmicity was assessed with the meta2d function of the Metacycle package [42]. KPC (WT) cells exhibited an intact and robust circadian clock with a q = 0.0004 and rAMP = 0.11 for Per1. Although still rhythmic, when compared to normal murine pancreas core clock gene function from our prior longitudinal transcriptional dataset (normal pancreas Per1: q = 0.0001, rAMP = 0.394), the relative amplitude was diminished [23]. To further evaluate the functionality of the clock in KPC cells, luciferase activity driven by the Per2 promoter was measured. Luciferase activity in two separate clones was highly rhythmic (Clone 1: q = 5.51E-5, rAMP = 0.44; Clone 2: q = 1.95E-7, rAMP = 0.39) (S3 Fig). Consistent with the comparison of Per1 expression in KPC versus our prior normal murine pancreas dataset, the relative amplitude of Per2 was diminished in the KPC lines compared to normal pancreas tissue (q = 1.39E-7, rAMP = 0.82). Thus, although rAMP was diminished in the murine pancreas cancer cell line compared to normal pancreas tissue, clock function was still intact. Conversely, KPC-BKO cells exhibited no detectable clock function with a q = 0.81 and rAMP = 0.003 for Per1 expression (Fig 5D). In addition, we The human pancreatic cancer tumor clock is dysfunctional relative to the normal pancreas. A. CYCLOPS was used to reorder samples from normal (n = 50) and pancreatic ductal adenocarcinoma (n = 318) TCGA and CPTAC-3 samples. Each point represents an individual patient with order determined by paired eigengenes. Plots from several core clock genes reordered by CYCLOPS in normal tissue as compared to best reordering in PDAC (A) are shownordered from 0 to 2π. Shading around the blue regression line indicates the 95% confidence interval. Relative amplitude (rAMP) for each gene is determined by dividing the amplitude by the fitted expression baseline (fitmean). The fitmean value can be conceptualized as a mean level of expression of that gene across the dataset, and therefore a minimum level of expression (fitmean > 16) is required for rhythmicity cutoff. The goodness of fit of the experimental values by cosinor regression was calculated by R 2 (rsq). Using these collective parameters, a p < 0.05, rAMP > 0.1, goodness of fit (rsq) > 0.1, and fitmean > 16 were considered rhythmic [27]. B. Graphical representation of Phase Set Enrichment Analysis (PSEA) of normal pancreatic samples, ordered by phase of expression.
[URL]004 looked for differences in the cell cycle consequent to clock dysfunction as the two pathways are known to interact [43,44]. For instance, Bieler et al. identified robust synchrony between cell division and circadian cycle in NIH3T3 fibroblasts that contained a Rev-Erba-YFP reporter [43], while Feillet et al. used a similar system (NIH3T3 fibroblasts) to identify a coordinate entrainment in unsynchronized cells as well as cells that were dexamethasone synchronized [44]. Consistent with this known cross-talk between the circadian clock and cell cycle,
Clock dysfunction accelerates pancreatic cancer growth
After establishing the model, we sought to understand how a dysfunctional clock impacted cell growth. KPC and KPC-BKO cells were grown in culture and injected into the right flanks of syngeneic C57BL/6J mice. Tumor growth was measured every 3-4 days for a total of 28 days to understand differences in primary tumor growth. We found that BKO caused an accelerated growth pattern, resulting in higher mean (± standard error) weight tumors at the study conclusion compared to KPC-derived tumors (438.02 ± 48.84 mg vs. 280.11 ± 42.73 mg; p = 0.02) (Fig 6A and 6B). These findings were evaluated and independently confirmed in a second identically created Bmal1 mutant (functional knockout) clone (S4A-S4C Fig). Perhaps more relevant to human PDAC-given the aggressiveness of this cancer-we assessed the effect of clock disruption on survival by implanting KPC and KPC-BKO heterotopic tumors and observing the mice until moribund status or lethality (Fig 6C). On Kaplan Meier log-rank analysis, mice harboring tumors derived from KPC-BKO cells experienced worse survival than mice bearing KPC-derived tumors (median survival: 52 versus 75 days, p = 0.04). Thus, clock dysfunction resulted in accelerated tumor growth in vivo, leading to worse overall survival.
Loss of Bmal1 promotes the enrichment of cell growth pathways
To determine the possible etiologies of clock disruption causing accelerated PDAC progression, we compared the transcriptomic profiles of KPC and KPC-BKO cells. The principal component analysis revealed clear transcriptional profile differences between each condition (Fig 7A). Differential gene expression was quantified with edgeR and of the 15,110 genes, 5,235 (34.65%) were upregulated and 5,113 (33.84%) were downregulated in KPC-BKO compared to KPC cells (Fig 7B and S2 Data) [45]. When we examined the CCGs known to control the circadian cycle, we found that 12/15 examined clock genes were differentially expressed, including Per1, Per2, Per3, Cry1, Cry2, Nr1d1, Nr1d2, Bhlhe40, Bhlhe41, Npas2, Arntl2, and Dbp (All q < 0.05) [40]. We then performed a KEGG pathway analysis to examine for enrichment of pathways as a result of Bmal1 knockout in the PDAC cells (Fig 7C). We found that there was an enrichment of pathways important for cellular adhesion, such as ECM-Receptor Interaction, Cell Adhesion, and Focal Adhesion, as well as several cellular growth pathways including PI3K-AKT Signaling Pathway, MAPK Signaling Pathway, and Rap1 Signaling Pathway. Collectively, these data indicate that clock disruption in the KPC cells results in significant core clock gene changes and a transcriptional shift that alters key cancer promotionrelated pathways such as cellular attachment and proliferation.
Cell survival is increased with the loss of Bmal1 through alterations in multiple cell death pathways
A hallmark of cancer progression is the inhibition of apoptosis [46]. This phenotype is readily apparent in response to chemotherapy. In particular, the mechanism of action of gemcitabine (inhibition of DNA synthesis) and paclitaxel (microtubule stabilization)-backbones in PDAC therapy-is to ultimately induce apoptosis [47,48]. To understand the potential implications of clock disruption for PDAC patients undergoing treatment, we subjected KPC and KPC-BKO cells to 72 hours of chemotherapeutic treatment with either gemcitabine or paclitaxel [49]. We found that the activity of cell death pathways through apoptosis (measured by Caspase 3/7 activity) was blunted in response to both chemotherapeutic agents as a result of clock dysfunction (Fig 8A and 8B). We also assessed cytotoxic cell death, as measured by Dead-Cell Protease activity, and found that clock disruption promoted resistance to cytotoxic cell death induced Volcano plot showing genes that were significantly upregulated (red) and downregulated (blue) on differential gene expression (DGE) analysis (q < 0.05). All significant core clock genes are highlighted C. KEGG analysis was then performed, and the top 9 pathways ordered by significance are shown. For each pathway, the size of each dot corresponds to the number of genes involved in each pathway. by gemcitabine and paclitaxel (Fig 8C and 8D). These findings were also confirmed in a separately evaluated independent Bmal1-mutant clone (S4D-S4G Fig). Prior work has implicated alteration of Trp53 signaling to impact cell survival by suppressed apoptosis [20], but KPC cells (both WT and BKO) are Trp53 mutant (Trp53 R172H ) indicating alternative mechanisms of heightened resistance in the KPC-BKO cells. Gemcitabine resistance in PDAC often occurs due to the downregulation of the channel protein hENT1 (SLC29A1 gene), or deoxycytidine kinase (DCK gene) which activates gemcitabine once inside the cell [50]. However, these were
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only marginally downregulated (1.05-fold, q = 0.04 and 1.08-fold, q = 0.02) due to Bmal1 knockout, and thus unlikely to contribute to the differences seen with clock disruption (Fig 9A). Furthermore, these were arrhythmic based on rhythmicity cut-off values (p < 0.05, rAMP > 0.1, goodness of fit (rsq) > 0.1, and fitmean > 16) when examining our human samples, suggesting a lack of circadian control (Fig 9B and S1 Table. Meanwhile, paclitaxel drug resistance is thought to occur mostly through upregulation of drug efflux transporter proteins (P-glycoprotein also known as multidrug-resistance associated protein), but these ATP-binding cassette transporter proteins (Abcb1a and Abcb1b genes) were instead significantly downregulated (8.2-fold, q < 0.0001 and 2.1-fold, q < 0.0001) in KPC-BKO vs KPC cells (Fig 9C) [48]. Concordantly, the ABCB1 gene was rhythmic with elevated expression in the human normal samples (p = 0.015, rAMP = 0.95, fitmean = 133, rsq = 0.57) compared to the human PDAC samples that showed low levels of expression (mean expression = 22) (Fig 9D and S1 Table). Thus, using this combined data analysis, we found that the commonly described resistance mechanisms for gemcitabine and paclitaxel were not identified, underscoring the complexity of clock disruption induced chemotherapeutic resistance in cancer. Regardless, these data demonstrate that clock dysfunction promotes broad resistance in PDAC including multiple cell death pathways in the setting of two different PDAC backbone agents.
Discussion
Our work herein used matched normal and tumor samples from TCGA and CPTAC-3 to provide the first substantive evidence, to our knowledge, that the circadian clock is disrupted in PDAC while the adjacent normal pancreatic clock is intact. Recently published work by Talamanca et al. also demonstrated an intact circadian clock in human pancreas, albeit pancreas from human donors and not tumor-adjacent pancreas [51]. The authors leveraged transcriptional data from the GTEx consortium and used an algorithm to project phases across tissues in postmortem human donors based on a donor internal phase. They presumed that relative circadian phases are consistent among individuals to identify sex-and age-dependent changes in circadian expression. Although the main metabolic organ of focus was the liver, in the pancreas they identified a relatively strong concordance of rhythmically expressed genes between males and females. However, when comparing pancreatic tissue from young versus old individuals, there was a significant decrease in the percentage of conserved rhythmically expressed genes. Meanwhile, Wucher et al. also used the GTEx consortium data and time of death (as well as the season of death) to determine differentially expressed genes between the day and night and across seasons [52]. With this method, they were able to identify differentially expressed genes between the day and night across tissues, and they found that among circadian clock genes, PER1-3 demonstrated the lowest logFC (night/day) while BMAL1 and NPAS2 showed the highest logFC (night/day). Interestingly, BMAL1 was the single gene with a daynight pattern across the greatest tissue number (33 tissues). Also of note, the authors identified a seasonal variation in gene expression with approximately 5% of the pancreatic transcriptome demonstrating differential expression in at least one season when compared to the other seasons. These results have implications when evaluating population-level data and indicate sex, age, and potentially the time of year are important factors depending on the tissue assessed. To evaluate clock functionality in the pancreas in our study, we examined the clock with three complementary analyses not previously performed in pancreatic tissues: nCV, clock correlation, and CYCLOPS. The nCV was pioneered and validated in several tissues by Wu and colleagues [25]. It is a measurement that is directly related to the relative amplitude (rAMP) of the oscillation and assesses overall and individual clock gene robustness [25]. For example, Wu and colleagues demonstrated that the nCV of core clock genes (CCGs) was consistently
PLOS GENETICS
and significantly diminished in clock-disrupted tissues versus wild-type tissues (e.g., Bmal1 knockout adipocytes vs wild-type adipocytes) and human datasets of tumor compared to matched (adjacent) normal tissue, where the timing of sample collection was unspecified [25]. It is therefore extraordinarily beneficial for understanding the rAMP of the circadian clock genes-a key measure of clock health-in population-level data where sample time acquisition is unknown. In conjunction with nCV, Shilts et al. developed the method of clock correlation to determine the progression of the clock in time-indeterminate datasets [22]. By capitalizing on the concept of co-expression of CCGs (intrinsic to the transcriptional translational feedback loop), Shilts and colleagues were able to computationally discern clock progression in transcriptomic data from numerous human datasets. Importantly, this technique is not dependent on full coverage of the period by samples, providing a beneficial approach for our normal data set which included only 50 samples and did not have phase representation across the 24-hour period. Furthermore, a direct comparison between heatmaps can be made (i.e., murine vs human or tumor vs normal) because each heatmap has the same corresponding color to rho correlation value [22]. Thus, when combining nCV and clock correlation analysis in unordered human samples, the health of the circadian clock in population-level data can be ascertained even when working with low sample numbers (at least 30).
Wu and colleagues applied nCV analysis to several paired tumor-normal datasets [25], while Shilts et al. examined clock correlation in normal tissues and paired tumor-normal datasets [22]. However, we are the first group, to our knowledge, to analyze the pancreas, possibly because our recent publication was the first, to our knowledge, to characterize the diurnal expression of mouse pancreatic genes over 48 hours [23]. Thus, an appropriate mouse reference group had not been published for comparison. In the study by Shilts et al., the human liver clock correlation heatmap (a similarly metabolic organ) demonstrated a weaker clock correlation versus the mouse reference, which they attributed to increased noise in the liver dataset [22]. Therefore, in our human data, we expected to identify an apparent 'diminished health' of the clock (lower nCV and weaker clock correlation) in the human pancreatic tissue versus mouse samples due to significant variation in the normal samples. This variability is contributed by human factors, such as type and timing of diet [53,54], underlying genetic differences, and alterations in the 'normal pancreas' that surrounds the tumor (pancreatic atrophy, fibrosis, inflammation, etc.) [55]. These can modulate the relative amplitude of oscillation or CCG correlation which contributes to significant noise in the data, as compared to the genetically identical, age-matched, and environmentally matched mouse samples [22,28]. Regardless of these limitations, the nCV and clock correlation revealed two key components of a healthy clock in human pancreas, which was an important component of the pipeline.
CYCLOPS is a machine learning method developed by Anafi et al [28], with subsequent elegant studies by Wu et al [26,27]. , to demonstrate how clock gene relationships can be used to infer and reorder samples by their predicted phase of expression to understand circadian biology in population-level data. The authors who developed CYCLOPS recommend roughly 250 samples for a complete phase distribution [28], which is dependent on differences in time of surgery (presume specimens acquired between 6 AM and 6 PM) as well as differences in genetics and environmental factors (e.g., differences in sleep-wake cycle, or shift worker status) [56]. CYCLOPS has been applied to the lung, liver, brain, hepatocellular carcinoma, and skin, but not the pancreas [27,28]. This is possibly due to known challenges associated with CYCLOPS, which includes optimizing the seed gene list for appropriate ordering [28]. We used the modified CYCLOPS approach by Wu et al. and were able to leverage our murine pancreatic longitudinal expression data to generate the seed gene list for use with human data, which increases the signal-to-noise ratio to optimize ordering capability [23,27,28]. Thus, despite the expected variability in human normal pancreas samples, CYCLOPS significantly ordered the samples across the period, with several clock genes, including CLOCK, PER1, PER3, NR1D2, RORC, NFIL3, TEF, NR1D1, BHLHE40, and NPAS2 demonstrating rhythmic expression. While BMAL1 and CRY1 did not pass our predetermined cutoff for rhythmicity in the normal samples, they demonstrated a robust rAMP with near-significant p values likely reflecting limitations with our sample number (i.e., distribution of samples across the period) than true lack of rhythmicity in the normal samples. As evidenced by the apparent gap in phase in the normal data, there was not uniform distribution of normal samples across the 24-hour period, which signifies a limitation of our analysis. Incorporating time-stamped normal samples (the hybrid design) can significantly increase the accuracy of CYCLOPS ordering results, as suggested in Wu et al. [26,27]. Currently, we cannot implement a hybrid experimental design in this study. However, despite these limitations, CYCLOPS statistically ordered the human normal data, and the phase-set enrichment analysis and predicted phase of expression (of most CCGs) aligned well with our prior mouse pancreas transcriptomic data (reference dataset). When combined with nCV and clock correlation, the data clearly demonstrate an intact clock in the human normal pancreas samples, which was an essential premise for evaluating the clock in human PDAC.
We then proceeded to use nCV, clock correlation, and CYCLOPS to provide compelling evidence of circadian clock disruption in human PDAC. While the concept of circadian disruption in PDAC has been posited by others, past studies examining global clock function in human PDAC have been limited by detecting binary differences in overall RNA and protein expression between normal pancreas and PDAC [17,18,20,21]. Relles et al. found that several CCGs demonstrated decreased expression in PDAC compared to benign tumors or normal pancreas [18], while Li et al. found that low Bmal1 expression (compared to 'higher expression') was associated with worse disease-free and overall survival in patients with PDAC [17]. Similar studies have been repeated with concordant findings [20,21]. However, binary comparisons of expression are unlikely to capture the complexities of clock health such as rhythmicity, phase changes, changes in rAMP, or changes in clock progression. With the 318 available PDAC samples, we showed that there was markedly diminished nCV and a much weaker correlation among clock genes in PDAC compared to normal pancreas, depicting a loss of clock health in the cancer tissue. Further, although the inability to reorder PDAC samples may be a limitation of CYCLOPS, it more likely indicates clock dysfunction [57]. In the PDAC samples, there was a sufficient sample number (n = 318) for phase distribution, but the rAMP of nearly all CCGs was markedly decreased (as visualized through best reordering), with many manifesting an arrhythmic pattern. Collectively, our approach to human data (nCV, clock correlation, and CYCLOPS) provided convincing evidence of clock disruption in PDAC.
The main limitation of our current strategy is the inability to discern why there was a loss of circadian signatures in human PDAC population data. While we have shown compelling evidence from the human sample transcriptomic data that circadian disruption is present in PDAC, the population-level data doesn't accurately represent individual patient tumors. The literature in cancer would suggest that cohorts of patients exhibit a differential extent of clock disruption in their tumors (i.e., some patient tumors have an intact clock while others are markedly disrupted) [15,17,18,39]. This is seen in other cancers. For example, in human neuroblastoma, MYC amplification distorts circadian repressors (NR1D1) and circadian activators (RORA and BMAL1) to cause metabolic rewiring that fuels cancer cell growth and promotes poor patient outcomes [15,39]. Consequently, clock function in neuroblastoma can be subcategorized based on low/normal MYC expression (intact clock) versus MYC amplification (clock disrupted). Similarly, in human PDAC, the overall loss of circadian signatures seen in the nCV, clock correlation, and CYCLOPS analysis could be the consequence of i) dampening of the clock to a significant extent in every patient (i.e., global phenomena), ii) differences in the extent to which the clock is disrupted (i.e., clock disrupted vs intact clock), or iii) biological heterogeneity in the PDAC samples surpassing circadian variability. The last consideration seems unlikely given the marked decrease in rAMP (nCV) and clock correlation showing significantly diminished clock health in PDAC. Furthermore, as seen with the CYCLOPS ordered PER1 expression data over the 24-hr period (Fig 3A), enhanced variability did not lead to the apparent arrhythmicity in PDAC. Unfortunately, we are currently unable to discriminate between the first two possibilities and test the hypothesis that patient tumor subgroups harbor differences in clock function. Ideally, we would be able to subcategorize the population of patients instead of evaluating the entire cohort of PDAC patients, given the known molecular heterogeneity in PDAC [58]. For example, there are alterations in different gene programs (e.g., Kras-mutant, TGF-β signaling, G1/S checkpoint signaling, etc.) that determine the molecular subtypes in PDAC such as squamous (including MYC pathway activation), pancreatic progenitor (including programs controlling fatty acid metabolism), immunogenic, and ADEX [58]. Therefore, separating the cohort into subtypes to evaluate clock function could help elucidate whether circadian disruption in human PDAC co-segregates with specific molecular subtypes or if there are alterations in specific gene programs that promote clock disruption. However, the strength in our current evaluation derives from the number of patients included in the analysis-and this is requisite for CYCLOPS analysis (minimum of~200 patients needed for optimal phase distribution and functioning of the algorithm) [28]. Thus, despite batch correction algorithms, the significant heterogeneity in transcriptomic data (e.g., institutional differences in experimental conditions, tumor purity, etc. [59][60][61][62]) results in challenges in interpreting differences in clock function and drawing conclusions from smaller subsets of patients, particularly with algorithms like CYCLOPS [28]. Therefore, the focus of the current work was to establish that clock dysfunction is present in human PDAC. In future work, we will build on this foundation and integrate whole genome sequencing with RNA sequencing from our institutional samples, combined with uniform pathologic evaluation and consistent sequencing conditions to minimize the variability/batch effect and optimize the signal-to-noise ratio. This strategy will enable us to investigate whether specific molecular subtypes of pancreas cancer co-segregate with clock dysfunction.
Our assessment of clock function in the KPC cells would also suggest that clock function is heterogeneous in PDAC, given that the clock was intact in the Kras-and Trp53-mutant pancreas cancer cells and necessitated Bmal1 mutagenesis to generate clock disruption. Further, we assessed the cycling of the Per1 gene and Per2-luciferase in the KPC cells and identified a robust rhythm; by comparison, the human PDAC samples demonstrated arrhythmic PER1 expression. This discrepancy highlights the challenge associated with the use of immortalized cancer cell lines for extrapolating to circadian clock function and cancer-specific distortion of the clock in human patients. These in vitro and in vivo models can be used to evaluate the impact of specific clock alterations, which can also yield information on potential novel therapeutic approaches (e.g., uncovering the mechanism through which Bmal1 knockout causes accelerated tumor growth could lead to therapeutic vulnerabilities). Yet, this type of model system is uniform with respect to underlying PDAC-specific mutations (all lines in our study are derived from the same Kras and Trp53 mutations) and does not recapitulate the unique nature of human PDAC in situ, where individual patient tumors exhibit mutational heterogeneity [58]. Therefore, we did not have a 'representative model' in our immortalized studies of each human PDAC subtype to determine which subtypes may harbor a disrupted clock. Unfortunately, there is currently no methodology to obtain longitudinal data from each individual patient tumor; such an approach would enable an understanding of the circadian clock at the individual level and would help to determine the circumstances under which tumors manifest clock disruption, such as with specific gene program alterations or in the setting of metastatic disease. As noted previously, the strategy of future work to uniformly evaluate subsets of PDAC to determine clock disrupted versus clock intact subtypes can help to address this significant discrepancy.
We generated the novel murine KPC-BKO cell line as a basis to evaluate clock disruption in PDAC. Using our KPC and KPC-BKO cells, we found accelerated tumor growth in our syngeneic in vivo model with Bmal1 functional knockout. Our preference was to use an immunecompetent model given the known impact of the circadian clock on the immune system (another strength of our model) [63,64]. Interestingly, our primary tumor growth results were similar to results by Jiang and colleagues, who used implanted human PDAC cells (BxPC-3 and PANC-1) into immunocompromised mice after shRNA knockdown of Bmal1 [20,21]. However, patients with PDAC ultimately succumb to distant metastatic spread, rather than local tumor growth, so examining the contribution of clock disruption to overall survival was of greater importance [65]. We found that Bmal1 mutation promoted earlier lethality, which has not been shown before. Concordant with the aggressive tumor phenotype, Bmal1 mutation also caused resistance to chemotherapy. While resistance to gemcitabine has been shown [21], we found chemoresistance to two different backbone anti-cancer agents (gemcitabine and paclitaxel), including suppressed apoptosis and cytotoxicity, indicating a more broad resistance to standard PDAC chemotherapy. Although suppressed Bmal1 expression in PDAC has been suggested to modulate Trp53 to promote a tumor suppressor effect, this was unlikely the case in our study considering KPC cells are a Trp53-mutant cell line [20]. Other work indicates the transcription factor YY1 is significantly overexpressed in PDAC and ultimately causes BMAL1 suppression with consequent PDAC progression and resistance to gemcitabine (unclear mechanism of resistance) [21]. Yet, when we examined our human PDAC and human normal samples, we found that YY1 expression was equivalent between the groups (mean expression 101.61 versus 102.87). Turning to other cancers where BMAL1 is suppressed, MYC amplification in neuroblastoma alters BMAL1 mRNA expression through the induction of NR1D1 [39,66]. Notably, this is associated with poor prognosis and is BMAL1 dependent since ectopic expression of BMAL1 inhibits tumor growth. Given the known interaction between MYC and the circadian clock, and the known presence of MYC pathway activation in PDAC (squamous subtype), this will be a focus in future work to evaluate the molecular subtypes of PDAC [15,39,[67][68][69]. However, this mechanism was not apparent at the global population-level since we did not identify upregulation of MYC, MYC-N, or NR1D1 gene expression in human PDAC compared to normal samples. While common resistance mechanisms of gemcitabine and paclitaxel were not reflected in the human PDAC versus human normal data or the KPC versus KPC-BKO cells (e.g., channel proteins), RNA sequencing identified several enriched pathways integral to cancer progression and chemoresistance in the Bmal1 functional knockout cells, such as the PI3K-AKT pathway [70,71]. The PI3K-AKT pathway is inextricably linked to cancer cell proliferation and resistance to apoptosis, indicating a plausible mechanism for inhibition of programmed cell death to multiple agents seen in our study [72,73]. Further, resistance to paclitaxel is associated with the activation of the PI3K-AKT pathway [74], and similar correlations have been identified with gemcitabine resistance [75,76]. However, the mechanism of chemoresistance is quite complex and the etiology for suppressed apoptosis and cytotoxicity in the KPC-BKO line versus the KPC line remains unclear. To add additional complexity (and highlight another limitation), it is currently unresolved whether the effects we have seen with the loss of functional BMAL1 protein are due to clock disruption or an independent role. Furthermore, clock-dependent effects can depend on which arm is perturbed, the positive arm (circadian activators) or negative arm (circadian repressors) of the clock. For example, in recent seminal work by Liu and colleagues, abolishing different arms of the circadian clock resulted in reciprocal effects on the expression of c-Myc and downstream target genes [68], with the knockout of Bmal1 causing increased expression and Cry1/2 knockout causing suppressed expression of c-Myc and c-Myc regulated genes, respectively. Therefore, in future work, we can employ a series of clock-manipulated PDAC cell lines (e.g., Per1/2 double knockout to disrupt the negative arm) to better understand the contributions of clock disruption and various components of the clock to chemoresistance and PDAC progression.
In conclusion, we used a comprehensive approach (nCV, clock correlation, and CYCLOPS) to evaluate the health of the circadian clock in human normal pancreas and demonstrated clock disruption in human PDAC. Additionally, we developed novel cell lines to evaluate the repercussions of clock disruption in PDAC and identified factors associated with poor prognosis (i.e., worse survival, resistance to chemotherapy, and enrichment of cancer-related pathways). While we acknowledge that significant work needs to be done to improve our understanding of the circadian clock in PDAC, we can use the foundation from this study as a basis for future work, where we will disentangle the clock-dependent effects of PDAC and consequently focus therapeutic efforts.
Ethics statement
All animal studies were conducted according to an approved protocol (M005959) by the University of Wisconsin School of Medicine and Public Health (UW SMPH) Institutional Animal Care and Use Committee (IACUC). This committee and approval ensures all animal studies were conducted by internationally-accepted standards.
Mouse care
Male and female C57BL/6J mice were housed in an Assessment and Accreditation of Laboratory Animal Care (AALAC) accredited selective pathogen-free facility (UW Medical Sciences Center) on corncob bedding with chow diet (Mouse diet 9F 5020; PMI Nutrition International) and water ad libitum. All in vivo experiments were performed with age-, cage-, and sexmatched mice under identical lighting conditions. All experiments were performed with mice 6-8 weeks of age, and cages were selected at random for each experiment.
Clock correlation, nCV, and CYCLOPS pipeline
We assessed the overall clock gene correlation and robustness of the clock with the clock correlation matrix and normalized coefficient of variation (nCV)-nCV is known to be correlated with the relative amplitude (rAMP) of oscillating clock genes, indicating the clock robustness as previously described [22,25]. The nCV was calculated for the overall condition with the nCVnet and nCVgene functions [25]. Clock correlation matrices were created using an available shiny app ( [URL]) which compares the correlation of clock components (17 individual clock genes) to a baseline correlation from the circadian atlas using the Mantel test [22,26,27,31,32].
Cyclic ordering by periodic structure (CYCLOPS) [28] was validated for use in the pancreas utilizing our existing murine normal circadian and chronic jetlag pancreas RNA sequencing (RNA-seq) data (Gene Expression Omnibus (GEO) Accession number: GSE165198) [23]. Specifically, the seed genes for use in CYCLOPS were selected by cross-referencing genes found to be rhythmically expressed in our dataset with those genes either rhythmically expressed in the liver (similarly metabolic organ) or those used by Wu et al. when validating CYCLOPS in the skin (S3 Data) [23,26,31,32]. The updated CYCLOPS pipeline by Wu et al. ( [URL]. com/gangwug/Oslops) was then used to reorder our murine pancreas datasets with known sample collection times [26]. Eigengenes were selected with the Oscope package to sharpen CYCLOPS [77]. Clusters with a p < 0.05 (also known as Stat err ) and Met smooth < 1 were considered to be significantly reordered [28]. Rhythmicity of the reordered genes was determined on cosinor analysis with a p < 0.01, rAMP > 0.1, goodness of fit (rsq) > 0.1, and fitmean > 16 [26]. Significantly rhythmic gene phase was then compared to the rhythmic gene phase detected from the known sample time collection using the meta3d function of Metacyle [42]. Clock genes were highlighted to demonstrate a correlation between the predicted and actual phase.
The clock was evaluated in human normal and human PDAC RNA-seq datasets from TCGA and CPTAC-3 [29,30]. After batch correction with ComBat and filtering, 50 matched normal and 318 PDAC samples were obtained for analysis [78]. The pipeline described above was then used to obtain the clock correlation matrix, nCV, and CYCLOPS reordering. Cosinor analysis was performed to test for rhythmicity. Given the additional biologic heterogeneity of the human data, a p < 0.05, rAMP > 0.1, goodness of fit (rsq) > 0.1, and fitmean > 16 were used as a rhythmicity cutoff. Rhythmic genes from normal samples were assessed with phase set enrichment analysis (PSEA) [79]. Rhythmic gene sets ordered by significance were inputted with their calculated phase of expression. Default settings were used for PSEA, including domain 0-24, min item 10, max sims/test 10,000. The gene set enrichment analysis (GSEA) gene ontology (GO) (c5.go.bp.v7.5.1.symbols) set was leveraged as the pathway input. The top 15 significant pathways (q < 0.05) were selected for representation.
Creation of Per2-dLuciferase reporter KPC cell line
KPC cells were stably transfected with a mammalian gene expression vector harboring a destabilized luciferase reporter driven by the Per2 promoter fused to intron 2 of Per2 and a puromycin resistance cassette (VectorBuilder, Santa Clara, CA) [81]. The vector was transfected with lipofectamine 2000 (ThermoFisher Scientific) and incubated for 2 days, and then exposed to media containing 2.5 μg/mL puromycin for 3 days, and surviving clones were selected. Luciferase activity was measured in the selected clones with the luciferase assay system (Promega, Madison, WI) on a BMG CLARIOstar (BMG Labtech, Ortenberg, Germany) plate reader. The two selected clones were then subcloned using a BD FACSAria III after staining with DAPI. Luciferase activity was again measured in the subclones to validate expression before use in downstream experiments.
Clock function testing
To evaluate for clock function, KPC and KPC-BKO cells were synchronized with 200 nM dexamethasone for 2 hours in FBS-free DMEM media, followed by RNA isolation 24 hours after the synchronization using the RNeasy protocol (Qiagen, Hilden, Germany) according to the manufacturer's recommendations [82]. Samples (n = 3 biologic replicates) were collected at 4-hour intervals for 24 hours. Quantitative real-time polymerase chain reaction (RT-qPCR) was performed (in technical triplicate) for the downstream core clock gene (CCG) Per1 (ID: Mm00501813_m1, Life Technologies, Carlsbad, CA) and the housekeeper gene Hprt (ID: Mm03024075_m1) using GoTaq Probe qPCR and RT-qPCR System (Promega, Madison, WI) and Quantstudio 7 flex RT-PCR system (ThermoFisher Scientific). Expression was measured and the ΔCT was calculated. The mean ΔCT values were then tested for rhythmicity using the meta2d function of Metacycle [42]. For Metacycle settings, the min period and max period were set to 24, and "JTK", "LS", and "ARS" were selected for cycMethod. An integrated FDR corrected q value < 0.05 and rAMP > 0.1 were taken as rhythmic as previously described [26]. To separately evaluate clock function in KPC cells, luciferase activity driven by the Per2 promoter was measured. Cells from two independent clones were synchronized with 200 nM dexamethasone for 2 hours in FBS-free DMEM media, and 24 hours later luciferase activity was measured at 4-hour intervals (n = 6 technical replicates per time point). To measure activity, 1x cell culture lysis reagent (Promega) was added and cells were incubated for 5 minutes. Firefly luciferase assay reagent (Promega) was added and luminescence was measured on a BMG CLARIOstar plate reader and tested for rhythmicity using Metacycle.
RNA isolation, sequencing, differential gene expression analysis
To evaluate for transcriptomic differences between wild-type and BKO KPC cells, bulk RNAseq was performed on 6 independent samples collected from each condition. Isolation was carried out as above and quality was tested for an RNA integrity number (RIN) > 7.5 on the Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA). A total of 300 ng of mRNA was enriched with poly-A selection and sequencing on the Illumina HiSeq2500 platform by the University of Wisconsin Biotechnology Sequencing Core. FASTq files were processed with Skewer and genes were filtered to remove those with low expression [83]. Samples were normalized by the method of trimmed mean of M-values [84]. Contrasts were drawn with the edgeR package, with differential expression taken when the FDR q < 0.05 [45]. Pathway testing was performed with the KEGG database (Kyoto Encyclopedia of Genes and Genomes) using previously described methods [85]. The top 500 significant genes were inputted, ordered by q value, and the top 9 significant pathways (where p < 0.05) were plotted for visualization. Pathway dot size is indicative of the number of genes in each pathway. The RNA-seq data is publicly available through GEO (Accession number: GSE213680).
Heterotopic tumor modeling
To create flank tumors, 1 x 10 5 KPC or KPC-BKO cells were injected into the right flanks of immunocompetent C57BL/6J mice obtained from Jackson Laboratory (RRID: IMSR_JAX:000664, Bar Harbor, ME). Cells were mixed in a 1:1 50 μL solution of DMEM media and Matrigel (Corning Inc, Corning, NY). In the tumor growth experiment, a single dose of KPC or KPC-BKO cells was injected into C57BL/6J mice (male: n = 5, female: n = 5, each group) and tumors were measured twice weekly for four weeks starting on day 6 with the caliper method as previously described [86]. Tumor length and width were measured and tumor volume was calculated using the formula: tumor volume = length x width 2 x ½. Tumor weight was also measured (in mg) at the conclusion of the study period. Two independent replicate experiments were performed for tumors derived from KPC cells and KPC-BKO cells (total n = 20 mice per group). Additionally, two separate KPC-BKO clones were tested. Our power analysis, based on prior literature, indicated that using 10 mice per group would have 80% power to detect a 1.5-fold difference (alpha = 0.05) in tumor size (21 days after heterotopic injection) between KPC and KPC-BKO cells [87]. Mean differences between groups were computed using a t-test. In the tumor metastasis/survival experiment, the same dose of KPC or KPC-BKO cells was injected into C57BL/6J mice (n = 7 in each group) and tumors were measured weekly until the mice became moribund or died. Kaplan Meier log-rank analysis was then performed to compare survival differences between conditions with the survival package [88]. A p < 0.05 is taken as significant for both experiments.
Cell cycle analysis
To evaluate the cell cycle of KPC and KPC-BKO cells, cells were grown to confluence, fixed with 70% ethanol, and stained with 50 μg/mL propidium iodide (Catalog: 421301, BioLegend, San Diego, CA) and 100 μg/mL RNAse A (Catalog: FEREN0531, Thermo Fisher). Samples (n = 3 technical replicates, each condition) were sorted with an Attune NxT flow cytometer (Thermo Fisher) and the DNA content was analyzed for the percent of cells in G1, S, and G2 phase with ModFit LT 6.0 software at the University of Wisconsin Carbone Cancer Center Flow Cytometry Laboratory. The average coefficient of variation (CV) of each sample was < 6%. Differences between conditions were tested with a t-test and a p < 0.05 was taken as statistically significant. This experiment was performed independently twice.
Chemotherapeutic resistance evaluation
To evaluate for chemotherapeutic resistance, an equal number of KPC or KPC-BKO cells (3000 each) were grown in 96-well plates for 24 hours. Cells were washed and media containing either vehicle (DMSO, Catalog: D2650-100ML, MilliporeSigma) or increasing concentrations of gemcitabine (dose range = 10 nM to 1μM) (Catalog: G6423-50MG, MilliporeSigma) or paclitaxel (dose range = 10 nM to 1μM) (Catalog: AAJ62734MC, Thermo Fisher) was added to each well. Apoptosis was measured by detecting Caspase 3/7 activity after 24 hours of treatment using the Caspase-Glo 3/7 Assay System (Promega) on a BMG CLARIOstar plate reader. Dead-cell protease activity was similarly measured in cells after 20 hours of treatment using the CytoTox-Glo Cytotoxicity Assay (Promega) on a BMG CLARIOstar plate reader. Mean differences in the fold change between chemotherapy-and vehicle-treated cells were made with a Student's t-test and a p < 0.05 was taken as statistically significant. Each experiment was completed with technical triplicates and independently replicated twice. A representative result is shown for each experiment.
Analysis
All analyses were performed in R version 4.2.0 or Julia version 0.3.12 unless otherwise indicated. Samples were not excluded from the analysis unless explicitly stated. Standard comparisons of the mean were performed with a t-test and survival was assessed with the log-rank test unless explicitly stated. Two independent clones were examined, and luciferase activity was measured at 4-hour intervals (n = 6 per time point). Rhythmicity was calculated with Metacycle. Clone 1 (left) was found to be rhythmic with a q = 5.51E-5 and rAMP = 0.44. Clone 2 (right) was found to be rhythmic with a q = 1.95E-7, rAMP = 0.39.
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Domain: Biology
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Systematic mapping of small nucleolar RNA targets in human cells
Altered expression of box C/D small nucleolar RNAs (snoRNAs) is implicated in human diseases, including cancer. Box C/D snoRNAs canonically direct site-specific, 2’-O-methylation but the extent to which they participate in other functions remains unclear. To identify RNA targets of box C/D snoRNAs in human cells, we applied two techniques based on UV crosslinking, proximity ligation and sequencing of RNA hybrids (CLASH and FLASH). These identified hundreds of novel snoRNA interactions with rRNA, snoRNAs and mRNAs. We developed an informatic pipeline to rigorously call interactions predicted to direct methylation. Multiple snoRNA-rRNA interactions identified were not predicted to direct RNA methylation. These potentially modulate methylation efficiency and/or contribute to folding dynamics. snoRNA-mRNA hybrids included 1,300 interactions between 117 snoRNA families and 940 mRNAs. Human U3 is substantially more abundant than other snoRNAs and represented about 50% of snoRNA-mRNA hybrids. The distribution of U3 interactions across mRNAs also differed from other snoRNAs. Following U3 depletion, mRNAs showing altered abundance were strongly enriched for U3 CLASH targets. Most human snoRNAs are excised from pre-mRNA introns. Enrichment for snoRNA association with branch point regions of introns that contain snoRNA genes was common, suggesting widespread regulation of snoRNA maturation.
INTRODUCTION
The small nucleolar RNAs (snoRNAs) are a class of abundant, small stable RNAs, most of which act as guides for site-specific RNA modification. Most members of the box C/D class of snoRNAs select sites of ribose 2'-O-methylation by extended regions of perfect complementarity with target sites (≥12 bp), in which the nucleotide to be modified is placed exactly 5 bp from the conserved box D or box D' motifs within the snoRNA (reviewed in (Tollervey and Kiss, 1997;Watkins and Bohnsack, 2012)). The box C/D snoRNAs associate with a group of four common proteins, NOP56, NOP58, 15.5K and the methyltransferase Fibrillarin. The snoRNAs have a partially symmetrical structure, in which stem structures bring together the highly conserved, terminal box C (RUGAUGA, R = A or G) and box D (CUGA) sequences and the related, but less conserved, internal box C' and box D' elements. These stem structures include a K-turn structural motif that is bound by the small 15.5K protein. In vitro structural analysis indicated that the box C/D stem is also bound by NOP58, while the box C'/D' stem is bound by the homologous NOP56 protein. Each region is bound by a copy of NOP1, so the regions flanking either box D, box D' or both can function as methylation guides. In human cells, snoRNA-directed methylation sites show variable stoichiometry, indicating regulation that is likely to be functionally important (reviewed in (Henras et al., 2017;Sloan et al., 2017)).
The strict requirement for a long region of perfect complementarity that extends to box D/D' for guide function implies that strong snoRNA base pairing could occur without eliciting target RNA methylation. Indeed, a small number of box C/D snoRNAs have essential functions in ribosome synthesis that require snoRNA/pre-rRNA base pairing without associated rRNA methylation. In vertebrates, these snoRNAs include U3, U14 and U8 (reviewed in (Watkins and Bohnsack, 2012)). In addition, pre-rRNA base-pairing by U13 snoRNA directs formation of N 4 -acetyl cytidine (ac4C) by NAT10, rather than methylation (Sharma et al., 2015;Sharma et al., 2017b).
Mutations in U8/snoRD118 cause the neurological disease leukoencephalopathy with calcification and cysts in humans (Frenk et al., 2014) and a Zebrafish model (Badrock et al., 2020). In addition, snoRNAs can apparently function by direct protein binding: Loss of specific snoRNAs reduced levels of the GTP-bound, active form of K-Ras with consequent hyperactivation of the Ras-ERK1/ERK2 signaling pathway (Siprashvili et al., 2016). snoRNAs were also implicated in activation of the immune regulator Protein Kinase RNA-activated (PKR) under conditions of metabolic stress (Youssef et al., 2015).
Bioinformatics approaches have been used to predict snoRNA binding sites in several systems, particularly where this is associated with methylation (Jorjani et al., 2016;Lowe and Eddy, 1999;Lu et al., 2016;Omer et al., 2000). In addition, a number of recent reports have described methods for the identification of RNA-RNA interactions through proximity ligation followed by
RNase treatment and formaldehyde crosslinking
RNP complexes bound to the beads were treated with 0.5 unit RNaseA+T1 mix (RNace-IT, Stratagene) in 100 μl PBS,2mM MgCl2 buffer for 10 min at 20°C. In CLASH 900 μl GDB denaturing buffer (6M GuCl2, 150mM NaCl, 20mM Tris pH=7.4) was added to the beads with RNase and mixed thoroughly. Supernatant with denatured complexes was removed and added to Ni beads (Gibco) washed in GDB, subsequent binding carried out for 1h at 4C. In FLASH to remove indirect RNA and protein binding from the complexes the beads were washed twice with PBS-WB buffer (PBS, +150mM NaCl, 2 mM MgCl2, 0.4% NP-40), twice with HS-PBS-WB (PBS, 0.3 M NaCl, 2 mM MgCl2, 0.4% NP-40) and once in 1xPBS. The complexes were cross linked on beads in 0.2% formaldehyde in PBS for 3 min, then formaldehyde was quenched by addition of glycine to 0.2M and Tris-HCl pH=8 to 0.1M and incubation for 5 min. Crosslinked complexes were subjected to 4x denaturing washes in UB (20 mM Tris pH=7.4, 8M UREA, 0.3M NaCl, 0.4% NP-40) and additional incubation in UB for 30 min at 4C to remove nonspecific interactions. Subsequent steps were identical for FLASH and CLASH.
In FLASH beads with bound RNA-protein complexes were washed 4x with PNK buffer.
To remove unwanted 3'phosphate groups from bound RNA fragments in both CLASH and FLASH the complexes were treated with TSAP phosphatase (Promega) using provided buffer for 40 min at room temperature. To inactivate the enzyme the beads were washed twice with UB (FLASH) or GDB (CLASH) and 4x with PNK buffer.
Then the 5' phosphorylation and radioactive labelling of RNA was carried out. The complexes on the beads were incubated with 40 units T4 Polynucleotide kinase (New England Biolabs), first with P32 labelled ATP for 45 min, then 20 more min with 1 mM cold ATP, in PNK buffer with RNase inhibitors (RNasin, Promega) at room temperature. The reaction should provide 5' phosphates needed for downstream ligations. The beads then were washed as before twice UB (FLASH) or GDB (CLASH) and 4x PNK buffer.
Linker ligation and RNA-protein complex elution
Protein-bound RNA molecules were ligated together and with 3' linker (1 μM miRCat-33, IDT), overnight using 40 units of T4 RNA ligase 1 (New England Biolabs) in PNK buffer with RNase 6 inhibitors at 16°C. This reaction created RNA hybrids and single RNA molecules ligated to miRCat linker. On the next day, the beads were washed as before 2x UB (FLASH) or GDB (CLASH) and 4x PNK buffer. Then using 40 units of RNA ligase 1, barcoded 5′ linkers (final conc. 5 μM; IDT, one for each sample) were ligated in RNA ligase 1 buffer with 1mM ATP for 3-6 h at 20°C. The beads were washed as before. In CLASH the complexes were eluted in EB (2x NuPage Sample buffer, 400 mM Imidazole, 10mM Tris-HCl pH=7.4, 10mM DTT). In FLASH the complexes were washed off the beads by partial destruction of formaldehyde crosslinking by boiling the samples in NuPAGE protein sample buffer plus 100 mM Tris-HCl, 1%SDS, 100 mM ME (β-mercaptoethanol) for 3 min. The supernatant with RNA-protein complexes was recovered from cooled samples.
SDS-PAGE, and transfer to nitrocellulose
Protein-RNA complexes in NuPAGE SB plus SDS, ME (Life Technologies) were resolved on a 4%-12% Bis-Tris NuPAGE gel (Life Technologies) in NuPAGE SDS MOPS running buffer then they were transferred to nitrocellulose membrane (GE Healthcare, Amersham Hybond ECL) in NuPage transfer buffer (Life Technologies) with 10% methanol for 1 hr at 100V. Depending on the strength of the signal the membrane was exposed on film (Amersham) for 1 hr or overnight at -70C. Developed film was aligned with the membrane and the radioactive bands corresponding to the Fibrillarin-RNA complexes were excised.
Proteinase K Treatment, RNA Isolation and cDNA Library preparation
Cut out bands were incubated with 150 μg of Proteinase K (Roche) and proteinase K buffer (50 mM Tris-HCl pH 7.8, 50 mM NaCl, 0.4% NP-40, 0.5% SDS, 5 mM EDTA) for 2 hr at 55°C. The RNA was extracted with phenol-chloroform-isoamyl alcohol (PCI) mixture and ethanol precipitated overnight with 10 μg Glycogen (Ambion, Life Technologies). The isolated RNA was dissolved in 12 mkL of distilled RNAse-free water and reverse transcribed using miRCat-33 primer (IDT) with Superscript III Reverse Transcriptase (Life Technologies) in its buffer for 1h at 50°C. RNA was then degraded by addition of RNase H (New England Biolabs) for 30 min at 37°C. cDNA was amplified using primers P5 and primer PE_miRCat_PCR and TaKaRa LA Taq polymerase (Takara Bio). PCR products were separated on a 2% MetaPhor agarose (Lonza) gel with SYBRSafe (Life Technologies) in 1 x TBE at +4C. The gel band corresponding to 150-200bp was cut out. cDNA was purified with MinElute Gel Extraction Kit (QIAGEN). Obtained cDNA libraries were sent for high-throughput sequencing.
5' linkers
The fixed barcodes are underlined. N indicates mixed nucleotides for random barcodes.
Analysis of CLASH data
Raw sequences were preprocessed prior to alignment using hyb (Travis et al., 2014) by running the hyb preprocess command with standard parameters. The preprocessed data were aligned to a custom database combining multi-exon transcripts and unspliced genes (with snoRNA genes extended by 20bps in each direction and masked out of the genes in which they are contained where appropriate). The custom database was built using reference data from Ensembl release 77 (www.ensembl.org). To facilitate the analysis of snoRNA/rRNA hybrids, the complete human ribosomal DNA repeating unit ( [URL]13369) was also included 8 in the database. Sequence alignment was performed using the blastall command, using the standard parameters from the hyb pipeline (Travis et al., 2014). The aligned reads were processed using a variant of the hyb pipeline, modified slightly to extract snoRNA hybrids rather than microRNA hybrids preferentially. Hybrids identified using this process were then filtered to exclude sequences that could be aligned as single reads to the human genome (Ensembl release 77) using Novoalign 2.07 (www.novocraft.com) to prevent single reads overlapping gene boundaries from being mistakenly identified as hybrids. Downstream analysis was performed on reproducible hybrids (in which both fragments were found to overlap in two or more hybrids) with a predicted folding energy of -12dG or below. Among these stable, reproducible hybrids, further filters were applied to ensure that hybrids between snoRNAs and other classes of RNA were not mismapped U3 stems, and that hybrids between snoRNAs and RNAs that were not snoRNAs or rRNAs were not mismapped snoRNA-rRNA hybrids. The analysis was performed using the hybtools python package ( [URL]), which was developed for this project. Reference data for the analysis of human methylation sites were obtained from (Krogh et al., 2016) and (Jorjani et al., 2016).
All interactions recovered are listed in Dataset 1.
Analysis of RNA-Seq data
RNA-Seq data were processed using STAR (Dobin et al., 2013) and DESeq2 (Love et al., 2014), using a human genome database from ensemble release 77 (www.ensembl.org).
Systematic mapping of box C/D snoRNA interactions by UV crosslinking
To identify potential novel snoRNA interactions we initially applied the CLASH technique ( Figure 1A) (Helwak et al., 2013;Kudla et al., 2011). This involves UV crosslinking of RNA complexes with tagged proteins in living cells and affinity purification of the RNP complexes under stringent conditions. Ligation of linker adaptors is performed in parallel with internal ligation of captured RNA fragments base paired to each other. RNA is isolated, including RNA hybrids, followed by reverse transcription and high throughput sequencing of cDNA libraries.
CLASH analyses require the use of a "bait" protein fused to a tandem affinity purification tag. For these analyses, we tagged the core box C/D snoRNP proteins Fibrillarin (FBL) or NOP56 with a C-terminal tag consisting of His6 -Precision protease cleavage site -Flag epitope. Tagged constructs were integrated into the chromosome in Flip-in HEK293 cells at a pre-inserted LoxP site. The fusion proteins were expressed under a regulated pCMV-2xTET O2 promoter.
Tetracyclin levels were titrated to achieve expression close to the endogenous protein level, as assessed by western blotting. The tagged proteins were previously shown to be functional in rRNA processing by rescue experiments, in which the endogenous proteins were depleted by RNAi (Knox et al., 2011).
In order to assess robustness of our dataset we performed orthogonal validation, using formaldehyde assisted crosslinking ligation and sequencing of hybrids (FLASH) (Bharathavikru et al., 2017). This approach is similar to CLASH in that RNA-protein interactions are captured by UV crosslinking in growing cells, but antibodies are used for affinity purification of endogenous RNA-protein complexes. During purification, brief formaldehyde crosslinking is used to stabilize binding of the covalent bait protein-RNA complex to the protein A beads, allowing column washes under highly denaturing conditions. Analyses of single hits for FBL and NOP56 showed that, for both proteins, snoRNA sites were most frequently recovered followed by rRNA and then mRNA hits, in both CLASH and FLASH analyses (Supplementary Figure 1), supporting the reliability of the crosslinking approaches.
In human cells we recovered 591,958 hybrids overall (Supplementary Figure 2A; Table S1; Dataset 1). Recovered sequences that could be confidently mapped to two distinct regions of the genome (see Methods) were regarded as representing chimeric cDNAs resulting from RNA-RNA ligation. Non-identical chimeric sequences, or sequences recovered from different analyses, in which both segments overlapped were regarded as demonstrating independent recovery of the same interaction. The recovered RNA sequences were folded in silico, using the ViennaRNA Package 2.0 (Lorenz et al., 2011), to assess whether they arose from a stable RNA-RNA duplex. Interactions supported by at least two independent sequences, with a predicted ∆G of less than -12 Kcal mol -1 , were considered stable and reproducible, and included in further analyses; this was the case for a total of 449,781 hybrids (Supplementary Figure 2A). Among stable, reproducible hybrids, further filters were applied to ensure that hybrids called between snoRNAs and other classes of RNA were not mismapped internal snoRNA stems, and that hybrids between snoRNAs and RNAs that were not snoRNAs or rRNAs were not mismapped snoRNA-rRNA hybrids.
We compared CLASH from the cells expressing tagged Fibrillarin with FLASH from untagged control cells using anti-Fibrillarin antibodies. Strikingly, 97% of stable, reproducible RNA-RNA interactions recovered by CLASH were mapped to sites of interactions also recovered in FLASH: ( Figure 1B). A lower fraction of hybrids recovered by FLASH corresponded to interaction sites also found in CLASH data (66% of hybrids), with 34% FLASH only hybrids ( Figure 1B).
The majority of hybrids mapping to snoRNAs were internal, representing stem structures (Supplementary Figure 2; Table S1). These potentially allow visualization and analysis of snoRNA structures. Among intermolecular snoRNA hybrids, snoRNA-rRNA hybrids were most frequently recovered. From the set of stable, reproducible intermolecular hybrids after filtering, 69% were snoRNA-rRNA interactions, 9% were snoRNA-mRNA interactions, and 17% were snoRNA-snoRNA. It is notable that some highly abundant RNA species were recovered at low To confirm the reliability of both methods we compared snoRNA-rRNA interactions recovered as hybrids in both types of experiments with the position of known rRNA methylation sites ( Figure 1C). Comparing CLASH and FLASH results for snoRNA-rRNA targeting in human HEK cells we noticed that although peak intensities varied to some extent, the same major interactions were recovered with both methods and correlated with known rRNA methylation sites. This was strongly supported by analyses of individual snoRNA interactions e.g. U14, which is known to interact at two positions on the 18S rRNA sequence ( Figure 1D). We conclude that both CLASH and FLASH provide consistent and reliable results. However, the background in FLASH analyses appeared higher than with CLASH, presumably reflecting the lower stringency of purification.
Identification of novel snoRNA-rRNA interaction sites
Modification sites in ribosomal RNAs have been well characterized by a variety of highly sensitive techniques, and it is very likely that all high-efficiency methylation sites have been identified (see (Marchand et al., 2016;Taoka et al., 2016) and references therein). Nonetheless, sub-stoichiometric modifications may have escaped direct identification. If these are mediated by a box C/D snoRNA, a hybrid must form that may be captured by CLASH. We therefore developed robust bioinformatics filters for snoRNA-rRNA interactions, to confidently identify putative novel sites of methylation.
We use the following strict filtering criteria to identify interactions that we can classify with high confidence as being capable of guiding methylation (Figure 2 We recovered 9,363 hybrids passing these criteria and thus potentially able to guide methylation In addition to known methylation sites we recovered novel snoRNA-rRNA interactions that could potentially guide methylation, including an orphan snoRNA SNORD126 (Figure 3; Supplementary Table S2). For SNORD14 (U14) interactions were recovered that would potentially guide methylation at 18S-462 and 18S-83. We conclude that the filters are quite conservative, and should recover only interactions with a high likelihood of representing methylation-guide RNA binding sites.
Due to the strict criteria, many confirmed methylation site interactions were filtered out. In order to ensure that hybrids that did not meet these strict criteria but were associated with methylation site interactions were not included in downstream analyses, we also used a weaker set of criteria to identify potentially methylating hybrids. Hybrids were classified as potentially methylating hybrids if they did not meet the strict methylation criteria, but the nucleotide 5 base pairs upstream of the D or D' box did base pair with the rRNA. Unlike the strict criteria, these weaker criteria did allow G-U base pairs. We recovered 7,971 hybrids passing these criteria, representing 412 interactions, including additional known methylation guide interactions for which no hybrids were found that passed the strict methylation criteria (Table S3).
The hybrids that did not meet the strict or lenient methylation criteria, and whose pre-47S fragment did not overlap with a nucleotide for which the snoRNA included in the hybrid was known to guide methylation, were separated in three further categories: 'ancillary' (Table S4), 'blocking' (Table S5), or 'structural' hybrids (Table S6), depending on whether they potentially assist or interfere with the methylation function of a snoRNA, or contribute to pre-rRNA folding during ribosomal subunit assembly (Figure 2).
Hybrids recovered 100 nt upstream or downstream of a methylation site directed by the same snoRNA, but not overlapping with it, were designated "ancillary" as they could give additional structural support to the guide snoRNA interaction ( Figure S3A; Supplementary Table S1) (van Nues et al., 2011). We recovered 692 ancillary hybrids.
Hybrids formed by snoRNAs at methylation site, that are not predicted to guide methylation but forming at least 22 perfect Watson-Crick pairs within 17 nt upstream or downstream of the site were called "blocking" interactions. In total, 11,028 non-methylating hybrids overlapped with interactions guiding methylation (Supplementary Table S1). Among these interactions, the majority guide methylation at neighboring sites. It is unlikely that closely located sites (less than 20 nt separation) could be methylated. Thus, over-expression of a snoRNA could lead to both increased methylation at its target rRNA site and suppression at neighboring sites. The presence of such overlapping methylation guide interaction sites suggests the need for a precise timing for snoRNA binding and methylation; such an ordered sequence may contribute to the correct folding of the pre-rRNAs and/or aid in avoiding kinetic traps (Huang and Karbstein, 2021;Steitz and Tycowski, 1995). For clarity, these interactions were not included in the "blocking" interactions list. However, 62 high confidence blocking hybrids were identified for snoRNAs that are not predicted to direct methylation at closely located sites ( Figure S3B; Supplementary Table S1). Recent reports have highlighted the variability in methylation efficiency at different sites in the human rRNA (Erales et al., 2017;Krogh et al., 2016;Sharma et al., 2017a;Zhou et al., 2017):, and we speculate that this may partly reflect competition for binding between snoRNA species. For instance, we observed interactions involving the abundant snoRNAs U3 and U8 that could block methylation sites.
All other confidently identified snoRNA-rRNA hybrids were termed "structural" interactions, reflecting potential structural roles in supporting conformational changes and avoiding kinetic traps during pre-ribosome assembly and/or pre-rRNA folding (Huang and Karbstein, 2021;Steitz and Tycowski, 1995). These included the small number of box C/D snoRNAs implicated in ribosome synthesis steps other than rRNA methylation ( Figure S3C): U3, U8, U14 and the acetylation guide U13.
Among novel structural interactions, we found hybrids between 18S rRNA and the 3' region of U8 snoRNA. It was previously reported that the 5' end of U8 snoRNA is critical for 5.8S and 28S rRNA maturation (Peculis, 1997). However, no interactions for the 3' region of U8 were described and the functional significance remains to be established. In Xenopus, the timing of association of the 3' end of 5.8S rRNA and 5' end of 28S was proposed to be regulated by initial binding of U8 at the 5' end of 28S, promote formation of a "bulge" in the 28S sequence. This might act as a "priming site" for base-pairing to 5.8S, leading to the eventual displacement of U8.
We note that a peak of U8 interaction is located at the 5' end of 28S ( Figure 4B), potentially corresponding to this predicted interaction.
These interactions suggest the existence of regulatory loops in snoRNA biogenesis and function, e.g. through possible titration/sequestration or "sponging". In addition, a small number of interactions predicted to guide snoRNA methylation were detected ( Figure S4C), using the same criteria as applied to rRNA (Figure 2).
snoRNA-tRNA interactions
We noted that although tRNAs represented only a small proportion of all snoRNA hybrids (0.2%), they were enriched for interactions that potentially direct methylation. Overall, from 591 reproducible snoRNA-tRNA hybrids, two met the criteria for classification as high confidence methylating hybrids, and 161 met the criteria for classification as potentially methylating hybrids.
Notably, for hybrids between snoRNAs and tRNAs that contain introns, 63% (71 out of 113 reproducible hybrids) were classified as high confidence or potentially directing methylation. In contrast, for hybrids between snoRNAs and tRNAs that do not contain introns, only 19% (92 of 478 reproducible hybrids) meet these criteria (Supplementary Table 1).
snoRNA-mRNA interactions
Perhaps the most interesting class of snoRNA chimeras involved snoRNA-mRNA interactions. It has been proposed that snoRNAs can influence pre-mRNA splicing, processing and stability in mammalian cells; for examples see (Falaleeva et al., 2015;Huang et al., 2017;Kishore and Stamm, 2006;Sharma et al., 2016). It is, however, also possible that snoRNAs might be sponged on abundant mRNAs.
Among reproducible, stable hybrids, 28,120 snoRNA-mRNA hybrids were recovered, representing 1,755 interactions between 149 snoRNA families and 967 mRNAs. To eliminate potential mis-mapping errors we removed all hybrids that were called as snoRNA-mRNA hybrids, but whose mRNA fragment could also be aligned to U3 or to an rRNA sequence (albeit poorly). This filtering step retained 7,209 hybrids involving 117 snoRNAs and 940 mRNAs.
The greatest number of filtered mRNA interactions was observed for U3 (50% of reproducible snoRNA-mRNA hybrids, interacting with 566 different mRNAs), followed by SNORD33 (8% of hybrids, 23 mRNAs), SNORD24 (3% of hybrids, 15 mRNAs), snoU83B (3% of hybrids, 63 mRNAs), and SNORD58 (3% of hybrids, 37 mRNAs). There was a clear correlation for mRNAs between filtered hybrids and single hits (p = 6e −166 ), showing enrichment of mRNA single hits in the regions of snoRNA interactions ( Figure 5B). In contrast, there was little correlation between mRNA expression levels and recovery in snoRNA hybrids (R 2 =0.0057) ( Figure 5C). These data support the conclusion that target mRNAs were specifically recovered and represent bona fide interactions.
To assess whether novel snoRNA-mRNA interactions have the potential to direct RNA methylation, the hybrids were analyzed using the same criteria those applied to rRNA (Figure 2).
This identified a small number of putative methylation guide interactions (Supplementary Figure S7).
Comparison of snoRNA-mRNA interactions revealed distinctly different patterns of targets between U3 and other snoRNAs ( Figure 6A). Interactions with all snoRNAs were recovered in mRNA coding sequences (CDS) and untranslated regions (UTRs), but were substantially enriched in pre-mRNA introns ( Figure 6A). However, more CDS interactions were recovered for U3 than for all other snoRNAs combined. Those U3 interactions that were identified within mRNA introns were predominately not in proximity to splice junctions. Moreover, most mRNA-U3 binding sites presented sharp peaks pointing to highly-specific interactions. The strikingly high number of U3-mRNA interactions suggest a special role for U3 in mammalian gene expression, which might be reflected in the substantially greater abundance of U3 than other human snoRNAs and detection of stable abundant U3-derived fragments.
To study the functional role of U3 interactions we depleted U3 in HEK cells with morpholino oligo and carried out transcriptome analyses of control (mock treated) and U3-depleted cells. RNA sequencing showed that 3 days following U3 depletion, substantially more mRNAs identified as U3 targets in CLASH showed altered levels than non-targets or total genes (Chi Square Test; p = 4e -6 ). U3 target RNAs showed both increased and decreased levels, but 20% of CLASH/FLASH U3 targets showed reduced abundance after U3 depletion (Supplementary Figure S8B). Notably, we observed a correlation between the presence of a snoRNA targeted by U3 in the intron and levels of its host mRNA after U3 depletion, pointing to general rule of interplay between biogenesis of snoRNAs and their host genes.
Initial comparison of the binding sites to the patterns of evolutionary conservation across 23 mammalian species did not identify enrichment for conserved regions in snoRNA binding sites relative to their flanking regions. However, further analysis revealed that the fall in conservation was due to the frequent presence of an exon near the snoRNA binding site. The 3' region of introns that bind snoRNAs was more conserved than, for instance, the 5' region of the same intron not harboring interactions. This observation supports the functional importance of the interactions (Supplementary Figure S6).
Binding is frequently found within introns that host snoRNAs
We noted that a substantial proportion of snoRNA hybrids with intronic regions represented interactions between snoRNAs and their host introns (5% or 220 hybrids), suggesting frequent connections between snoRNA biogenesis and host gene splicing ( Figure 6). Around 16% of all C/D snoRNA-mRNA interactions were represented by peaks towards the 3' ends of introns, in cis or in trans, in the region of potential intron branch point (25 to 50 bp from the 3' SS) ( Figure 6) pointing to the possibility of their involvement in splicing of the target mRNAs.
Intronic snoRNAs frequently formed predicted duplexes with the 3' flanking region that included the intron branch point and/or of the polypyrimidine tract, both of which are important signals for pre-mRNA splicing. For examples see Figure 7. We speculate that these interactions may slow pre-mRNA splicing allowing sufficient time for assembly of snoRNP proteins with the nascent transcript prior to pre-mRNA intron cleavage and degradation.
DISCUSSION
Here we systematically mapped the interactions between box C/D snoRNAs and the human transcriptome by the use of UV crosslinking, followed by generation and sequencing of RNA hybrids. To achieve high specificity and robustness, we used two complementary approaches: CLASH relies on high-affinity purification tags, suitable for extensive washes in denaturing conditions, whereas FLASH utilizes specific antibodies in combination with mild chemical crosslinking with formaldehyde to stabilize the interactions. Comparison of snoRNA hybrids recovered with CLASH and FLASH revealed a high degree of overlap, and we therefore combined these datasets for most analyses.
As expected, we recovered many interactions between the cognate snoRNAs and known or predicted sites of rRNA methylation. It was, however, notable that at many sites of methylation the human rRNA, multiple snoRNAs could be identified with complementary base-pairing that matched the features previously defined as required for 2'-O-methylation; ≥12 bp showing perfect complementarity, with the modified nucleotide positioned 5 bp from a site D motif (Kiss-László et al., 1996). In several cases previously predicted snoRNAs were not recovered, although these may well bind under altered metabolic conditions, in different cells types or during cell differentiation and tumorigenesis. These data underline the surprisingly high degree of plasticity and redundancy in human snoRNA-rRNA interactions. Recent quantitative analyses of human rRNA methylation have identified many sites that show partial methylation suggesting that functionally distinct ribosomes are generated under different growth conditions (Erales et al., 2017;Hebras et al., 2020;Krogh et al., 2016;Sharma et al., 2017a). We also found numerous cases in which snoRNAs were recovered bound to the pre-rRNA close to methylation sites with strong Watson-Crick base-pairing, but in a configuration that is not expected to guide RNA modification. We speculate that these interactions regulate the timing and/or efficiency of rRNA modification by competing with cognate methylation-guide snoRNAs. In addition, they may contribute to pre-rRNA folding dynamics during ribosomal subunit biogenesis, as previously proposed (Huang and Karbstein, 2021;Steitz and Tycowski, 1995). The emergence of snoRNAs with overlapping specificities and overlapping binding sites, indicate that the site-specific regulation of rRNA methylation is both functionally important and complex.
A large number of reproducible snoRNA-mRNA hybrids represented snoRNA-mRNA interactions. Human U3 is 20-50 fold more abundant than most methylation-guide snoRNAs and was responsible for 50% of all filtered snoRNA-mRNA hybrids. These interactions involved a 3' sequence in U3, homologous to a guide region in yeast U3 previously implicated in noncanonical interactions (Dudnakova et al., 2018;Kudla et al., 2011). Following U3 depletion, mRNAs identified as bound by U3 were highly over-represented among mRNAs showing altered abundance. Moreover, U3 target sites were largely located in exons, whereas other snoRNAs predominately target conserved regions of pre-mRNA introns. However, the mechanistic links between U3 interactions and altered mRNA abundance remain unclear. Notably, snoRNA-mRNA interactions did not resemble those formed by miRNAs in structure, distribution or, most likely, in function. The length distribution of the snoRNA fragment of snoRNA-mRNA chimeras showed an average length of around 35 to 40-nt with base pairing region of at least 12nt. This is much longer than expected for miRNA interactions. We also did not observe accumulation of the previously reported, short (≤ 22nt), miRNA-like fragments of snoRNAs in single reads.
In human cells, the majority of snoRNA genes are located within introns in protein coding genes.
It was previously suggested that processing of snoRNAs and splicing of the host gene may be connected (Hirose et al., 2003). Indeed, folding of the snoRNA snoRD86, which is encoded in an intron of the NOP56 gene, acts as sensor in controlling the abundance of this snoRNP core protein (Lykke-Andersen et al., 2018). We observed that intronic regions flanking snoRNAs were frequently recovered in hybrids with the same snoRNA, as well as with other snoRNAs. We suggest that such interactions facilitate coordination between the maturation of snoRNA and splicing of the host gene. Debranched introns are expected to be rapidly degraded by both the 5' exonuclease Xrn2 and 3' exonucleases of the exosome complex. It is therefore important that snoRNA folding and snoRNP assembly precedes intron excision. The observed interactions may coordinate these steps.
Despite the essential roles they play during ribosome biogenesis through their involvement in pre-rRNA modification, processing, and folding remains unclear to what extent box C/D snoRNAs contribute to regulating the homeostasis of other cellular RNAs, including mRNAs. The data on interaction sites reported here may aid the elucidation of non-conventional roles of box C/D snoRNAs and the potential links between altered expression and cell differentiation, embryogenesis or human disease.
Figure S9. Filtering steps applied to hybrids
(A) A schematic representation of the filtering steps applied to different classes of RNA-RNA hybrids. Initial, single hit filtering was applied to all hybrids to filter out miscalled single reads.
Filtering for reproducibility and stability was then applied to all hybrids. Additional U3 filtering was applied to all hybrids between snoRNAs and other biotypes to filter out miscalled U3-U3 intermolecular hybrids. Finally, additional rRNA filtering was applied to all hybrids between snoRNAs and other non-rRNA biotypes, to filter out miscalled snoRNA-rRNA hybrids.
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Domain: Biology
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Virulence of four Steinernema species as a biological control agent in controlling the termite, Coptotermes heimi (Wasmann) (Isoptera: Rhinotermitidae)
Subterranean termites are an ancient group of social insects, broadly spread, known primarily as economically important pests for the destruction of wooden structures and also as agricultural pests. Many of the banned chlorinated hydrocarbon insecticides used to be recommended for the control of termites. Hence, it has become necessary to find alternative measures for termite control in the natural diverse habitats as well as in the cultivated soil to diminish use of these chemicals. Therefore, in the present study, 4 strains of entomopathogenic nematodes (EPN) belong to the genus Steinernema were assessed against Coptotermes heimi (Wasmann). These EPN included Steinernema pakistanense NNRC-AS.04, S. siamkayai NNRC-As.12, S. bifurcatum NNRC-As.65, and S. maqbooli NNRC-As.88. Virulence of all strains was determined at 3 different EPN inocula in plastic containers layered with sand. A significant nematode inoculum effect was detected for all the tested EPN species. NNRC-AS.04 and NNRC-As.65 showed the highest virulence effects of 95 and 100%, respectively at 150 IJs/ml.
Background
Worldwide, termites are a massive dilemma in both urban and agricultural areas, as they are the source for considerable devastation to plants, agricultural crops, wood structures, and account for financial loss. High-quality wood products are often preferred by customers, but physical or biological damages reduce their worth (Uzunovic et al. 2008). The incidence of termites is habitually not eagerly observed as of concealed behavior. They act as decomposers as well as herbivores feeding on a spacious variety of dead, rotten, or fresh plant material (Traniello andLeuthold 2000 andBignell andEggleton 2000). Coptotermes heimi (Wasmann) (Isoptera: Rhinotermitidae) has been reported from urban and agricultural fields of Pakistan as a serious pest Mir 2010 andManzoor et al. 2011). Successful management for termite colonies needs many particular skills depending on the species origin invasion. Knowledge of termite ecology and its identification can help to spot damage and ways of control (Khan et al. 2016). Chemical troubles made a big impact on the agricultural society and drew attention to the use of biocontrol agents as a safe and effective biopesticide alternative. It can be used in many diverse agricultural systems rather than an immediate solution. Therefore, biocontrol should be considered as a long-term research aim. The two important nematode families falling in the group of entomopathogenic nematodes (EPNs) are Steinernematidae Travassos, 1927, andHeterorhabditidae Poinar, 1975, which are considered as one of the most successful examples of biological tools used to control soil dwelling insect pests. They possess virtually all the attributes of an ideal biological control agent. They enter the host through the natural openings such as mouth, anus or spiracles, or sometimes by abrasing the intersegmental membranes of the insect cuticle (e.g., Heterorhabditis spp.) (Grewal et al. 2005), reach the hemocoel, release there the cells of symbiotic bacteria from their intestine, which ultimately results in killing the host within 48 h (Askary 2010;Askary 2012). Because the nematode symbiotic bacterium kills insects, so quickly, there is no intimate host parasite relationship as characteristic for other insect parasitic nematodes (Shapiro-Ilan et al. 2012).
The aim of this research was to assess the virulence of EPNs, Steinernema species against C. heimi, under laboratory conditions.
Target pest
Alive colonies of C. heimi were collected from infested fallen wooden logs of Mangifera indica (Sapindales: Anacardiaceae) from main campus University of Karachi, Karachi, Pakistan (24.9418°N, 67.1207°E) and identified with a help of the key of (Akhtar 1983). The termite colonies were bought to the culturing room of National Nematological Research Center (NNRC), University of Karachi (Uok), Karachi, Pakistan and maintained in 1000 ml plastic containers with wooden logs at 28°C and 75-80% RH till the experiment was executed.
Virulence assay
Active C. heimi termite individuals were collected from a rearing container for virulence assay of 4 EPN species in a plastic container (280 × 160 × 80 mm) separately for each nematode species and concentration. Containers were layered with 45-g sterilized moist soil. Twenty termites were added in each container exposed to 3 different numbers of nematodes viz., 50, 100, and 150 IJs/ml in 2.5 ml distilled water suspension covered with a plastic lid. Concentrations were dropped evenly in containers by a 1000 μl pipette, to evade mingle sterile pipette tips that were changed after each conduct. Simple distilled water 2.5 ml was dispensed in a control treatment. Mortality rate was calculated after 48 h of exposure and the containers were kept at 28 ± 2°C. Dead termites were transferred in a plastic cavity block (4.5 × 4.5 cm) layered with moist filter paper disk (Whattmann No. 1) to record a nematode emergence. Experiment was carried out twice with 3 replicates at each concentration and EPN species.
Statistical analysis
Data were subjected to analysis of variance in SAS (ver. 9.1, SAS Institute, Cary, NC). If the interaction in EPN species and numbers was significant, it was used to explain results. If the interaction was non-significant (P < 0.05), means were separated with DMRT Duncan's multiple range test (Duncan 1955). Lethal concentration 50 and 90% (LC 50 and LC 90 ) values, intercept, and chisquare values were analyzed by PROC PROBIT routine of SAS, 2000. Abbott (1925) formula was used to correct mortality percentages as follows.
Results and discussion
The comparative virulence assay of the 4 EPN species against C. heimi termite was investigated plastic container layered with 45-g sterilized moist soil at 28 ± 2°C in laboratory of NNRC conditions. The analysis of variance showed significant differences among nematode species efficacy on termites (ANOVA F = 201.5; df = 3; P = 0.05). Nematode inocula also differed significantly (ANOVA F = 6.6; df = 3; P = 0.05) and interaction of the 3 inocula with 4 nematode species, also had marked effect on the pest (ANOVA F = 0.8; df = 3; P = 0.05). Results demonstrated that the nematode could suppress the populations of C. heimi termite. S. pakistanense NNRC-AS.04 and S. bifurcatum NNRC-As.65 showed higher effects at all application concentrations than S. siamkayai NNRC-As.12 and S. maqbooli NNRC-As.88. The highest mortality rate was achieved when EPNs were applied at the concentrations of 150 IJs/ ml after 48 h (Fig. 1). At the lowest concentration of 50 IJs/ml after 48 h of exposure time, at least 50% of termite were killed by S. pakistanense NNRC-AS.04, showing significant differences with S. siamkayai NNRC-As.12 30% and S. maqbooli NNRC-As.88 25%. Concentrations had a great impact on the efficacy of species of nematode (Trdan et al. 2009). The increased mortality of termite caused was concentration dependent. At 150 IJs/ml, the highest mortality rate was induced by NNRC-AS.04 (95 and 100%) by NNRC-As.65, while the lowest one was (62%) by NNRC-As.88. Nematode progenies can reproduce in termites, they were clearly seen when dead termites were transferred to vacant cavity block. The LC 50 , LC 90 values with P value are shown in Table 1. The overall result showed that S. pakistanense and S. bifurcatum were highly virulent against the target subterranean termite, C. heimi 48 h after application. If nematode reproduction can occur in the target insect, long-term management might be achievable. Similar results of maximum mortality response against termite species Macrotermes in a sand and filter paper assay caused by S. pakistanense were also stated by Shahina and Tabassum (2010). Razia and Sivaramakrishnan (2016) evaluated 3 species of EPN, S. siamkayai, S. pakistanense, and H. indica against subterranean termites, Reticulitermes flavipes and Odontotermis hornei under laboratory conditions. In sand assay method, S. pakistanense showed significant results, causing 100% mortality of both pests within 24 h, followed by S. siamkayai and Heterorhabditis indica, which were applied at 250 IJs/ml at 48 h. Wilson-Rich et al. (2007) reported that Zootermopsis angusticollis termite of wet timber showed a susceptibility to concentration-dependent by S. carpocapsae (Mexican strain). Under field conditions, few research studies have been accomplished, using EPNs as a biocontrol agent for termites (Dolinski and Lacey 2007). For the pathogenicity against the termite, Macrotermes bellicosus one strain of H. indica and 29 strains of H. sonorensis (Beninese) were tested by Zadji et al. (2014) and reported that 73% of the isolates parasitized more than 80% of the termite and was influenced by a grouping of biotic and abiotic factors, nematode strain and species. Divya and Sankar (2009) reported 50% mortality of the termite species, Odontotermes obesus after 36 h of post H. indica application. Termites, strained by chemical and pathogen sub-lethal doses, were more vulnerable by EPNs (San-blass and Gowen 2008). Termites exist and feed in environment that are cool, damp, and with no direct sunlight such as wood materials or soil. These ecological surroundings are perfect for the endurance and association of EPNs, providing the source for the concerned pest management. Investigations suggested that the nematodes are effective to control termite colonies as environmentally secure approach (Askari et al. 2012). During the last 30 years of research, EPNs gain common acceptance, and their commercial applications are being developed as environmentally alternatives to chemical pesticides further noticed in decision of nematodes as better application methods against subterranean termites (Khan et al. 2016). In the fields, species of EPNs that are noted as heat tolerant or environmental stress are requisite for management of termites.
Conclusion
Obtained results are evidence that EPNs can be efficient tactics to control the termites but further studies under field conditions are required.\===
Domain: Biology. The above document has 2 sentences that start with 'If the interaction',
2 sentences that end with '(Khan et al',
2 sentences that end with 'species against C',
2 sentences that end with '= 3; P = 0.05)'. It has approximately 1560 words, 107 sentences, and 15 paragraph(s).
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The voltage and spiking responses of subthreshold resonant neurons to structured and fluctuating inputs: persistence and loss of resonance and variability
We systematically investigate the response of neurons to oscillatory currents and synaptic-like inputs and we extend our investigation to non-structured synaptic-like spiking inputs with more realistic distributions of presynaptic spike times. We use two types of chirp-like inputs consisting of (i) a sequence of cycles with discretely increasing frequencies over time, and (ii) a sequence having the same cycles arranged in an arbitrary order. We develop and use a number of frequency-dependent voltage response metrics to capture the different aspects of the voltage response, including the standard impedance (Z) and the peak-to-trough amplitude envelope (VENV\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_{\text {ENV}}$$\end{document}) profiles. We show that Z-resonant cells (cells that exhibit subthreshold resonance in response to sinusoidal inputs) also show VENV\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ V_{\text {ENV}} $$\end{document}-resonance in response to sinusoidal inputs, but generally do not (or do it very mildly) in response to square-wave and synaptic-like inputs. In the latter cases the resonant response using Z is not predictive of the preferred frequencies at which the neurons spike when the input amplitude is increased above subthreshold levels. We also show that responses to conductance-based synaptic-like inputs are attenuated as compared to the response to current-based synaptic-like inputs, thus providing an explanation to previous experimental results. These response patterns were strongly dependent on the intrinsic properties of the participating neurons, in particular whether the unperturbed Z-resonant cells had a stable node or a focus. In addition, we show that variability emerges in response to chirp-like inputs with arbitrarily ordered patterns where all signals (trials) in a given protocol have the same frequency content and the only source of uncertainty is the subset of all possible permutations of cycles chosen for a given protocol. This variability is the result of the multiple different ways in which the autonomous transient dynamics is activated across cycles in each signal (different cycle orderings) and across trials. We extend our results to include high-rate Poisson distributed current- and conductance-based synaptic inputs and compare them with similar results using additive Gaussian white noise. We show that the responses to both Poisson-distributed synaptic inputs are attenuated with respect to the responses to Gaussian white noise. For cells that exhibit oscillatory responses to Gaussian white noise (band-pass filters), the response to conductance-based synaptic inputs are low-pass filters, while the response to current-based synaptic inputs may remain band-pass filters, consistent with experimental findings. Our results shed light on the mechanisms of communication of oscillatory activity among neurons in a network via subthreshold oscillations and resonance and the generation of network resonance.
; Baroni et al. 2014;Khosrovani et al. 2007;Bilkey and Heinemann 1999;Manis et al. 1999;Chapman and Lacaille 1999;Bracci et al. 2003;Golomb et al. 2007;Surmeier et al. 2005;Wilson and Callaway 2000;Einstein et al. 2017;Amir et al. 2002). In brain areas such as the entorhinal cortex, the hippocampus and the olfactory bulb, the frequency of the STOs is correlated with the frequency of the networks of which they are part (Alonso and Llinás 1989;Klink and Alonso 1993;Giocomo et al. 2007;Cobb et al. 1995;Colgin 2013;Chapman and Lacaille 1999;Desmaisons et al. 1999;Balu et al. 2004;Kay et al. 2008;Li and Cleland 2017), thus suggesting STOs play a role in the generation of network rhythms (Desmaisons et al. 1999;Brea et al. 2009;Wang 2010), the communication of information across neurons in a network via timing mechanisms (Izhikevich 2002;Stiefel et al. 2010;Dwyer et al. 2012;Lampl and Yarom 1993), cross-frequency coupling in neurons where STOs are interspersed with spikes (mixed-mode oscillations; MMOs) (Bragin et al. 1995;Chrobak and Buzsáki 1998;Colgin et al. 2009;Gireesh and Plenz 2008;Axmacher et al. 2006;Jensen and Colgin 2007;Gloveli et al. 2005;Belluscio et al. 2012), and the encoding of information (Hinzer and Longtin 1996;Burgess et al. 2011) and sensory processing (Einstein et al. 2017). STOs can be generated by cellular intrinsic or network mechanisms. In the first case, STOs result from the interplay of ionic currents that provide positive and slower negative effects (e.g., (Dickson et al. 2000a, b;Rotstein et al. 2006;). (Examples of the former are the persistent sodium and calcium activation and examples of the latter are h-type hyperpolarization-activated mixed sodiumpotassium, M-type slow potassium and calcium inactivation.) In the second case, STOs are generated in networks, but the individual cells cannot robustly oscillate when isolated (e.g., (Manor et al. 1997;Chorev et al. 2007;Loewenstein et al. 2001)).
The communication of oscillatory information among neurons in a network and across brain areas requires the generation of spiking patterns that are correlated with the underlying STOs (e.g., MMOs where spikes occur at the peak of the STO or at a consistent phase referred to this peak). It also requires the system to be able to respond to external inputs in such a way as to preserve the oscillatory information. Studies on the latter are typically carried out by using sinusoidal inputs. Subthreshold (membrane potential) resonance (MPR) refers to the ability of a system to exhibit a peak in their voltage amplitude response to oscillatory inputs currents at a preferred (resonant) frequency (Hutcheon and Yarom 2000;Richardson et al. 2003;Rotstein and Nadim 2014;Rotstein 2015) (in voltage-clamp, the input is voltage and the output is current). MPR has been investigated in many neuron types both experimentally and theoretically (Pena et al. 2018;Hutcheon and Yarom 2000;Richardson et al. 2003;Lampl and Yarom 1997;Llinás and Yarom 1986;Gutfreund et al. 1995;Erchova et al. 2004;Schreiber et al. 2004;Hutcheon et al. 1996a;Gastrein et al. 2011;Hu et al. 2002Hu et al. , 2009Johnston 2007, 2008;Marcelin et al. 2009;D'Angelo et al. 2009;Pike et al. 2000;Tseng and Nadim 2010;Tohidi and Nadim 2009;Solinas et al. 2007;Wu et al. 2001;Muresan and Savin 2007;Heys et al. 2010Heys et al. , 2012Zemankovics et al. 2010;Nolan et al. 2007;Engel et al. 2008;Boehlen et al. 2010Boehlen et al. , 2013Narayanan 2012, 2014;Fox et al. 2017;Chen et al. 2016;Beatty et al. 2015;Song et al. 2016;Art et al. 1986;Remme et al. 2014;Higgs and Spain 2009;Yang et al. 2009;Mikiel-Hunter et al. 2016;Rau et al. 2015;Sciamanna and Wilson 2011;D'angelo et al. 2001;Lau and Zochowski 2011;van Brederode and Berger 2008;Rotstein and Nadim 2014;Rotstein 2014Rotstein , 2015Szucs et al. 2017) and it has been shown to have functional implications for the generation of network oscillations (Chen et al. 2016;Bel and Rotstein 2019).
The choice of sinusoidal inputs is based on the fact that for linear systems they can be used to reconstruct the response to arbitrary time-dependent inputs, and relatively good approximations can be obtained for mildly nonlinear systems. However, although neurons may be subject to oscillatory modulated inputs, the communication between neurons in a network occurs via synaptic connections whose waveforms are significantly different from pure sinusoidal functions. Synaptic inputs such as these associated with AMPA or GABA A synaptic currents rise very fast (almost instantaneously) and then decay exponentially on a slower time scale. In contrast to sinusoidal inputs, the rise and decay of the periodic synaptic inputs occur over a small portion of the input periods for the smaller input frequencies. The gradual variation of the sinusoidal inputs causes the voltage response to reach the stationary regime after a very small number of cycles, while the abrupt changes in the synaptic inputs over a small time interval sequentially activate the autonomous transient dynamics at every cycle, and therefore is expected to produce different response patterns than these for sinusoidal inputs (Pena and Rotstein 2021).
The main goal of this paper is to understand whether and under what conditions the presence of MPR in a neuron is predictive of the preferred frequency at which the neuron will spike in response to periodic presynaptic inputs when the input amplitude is increased above subthreshold levels.
From a dynamical systems perspective, single-cell sustained STOs can be in the limit cycle regime (robust to noise, driven by DC inputs) or in the fluctuation-driven regime (vanishing or decaying to an equilibrium in the absence of noise). Noisy STOs in the limit cycle regime reflect the stationary dynamics of the system in the absence of noise. In contrast, fluctuation-driven STOs reflect the autonomous transient dynamics (the transient dynamics of the underlying unperturbed system) (Pena and Rotstein 2021). The effects of the autonomous transient dynamics are captured by the system's response to abrupt changes in constant inputs (Pena and Rotstein 2021). There, the values of the voltage and other variables at the end of a constant input regime become the initial conditions for the new one. By repeated activation of the autonomous transient dynamics, piecewise constant inputs with short-duration pieces and arbitrarily distributed amplitudes (not necessarily randomly distributed) are able to produce oscillatory responses (Pena and Rotstein 2021). Noise-driven oscillations are a limiting case of this mechanism where the input's constant pieces have randomly distributed amplitudes and their durations approach zero. If the amplitudes are normally distributed, these piecewise constant inputs provide an approximation to Gaussian white noise (Allen et al. 1998). Roughly speaking, each "kick" to the system by the noisy input operates effectively as an abrupt change of initial conditions to which the system responds by activating the transient time scales, and the voltage and other state variables evolve according to the vector field away from equilibrium (or stationary state). For example, noise-driven STOs (White et al. 1998;Rotstein et al. 2006;Chow and White 1996;Pena and Rotstein 2021) can be generated when damped oscillations are amplified by noise, and this may extend to situations where the noiseless system exhibits overdamped oscillations (overshoots) (Pena and Rotstein 2021).
Along these lines of the previous discussion, a series of dynamic clamp experiments (Fernandez and White 2008) using artificially generated synaptic conductances and currents driven by high-rate presynaptic Poisson spike trains showed that medial entorhinal cortex layer II stellate cells (SCs) are able to generate STOs in response to current-based synapses, but not in response to conductance-based synaptic currents. SCs are a prototypical example of an intrinsic fluctuation-driven STO neuron (Dickson et al. 2000a, b;Dorval and White 2005;Rotstein et al. 2006) and resonator (Schreiber et al. 2004). In the response to current-based synaptic inputs, the STOs have similar frequencies and amplitudes as the spontaneous STOs (Dickson et al. 2000a, b) and the resonant responses to sinusoidal inputs (Schreiber et al. 2004). In response to conductance-based synaptic currents, STOs may still be present, but highly attenuated as compared to current-based synaptic inputs. Similar results were found in Kispersky et al. (2012) for hippocampal CA1 OLM (oriens lacunosum-moleculare) cells and in Farries and Wilson (2012a, b) for phase-response curves in subthalamic neurons.
This raises a seeming contradiction between the ability of the impedance (Z -) profile (curve of the voltage Vresponse normalized by the amplitude of the oscillatory inputs as a function of the input frequency) to predict the existence of STOs for arbitrary time-dependent inputs, in particular Gaussian white noise, and the absence of STOs for conductance-based synaptic inputs in response to Poissondistributed spike trains whose effect on the target cells have been approximated by Gaussian white noise (Brunel 2000;Amit and Tsodyks 1991;Tuckwell 1989Tuckwell , 1988Amit and Brunel 1997;Brunel et al. 2001). This can be partially explained by the fact that synaptic currents "add linearity" to the system, but fluctuation-driven STOs can be generated in linear systems and therefore one could expect only changes in amplitude and frequency. Another possible explanation is that while the Z -profile is independent of the input waveform, the voltage response power spectral density (PSD) is not and depends on the current input waveform. The expectation that the PSD be similar for current-based Gaussian white noise and Poisson-driven current-/conductance-based synaptic inputs would assume similarity between the different input types.
In this paper, we systematically address these issues in a broader context. Our results shed light on the mechanisms of communication of oscillatory activity among neurons in a network via subthreshold oscillations and resonance and the generation of suprathreshold and network resonance.
Models
In this paper, we use relatively simple biophysically plausible models describing the subthreshold dynamics of individual neurons subject to both additive and multiplicative inputs.
Linear model: additive input current
For the individual neurons we use the following linearized biophysical (conductance-based) model where v (mV) is the membrane potential relative to the voltage coordinate of the fixed-point (equilibrium potential) of the original model, w (mV) is the recovery (gating) variable relative to the gating variable coordinate of the fixed-point of the original model normalized by the derivative of the corresponding activation curve, C ( μF/cm 2 ) is the specific membrane capacitance, g L (mS/cm 2 ) is the linearized leak conductance, g 1 (mS/cm 2 ) is the linearized ionic conductance, τ 1 (ms) is the linearized gating variable time constant and I in (t) (μA/cm 2 ) is the time-dependent input current. In this paper we consider resonant gating variables (g 1 > 0; providing a negative feedback effect). We refer the reader to Richardson et al. (2003); Rotstein and Nadim (2014) for details of the description of the linearization process for conductance-based models.
Conductance-based synaptic input model: multiplicative input
To account for the effects of conductance-based synaptic inputs we extend the model (1)-(2) to include a synaptic current and G syn (mS/cm 2 ) is the maximal synaptic conductance, E syn (mV) is the synaptic reversal potential (E ex for excitatory inputs and E in for inhibitory inputs) and S in (t) is the time-dependent synaptic input.
I Nap + I h conductance-based model
To test our ideas in a more realistic model we will use the following conductance-based model combining a fast amplifying gating variable associated to the persistent sodium current I Nap and a slower resonant gating variable associated to the hyperpolarization-activated mixed cation (h-) current I h . The model equations for the subthreshold dynamics read where and This model describes the onset of spikes, but not the spiking dynamics (Rotstein et al. 2006). Spikes are added by including a voltage threshold (indicating the occurrence of a spike after its onset) and reset values V rst and r rst for the participating variables.
Input functions: periodic inputs and realistic waveforms
The input functions I in (t) and S in we use in this paper have the general form
Chirp-like input functions: increasingly ordered frequencies
We will use the three chirp-like input functions F(t) shown in Fig. 1-a. The sinusoidal chirp-like function ( Fig. 1-a1) consists of a sequence of input cycles with discretely increasing frequencies over time ( Fig. 1-a4). We use integer frequencies in the range 1 − 100 Hz. These chirp-like functions are a modification of the standard chirp function (Hutcheon et al. 1996a) where the frequency of the sinusoidal input increases (or decreases) continuously with time (Hutcheon et al. 1996a). Sinusoidal inputs of a single frequency and sinusoidal chirps with monotonically and continuously increasing (or decreasing) frequencies with time have been widely used to investigate the resonant properties of neurons both in vitro and in vivo (Hutcheon et al. 1996a;Stark et al. 2013;Tseng and Nadim 2010). The square-wave ( Fig. 1-a2) and synaptic-like ( Fig. 1-a3) chirp-like functions are constructed in the same manner as the sinusoidal one by substituting the sinusoidal functions by square waves (duty cycle = 0.5) and exponentially decreasing functions with a characteristic decay time τ Dec , respectively. We refer to them as sinusoidal, square-wave, and synapticlike inputs or chirps, respectively (and we drop the "chirplike").
The discretely changing chirp-like functions we use here are a compromise between tractability and the ability to incorporate multiple frequencies in the same input signal. They combine the notion of input frequency with the notion of transition between different frequencies in the same signal. Also, note that the square-wave inputs are an intermediate between sinusoidal and synaptic-like inputs in the sense that square-wave inputs have abrupt transitions as synaptic-like inputs but are closer in shape to the sinusoidal input that is changed gradually.
Chirp-like input functions: arbitrarily ordered frequencies
To examine the variability of the cell's response to the chirp signals described above and to capture the fact that information does not necessarily arrive in a regularly ordered manner, we will use modified versions of these chirp inputs where the cycles are rearranged in an arbitrary order ( Fig. 1-b). The regularly ( Fig. 1-a4) and arbitrarily ( Fig. 1-b4) ordered input signals have exactly the same cycles (one cycle for each frequency value within the considered range) and therefore the same frequency content.
Poisson distributed spikes and white Gaussian noise
To test the oscillatory responses to more realistic inputs we use spike-trains with distributed spikes following a homogeneous Poisson process with rate ν. Each input spike evokes a synaptic-like input function as described above. In addition, we use an additive Gaussian noise current I noise = √ 2Dη(t) where η(t) is a Gaussian white noise input with zero mean and unit variance (I in (t) has zero mean and variance 2D). Unless stated otherwise, ν = 1000 Hz and D = 1 (additional information is provided in the figure captions).
Impedance (amplitude) profile
The impedance (amplitude) profile is defined as the magnitude of the ratio of the output (voltage) and input (current) Fourier transforms where In practice, we use the Fast Fourier Transform algorithm (FFT) to compute F{x(t)}.
Note that Z ( f ) is typically used as the complex impedance, a quantity that has amplitude and phase. For simplicity, here we used the notation Z ( f ) for the impedance amplitude.
Voltage and impedance (amplitude) envelope profiles
The upper and lower envelope profiles V +/− E N V are curves joining the peaks and troughs of the steady state voltage response as a function of the input frequency f . The envelope impedance profile is defined as Rotstein (2014Rotstein ( , 2015 Z where A in is the input amplitude. For linear systems,
Voltage power spectral density
In the frequency-domain, we compute the power spectral density (PSD) of the voltage as the absolute value of its Fourier transform F{v(t)}. We will refer to this measure as PSD or V PSD .
Firing rate (suprathreshold) response
We compute the firing rate response of a neuron by counting the number of spikes fired within an interval of length T and normalizing by T where the neural function x is given by and t i are the spike times within the considered interval.
Numerical simulations
We used the modified Euler method (Runge-Kutta, order 2) (Burden and Faires 1980) with step size t = 0.01 ms. All neural models and metrics, including phase-plane analysis, were implemented by self-developed MATLAB routines (The Mathworks, Natick, MA) and are available in [URL]_ dependent.
Transient and steady-state neuronal responses to abrupt versus gradual input changes
The properties of the transient responses of dynamical systems to external inputs depend on the intrinsic properties of the target cells, the initial conditions of the participating variables and the nature of the attractors (assumed to exist). The complexity of the autonomous transient dynamics, defined as the transient response to abrupt changes in constant inputs, increases with the model complexity. For example, for the simplest, passive neuron (a one-dimensional system), the voltage V evolves monotonically towards the new equilibrium value determined by a constant input. Two-dimensional neurons having a restorative current with slow dynamics (e.g., I h , I M , I CaT inactivation) may display overshoots and damped oscillations (Fig. 2), which can be amplified by fast regenerative currents (e.g., I Nap , I K ir , I CaT activation), and are more pronounced the further away the initial conditions are from the equilibrium (not shown) and the more abrupt is the input change. Here we review the dependence of the transient response properties of relatively simple models with the properties of the input and discuss some of the implications for the steady-state responses of the same systems to periodic inputs.
The strength of the transient responses to input changes decreases as the input changes transition from abrupt to gradual
An abrupt change in the input current (e.g., step DC input) can be interpreted as causing a sudden translation of the equilibria (for the voltage and other state variables) in the phase-space diagram from its baseline location (e.g., Fig. 3-a, I = 0, intersection between the V -and w-nullclines, solid-red and green, respectively) to the to a new location determined by the DC value (e.g., Fig. 3-a, I = 1, intersection between the displaced V -and w-nullclines, dashed-red and green, respectively). The values of these state variables prior to the transition become the initial conditions with respect to this new equilibrium. Therefore, the voltage responses to abrupt changes in the input currents are expected to exhibit overshoots and damped oscillations ( Fig. 3, insets, and Fig. 2, left ), which are more pronounced the stronger the input (not shown). As the change in input current becomes more gradual, the transient effects become more attenuated (Fig. 2, middle) and eventually the voltage response becomes almost monotonic (Fig. 2, right). This transition in the strength of the transient responses is expected since a monotonic input change can be approximated by a sequence of smaller step input changes of increasing (constant) magnitude, each one producing a transient response, which becomes smaller the larger the number of steps (the smaller the step size) since the initial conditions for each step in the partition are very close to the corresponding (new) steady state. Therefore, for input transitions between the same constant values, but with different slopes, the amplitude of the transient response becomes more attenuated the more gradual the transition since a larger partition is required in order to keep the step size constant.
Nonlinear amplification of the transient and steady-state response to constant inputs
Certain types of nonlinearities have been shown to amplify the response of neuronal systems (and dynamical systems in general) to the same input. This is reflected in both the responses to constant and oscillatory inputs. For illustrative purposes, in Figs. 3-b and -c we use a piecewise linear (PWL) model obtained from the linear model (1)-(2) by making the v-nullcline a continuous PWL function. It was shown in Rotstein (2014) that this type of model displays nonlinear amplification of the voltage response to sinusoidal inputs and captures similar phenomena observed in nonlinear models, in particular these having parabolic-like V -nullclines describing the subthreshold voltage dynamics (Rotstein 2015). Phase-plane diagrams for I = 1. The solid-red curve represents the V -nullcline (dv/dt = 0) for I = 0, the dashed-red curve represents the V -nullcline (dv/dt = 0) for I = 1, the solid-green curve represents the w-nullcline (dw/dt = 0) for I = 0, the solid-blue curve represents the trajectory, and the dashed-gray lines are marking the point where the trajectory initially starts at (0, 0) (the fixed-point for I = 0), converging to the fixedpoint for I = 1. The insets show the V traces. The 2D linear system exhibits an overshoot in response to step-constant inputs and resonance in response to oscillatory inputs (Richardson et al. 2003;Rotstein and Nadim 2014;Rotstein 2014). a. Linear (LIN) model described by eqs.
(1)-(2). b. Current-based piecewise linear (PWL) model described by We used the following parameter values: C = 1, g L = 0.25, g 1 = 0.25, τ 1 = 100 (same as in Fig. 4), v c = 1 and g c = 0.1 I = 0 (solid-red, baseline) and I = 1 (dashed-red). The wnullcline is unaffected by changes in I . The trajectory (blue), initially at the fixed-point for I = 0, converges towards the fixed-point for I = 1. For low enough values of I (lower than in Fig. 3-a and -b) the trajectory remains within the linear region (the trajectory does not reach the V -nullcline's "breaking point" value of V ) and therefore the dynamics are not affected by the nonlinearity. In both cases (panels a and b), the response exhibits an overshoot. The peak occurs when the trajectory is able to cross the V -nullcline. Because the V -nullcline's "right piece" has a smaller slope than the "left piece", the trajectory is able to reach larger values of V before turning around. This amplification is particularly stronger for the transient dynamics (initial upstroke) than for the steady-state response. Nonlinear response amplifications in this type of system are dependent on the time scale separa-tion between the participating variables. For smaller values of τ 1 this nonlinear amplification is reduced and although the system is nonlinear, it behaves quasi-linearly (Rotstein 2014(Rotstein , 2015.
Attenuation of the transient and steady-state response of conductance-based versus current-based (constant) synaptic inputs
From the phase-plane diagram in Fig. 3-c we see that increasing values of I (here we have a conductance and a driving-force in the model) reduces the nonlinearity of the V -nullcline (dashed-red) and increases (in absolute value) its slope. Both phenomena oppose the response amplification (blue) and the overshoot becomes much less prominent. The triangular region (bounded by the V -axis, the displaced Vnullcline (dashed-red) and the w-nullcline (green)) is reduced in size as compared to the current-based inputs (panel b) and therefore the response is reduced in amplitude. Moreover, because the displaced V -nullcline in panel c is more vertical than the baseline V -nullcline, the size of the overshoot in response to constant inputs is reduced and, in this sense, the responses become quasi-1D. As a consequence, the initial portion of the transient responses to abrupt changes in input is reduced in size and the oscillatory response to PWC inputs is also attenuated and the resonant peak disappears (Pena and Rotstein 2021).
Implications for the neuronal responses to structured (periodic) and non-structured inputs: hypotheses and questions
An immediate consequence of these observations is the prediction that a system's responses to square-wave and sinusoidal inputs of the same frequency (and duty cycle) will be qualitatively different, and these differences will depend on the stability properties of the unperturbed cells (e.g., stable nodes versus foci, overshoots versus damped oscillations). The steady-state response to periodic inputs can be interpreted as a sequence of transient responses to input changes. These transient effects (autonomous transient dynamics) are expected to be prominent in the steady-state responses to square-wave inputs, but not in the steady-state responses to sinusoidal inputs. Sinusoidal and square-wave inputs are representative of gradually and abruptly changing signals, respectively, and are amenable for comparison. The result of these comparisons sheds light on more realistic signals such as synaptic-like ones. To test these ideas we will use the chirp-like input currents shown in Fig. 1 (see Section 2.2.1).
A second immediate consequence of the observations referred to above is the finding that a system's amplitude response to piecewise constant (PWC) inputs having the same set of constant pieces arranged in different order is variable with respect to each other (Pena and Rotstein 2021). At the population level (same cell receiving a number of input signals consisting of permutations of the order of the same constant pieces of the same baseline signal), the properties of this variability crucially depend on the cell's autonomous transient dynamics. These reflect the multiple ways in which the cell responds to a given constant piece input from the values determined by the responses to the previous piece in the inputs signal, which change across trials. Interestingly, this phenomenon does not require the constant piece amplitudes to be randomly distributed, but they can be generated by a deterministic rule consisting of a baseline input pattern (e.g., increasing order of amplitudes) and a subset of all possible permutations of the constant piece amplitude order. We analyzed this in detail in the companion paper (Pena and Rotstein 2021).
The issues discussed above raise a number of questions. First, whether and under what conditions the frequencypreference properties of a system's response to sinusoidal inputs are predictive of the response properties of the same system to other types of inputs. While the Fourier theorem guarantees that the latter can be reconstructed from the former if properly normalized, it does not guarantee that the two will have the same waveform and the same frequencydependence properties using metrics that depend on these waveforms since the normalization factors (related to the input) may have different frequency dependencies. Second, whether and under what conditions the differences between the preferred frequency-band response of sinusoidal and nonsinusoidal inputs, if they exist, persist in the spiking regime. Given that the communication between neurons occurs via synaptic interactions, the failure of the responses to synapticlike inputs to replicate the frequency-preference properties in response to sinusoidal inputs would indicate that the latter, although useful for the reconstruction of signals, does not have direct implications for the spiking dynamics. Third, whether and under what conditions the frequency-preference properties of a system's response to structured (deterministic) inputs are predictive of the responses of the same system to unstructured (noisy) inputs. Fourth, whether and under what conditions the oscillatory (intrinsic) and resonant properties of cells result from the very brief initial transients of their autonomous dynamics. Fifth, how does the variability of a cell's response to different input trials is processed by the feedback effects operating at the cell level. We address these issues in the next sections.
Subthreshold resonance
A cell is said to exhibit subthreshold resonance if its voltage amplitude response to subthreshold oscillatory inputs peaks at a preferred (resonant) frequency (Figs. 4-a and 5-a). These responses are typically measured by computing the impedance Z , defined as the quotient of the power spectra of the output and input (see Methods). In current clamp, the input is current and the output is voltage. In controlled experiments and simulations, the unperturbed cells are in equilibrium in the absence of the oscillatory inputs. In response to constant inputs, resonant cells may be non-oscillators, typically exhibiting an overshoot, or exhibit oscillatory behavior (e.g., damped oscillations) (e.g., see Figs. 4-b1 and 5-b1, respectively, where the behavior can be observed in the first cycle). Therefore, resonance is not uncovering an oscillatory property of the unperturbed cell, but rather it is a property of the interaction between the cell and the oscillatory inputs (Rotstein 2014 We used the following parameter values: C = 1, g L = 0.05, g 1 = 0.3, τ 1 = 100 ms, and A in = 1 (curve of the impedance amplitude as a function of the input amplitude) measures the signal frequency content ( Fig. 4-a3, green). The upper and lower envelope profiles V Fig. 4-a2) capture the stationary peaks and troughs of the voltage response, respectively as a function of the input frequency. The peak profiles, in particular, are a relevant quantity since spikes are expected to occur at the response peaks as the input amplitude crosses threshold (the voltage response to this amplitude crosses the voltage threshold). The envelope impedance Z E N V profiles ( Fig. 4-a3, blue), consisting of the stationary peak-to-trough amplitude normalized by the input amplitude as a function of the input frequency and serves to connect and compare between the two previous profiles.
For sinusoidal inputs, the Z -profile in response to chirp inputs typically coincides with the Z E N V -profile computed by using sinusoidal inputs of a constant frequency (over a range of input frequencies). This remains true for the sinusoidal chirp-like input we use here (Fig 4-a). It is always true for linear systems (Richardson et al. 2003;Rotstein and Nadim 2014) and certain nonlinear systems (e.g., (Rotstein and Nadim 2019)). In other words, the frequency content of the voltage response (green) is reflected by the voltage upper and lower envelope response profiles and the response to non-stationary chirp-like inputs coincides with the stationary response to sinusoidal inputs of a single frequency. This is a direct consequence of the fact that the input changes are gradual.
Communication of the preferred frequency responses from the sub-to the supra-threshold regimes
The frequency-dependent suprathreshold response patterns to periodic inputs result from the interplay of the frequencydependent subthreshold voltage responses to the same inputs and the spiking mechanisms. The subthreshold resonant frequency is communicated to the suprathreshold regime when neurons selectively fire action potentials in response to oscillatory inputs only at frequencies within a small enough range around the subthreshold resonant frequency. This type of evoking resonance can be obtained for input amplitudes sightly above these producing only subthreshold responses, for example for neurons for which the spiking response to oscillatory inputs can be thought of as spikes mounted on the corresponding subthreshold responses. Evoked spiking resonance captures a selective coupling between the oscillatory input and firing, and it has been observed experimentally and theoretically (Hutcheon et al. 1996a, b) and the underlying dynamic mechanisms have been investigated in detail . A related measure of the communication of the subthreshold resonant frequency to the suprathreshold regime is that of firing rate resonance (Richardson et al. 2003) where the firing rate in response to oscillatory inputs peaks at or within a small range around the subthreshold resonance frequency. We note that subthreshold resonance does not necessarily imply evoked spiking resonance (Hutcheon et al. 1996a), evoked spiking resonance may be observed as the input amplitude crosses threshold, but lost for higher input amplitudes , the firing rate (or spiking frequency) at the firing rate (evoked) resonant frequency band is not necessarily the same as that frequency band (Hutcheon et al. 1996b;Richardson et al. 2003;, and evoked spiking resonance may be occluded in the presence of spontaneous firing, a situation likely to occur in vivo. When the spontaneous (or intrinsic) firing frequency is relatively regular, the associated time scale may dominate over the subthreshold resonant time scale and determine the firing rate resonant frequency (Richardson et al. 2003). A third form of preferred frequency response to oscillatory inputs is the so-called output spiking resonance where the spiking frequency response to oscillatory inputs remains within a relatively narrow range independently of the input frequency range. The output spiking resonant frequency and the subthreshold resonant frequency are not necessarily the same, but the mechanisms that give rise to both are dynamically related . While there is no guarantee that subthreshold resonance implies any of the various types of supra-threshold resonance, the communication of the resonant frequency to the suprathreshold regime is favored when the neuron's upper envelope profile V + E N V exhibits a peak at the subthreshold resonant frequency (V + E N V resonance). For the examples in Figs. 4-a and 5-a, the models, supplemented with a voltage threshold for spike generation and a reset mechanism, will exhibit evoked spiking resonance in response to sinusoidal inputs at the subthreshold resonant frequency band, and this is well captured by both the Z and Z E N V profiles. However, while this remains true for a larger class of systems, we note that this is not necessarily the case for nonlinear systems exhibiting, for instance if the V + E N V and V − E N V are asymmetric with respect to the equilibrium voltage (Pena et al. 2019). For example, a cell that is an upper envelope low-pass filter (V + E N V is a decreasing function of the input frequency), but a lower envelope band-pass filter (V − E N V has a trough at an intermediate input frequency) will show a peak in the impedance profile Z and therefore will be considered resonant, but this will not necessarily be reflected in the spiking response since the lower frequencies will be communicated better to the spiking regime than the intermediate frequencies as the input amplitude increases above threshold, in particular, these within the subthreshold resonant frequency band.
This together with our discussion in the previous section raises the question of whether the responses of resonant cells to non-sinusoidal periodic inputs will also show a preferred frequency response in the resonant frequency band and whether the Z -and V E N V -profiles exhibit the same filtering properties. This has implications for the frequencydependent supra-threshold response patterns to periodic inputs since the communication between neurons occurs via synaptic interactions, which exhibit abrupt changes as compared to the sinusoidal inputs, raising in turn the possibility of a competition between Z -and V E N V -profiles in determining the spiking frequency filtering properties.
ENV resonance in response to chirp-like square-wave inputs
Figures 4-b1 and -b2 illustrate that (Z ) resonant cells (see Fig. 4-a) may not exhibit envelope band-pass filter in responses to square-wave inputs. The V + E N V resonant response for sinusoidal inputs (Figs. 4-a1 and -a2) is lost for square-wave inputs and, consequently, these inputs would produce spiking activity preferentially at the lowest frequencies (no evoked spiking resonance) for input current amplitudes above threshold. The absence of V + E N V -resonance does not imply the absence of Zresonant frequency content. In fact, the power spectra for the responses to sinusoidal and square-wave inputs ( Fig. 4-b3, green) are very similar to the power spectra for sinusoidal inputs (Fig. 4-a3, green) and all show Zresonance (see schematic explanation in Fig. S1). However, this Z -resonance is not reflected in the V response and therefore it does not have a direct effect on the communi-cation of the subthreshold frequency content to the spiking regime. Figure 5-b1 and -b2 shows a representative case where V + E N V resonance is still present for square-wave inputs, but the resonance amplitude Q + E N V (defined as the quotient of the values of V + E N V at the peak and at f = 0) is very small as compared to Q + E N V in response to sinusoidal inputs (Fig. 5-a2). In these cases, the subthreshold resonant frequency will be communicated to the spiking regime, but only for a small range of input amplitudes as compared to the responses to sinusoidal inputs (Fig. 5-a), above which spiking would occur for the lowest frequencies. The frequency content of the voltage response ( Fig. 5-b3, green) is not reflected by the V + E N V and V − E N V response profiles (Fig. 5-b2) and consequently by the Z E N V profiles (Fig. 5-b3, blue).
The main difference between the two cases presented in Figs. 4 and 5 is the type of autonomous transient dynamics of the two cells. For the parameter values used in Fig. 4 the equilibrium for the isolated cell is a stable node (real eigenvalues, no intrinsic damped oscillations) and the cell displays overshoot transient responses to input changes (e.g., Fig. 2-a), while for the parameter values used in Fig. 5, the equilibrium for the isolated cell is a stable focus and the cell displays damped oscillations in response to input changes (e.g., Fig. 2-b). Biophysically, the transition from stable nodes to stable foci is associated with an increase in the levels of the amplifying currents. In Fig. 5, this is reflected as a decrease in the linearized conductance g L , which contains information about fast amplifying currents such as I Nap present in the original biophysical models (Richardson et al. 2003;Rotstein and Nadim 2014).
The persistence of V + E N V resonance in cells having stable foci is due to a combination of the (damped) oscillatory response after the abrupt input increase/decrease and summation. More specifically, the response to the abrupt input changes (square-wave or synaptic) has two regimes: a relatively large amplitude response, reflecting the biophysical amplification levels, and a smaller amplitude response reflecting the stability properties of the equilibrium V eq . The location of the voltage response V right before the arrival of the input from the next cycle determines the response amplitude to this input and this location depends on the stability properties of V eq . When V eq is a node, the voltage response V decreases below V eq immediately after the abrupt input change, and then returns to V eq . When the input from the next cycle arrives, V is below V eq . In contrast, when V eq is a focus, the oscillatory voltage response may be above V eq when the input from the next cycle arrives, and therefore, because it starts at a higher value, V reaches higher values. This depends on the frequency of the damped oscillations and the input frequency. If the input frequency is too low, then the damped oscillations die out before the next input arrives, while if the input frequency is high enough, then the damped oscillations are close to their first peak. If the input frequency is higher, then the value that V has when the next input arrives is lower because is further away from the first peak. For still higher input frequencies, the standard summation takes over. Figures S2 and S3 show similar results for the nonlinear conductance-based I h + I Nap model (the linear models used for Figs. 4 and 5 can be considered as linearized versions of this I h + I Nap model). For the lower levels of the I Nap conductance G p the I h + I Nap shows no V + E N V resonance, while V + E N V resonance persists for the higher levels of G p , consistent with the transition of the equilibrium from a stable node to a stable focus.
ENV resonance in response to excitatory synaptic-like inputs currents
As for the square-wave inputs described above, V + E N V is absent when V eq is a node (Figs. 4-b1 and -b2) and present when V eq is a focus (Figs. 5-b1 and -b2), but with a smaller Q + E N V than the response to sinusoidal inputs (Fig. 5a). Also similarly to square-wave inputs, the absence of V + E N V -resonance does not imply the absence of Z -resonant frequency content; The power spectra of the responses to sinusoidal, square-wave and synaptic-like inputs are very similar and all show Z -resonance green,green), and therefore this (Z -) resonance may have no direct effect on the communication of the subthreshold frequency content to the spiking regime (since the frequency-dependent properties that govern the generation of spikes are captured by V + E N V profiles and not on the frequency content captured by the Z -profiles).
In contrast to the responses to square-wave inputs, both the V + E N V and V − E N V responses to excitatory synaptic-like inputs exhibit a trough before increasing due to summation, which is more pronounced in Fig. 4c (V eq is a node) than in Fig. 5c (V eq is a focus). The generation of these troughs are the result of the interplay of the accumulation of synaptic inputs and the intrinsic properties of the cell reflected by the transient responses to individual inputs (overshoots, damped oscillations), and occur at a different frequency than the Z -resonant frequency. More specifically, for the lower frequencies in Fig. 4-c, V exhibits a sag before returning to a vicinity of V eq . The value V reaches before the arrival of the next input serves as the initial condition for the next cycle.
As the input frequency increases, these initial conditions are lower than for the previous cycles since the periods decrease, and therefore V returns to an even lower value after the synaptic input wears off. As the input frequency increases further, standard summation takes over and the combination of summation and the higher frequency input creates the high-pass filter V E N V patterns with an amplitude that decreases with frequency. This phenomenon is watered down when V eq is a focus because the amplification associated to the presence of damped oscillations as compared to overshoots causes the voltage response troughs at every cycle to reach lower values when V eq (Fig. 5c) is a focus than when V eq is a node (Fig. 4c). Figures S2 and S3 show similar results for the nonlinear conductance-based I h + I Nap model.
Z-resonant cells show V + ENV resonance in response to inhibitory synaptic-like inputs currents in a V eq -stability-dependent manner
For inhibitory synaptic-like input, the V + ENV and V − ENV responses are qualitatively inverted images of the ones described above. For linear cells, in particular, the responses for excitatory and inhibitory synaptic-like inputs are symmetric with respect to V eq (= 0). The most salient feature is the presence of a peak in both V + ENV and in V − ENV (Figs. S4-a1 and -b1 and Figs. S4-a2 and -b2), indicating the occurrence of V + ENV resonance at a frequency, which is different from the Z -resonant frequency (Figs. S4 c1 and c2). The mechanism of generation of this V + ENV band-pass filters is similar to the one described for the troughs in excitatory synapticlike inputs and involves a combination of summation and intrinsic properties of the cell, reflected in the properties of the transient response of the cells to individual inputs. More specifically, the summation acts as a low-pass filter and the effects of the transient responses to individual neurons, associated with the presence of Z resonance, act as a high-pass filter. We emphasize that the V + ENV and Z resonances are significantly different. We also emphasize that V + ENV resonance is not significant when V eq is a focus (Fig. S4-b2) since the amplification associated to the presence of damped oscillations referred to above obstructs the envelope high-pass filtering component. Figures S5 and S6 show similar results for the nonlinear conductance-based I h + I Nap model.
V + ENV low-and high-pass filtering properties of synaptic-like currents
The responses to synaptic-like inputs are affected by the summation effect, which depends on the synaptic decay time τ Dec . Figs. S7 and S8 illustrate the transition of the response (middle and right panels) to excitatory synaptic-like inputs (left panels) for representative values of τ Dec (including these used in Figs. 4-c and 5-c). In all cases, the frequency content measured by the impedance Z (Figs. S7 and S8, green) remains almost the same. The summation effects, which increases as τ Dec increases, strengthens the low-pass filter properties of the Z E N V response. The results for inhibitory synaptic-like inputs are symmetric to these in Figs. S7 and S8 with respect to V eq (= 0) (not shown), and therefore, increasing values of τ Dec strengthens the low-pass filter properties of Z ENV .
The autonomous transient dynamic properties are responsible for the poor upper envelope (V + ENV ) resonance (or lack of thereof) exhibited by (Z-) resonant cells in response to non-sinusoidal chirp-like input currents
As discussed above, the differences between the V + E N V response patterns to square-wave/synaptic-like and sinusoidal inputs and the differences between the V + ENV response patterns to different types of synaptic-like inputs (excitatory, inhibitory) are due to the different ways in which individual cells transiently respond to abrupt and gradual input changes, which operate at every cycle. Both and the corresponding sinusoidal inputs share the primary frequency component determined by the period (see Fig. S1). However, the sinusoidal input is gradual and causes a gradual response without the prominent transients (overshoots and damped oscillations) observed for the square-wave input, which together with the summation phenomenon produces V ENV peaks. The responses to square wave inputs, in particular for the lower frequencies, reach a steady-state value as the responses to sinusoidal inputs do, but in contrast to the latter, the voltage envelope for the former is determined by the transient peaks. These transients appear to be "getting in the way" of the voltage response to produce V ENV resonance. However, they are not avoidable. In fact, overshoots in non-oscillatory systems are an important component of the mechanism of generation of resonance in response to sinusoidal inputs (Rotstein 2014(Rotstein , 2015 as are damped oscillations. For comparison, Fig. S9 shows the responses of a passive cell (g 1 = 0) to the three types of inputs. Passive cells exhibit neither overshoot nor damped oscillatory transient responses to abrupt input changes, but monotonic behavior. As expected, this cell does not exhibit resonance in response to oscillatory inputs, but a low-pass filter response in both Z and V ENV (Figs. S9-a). The envelope response to square-wave inputs is also a low-pass filter ( Fig. S9-b), though it decays slower with increasing values of the input frequency since, because of the waveform, the square-wave input stays longer at its maximum value than the sinusoidal input at each cycle. In contrast to Figs. 4 and 5, the cell's response to synaptic-like inputs is a V E N V high-pass filter ( Fig. S9-c) due to summation and the lack of interference by the transient effects. Fig. S10 shows similar results for a two-dimensional linear model with a reduced value of the negative feedback conductance g 1 where overshoots and damped oscillations are not possible. The analogous results for inhibitory synaptic-like inputs are presented in Figs. S4 (rows 3 and 4).
Current-and conductance-based synaptic-like inputs produce qualitatively different voltage responses and synaptic currents
The synaptic-like inputs considered so far are additive current inputs. However, realistic synaptic currents involve the interaction between the synaptic activity and the postsynaptic voltage response. In biophysical models, the synaptic currents terms consist of the product of synaptic conductances and the voltage-dependent driving force (Eq. 3). Because the voltage response contributes to the current that produces this response, the frequency-dependent response profiles for current-and conductance-based inputs may be qualitatively different. Fig. 6-b, blue), the conductance-based synaptic current shows a peak in the upper envelope ( Fig. 6-a2), but not in the voltage response ( Fig. 6-b, red), which, instead, shows a trough as for the current-based synaptic input ( Fig. 6-b, blue). In spite of the similarities between the two profiles, the Z ENV profile for the conductance-based input shows a peak ( Fig. 7-c1, red), but this peak does not reflect a true Z ENV preferred frequency response.
Z-resonant cells do not show V + ENV resonance in response to conductance-based excitatory synaptic-like inputs, but they do show troughs in the
For the parameter values corresponding to Fig. 5 (mild V + E N V resonance in response to current-based synaptic-like inputs, Fig. 7-b, blue), the response to conductance-based synaptic inputs is similar to that in Fig. 6, but more amplified. In particular, there is no V + ENV resonance in response to conductance-based synaptic-like input ( Fig. 7-b, red). In both cases, the cells show Z resonance (Figs. 6 -c2 and 7-c2).
For comparison, Fig. S11 shows the result of repeating the protocols used above for a passive cell (g 1 = 0, same as Fig. S9). The frequency response patterns are the standard Zand Z E N V low-pass filters and the expected V + ENV high-pass filter.
Z-resonant cells show V + ENV resonance in response to conductance-based inhibitory synaptic-like inputs
Figures S12, S13, and S14 shows the result of repeating the protocols described above (Figs. 6, 7 and S11, respectively) using synaptic-like inhibitory conductance-based inputs. The Z -and Z ENV -profiles are qualitatively similar, except for the relative magnitudes of the synaptic-and conductance-based Z ENV that are inverted. The V + ENV profile shows a significant (resonant) peak when the cell has a node ( Fig. S12-b), which is almost absent when the cell has a focus ( Fig. S13-b), but Z E N V has a peak when the cell is a focus (Fig. S13-c1), while it is a low-pass filter when the cell has a node (Fig. S12-c1).
Together, these results and the result from the previous section shows that the frequency content (in terms of Z ) of resonant cells persists in response to synaptic-like currentand conductance-based inputs, but the V + ENV responses are different between synaptic-like current-and conductancebased inputs, and these differences depend on whether the cell has a node or a focus and whether the synaptic-like input is excitatory or inhibitory. Of particular interest are the peaks in the conductance-based synaptic inputs (Figs. 6-a2 and 7-a2, red).
Amplitude variability in response to chirp-like inputs with arbitrarily distributed cycles results from the transient response properties of the autonomous system
In the previous sections we used discretely changing frequencies (chirp-like or, simply, chirps, see Sect. 2.2.1) as a compromise between tractability and the ability to incorporate multiple frequencies in the same input signal, and we extended this type of inputs to waveforms with more realistic time-dependent properties. In all the cases considered so far, the chirp-like input cycles were "regularly" ordered in the sense that the input frequency monotonically increases with the cycle number (and with time). In this section, we move one step forward and consider chirp-like inputs where the cycles are arbitrarily ordered (see Sect. 2.2.2 and, Fig. 1-b) in an attempt to capture the fact that information does not necessarily arrive in a regularly ordered manner while keeping some structure properties (sequence of oscillatory cycles), which ultimately allows for a conceptual understanding of the responses. Each trial consists of a permutation of the order of the cycles using the regularly ordered cycles as a reference. The regularly and arbitrarily ordered input signals have exactly the same cycles (one cycle for each frequency value within some range) and therefore the same frequency content. The corresponding responses are expected to have roughly the same frequency content as captured by the Z -profiles within the range of inputs considered. However, we expect the a1 b c1 c2 a2 frequency-content). We used the following parameter values: C = 1, g L = 0.05, g 1 = 0.3, τ 1 = 100 ms, and A in = 1, same model as in Fig. 5 voltage responses to have different frequency-dependent V responses, captured by the peak-and-trough envelopes V +/− ENV . The differences between the V responses for two different inputs (different cycle orders) are due to the different ways in which the autonomous transient dynamics are activated across cycles for these inputs as the result of the transition between cycles. The values of the participating variables at the end of one cycle become the initial conditions for the subsequent cycle.
Emergence of the amplitude variability
In Sect. 3.2.1 (Figs. 4-a and 5-a) we showed that (Z -) subthreshold resonance is well captured by the V profiles). In Sect. 3.5 we argued that the transient response properties of the autonomous (unforced) cells (overshoots, damped oscillations, passive monotonic increase/decrease) are responsible for the (frequency-dependent) differences between the V +/− ENV profiles and the Z -profiles in response to both square-wave and synaptic-like chirp-like inputs, and for the (frequency-dependent) differences among the V +/− ENV profiles in response to the three types of chirp-like inputs.
The ordered chirp-like input signals produced V +/− ENV profiles with gradual amplitude variations along with the input frequency range (and a very small number of increasing and decreasing portions). The peaks and troughs for each frequency are determined by two parameters: the values of the variables at the beginning of the corresponding cycle and the duration of the cycle (the intrinsic properties of the cell are the same for all input frequencies), which in turn determines the initial values of the variables in the next cycle. The monotonic increase of the input frequency causes a gradual change in these parameters along the frequency axis, which in turn causes gradual changes in the V +/− ENV profiles. Because of this dependence of the values of the variables at the beginning of each cycle with the values of these variables at the end of the previous cycle, we reasoned that the voltage response to chirp with arbitrarily distributed cycles in time will exhibit non-regularly distributed peak and troughs, leading to amplitude variability in the V +/− ENV profiles, while producing at most minimal changes in the Z profiles (as compared to the responses to input signals with order cycles) within the frequency range considered. Moreover, this variability will depend on the type and properties of the autonomous transient dynamics of the participating cells. The arbitrary distribution in the order of the cycles in the input signal is achieved by considering one permutation of the regularly ordered signal (signal with regularly ordered cycles). The randomness in the input signals lies in the choice of a subset of all possible permutations for the considered trials.
Our results are presented in Figs. 8 and 9 for a cell exhibiting an overshoot ( Fig. 8; stable node; same parameter values as in Fig. 4) and damped oscillations ( Fig. 9; stable focus; same parameter values as in Fig. 5) in response to stepconstant inputs. For sinusoidal and square-wave chirp-like inputs, the output V + ENV and V − ENV frequencies were computed as the differences between two consecutive troughs and two consecutive peaks, respectively, normalized so that the resulting frequencies have units of Hz. The V + ENV and V − ENV profiles consist of the sequence of maxima and minima for each frequency (dots superimposed to the v time courses in the left columns) and include the damped oscillations for the lower frequencies (e.g., Fig. 9-b1). For the synaptic-like chirp inputs, we used the V + ENV and V − ENV profiles consisting of the sequence of maxima and minima for each input frequency and do not include the damped oscillations for the lower frequencies (e.g., shown in Fig. 9-c1, but not present in Fig. 9-c2).
We use the (regularly changing) responses to inputs with regularly ordered cycles (blue) as a reference for the variability of the responses to arbitrarily ordered cycles (red). In both Figs. 8 and 9 the responses to the input with randomly ordered cycles (red) have random amplitudes organized around (sinusoidal and square-wave; panels a and b) or in a vicinity (synaptic-like panel c) of the responses to regularly ordered cycles. The amplitude response variability is stronger for higher frequencies than for the lower frequencies, since the responses for the former are more affected by changes in the initial conditions at the corresponding cycles. Importantly, the variability is stronger for cells having stable foci (exhibiting transient damped oscillations; Fig. 9) than for cells having stable nodes (exhibiting transient overshoots; Fig. 8), reflecting the higher complexity of the latter cells' autonomous part. In all cases considered, the Z profiles remain almost unaffected by the order of cycles within the input frequency range. For comparison and completeness, Figs. S15 and S16 show similar graphs for a passive cell and synaptic-like inhibition, respectively. An important observation common to the responses of the three types of cells to synaptic-like inputs is the identification of the summation effects in the generation of V +/− ENV resonances. For example, Fig. S15-c2 (passive cell) shows that the summation effect in response to regularly ordered cycles (blue) disappears in the responses to randomly ordered cycles (red). Furthermore, the V + ENV resonance in response to regularly ordered synaptic-like inhibitory inputs ( Fig. S16-a2, blue) also disappears in the responses to randomly ordered synaptic-like inhibitory inputs (Fig. S16-a2, red).
Together these results show that the disruption of the regular order of a set of basic input signals, while the basic signals and their shapes remain unchanged, is translated into the amplitude variability of the response as compared to the responses to the regularly ordered sequence of signals, and a3 a2 a1 b3 b2 b1 c3 c2 c1 Fig. 8 Comparison of neuronal response between ordered and random input (linear model, overshoot). a Sinusoidal chirp. b Square-wave chirp. c Excitatory synaptic-like chirp. (a1, b1, and c1) voltage traces with peaks and troughs marked by red circles. (a2, b2, and c2) Voltageresponse envelopes in the frequency domain (blue is ordered input; red is random input as in Fig. 1b). (a3, b3, and c3) Z ( f ) (frequency-content) for ordered and shuffled inputs. In this 2D linear model, we used the following parameter values: C = 1, g L = 0.25, g 1 = 0.25, τ 1 = 100 ms, and A in = 1. Same model as in Fig. 4 this variability results from the properties of the transient dynamics of the (unforced) cells receiving the input.
Dependence of the amplitude response distribution variance with the cycle frequency and the properties of the receiving cell
Here we focus on synaptic-like inputs since they are the most realistic signals cells receive and we considered both currentand conductance-based synaptic-like inputs. We used the model (1)-(2) for current-based inputs and the model (3)-(4) for conductance-based inputs. We use the same input signal for both (I (t) = S(t)).
In order to quantify the variability of the voltage response envelopes (V + ENV and V − ENV ) to arbitrarily ordered chirp-like inputs we considered a number of trials (N trials = 100) and computed the cycle-by-cycle variance for the corresponding peaks (V + ENV ) and troughs (V − ENV ). Our results for some representative cases are presented in Fig. 10. In all cases, Var(V + ENV ) (blue) is less variable across frequencies than a3 a2 a1 b3 b2 b1 c3 c2 c1 Fig. 9 Comparison of neuronal response between ordered and random input (linear model, subthreshold oscillations). a Sinusoidal chirp. b Square-wave chirp. c Excitatory synaptic-like chirp. (a1, b1, and c1) voltage traces with peaks and troughs marked by red circles. (a2, b2, and c2) Voltage-response envelopes in the frequency domain (blue is ordered input; red is random input as in Fig. 1b). (a3, b3, and c3) Z ( f ) (frequency-content) for ordered and shuffled inputs. In this 2D linear model, we used the following parameter values: C = 1, g L = 0.05, g 1 = 0.3, τ 1 = 100 ms, and A in = 1. Same model as in Fig. 5 Var(V − ENV ) (red). The latter significantly increases for the higher frequencies. This high variability is associated with the phenomenon of summation observed in the regularly ordered cell. In other words, while summation is not observed in the responses to arbitrarily distributed cycles (e.g., Figs. 8-c, 9-c, and S15-c), it is translated into a high response variability. In Fig. 10-a, the transition from the P-cell (passive cell) to the N-cell (node cell) is due to a small increase in g 1 and therefore the Var patterns are similar. The transition from the N-cell to the F-cell (focus cell) involves changes in both g L and g 1 in order to maintain f res within the same (small) range. Both V + E N V and V − E N V are significantly larger for the F-cell than for the N-cell, consistent with Figs. 8 and 9. The amplification of the initial portion of the transient response to constant inputs caused by differences in cell type is translated into a higher response variability. In Fig. 10-b, the transition from the P-cell to the N-cell to the F-cell is due to an increase only in g 1 (at the expense of having values of f res distributed on a longer range than in panels a). The Var magnitudes are similar among the different cases. Together, these results reflect the fact that changes in the levels of the positive feedback effects (captured by the parameter g L in linear models) have stronger effects on the response variabil-Focus Node Passive a3 a2 a1 b3 b2 b1 Fig. 10 Peak and trough envelope (V + ENV and V − ENV ) variability in response to synaptic-like chirp-like inputs with arbitrarily distributed cycles for current-and conductance-based models. We used the linear model (1)-(2). Each trial (N trials = 100) consists of a permutation of the cycle orders using as reference the ordered input patterns in Figs. 8-c to S15-c. The blue and red curves represent the variances across trials for V + ENV and V − ENV in response to synaptic-like current-based inputs. The light-blue and light-coral curves represent the variances across trials for V + E N V and V − ENV in response to synaptic-like conductance-based inputs. Column 1. Passive cells. Column 2. Node (N-) cells. Column 3. Focus (F-) cells. a1. g L = 0.25 and g 1 = 0 ( f nat = f res = 0). a2. g L = 0.25 and g 1 = 0.25 ( f nat = 0 and f res = 9). a3. g L = 0.05 and g 1 = 0.3 ( f nat = 8.1 and f res = 8). b1. g L = 0.1 and g 1 = 0 ( f nat = f res = 0). b2. g L = 0.1 and g 1 = 0.2 ( f nat = 0 and f res = 7). b3. g L = 0.1 and g 1 = 0.8 ( f nat = 12.3 and f res = 14). We used the additional parameter values: C = 1, τ 1 = 100, A in = 1, G syn = 1, E syn = 1 ity than changes in the negative feedback effect (captured by the parameter g 1 ).
The envelope response variabilities are stronger for current-than for conductance-based synaptic-like inputs
The Var(V + ENV ) and Var(V − ENV ) for the conductance-based synaptic-like inputs follow a similar pattern as these for the current-based inputs (Fig. 10, light-blue and light-coral), but the magnitudes for the former are lower than these for the latter inputs, consistent with the attenuation of the initial portion of the transient response to conductance-based constant synaptic inputs as compared to current-based constant synaptic inputs discussed in Section 3.1.2. These relationships persist when the Var(V + ENV ) and Var(V − ENV ) patterns are normalized by the amplitude of the response of the first synaptic-like input in the ordered patterns (a metric that takes into account the effects of the differences in parameter values by the relative magnitude of their responses to the same input pattern). Figure 11-a (blue) corresponds to the same parameter values as Figs. 8-c, 9-c, and S15-c. The most salient feature is the low-pass filter in the middle panel (a2), while the corresponding Z -profile shows a band-pass filter (Figs. 8-c). For the parameter values in Fig. 11-b2 both the PSD-profile and Z -profile (not shown) display a band-pass filter, but the resonant peak in the Z -profile is more pronounced than in the PSD-profile and relatively bigger in comparison to the values of the same quantities at f = 0. This reflects the different ways in which the autonomous transient dynamics can be evoked by different types of input patterns, leading to significantly different results. This is not unexpected from the sets of parameter values in this graphs (panels a2 and b2) since Focus Node Passive a3 a2 a1 b3 b2 b1 Fig. 11 Average PSD for the V response to synaptic-like chirp-like inputs with arbitrarily distributed cycles for current-and conductancebased models. For current-based synaptic-like inputs we used Eqs.
Oscillations (and lack of thereof) in response to synaptic-like chirp inputs
(1)-(2). For conductance-based synaptic-like inputs we used the linear component of eqs.
The oscillatory voltage responses are stronger for current-than for conductance-based synaptic-like inputs
This is readily seen by comparing the blue and red curves in Fig. 11. These results are inherited from the responses of linear systems to synaptic current-and conductance-based constant inputs discussed in Section 3.1.2, and they can be understood in terms of our phase-plane diagrams discussion (compare Figs. 3-a and -c). Importantly, while in Fig. 11-a2 both responses show a low-pass filter, in Fig. 11-b2, the response to conductance-based synaptic inputs is close to a low-pass filter, while the response to current-based synaptic inputs is a well developed band-pass filter. In both cases, the autonomous cells have a stable node, exhibiting resonance, but not intrinsic (damped) oscillations.
Oscillations (and lack of thereof) in response to Poisson distributed synaptic-like inputs
Poisson distributed inputs have in principle a very different structure than the synaptic-like chirp inputs we discussed above. However, there is a natural transition between the two types of patterns. Roughly speaking, synaptic-like chirp patterns can be first extended to include a larger number of frequencies (not necessarily integer), and more than one cycle for each input frequency according to some distribution. Therefore one expects the results discussed above to extend to Poisson distributed inputs. This is supported by the fact that for high enough Poisson input rates ν, synaptic-like inputs approximate constant inputs (this is true for Poisson distributed pulses with amplitude g → 0 and rate ν → ∞), and therefore one should expect the voltage response PSD to approach these for constant inputs reflecting overshoots (low-pass filters) and damped oscillations (band-pass filters). However, for cells exhibiting overshoots there is a conflict between the response pattern "dictated" by the Z -profile (band-pass filter) and the low-pass filter pattern in response to constant inputs. For these cases, we expect the response pattern to be highly sensitive to the interplay of the Poisson rate ν and model parameters. For other rates we expect a departure from the overall behavior described above, but less pronounced.
Our results are presented in Fig. 12 (see also Figs. S17 to S20). The blue and red curves correspond to the V responses to synaptic-like current-and conductance-based inputs respectively. The solid curves are smoothed versions of the dotted ones (to which they are superimposed). The green dots/solid curves correspond to the V responses to white noise and are used as a reference for comparison. The blue and red dashed curves are rescaled versions of the corresponding dot/solid curves so that they match the values of the green curves at f = 1. In all cases, the responses to synaptic-like inputs are attenuated as compared to the responses to white noise. The level of attenuation increases with increasing values of the input Poisson rate (ν) as we discussed below. Low-pass filters (panels a1 and b1) and strong band-pass filters (panels a3 and b3, F-cell) remain so with some variations. The responses of N-cells (panels a2 and b2) vary according to the input rate ν. Consistent with our results discussed above, for the cells that have resonance but not intrinsic (damped) oscillations (panels a2 and b2) and ν = 1000 (Fig. S19), the response can be either a low-pass filter or a band-pass filter depending on the model parameters, in particular the level of the (amplifying) positive feedback effect that increases with decreasing values of g L (compare panels a2, for g L = 0.25, and b2, for g L = 0.1). This remains the case for ν = 500 (Fig. S18) for the conductance-based response, but not for the current-based response (a band-pass filter emerges in panel a2). For ν = 100 and ν = 10 (Figs. S17 and S20), both the current-and conductance-based responses show a band-pass filter.
The results described above persist when the input synaptic train consist of both excitatory and inhibitory synaptic-like inputs (e.g., Fig. S21).
The emergence of band-pass filters in response to synapticlike inputs is strongly dependent on the synaptic input decay time τ Dec . For values of τ Dec (= 25 ms) larger than in Figs. S17 to S21 and not realistic for fast synapses the band-pass filters are attenuated for F-cells and the N-cells show low-pass filters (compare Figs. 12-a2 and -b2 with Figs. S19-a2 and -b2).
The oscillatory voltage responses are stronger for current-than for conductance-based synaptic-like inputs and biophysically plausible in vivo input rates
This is readily seen in Figs. 12 and S19 for the realistic values of in vivo input rates (ν = 1000) also used in controlled experiments (Fernandez and White 2008). For lower values of ν ( Fig. S17 for ν = 100, Fig. S18 for ν = 500, Fig. S20 for ν = 10) and when synaptic inhibition is incorporated (Figs. S21 for excitatory ν = 1000 and inhibitory ν = 500) the relative magnitudes of the current-and conductancebased responses depends on the input frequency regardless of whether the response has a low-or a band-pass filter. For even lower values of ν (Figs. S17 for ν = 10), the conductancebased response is stronger than the current-based response). Together these results and the results from the previous Sections shed some light on the implications of the experimental findings in (Fernandez and White 2008) where the intrinsically generated subthreshold oscillations observed in medial entorhinal cortex layer II stellate cells (SCs) have been shown to be strongly attenuated by current-based synapticlike inputs and absent (or almost absent) in response to conductance-based synaptic-like inputs. Our findings suggest that STOs in SCs are generated by noise-dependent mechanisms in the presence of subthreshold resonance with at most strongly damped intrinsic oscillations (Rotstein et al. 2006), but not sustained limit cycle oscillations (Remme et al. 2012) in the presence of noise variability.
Discussion
Subthreshold (membrane potential) oscillations (STOs) have been observed in many neuron types in a variety of brain areas and have been argued to be functionally important for the generation of brain rhythms, sensory processing, encoding of information, communication of information via timing mechanisms and cross-frequency coupling (see more details and references in the Introduction).
Intrinsically generated STOs in single neurons require the presence of relatively slow restorative currents providing a negative feedback effect (currents having a resonant gating variable) and are amplified by fast regenerative currents providing a positive feedback effect (currents having an amplifying gating variable) (see Sect. 3.1 for more details). From a dynamical systems perspective, sustained STOs can be generated by limit cycle mechanisms or be noise-driven. In the latter case, the noiseless system may exhibit either damped oscillations (F-cells; the equilibrium has complex eigenvalues) or even overshoots (N-cells; the equilibrium has real eigenvalues) in response to abrupt changes in constant inputs. The interaction between Gaussian white noise and these autonomous transient dynamics may create sustained STOs (Rotstein et al. 2006;Pena and Rotstein 2021).
Neurons are subject to fluctuating inputs from a large number of synaptic currents generated by action potentials whose collective dynamics can be modeled as a high-rate Poisson process. Due to its high-rate, this synaptic noise has been approximated by Gaussian white noise or Ornstein-Uhlenbeck processes (Uhlenbeck and Ornstein 1930) (lowpass filtered versions of Gaussian white noise) (Brunel 2000;Amit and Tsodyks 1991;Tuckwell 1989Tuckwell , 1988Amit and Brunel 1997;Brunel et al. 2001). Recent experimental results (Fernandez and White 2008) on medial entorhinal cortex SCs, a prototypical intrinsic STO neuron (Dickson et al. 2000a, b) and resonator (Schreiber et al. 2004), using artificially generated current-and conductance-based synaptic inputs driven by high-rate presynaptic Poisson spike trains, showed that STOs are still present in response to currentbased synaptic inputs, but absent or strongly attenuated in response to conductance-based synaptic inputs. This would suggest that in realistic conditions the STO properties of SCs are not communicated to the network regime via synaptic mechanisms. On the other hand, in SCs and other cell types exhibiting STOs, the frequency of the STOs has been found to be correlated with the frequency of the networks in which they are embedded (Alonso and Llinás 1989;Klink and Alonso 1993;Giocomo et al. 2007;Cobb et al. 1995;Colgin 2013;Chapman and Lacaille 1999;Desmaisons et al. 1999;Balu et al. 2004;Kay et al. 2008;Li and Cleland 2017), suggesting intrinsic STOs in individual neurons may play, at least, an indirect role in the generation of network oscillations.
These issues are part of the more general question of how the response of neurons to periodic inputs (and to external inputs in general) depends on the interplay of the neuronal intrinsic properties and the properties of the input. Typical experiments on subthreshold and suprathreshold resonance use sinusoidal inputs, which change gradually with time. These studies are motivated by the fact that the resulting patterns can be used for the reconstruction of the system's response to arbitrary time-dependent inputs under certain assumptions on both the input and the system (e.g., quasi-linearity). However, systems are not necessarily close to linearity and neuronal communication occurs via relatively fast synapses (e.g., AMPA and GABA A ), which change more abruptly. These abrupt input changes evoke the autonomous intrinsic dynamics (damped oscillations or overshoots), which are occluded in response to gradual input changes. As a result, periodic (and also non-periodic) sequences of sinusoidal and synaptic inputs are expected to produce different patterns and therefore the impedance profile will not be a good predictor of the voltage response to trains of synaptic inputs under general assumptions.
We set out to clarify these issues in a broader context. To develop the main set of ideas, we used a relatively simple neuronal model, the linearization of conductance-based models subject to additive current-based inputs and multiplicative conductance-based synaptic inputs. We then tested these ideas using a conductance-based model. We used three representative waveforms over a range of frequencies: sinusoidal, synaptic-like and square-wave (duty cycle equal to 0.5). Sinusoidal inputs are typically used to uncover the preferred oscillatory responses to external inputs as discussed above. Synaptic-like inputs represent the realistic ways in which communication between neurons occurs. Square-wave inputs can be considered as an intermediate between the first two. Sinusoidal and square-wave inputs share the waveform skeleton (they have the same frequency content except for the high frequency associated with the abrupt changes between phases), but sinusoidal inputs change gradually. Square-wave and synaptic inputs involve abrupt changes between minima and maxima, but the active part of the synaptic-like waveforms is independent of the period for a relatively large range of input frequencies. In addition, we used chirp-like (sinusoidal) inputs with discretely changing frequencies in order to be able to incorporate multiple frequencies in the same signal and we extended these chirps to include square-and synaptic-like waveforms.
We developed the notion of the peak/trough voltage envelope profiles V ± ENV ( f ) and the peak-to-trough impedance profiles Z ENV ( f ) as metrics to investigate the frequencydependent voltage responses to periodic inputs in addition to the (standard) impedance amplitude profiles Z ( f ) and the corresponding voltage PSD (computed using Fourier transforms of the whole signal). Because the upper (peak) envelope is the most important quantity regarding the communication of information to the suprathreshold regime, we often refer to it indistinctly as V ENV or V + ENV . The differences between V ENV (or V + ENV − V − ENV ) and the voltage PSD are due to the signal structure (V ENV captures only the envelope of the voltage response). The differences between Z ENV and Z capture the effect of the input signal (Z ENV is normalized by the input signal's amplitude A in , while Z is normalized by the amplitude of its PSD). The differences between the V ENV and Z ENV profiles are due to the asymmetries in the voltage responses.
We showed that cells that exhibit resonance in response to sinusoidal inputs (Z -resonant cells) also show resonance in the V ENV -and Z ENV -responses to sinusoidal chirp inputs independently of whether they were N-cells or F-cells. This was expected given the gradual increase of the sinusoidal waveforms, but it served as a baseline for comparison with the other input types. For suprathreshold input amplitudes within some range, the frequency properties of Z -resonant cells in response to sinusoidal inputs are communicated to the suprathreshold regime in the form of evoked spiking resonance or firing-rate resonance (Richardson et al. 2003). In contrast, Z -resonant N-cells are V ENV and Z ENV low-pass filters and Z -resonant F-cells have mild V ENV and Z ENV resonant properties. In other words, the cells' subthreshold frequency-dependent properties, are not necessarily communicated to the spiking regime in response to non-sinusoidal inputs. The V ± ENV patterns in response to these two types of inputs are dominated by the autonomous intrinsic dynamics and this is particularly strong for the lower frequencies (longer periods) where the overshoots and damped oscillations can be prominent. In response to sinusoidal inputs, the autonomous transient dynamics develops gradually and their contribution to the V ± ENV patterns remains occluded.
We used these protocols to compare the response of these neuron types to current-vs. conductance-based synapticlike inputs. In all cases, the response to conductance-based inputs was attenuated as compared to the response to currentbased inputs. Each one of the metrics produced different results, capturing different aspects of the voltage responses to these two types of inputs and their relationship with the input signals. For N-cells the responses to both currentand conductance-based inputs are V ENV low-pass filters. In contrast, for F-cells the V ENV responses to currentbased inputs were band-pass filters, while the responses to conductance-based inputs were low-pass filters. These bandpass filters were generated as the result of the interplay of the autonomous transient dynamics (damped oscillations) and summation.
The Z ENV profiles tell a different story. For N-cells, the Z ENV responses to current-based inputs are low-pass filters, while they are band-pass filters for conductance-based inputs. For F-cells, the Z ENV responses to both currentand conductance-based inputs are band-pass filters. In both cases, the Z ENV band-pass filters in response to conductancebased inputs reflect troughs in the V − ENV profiles rather than a real preferred voltage response. The Z profiles tell yet a different story. In all cases considered (N-and F-cells, current-and conductance-based inputs), the Z profiles are band-pass filters. For passive cells, where the autonomous transient dynamics are relatively simple (monotonic increase or decrease), the response patterns are dominated by summation and, while Z ENV and Z are low-pass filters, V ENV are high-pass filters.
In order to understand the contribution of the autonomous intrinsic dynamics to the generation of variability in the neuronal response patterns to external inputs, we used the three types of chirp-like inputs with arbitrarily ordered cycles. These inputs are an intermediate step between the regularly ordered and the fully irregular chirp-like inputs. The prototypical example of the latter (and the one we had in mind) are the synaptic-like inputs generated in response to spike-trains with Poisson-distributed spike times. The inputs have the same cycles for all trials, and hence the same frequency content, but each trial corresponded to a different permutation of the order of the cycles. The only source of uncertainty was the subset of all possible permutations of the cycle period. The differences in the voltage responses for cycles with the same period across trials were due to the differences in the initial conditions across trials for the same period. More specifically, for a given period (T k ) the previous cycle has different periods across trials and therefore different voltage values at the end of these periods, which become the initial conditions for period T k . The variability of these initial conditions across trials involves not only the voltage but the (hidden) recovery variables. Our results demonstrated the emergence of variability of the voltage responses across trials for all input waveforms inherited from this mechanism. This variability was stronger for the F-cells than for the N-cells considered and, again, it did not require stochastic input fluctuations, but it was the result of the multiple different ways in which the inputs evoked the autonomous intrinsic dynamics.
The average voltage PSD (< P S D >) responses for F-cells were band-pass filters for both current-and conductance-based synaptic-like inputs with arbitrarily order periods. For N-cells, in contrast, the < P S D > responses to current-based input were low-pass filters, while the < P S D > responses to conductance-based inputs were lowpass filters or mild band-pass filters. This is consistent with the results in Fernandez and White (2008) and previous results showing that STOs in SCs are noise-driven (Dorval and White 2005;Rotstein et al. 2006) (but see (Remme et al. 2012)). Our protocols consisted of the response of one cell type to variable inputs. More research is needed to understand the effects of variability across cells using some baseline attribute to all of them (e.g., same resonant properties or the same noise-driven oscillation properties).
Armed with these results, we compared the voltage responses (V PSD ) of these cell types to high-rate Poisson distributed current-and conductance-based synaptic inputs and additive Gaussian white noise (noise-driven oscillations). The V PSD -profiles in response to both current-and conductance-based synaptic inputs were attenuated with respect to the response to white noise. The V P S D -profiles in response to current-based synaptic inputs were low-pass filters for F-cells and low-pass filters (or mild band-pass filters) for N-cells. The V PSD -profiles in response to conductancebased synaptic inputs were low-pass filters for all cell types. This is, again, consistent with the results in Fernandez and White (2008) and suggests that in contrast to the noise-driven oscillations that emerge in both F-and N-cells, the currentbased synaptic-like Poisson-driven oscillation requires a stronger intrinsic oscillatory structure. These results also show that the responses to synaptic-like high-rate Poissondriven inputs are not necessarily captured by the response to additive Gaussian white noise in contrast to standard assumptions. More research is needed to establish the conditions under which oscillations emerge in response to synapticlike inputs. Representative examples show that oscillatory responses for current-and conductance-based synaptic-like inputs emerge for both F-and N-cells for lower Poisson rates. More research is also needed to establish the conditions under which the Gaussian white noise approximation provides good approximations to high-rate synaptic inputs.
The question arises whether the lack of oscillatory responses to synaptic-like inputs (almost complete for conductance-based and partial for current-based) implies the lack of communication of the intrinsic (noise-driven) oscillatory and resonant properties to the suprathreshold regime. While this requires a detailed analysis and is beyond the scope of this paper, we conducted a number of simulations to explore a few representative cases. We used the biophysical (conductance-based) I h + I Nap model (6)-(7) having two-dimensional subthreshold dynamics (Figs. S22 to S25) and compared them with the results using an integrate-andfire model (Fig. S26) for which the subthreshold dynamics is one-dimensional. The results are mixed, but an important common theme is that the responses show output firing rate resonance (the response firing remains within a relatively small bounded range) even when the subthreshold resonance is not present. The most salient cases are shown in Fig. S22a and -b. This phenomenon is absent in the absence of the intrinsic oscillatory dynamics for the leaky integrate-andfire model (Fig. S22-d, Fig. S26). These results strongly depend on the input amplitude ( Fig. S23 to S26). These results suggest that the oscillatory properties of individual neurons may be occluded at the subthreshold level, but they are still communicated to the suprathreshold regime. One may potentially contrast these observations against the cancellation of frequency-dependent redundant sensory stimuli in electric fish (Mejias et al. 2013;Benda et al. 2005;Bol et al. 2011Bol et al. , 2013. In such experiments, chirps create responses that are significantly enhanced compared with slower beats. This type of experimentation would certainly benefit from future discussions that derive from our work given the nature of their oscillatory inputs. Our study is focused on a specific type of resonance in response to deterministic periodic inputs (in the form of chirps) and leaves out the response of systems to stochastic inputs that may lead to stochastic and coherence resonance (Benzi et al. 1982, Gammaitoni et al. 1998McNamara and Wiesenfeld 1989;Mato 1989;McDonnell and Abboott 2009;Douglass et al. 1993;Wiesenfeld and Moss 1995;Collins et al. 1996;Muratov et al. 2005;Pikovsky and Kurths 1997;Lindner et al. 2004;Neiman et al. 1997;Lee et al. 1998;Pradines et al. 1999;Tateno and Pakdaman 2004;DeVille et al. 2005;Baspinar et al. 2021) (and references therein). These are related, but different phenomena, which may be affected by the use of non-sinusoidal inputs of the form we use here. More research is required to understand these issues.
The synaptic-like inputs we use in this paper have constant amplitude across cycles. Previous work showed that synaptic short-term plasticity (STP, depression and facilitation) can lead to the emergence of temporal filters (Fortune and Rose 2001;Mondal 2021;Mondal et al. 2021) and synaptic resonance (Drover et al. 2007;Mondal 2021). Additionally, STP has been shown to play a role in modulated intrinsic subthreshold oscillations (Flores et al. 2016;Torres et al. 2011;Uzuntarla et al. 2017). Future work should consider the effects of adding STP to the protocols we used here. However, this may require the use of full periodic signals instead of chirps to capture phenomena mentioned above. A step in this direction has been done in Mondal et al. (2021).
Simply put, under rather general circumstances, neurons that exhibit subthreshold resonance in response to sinusoidal inputs would exhibit spiking resonance in response to the same inputs (for small enough values of the input amplitude), but would not exhibit spiking resonance for other types of inputs, in particular for synaptic-like waveforms, which are the ones that operate in networks. This includes the difference between the responses to current-and conductance-based inputs. These issues have been overlooked in the literature and, often, claims are made about the implications of resonance in response to sinusoidal inputs for spiking activity in realistic setups. Our results generate a number of predictions that can be tested experimentally in vitro using the dynamic clamp technique (Sharp et al. 1993;Prinz et al. 2003) or in vivo using optogenetic tools (Zhang et al. 2007;Deisseroth 2011;Bernstein and Boyden 2012;Stark et al. 2013).
The resonance frequency f res (in Hz if t has units of ms) is the frequency at which Z reaches its maximum f res = −d 2 + √ b 2 c 2 − 2 a b c d − 2 d 2 b c 2π 1000. (23)
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Domain: Biology
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Biophysical Modeling of Actin-Mediated Structural Plasticity Reveals Mechanical Adaptation in Dendritic Spines
Synaptic plasticity is important for learning and memory formation; it describes the strengthening or weakening of connections between synapses. The postsynaptic part of excitatory synapses resides in dendritic spines, which are small protrusions on the dendrites. One of the key features of synaptic plasticity is its correlation with the size of these spines. A long-lasting synaptic strength increase [long-term potentiation (LTP)] is only possible through the reconfiguration of the actin spine cytoskeleton. Here, we develop an experimentally informed three-dimensional computational model in a moving boundary framework to investigate this reconfiguration. Our model describes the reactions between actin and actin-binding proteins leading to the cytoskeleton remodeling and their effect on the spine membrane shape to examine the spine enlargement upon LTP. Moreover, we find that the incorporation of perisynaptic elements enhances spine enlargement upon LTP, exhibiting the importance of accounting for these elements when studying structural LTP. Our model shows adaptation to repeated stimuli resulting from the interactions between spine proteins and mechanical forces.
Introduction
Dendritic spines are small protrusions from dendrites that form the postsynaptic part of a vast majority of excitatory synapses (Nakahata and Yasuda, 2018;Harris, 2020;Okabe, 2020). In response to glutamate signals released by stimulated presynaptic neurons, spines undergo both biochemical and morphological changes that can be long-lasting (Matsuzaki et al., 2004;Okamoto et al., 2004;Kasai et al., 2010;Fig. 1). Such changes have been long hypothesized as the biological mechanisms underlying memory storage in the brain (Yuste and Bonhoeffer, 2001). One of the most-studied long-lasting changes is long-term potentiation (LTP).
LTP induction alters the dendritic spine morphology. It has been shown that spines increase their volume dramatically, by up to 390%, after stimulation (Tønnesen et al., 2014;Chang et al., 2017). This increase is associated with an increase in AMPAR-mediated currents and depends on NMDAR activation and actin polymerization (Matsuzaki et al., 2004). Actin is highly concentrated in dendritic spines (Matus et al., 1982) and plays a key role in LTP (Cingolani and Goda, 2008;Okabe, 2020). Around 95% of F-actin in the spine undergoes rapid treadmilling (time scale ∼40 s), generating an expansive force caused by actin polymerization (Honkura et al., 2008). Moreover, the equilibrium between actin monomers (G-actin) and F-actin is affected by stimulation (Okamoto et al., 2004), suggesting that structural reconfiguration of actin is necessary for spine enlargement.
Actin-binding proteins (ABPs) aid the reconfiguration of the cytoskeleton by promoting G-actin polymerization, F-actin depolymerization, and capping the F-actin ends, which prevents their polymerization and depolymerization (Pollard and Borisy, 2003). Experiments have shown that ABPs are necessary for LTP (Cingolani and Goda, 2008;Fortin et al., 2012). To achieve the transient size increase seen during the first 4-7 min of LTP (Tønnesen et al., 2014;Chang et al., 2017), translocation of ABPs into the spine is necessary (Bosch et al., 2014), in addition to the Arp2/3 and cofilin activation due to Ca 2+ influx (Rangamani et al., 2016) (Fig. 1B). After 3 h, the spine settles to a size that is 40% larger than that prior to LTP induction (Tønnesen et al., 2014;Chang et al., 2017). Hence, the reconfiguration of the actin cytoskeleton of dendritic spines, and thus, structural plasticity is possible by an orchestrated interplay between actin and ABPs triggered by different signaling pathways (Bosch et al., 2014).
Besides changes in size promoted by the cytoskeleton reconfiguration, spines experience further mechanical modifications upon LTP. For example, mechanical coupling of the actin filaments with the extracellular environment through molecular clutches is necessary to push the membrane forward (Kastian et al., 2021). Degradation of the extracellular matrix (ECM) by proteases promotes structural and functional LTP (Wang et al., 2008). The elastic storage modulus and viscous loss modulus of the spine increase, which facilitate its mechanical stabilization due to the stiffening of the internal structure (Smith et al., 2007). Therefore, both biochemical reactions and mechanical force generation are required for structural LTP (sLTP).
In this work, we seek to answer the following questions: can a minimal model of actin-membrane interactions capture the dynamics of spine changes during sLTP?How spine enlargement upon LTP can be enhanced by the mechanical changes induced through the interaction with other perisynaptic elements?And finally, how do repeated stimuli affect sLTP?To answer these questions, we develop a 3D computational model using a system of partial differential equations (PDEs) with moving boundaries to incorporate the spatio-temporal dynamics of actin and ABPs in the expanding dendritic spines. We systematically investigate the contribution of ABPs and mechanics to sLTP under different conditions that mimic the alteration of spine properties. Our results predict that actin interaction with ABPs is sufficient to capture the spine growth during sLTP and that further increase can be obtained when including the interaction with other perisynaptic elements. Moreover, the spine enlargement capacity diminished with repeated stimuli, hinting at a homeostatic mechanism related to its mechanical properties.
Figure 1. Biophysical events involving early structural plasticity in the dendritic spine. A, Upon LTP induction, glutamate is released from the presynaptic neuron and taken up by the postsynaptic neuron. Consequently, a cascade of chemical reactions is initiated (B), and an influx of actin, cofilin, and Arp2/3 into the spine is triggered. This remodels the spine cytoskeleton allowing for spine enlargement. C, Actin filament treadmilling, branching, and severing events remodel the cytoskeleton. In this model, we focus on how the spatio-temporal dynamics of actin, cofilin, and Arp2/3 dictate the shape of the dendritic spine. Image created with BioRender.com.
Materials and Methods
We develop a mathematical model in which F-actin dynamics in the dendritic spine are affected by Arp2/3 and cofilin. As in cell motility models (Mogilner and Edelstein-Keshet, 2002;Tania et al., 2011;Tania et al., 2013), we assume that these ABPs are sufficient to promote membrane protrusion because their interaction with F-actin increases the force generated by actin polymerization which helps to overcome the membrane resistance (Mogilner and Oster, 1996;Xiong et al., 2010). Thus, we expect that the spine enlargement seen shortly after LTP induction will be driven by the reconfiguration of the cytoskeleton, similar to the reconfiguration needed for cell motility, and that other proteins minimally affect spine expansion. In the model, actin, Arp2/3, and cofilin are free to diffuse in the spine volume. Upon LTP, there is an influx of these proteins into the spine that represents the translocation of ABPs. Note that the ABPs chosen in our model are required for synaptic function. For example, conditional mutagenesis of Arp2/3, which promotes actin branching (Pollard et al., 2000;Pollard and Borisy, 2003), hinders spine enlargement upon LTP and is implicated in psychiatric disorders (Kim et al., 2013). Loss of cofilin impairs learning (Rust et al., 2010). Cofilin is an ABP whose function depends on its relative concentration with actin; it severs F-actin at low concentrations but promotes actin nucleation at high concentrations (Andrianantoandro and Pollard, 2006).
Governing equations. Based on the assumptions described above, we formulate a system of PDEs that describe the spatio-temporal dynamics of F-actin with uncapped (+) ends (or barbed ends), Arp2/3, and cofilin. Note that barbed ends polymerize G-actin, which generates an expanding force. Moreover, the action of Arp2/3 and cofilin, in addition to the actin influx upon LTP induction, increase the number of barbed ends in the spine. Thus, in our model, the increment of barbed ends during stimulation allows us to approximate the dynamics of barbed ends with PDEs instead of considering single filaments. This system is coupled with the spine membrane dynamics, as described in Doubrovinski and Kruse (2011). To examine the dendritic spine size and shape changes, we implement a moving boundary framework. The forces generated by actin polymerization F actin , the membrane F mem , and drag F drag dictate the displacement of the membrane G, which enclose the spine to an evolving domain V, hence G = ∂V.
In the absence of inertia, the force balance becomes Note that vectors are in bold font. As in Gonçalves and Garcia-Aznar (2021), we describe the interactions between the spine and the extracellular environment through F drag , a dissipative force that can represent the contributions of fluid drag and ECM adhesion. The drag force is given by where v represents an effective drag coefficient and v p is the protrusion velocity, i.e., the displacement of the membrane over time. Thus, the membrane evolves according to where F actin is the force generated by the polymerization of F-actin near the dendritic spine membrane that pushes the membrane forward (Mogilner and Oster, 1996;Lacayo et al., 2007;Honkura et al., 2008), given by Following Doubrovinski and Kruse (2011), the force applied to the membrane is dictated by the total potential generated by the number of barbed ends per cubic volume B inside the spine where is a soft repulsive potential (Fig. 2L) that depends on the distance d s (r, G) (Fig. 2B and C), given by Thus, barbed ends closer to the membrane have greater contributions to the potential energy. Note that when a, b − 1, the membrane is a reflective boundary. We use a signed distance to track the relative position of r with respect to the spine membrane.
The force generated by actin is balanced by an opposing force generated by the membrane F mem , which counteracts membrane deformations and is given by where represents the membrane Helfrich free energy due to bending (Helfrich, 1973). Here, H is the mean curvature, and k is the bending modulus. Note that we consider the membrane as a 2D elastic continuum with negligible thickness, as in Krüger (2012) and Deserno (2015). The volume and surface area of the spine membrane are unconstrained due to the influx of proteins and membrane addition through trafficking mechanisms (Yang and Liu, 2022). Thus, we do not consider membrane tension and osmotic pressure in our model.
Actin dynamics. F-actin in dendritic spines is distributed in two different pools, the stable pool and the dynamic pool. In contrast to the stable pool of actin, with a lifetime of ∼17 min, the dynamic pool undergoes rapid treadmilling (∼40 s) (Honkura et al., 2008). We assume that the structural changes in dendritic spines are mostly driven by the dynamic pool that has short filaments with uncapped (+) ends undergoing continuous polymerization, which generate F actin . Therefore, we do not explicitly model the stable pool, which only accounts for 5% of the total F-actin (Honkura et al., 2008). We keep track of the number of barbed ends per unit volume B(r, t) at position r [ L , R 3 , where L is a cubic lattice domain (Fig. 2F and G), instead of F-actin or G-actin concentration. Note that keeping track of the number of barbed ends instead of the number of actin filaments reduces the complexity of the model because otherwise, we would have to account for the length of the filaments. Here, we assume that actin filaments with barbed ends have similar lengths. Although this is a simplifying assumption, the modeled F-actin is dynamic and has a short length (Honkura et al., 2008), which we expect to exert similar force to the membrane; hence, differences in F-actin length are negligible. Lastly, we assume that ATP is not depleted throughout the simulation.
The dynamics of the number of barbed ends per unit volume are given by where k b is a constant basal efflux rate. The basal influx I b of actin is due to local actin synthesis (Tiruchinapalli et al., 2003;Cajigas et al., 2012). Because the proteins used in the model are dispersed in the spine head, we assume a homogeneous basal influx. The stimulus-triggered influx I S,b mimics the transient influx upon stimulation (Bosch et al., 2014). Hence, it is nonzero only during a brief window of time after stimulus initiation (1 min). In line with experimental observations (Bosch et al., 2014), we localize the stimulus-triggered influx to the spine head. For consistency of units, these terms are multiplied by a conversion factor C 0 that changes from concentration units in μM to the number of barbed ends per μm 3 .∇ represents the gradient operator. Note that the number of barbed ends per unit volume is not conserved due to the stimulus-triggered influx. The first term in Equation 10, −∇ • (ndB), denotes the change in the barbed ends per unit volume due to the polymerization of G-actin in the direction d = d(r, G) = ∇d s (r, G), where d s is defined in Equation 7 (Fig. 2D). Hence, d is the unit vector emanating from r and directed to the closest point in the membrane. F-actin (+) ends are continuously polymerizing G-actin at a speed n. Since the dynamic pool treadmills fast and accounts for 95% of F-actin in the spine (Honkura et al., 2008), we take the actin polymerization velocity to be fixed and independent of actin concentration. Thus, v = nd represents the velocity field of actin polymerization.
The second term in Equation 10, h∇ • (f u B), accounts for the change in B due to a force density f u , given by where c is a soft repulsive potential defined in Equation 6. Actin filaments interact with the intracellular environment, which includes transient attachments to the substrate (Doubrovinski and Kruse, 2011). The effects of such interactions are represented by effective filament motility parameter h that reduces the impact of ∇ • f u on B. The force field f u confines the system to V (Doubrovinski and Kruse, 2011). Note that the vectors of f u have direction d (Fig. 2D and H ). Hence, f u can describe the force that generates a retrograde flow of F-actin to create a gap between the barbed end and the membrane to fit G-actin during polymerization. The nucleation function accounts for the nucleation of new filaments with uncapped (+) ends by Arp2/3 binding to actin filaments. We choose the nucleation function proposed by Carlsson et al. (2004) which reflects the side-branching of Arp2/3. This function agrees with the observations in Risca et al. (2012), where the nucleation of new filaments is enhanced on the side of bent filaments. Here, k nuc represents the nucleation rate, A is the Arp2/3 concentration, and C 1 is a unit conversion factor. Cofilin severs F-actin in a concentration-dependent manner (Andrianantoandro and Pollard, 2006), creating new filaments with uncapped (+) ends. Based on observations of Bosch et al. (2014), we assume that during the first few minutes after LTP, the concentration of F-actin in the spine is higher than the concentration of cofilin. Because cofilin severs F-actin at low concentrations (Andrianantoandro and Pollard, 2006), we assume that cofilin binding to F-actin is cooperative (De La Cruz, 2005). This is represented by the severing rate function where k sev is the severing rate, k n is the dissociation constant, n is the Hill coefficient to capture the cooperative nature of the kinetics, and C represents the concentration of cofilin.
Arp2/3 dynamics. The evolution of Arp2/3 concentration over time is given by where k A is a constant degradation rate. We assume that as the dendritic spine expands, the Arp2/3 molecules are transported toward the membrane by a bulk flow, as in Tania et al. (2013). Note that the motion of the bulk flow is driven by the motion of F-actin since actin is highly dense in the spine. The speed of the bulk flow is given by jn mp with n mp representing the protrusion velocity. To ease the model simulations, we make the following simplifications: (1) instead of calculating n mp at each node of the mesh representing the spine membrane, we take the velocity of the protrusion at one side of the spine head for all nodes, as described in "Force-velocity calculation" section.
(2) If the protrusion shrinks at that location, i.e., the velocity direction is opposite to the expanding direction, we take n mp = 0 to avoid numerical instability.
(3) The polymerization velocity (n in Eq. 10) is lower than n mp . Thus, we multiply n mp by 0 < j < 1 to have similar velocities in all the variables and avoid numerical problems. This can represent the hindering of Arp2/3 and cofilin by the high density of proteins inside the dendritic spine (Helm et al., 2021).
Cofilin dynamics. The evolution of cofilin concentration is given by where k C is the degradation rate. Note that cofilin is also transported by bulk flow toward the membrane at speed jn mp .
Numerical implementation. In the model, the dynamics of actin barbed ends, Arp2/3, and cofilin are given by Equations 10, 14, and 15. In the simulation, these equations are solved over time in a cubic domain with an embedded triangular mesh representing the dendritic spine membrane (Fig. 2E). A signed distance function is used to calculate the proximity of the chemical species to the membrane (Fig. 2B). The force generated by actin polymerization and the force generated by the membrane are calculated from the spatial location of the barbed ends and the geometry of the spine mesh, respectively. These forces dictate the evolution of the spine mesh (Eq.3). To reach a stable spine morphology, we set the basal influx to be homogeneously distributed inside the spine head and impose absorbing boundary conditions because the proteins have a stable arrangement in the spine neck (Bär et al., 2016). The basal influx remains in the same location throughout the simulation (cyan circles in Fig. 2I). We assume that the stimulus-triggered influx rapidly reaches the spine head at Δ x distance from the membrane and within z = 0.7 and z = 1 μm (red triangles in Fig. 2I). We restrict the height of the stimulus location to capture the observations that the configuration of the postsynaptic density (PSD), a dense receptor site at the tip of the spine, remains stable during the early phase of sLTP (Bosch et al., 2014;Tønnesen et al., 2014), i.e., the duration of the simulations. Finally, we scale the stimulus-triggered influx to the initial head size to ensure that the same amount of proteins flows into the spine during the stimulus time window. We solve the system of PDEs using MATLAB's (MATLAB, 2021) ode45 solver at each time-step Δ t for all positions r of a discretized cubic lattice domain L [ R 3 with Δ x spacing (Fig. 2J ). The gradient operator is discretized using an explicit finite difference scheme. The spine membrane G is approximated by a 3D polygon with a triangular isotropic mesh consisting of n v vertices located at s i [ R 3 , i [ {1, 2, . . ., n v } (Fig. 2K ). G is updated according to Equation 3 at each time-step. For numerical accuracy, the membrane is remeshed using an isotropic remesher (Helf, 2021) (based on OpenMesh; OpenMesh, 2020), with a target edge length of Δ s . The points corresponding to the base of the dendrite are fixed throughout the simulation (Fig. 2A). See Figure 3 for a flowchart of the simulation.
Membrane discretization. In this section, we present the discretized version of the continuous PDE system that we implement in the simulation. Note that we use the concentration of Apr2/3 and cofilin, or the number of barbed ends per unit volume (Fig. 2G) instead of modeling the structure of the dendritic spine cytoskeleton (Fig. 2F ). The points of the cubic domain mesh r remain constant over the simulation (Fig. 2E and J ). A triangular mesh that represents the spine membrane is embedded in this cubic domain (Fig. 2K). The position of the nodes of the spine mesh s change at each time-step according to Equation 3.
The distance d(r, G), defined in Equation 7, corresponds to the minimum distance from the lattice point r to the hexagonal mesh representing the membrane V. For its calculation, there are two cases (Fig. 2D): 1. The closest point to the membrane from r is a vertex s. Then, 2. The closest point to the membrane from r is the triangular face i that spans through the vertices s i , s i+1 , s i+2 . Then, where N i is the normal vector and × represents the cross product.
In our numerical implementation, the normal vector to the mesh surface points to the outside of the spine, and the polymerization direction is −w because we are using the signed distance. This is considered in the calculations by changing the signs accordingly.
For the calculation of the force generated by the membrane F mem (Eq.8), we follow Zhu et al. ( 2022) and take the bending force with the spontaneous curvature equal to zero. To obtain the initial spine shape, we construct a mesh with a spheroid connected to the x-y plane representing the membrane via a cylinder, which represents the spine neck. To obtain a stable shape, we let the position of the vertices evolve according to Equation 3, but only considering the force generated by the membrane (i.e., F actin = 0). We anchor the spine to the dendrite by fixing the vertices corresponding to the dendrite (magenta points in Fig. 2A).
Constraining kinetic parameters to experimental measurements. Experimental results from Bosch et al. (2014) form the foundation of the temporal dynamics of the number of barbed ends, and Arp2/3 and cofilin concentration in our model. Therefore, we fit the model parameters to the data in Bosch et al. (2014) that shows the normalized concentrations of β-actin, Arp2/3, and cofilin-1 in dendritic spines of hippocampal CA1 neurons after inducing sLTP by 2 photon glutamate uncaging for 1 min (Fig. 4A and B). Other parameters are taken from the literature or set to a physiological range (Table 1).
Protein dynamics. We obtain the data points from Figure 1 in Bosch et al. (2014) by annotating the plots in Fiji (Schindelin et al., 2012) with the multi-point tool. This figure shows the volume (RFP) and amount of GFP protein quantified by the relative fluorescence intensity (F) to the average baseline (F 0 ). The points are exported to Matlab (MATLAB, 2021) using the ReadImageJROI.m function (Muir and Kampa, 2015). After scaling the points to the corresponding scale set in Fiji, we calculate the normalized concentration of the proteins as the ratio between the protein fluorescence intensity and the spine volume, as in (Bosch et al., 2014;Fig. 4A).
For estimation of the parameters corresponding to the actin, cofilin, and Arp2/3 influx into the dendritic spine upon LTP, we develop a minimal model that assumes that these proteins are continuously entering and exiting the spine. Moreover, we consider protein degradation and recent experimental findings showing that some proteins, like β-actin, are synthesized locally in the spine (Tiruchinapalli et al., 2003;Cajigas et al., 2012). For simplicity, we have gathered the continuous protein influx and synthesis in a source term I, and the efflux and degradation in a decaying rate k. Besides these continuous fluctuations, we consider a protein influx triggered by LTP induction I S (t), only present during the glutamate uncaging (1 min). We assume that I S (t) are constant values and that during the stimulus window, corresponding to the 1-min of glutamate uncaging, I S (t) > 0 and I S (t) = 0 otherwise. Hence, in this minimal model, the dynamics for the normalized To reduce the number of parameters and guarantee that the pre-stimulus (i.e., I S,b (t) = I S,A (t) = I S,C (t) = 0) level of protein concentration equals one for all the proteins, we first calculate the steady state of the system (b Then, we scale the system using the nondimensional quantities (b(t), a(t), c(t We fit the data points from Bosch et al. (2014) We use the ode45 solver to evolve the system in Equation 19. Figure 4B shows the resulting fit (squared norm of the residual = 0.0538), and the resulting parameters are in Table 1. We obtain the value of actin basal concentrations from Helm et al. (2021), and for Arp2/3 and cofilin, we take the mean of the molarity of the cytoskeleton proteins, hence (b * , A * , C * ) = (3000, 20, 40) μM.
We use a simplified version of our model that neglects the spatial component (i.e., setting to zero the F-actin elongation, repulsive force density, and bulk flow terms in Eqs. 10, 14, and 15) to compare the model output against experimental measurements (Fig. 4C ). Note that the evolution of the normalized concentration of barbed ends in the model is similar to the normalized concentration of β-actin from the experimental data, despite the differences in the evolution of the concentrations of Arp2/3 and cofilin (Fig. 4D). This discrepancy between the evolution in the experimental data and the model arises because the nucleation and severing events in the model reduce the protein concentrations (terms −f nuc and −f sev in Eqs. 14 and 15, respectively). During our data fitting, we did not account for the interactions between ABPs and actin because the experimental setup does not distinguish when cofilin is bound or unbound to F-actin (Bosch et al., 2014;Eq. 19). For example, the slow decay of cofilin normalized concentration in the data is attributed to cofilin binding to F-actin and Edge length μm 0.05 Fitted stabilizing it (Bosch et al., 2014). However, in the model, cofilin normalized concentration sharply decays after stimulation to a value below pre-stimulation due to the enhancement of severing events induced by the increase of F-actin. The concentration of Arp2/3 in the model exhibits a similar decay due to the increase of nucleation events promoted by the increase of F-actin. We are interested in the evolution of the spine expansion upon LTP, which we assumed to be regulated by the force generated by actin polymerization. Hence, we considered this model to be a good proxy for the interactions between proteins upon LTP because it exhibits a trend in the evolution of barbed ends similar to the evolution of β-actin in the experimental data.
Scale factors. To convert the concentrations of β-actin to number of barbed ends per μm 3 , we assume that there are 167 G-actin per F-actin since the mean length of dynamic F-actin in dendritic spines is around 450 nm (range: 200-700 nm; Honkura et al., 2008) and a monomer of actin contributes to 2.7 nm of the filament length (Mogilner and Oster, 1996). To change from number of molecules to μM, we use Avogadro's number and obtain C 0 ≈ 3.6 number of barbed ends per (μm 3 μM). To convert number of barbed ends per μm 3 to concentration of Arp2/3, we follow Tania et al. (2013) and assume that there is a minimal distance of 37 nm between branches of a filament. Hence, there are 12 molecules of Arp2/3 per F-actin, which gives a scale factor of C 1 ≈ 0.2 μm 3 μM/ number of barbed ends.
Force-velocity calculation. To calculate the protrusion velocity of the spine (Eqs.14 and 15), we select a node in the mesh corresponding to the middle of the spine head at the start of the simulation and keep track of its horizontal displacements in a fixed x-direction at each time-step. The velocity is calculated by dividing the displacement by the time-step duration in every iteration of the model. We assume that negative displacements have zero protrusion velocity. For the force-velocity relationship, the forces are measured locally, i.e., we take the average force generated by the nodes of the triangular face that intersects the cell displacement trajectory.
Stimulus. In our model, the stimulus from the presynaptic terminal triggers an influx of proteins into the spine, consistent with the observations in Bosch et al. (2014). Therefore, we assume that the stimulus-triggered terms I S,b , I S,A , and I S,C in Equations 10, 14, and 15, respectively, are set to the values in Table 1 divided by the number of basal protein locations during the 1 min period, and zero otherwise. The division ensures that the total levels of proteins in the model match experimental data. The basal influxes are also divided by the number of basal protein locations. Before and after the stimulus window, these terms are equal to zero. The stimulus-triggered influx is normalized for the spine volume, so the amount of stimulus-triggered influx is independent of the size of the spine. Note that we localize the stimulus-triggered influx to the spine head instead of simulating its transport from the dendrite through the spine neck because the experimental data only show the increase of the proteins in the spine head. Moreover, the diffusion of G-actin from the shaft to the spine is fast (time constant of 0.005-0.67s; Honkura et al., 2008), and the available data do not determine whether the proteins enter the spine using vesicular transport or diffusion. Thus, modeling the transport of actin and ABPs through the spine neck would represent an additional delay to the dynamics for which we do not have experimental data. Moreover, most actin filaments in the spine neck are stable and form rings (Honkura et al., 2008;Bär et al., 2016), suggesting that the dynamics between actin and ABPs can differ in that domain.
Results
Using a minimal model for spine actin-membrane interactions, we investigated the spatio-temporal evolution of the number of barbed ends and concentration of Arp2/3 and cofilin. We used 3D numerical simulations to validate our model against experimental observations of spine growth during sLTP qualitatively. We investigated how different mechanical parameters can affect spine growth dynamics during a single stimulus and finally, predict how spine volume can change due to multiple stimuli. These results are discussed in detail below.
Stimulus-triggered influx reproduces experimentally observed spine growth dynamics
We begin with an investigation of the spatial distribution of the number of barbed ends and the concentration of Arp2/3 and cofilin over time. First, we ran the simulation for 1 min while keeping the membrane fixed. We observed that the ABPs kept a similar spatial configuration at the end of 1 min while the barbed ends increased at the center of the spine head (Fig. 5C). We speculate that the increase is due to the repulsive force density term in the barbed ends dynamics (Eq.10). Therefore, we allowed membrane evolution for 3 min (Eq.3) and investigated whether the barbed end concentration reached a steady state when the force generated by them pushed the membrane forward. We found that the increase in the number of barbed ends slowed down at the end of the 3 min. Moreover, the shape of the dendritic spine settled to a new equilibrium shape in which the length of the neck was reduced and its width increased while the width of the head was reduced (Fig. 5A). Therefore, for the initiation of each simulation condition, we use this framework in which mechanical equilibrium is achieved.
Having established the mechanical equilibrium, we next simulated sLTP induction in the spines by activating the stimulus-triggered influx inside the head (Fig. 2I ) for 1 min, which results in a transient spine enlargement (Fig. 5C). Four minutes after the stimulus, the spines settled to a new larger size with a shorter and wider neck consistent with experimental observations (Bosch et al., 2014;Tønnesen et al., 2014;Yang and Liu, 2022). The final shape is shown in Figure 5B.
Next, we integrated the values of the number of barbed ends and concentration of ABPs over time in the cubic domain to understand how the stimulus alters the dynamics of ABPs. Figure 5D shows that the total values of the number of barbed ends, Arp2/3, and cofilin concentration equilibrate before the stimulus is added at 4 min. When the stimulus was triggered, we observed that the number of barbed ends increased, as in the experimental data (Fig. 4A). The cofilin concentration also increased while the Arp2/3 concentration decrease slowed down (Fig. 5D). Arp2/3 decreased throughout the simulation because it is sequestered for nucleation events (see Eqs. 14 and 12). After stimulation, the variables decayed at rates similar to the experimental observations (Bosch et al., 2014; compare with Fig. 4A). These dynamics, informed by the parameter estimation, qualitatively replicate the experimentally observed protein dynamics.
We next calculated the spine volume and surface area over time (Fig. 5E). Note that small fluctuations appeared when we allowed membrane changes (after minute 1) driven by the balance between the force generated by actin polymerization and the force generated by the membrane. The spine volume increased during the stimulus and continued to increase at a slower rate after the stimulus was turned off. The surface area also increased during the stimulus but settled to a new equilibrium value afterward. The first phase of growth is consistent with the main features of sLTP, where the spine head size increases in response to a stimulus (Matsuzaki et al., 2004;Bosch et al., 2014;Tønnesen et al., 2014). However, in experimental data, the spine shrinks after the first phase but we do not see this shrinkage in our model, which is likely a result of our simplifying assumptions. The addition of further mechanisms to our model could prevent such an increase.
We also measured the radius of the spine neck and head at the same height over time (z ∼ 0.63 μm and z = 0.84 μm, respectively; Fig. 5F). While the spine head radius showed a similar trend to the spine surface area, the spine neck increased after the stimulus finished, consistent with Tønnesen et al. (2014). After normalizing the values of volume, surface area, spine neck radius, and head radius (Fig. 5H ), we observed an increase in the spine volume (14.92%) and a small increase in the spine surface area (3.03%) and spine head radius (4.6139%) after stimulation. Figure 5G shows the normalized variables of Figure 5D divided over the normalized volume. The increase in cofilin concentration is larger than the increase in the number of barbed ends and similar to experimental data (Fig. 4B). Note that in the model, the decrease of the ABPs and number of barbed ends after stimulation is faster. Moreover, Arp2/3 shows a sustained decrease. Overall, we found that our 3D model qualitatively replicates the temporal trends in protein concentration observed in experiments (Bosch et al., 2014).
To further inspect how the forces generated by the membrane and actin polymerization influence the shape evolution of the spine, we plotted the membrane mesh color-coded by the norm of these forces (Fig. 6A). During the first phase of the simulation, where the membrane is fixed, we observed that the actin polymerization force dominates in the spine head while the membrane force dominates in the spine neck. When the membrane is allowed to move, the actin polymerization force decays at the tip of the spine head, and the membrane force increases at the base of the spine neck. As expected, the force generated by actin polymerization increased in the spine head during the stimulation window but settled to a new steady state after 4 min. Throughout the simulation, the force generated by the membrane is higher at the spine neck and the base of the spine rather than at the spine head. Note that the higher force in the spine neck arises from the smaller radius of the neck compared to the spine head radius while the higher force at the shaft is due to the sharp change in curvature in the junction of the spine neck and the dendrite.
To quantify the evolution of the forces generated by the membrane and actin polymerization over time, we integrated the norm of the force vectors corresponding to each node of the spine membrane (Fig. 6B). At the start of the simulation, where the spine membrane is fixed, there is a rapid decay of the total force generated by actin polymerization because the barbed ends are pushed back by the repulsive force density (Fig. 5C ). The forces settled to a stable value when the membrane was allowed to evolve. During the stimulation window, the force generated by actin polymerization increased while the force generated by the membrane showed a smaller increase. We further analyzed this difference by obtaining the normalized change in the forces (Fig. 6C). Although the total force generated by the membrane has a smaller increase than the force generated by actin polymerization, it decreases the effect of the force generated by actin when the forces are added (Fig. 6C). After stimulation, the force generated by actin polymerization decays to a value smaller than that before stimulation, which relates to the trend shown by the barbed ends (Fig. 5G). The sum of the forces reaches an equilibrium at the end of the simulation. The force-velocity relationships are shown in Figure 6D-F for the forces generated by the membrane, actin polymerization, and their sum, respectively. We calculated the force-velocity for spine growth by calculating the membrane horizontal displacement at the middle of the spine head and measured the forces locally. This relationship is nonlinear (could not be fitted to a line). Consistent with other actin-mediated force-velocity relationships, we find that the velocity is higher for smaller forces (McGrath et al., 2003;Brangbour et al., 2011). We observed that higher actin polymerization forces do not correspond to faster protrusions (Fig. 6E), which signals a delay in the membrane response to polymerization forces.
Barbed ends determine spine volume change
The Ca 2+ entry due to the spine activation of the NMDARs triggers a signaling cascade that leads to activation of Arp2/3 and cofilin (Rangamani et al., 2016;Fig. 1B). Because, in our model, we only account for the external influx of these proteins upon LTP, we investigated whether a further increase due to NMDAR activation enhances spine enlargement. For this, we assumed that the activated proteins contribute to the proteins entering upon LTP induction. Thus, we increased the value of I S,A and I S,C by 50%. To have a better representation of the changes in volume, number of barbed ends, and concentration of proteins, we obtained their normalized values at each minute of the simulation (Fig. 7). We found that the increase of Arp2/3 and cofilin (Fig. 7C and D) slightly increases the production of barbed ends (Fig. 7B). However, there is a slight reduction in the normalized volume after stimulation, from 1.1493 to 1.1287 (Fig. 7A). Thus, we concluded that the increase in Arp2/3 and cofilin might produce a negative effect on spine enlargement., Normalized (to t = 0 min) total force F. D-F, Plot of the protrusion velocity over force generated by the membrane, actin polymerization, and both, respectively. The protrusion velocity was calculated as the horizontal displacement of the membrane at the middle of the spine head. The speed with negative directions was set to zero. The force was taken locally, i.e., the force corresponding to the triangular face intersecting the displacement vector.
We then examined the effect of disrupting the stimulus-triggered influx of barbed ends, Arp2/3, and cofilin on spine enlargement. We observed that setting I S,A = 0 or I S,C = 0 during stimulation had an insignificant effect on spine enlargement: the normalized increase in volume at the end of the stimulation changes from 1.1493 to 1.1417 and 1.14926, respectively (Fig. 7A). Only impeding the stimulus-triggered influx of actin hindered the increase in spine volume to 3.72% after stimulation. Note that when I S,b = 0, the peak of normalized Arp2/3 and cofilin concentration after stimulation exhibits an increase, proving that these proteins are sequestered by the stimulus-triggered influx of actin through nucleation and severing events (Fig. 7C and D). From these simulations, we conclude that as long as there are sufficient barbed ends, spine volume change will be robust. The biochemical details of Arp2/3 and cofilin are critical for governing the dynamics of actin reorganization (Pollard and Borisy, 2003) but the number of barbed ends determines the growth itself. We inferred that the enhancement of barbed ends after LTP induction is sufficient to overcome the membrane force and allow spine enlargement, in line with previous theoretical work in cell protrusion (Lacayo et al., 2007;Rangamani et al., 2011).
Effect of membrane bending stiffness on spine morphology
The bending stiffness of neuronal membranes varies depending on the neuron type and compartment (i.e., cell body, neurite, growth cone) from 1.8 × 10 −19 to 2.3 × 10 −19 J (Pontes et al., 2013). In our model, we noticed that the total membrane force exhibited only a small increase during the stimulation window (Fig. 6C). However, the total membrane force diminished the increase of the force generated by actin polymerization. Therefore, we further investigated the effect of the force generated by the membrane on spine growth upon stimulation. To do this, we varied the bending stiffness k in Equation 9 by 25% its value in the simulations. We observed changing membrane stiffness altered the spine shape after stimulation. With a larger membrane stiffness, the spines show a wider neck and thinner head (Fig. 8A), suggesting that the increase in the membrane stiffness counteracts the high curvature of the neck and the side of the spine head. Decreasing the bending stiffness results in spines with a thinner neck (Fig. 8B).
Interestingly, we observed that the increase of volume of the spine during stimulation is similar for the different values of membrane stiffness (V = 1.1493, 1.1447, and 1.1347 for k, 1.25 k, and 0.75 k, respectively; Fig. 8C). However, the spine with reduced membrane stiffness showed a smaller change in volume after stimulation due to the reduced spine neck radius. The most notable change in the normalized number of barbed ends, Arp2/3, and cofilin for different values of k occurs after the stimulation window (Fig. 8D-F). The normalized concentrations of B, A, and C are smaller for increased bending stiffness. There is an increase in the force generated by actin polymerization upon stimulation when the membrane stiffness increases (Fig. 8H ). We assumed that such an increase is due to the resistance of the membrane in the middle of the membrane head, where the curvature is smaller and the barbed ends need to produce a higher force to enlarge the spine head. As expected, the changes in the force generated by the membrane are directly related to the membrane stiffness: when the membrane is stiffer it generates a larger force (Fig. 8G). Note that a decrease in k results in a decrease of the overall force (i.e., ||F mem + F actin ||; Fig. 8I). Taken together, we conclude that the resistance offered by the membrane affects the spine shape dynamics and the membrane mechanical properties due to lipid and protein composition could play an important role in sLTP.
Perisynaptic mechanical forces promote spine enlargement
Dendritic spines are embedded in an ECM and surrounded by other neurons and glia cells (Fig. 9A). Hence, spine enlargement can be promoted or hindered by these perisynaptic elements (Dityatev and Rusakov, 2011). Recent experimental results show that coupling F-actin in the spine head with the extracellular space via a molecular clutch is necessary to achieve spine enlargement upon LTP (Kastian et al., 2021;Fig. 9A). This molecular clutch mechanism was proposed from the observation that the forward movement of the protrusion growth is variable despite the retrograde flow being continuous. Such variation could be due to the transient linkage of F-actin with membrane proteins bound to ligands on the substrate (Mitchison and Kirschner, 1988). In their experiments, Kastian et al. (2021) showed that shootin1a couples polymerizing F-actin to cell adhesion molecules N-cadherin and L1-CAM. Moreover, LTP induction triggered Pak1-mediated shootin1a phosphorylation, promoting the coupling between F-actin and adhesion molecules. This clutch coupling is thought to reduce the retrograde flow of F-actin and increase the force generated by actin polymerization (Kastian et al., 2021). Here, we investigated the impact of such coupling on volume growth.
We assumed that upon LTP induction, shootin1a is highly phosphorylated, and hence, it mechanically couples F-actin to the extracellular adhesive substrates for the extent of the stimulation window. To simulate the reduction of retrograde flow and the resulting enhancement of actin polymerization caused by this coupling, we first decreased the value of the effective filament mobility h in Equation 10. We found that reducing h by 40% decreases the normalized volume after stimulation (from 1.1493 to 1.1268) and affects the volume evolution of the spine (Fig. 9C). Therefore, we instead increased the polymerization velocity n by 40% in Equation 10, which caused a further reduction in the normalized volume at the end of the stimulation window (ΔV = 1.1085;Fig. 9C). Since the normalized number of barbed ends per unit volume increases during this period (Fig. 9D), we concluded that the reduced change in volume results from the interaction between forces: when the polymerization force increases, it increases the counteracting forces generated by the membrane. Interestingly, combining the reduction in h and the increase in n lessens the volume increment at the end of the simulation (Fig. 9C ).
We next investigated which other mechanisms could enhance spine expansion. To allow spine enlargement upon LTP, the ECM remodels through the action of matrix matalloproteinase-9 (MMP-9; Wang et al., 2008). This protease degrades the ECM after LTP until it is inhibited by the tissue inhibitor of matrix metalloproteinases-1 (Magnowska et al., 2016). We mimicked these interactions by decreasing the effective drag coefficient v by 40% during the stimulation window. We observed an enhancement in the normalized volume of 6.73% after the stimulation (Fig. 9C and E). Moreover, when we combined the decrease in the drag coefficient with a rise in polymerization speed, the normalized volume change after the stimulation window was enhanced by 8.65% from control (Fig. 9C and E). Interestingly, the normalized sum of the forces generated by the membrane and actin polymerization reduces (Fig. 9B and F ) indicating that the enhancement of spine enlargement after stimulation is achieved by a membrane that is more sensitive to changes in the forces.
Repeated LTP inductions lead to a reduction in spine growth rate
Since synapses receive a series of stimuli (Huang and Kandel, 1994), we finally investigated what would happen to spine enlargement upon repeated LTP inductions. To do so, we simulated repeated LTP inductions: the first at the start of the simulation, the second at minute 2, and the third at minute 4. Thus, the spine was stimulated for 1 min (i.e., I S > 0 in Eqs. 10, 14, and 15) and let to rest for the following minute. We chose this stimulation protocol because the spine volume increase is reduced after 1-min rest (Fig. 10B).
We observed an increase in the size of the spine head and a shortening and widening of the spine neck at the end of each stimulus (Fig. 10A). The spine normalized volume increased with every LTP induction (Fig. 10B). Note that the volume increase after each induction is larger than the increase between stimuli (Fig. 10E). However, the volume increase lowers after each instance of the stimuli (14.92%, 12.6%, and 10.36% after the first, second, and third stimulus, respectively). Interestingly, the peak of the normalized number of barbed ends per unit volume was smaller after each stimulus, while the peak in normalized cofilin concentration was higher in the last stimulus (Fig. 10C). The normalized concentration of Arp2/3 reduced its decreasing rate during each stimulation window (Fig. 10C).
The peak of the total normalized actin force during the stimulation window decreases over time while the force generated by the membrane has a steady increase during the simulation that is independent of the repeated stimulations (Fig. 10D). Note that after the third stimulation, the normalized force generated by actin is lower than the normalized force generated by the membrane, which can explain the reduction of the respective volume increase. Taken together, the change in the trends of the normalized protein concentration and forces hint at a complex relationship between the stimulus-triggered influx and the spine volume. In our simulation, spine enlargement was dependent on the spine size at the start of the LTP induction: larger spines showed a smaller increase, consistent with experimental observations (Matsuzaki et al., 2004;Hobbiss et al., 2018).
Discussion
In this work, we proposed a minimal biophysical model in which spine enlargement upon LTP is driven by similar mechanisms to those of cell motility. Our model accounts for the spatial localization and chemical reactions of actin barbed ends, Arp2/3, and cofilin, and their interactions with the spine membrane. For purposes of computational tractability, we only focused on a few key ABPs. We chose ABPs that capture minimal actin remodeling events (Pollard et al., 2000;Pollard and Borisy, 2003) and are known to be important for healthy brain function. For example, failure of Arp2/3 function leads to spine loss and abnormal synaptic function, enhancing excitation and leading to similar symptoms to psychiatric disorders (Kim et al., 2015), and experimental studies indicate that cofilin is involved in Alzheimer's disease synaptic dysfunction (Ben Zablah et al., 2020). Importantly, the kinetic parameters of these species were fitted to experimental data (Bosch et al., 2014), indicating that our model predictions represent plausible dynamics. The spatio-temporal maps from the simulations give us a sense of how these molecules may arrange themselves in the spine during active remodeling. It is possible that there may be mislocalization of the proteins in the experiments due to the challenges associated with the overexpression of recombinant proteins fused to GFP (Suratkal et al., 2021), resulting in miscalibration. Nonetheless, the main dynamic events of increase in actin barbed ends and the net force generated are consistent with the literature (Mogilner and Oster, 1996;Pantaloni et al., 2001).
Our simulations replicate the initial phase of rapid volume increase seen in experiments (Matsuzaki et al., 2004;Okamoto et al., 2004;Bosch et al., 2014). The force-velocity relationships predicted from our model are consistent with other actinmediated force-velocity relationships (Mogilner and Oster, 1996;McGrath et al., 2003;Brangbour et al., 2011). The spatial nature of our model allows us to investigate the forces distribution along the spine membrane over time. We observed that rapid volume increase in our model is driven by the enhancement of the force generated by actin polymerization due to the stimulus-triggered influx of actin. The neck becomes wider and shorter after LTP induction, as observed in experimental data (Tønnesen et al., 2014), hinting that membrane forces could drive changes in the spine neck. Concurrently, the total number of barbed ends decreases below basal levels, thereby reducing the force generated by actin polymerization. Then, the force generated by the membrane dominates, driving spine neck expansion, and hence the slow increase in the total volume of the spine. Therefore, the rapid spine volume increase upon stimulation is due to an enlargement of the spine head by increased actin polymerization while the slow increase in the volume after stimulation is driven by the membrane counteracting the large curvature of the spine neck. Further experiments are needed to test whether the interplay between the polymerization and membrane forces explains the expansion and shrinkage of the spine. Although we did not consider the effect of the periodic actin rings in the neck (Bär et al., 2016;Alimohamadi et al., 2021), we observed that the neck retained its cylindrical structure. Future extensions of our work could explore a previously proposed theoretical hypothesis suggesting that such rings promote mechanical stability of the spine (Alimohamadi et al., 2021).
We showed that the increase of the spine volume upon LTP is enhanced when the interactions between the spine and perisynaptic elements are included. Indeed, recent experimental data found that the clutch molecules, which couple F-actin with the extracellular space, reduce the speed of the retrograde flow and hence, promote the actin polymerization force driving spine enlargement (Kastian et al., 2021). When the coupling with F-actin was disrupted, polymerization of F-actin increased the retrograde flow (Kastian et al., 2021). Furthermore, the activity of MMP-9, which drives extracellular proteolytic remodeling, is necessary and sufficient for spine enlargement and synaptic potentiation (Wang et al., 2008). However, this activity has to be timely inhibited to ensure synaptic responsiveness (Magnowska et al., 2016) hinting to complex dynamics in the ECM.
We examined the spine response under repeated stimuli and observed that their volume expansion reduced after each stimulation. Thus, in line with experimental data (Matsuzaki et al., 2004), larger simulated spines experience less volume increase upon LTP (Matsuzaki et al., 2004). This volume saturation is thought to be regulated by homeostatic mechanisms (Turrigiano, 2008), where the spine regulates its synaptic strength by increasing or decreasing the number of AMPARs or NMDARs at the PSD. It has been shown that dendritic spines that experience these homeostatic mechanisms show larger volume increases upon LTP induction (Hobbiss et al., 2018). Interestingly, spines modulate mechanically their response to multiple stimuli by stiffening (Smith et al., 2007). Here, we show that such homeostasis may be achieved by the interaction between proteins and the forces that drive spine expansion (Fig. 11).
Computationally, to our knowledge, this is the first 3D model that allows simultaneous protein temporal and spatial evolution, described by PDEs in a moving boundary framework, leading to asymmetric shape changes for sLTP. To facilitate computational simulations and mathematical description of the model, we made some simplifying assumptions, such as: (1) There is a large enhancement of protein concentration upon LTP, which allows us to describe their dynamics using a continuum description (PDEs). This limits the model to a short time window after stimulation.(2) Instead of modeling the stimulus-triggered transport of proteins from the dendrite to the spine head through the spine neck, we implemented a localized increment of proteins in the spine head due to the lack of experimental data to determine the type and dynamics of such transport.(3) We assumed that F-actin is constantly branching and severing and only accounted for the number of barbed ends at each location instead of tracking the individual actin filaments. Therefore, our model is not suitable to examine the length or orientation of F-actin.(4) We modeled the dynamics of free cofilin and Arp2/3, and hence, removed the bound cofilin and Arp2/3 through the terms f sev and f nuc , respectively (Eqs.10-15). Although these functions describe the complex binding dynamics between the proteins, our model does not account for the localization of the stable cofilin bound to F-actin.(5) We assumed unconstrained membrane addition through trafficking mechanisms without accounting for the localization of exocytic and endocytic zones (Park et al., 2006), which could influence the resulting spine shape.(6) Instead of modeling the interactions with the ECM and other perisynaptic elements, we modified model parameters. However, molecular clutches are complex and dynamic structures (Giannone et al., 2009) and future extensions can incorporate the coupling of F-actin with the substrate through adhesions binding and unbinding, as in cell protrusion models (Alert et al., 2015;Sens, 2020). We also focused on the biophysical aspects of spine growth but did not include the signaling events that are a part of the process (Bell et al., 2019;Ohadi et al., 2019;Bell et al., 2022a). These signaling events are known to be regulated by the spine shape (Ohadi and Rangamani, 2019;Bell et al., 2022a,b). In future work, our framework can be extended to include these upstream signaling events and downstream receptor trafficking events. Furthermore, because simulations involved the whole cubic domain, presynaptic and perisynaptic elements can be added at some computational cost. Recent progress in the analysis of moving boundary problems could elucidate an understanding of the relationship between model parameters and its dynamics (C ̌anic, 2021).
In summary, we have shown that the simplest biochemical events associated with actin remodeling result in a volume increase upon LTP, which is enhanced when we account for the interaction with the ECM. The spine volume increase lessens after multiple stimuli, which hints at a possible homeostatic mechanism by the interaction between the proteins and the forces generated by the membrane. We anticipate that this work will set the stage for coupled modeling and interrogation of the biochemical and mechanical events of sLTP in closer proximity than before.
Figure 2 .
Figure 2. Simulation setup. A, Initial configuration of the dendritic spine. Magenta dots correspond to the nodes of the dendritic shaft that remain fixed throughout the simulation. B, Slide of a dendritic spine at x = 0 μm showing the value of the signed distance function in the cubic domain L. C, Zoom of B. Each dot represents a position r, color-coded to the signed distance from the membrane (gray mesh). D, Scheme of the different cases for calculating the distance to the spine membrane. Arrows correspond to d = ∇d s . E, Dendritic spine (blue mesh) embedded in a cubic domain L. Dots correspond to the positions r (the discretization of this domain). F, We assume a given concentration of Arp2/3, cofilin, and number of barbed ends per discretized volume (G), instead of modeling the individual filaments. H, Scheme of the different types of motion for F-actin. I, Slide of a dendritic spine at x = 0 μm showing the spatial locations of basal influx (cyan circles) and stimulus-triggered influx (red triangles). Blue dots indicate the initial position of protein densities. Note that the red triangles, blue dots, and cyan circles overlap and that the stimulus-triggered influx is homogeneous in the spine head. J, Zoom to the cubic domain L in E, discretized in intervals of length Δ x . The intersection of the grid lines corresponds to the discretized positions r. K, Zoom of the spine membrane (in blue) embedded in the cubic domain. This membrane is discretized using a triangular mesh, where the node positions s are time evolving. L, Plot of the function c in Equation 6.
Figure 4 .
Figure 4. Obtaining model parameters from data. Stimulus is present from t = 0 to t = 1 min, indicated by the black vertical lines in the plot. During this time, there is glutamate uncaging and the stimulus-triggered influx I S > 0. A, Experimental data taken from Bosch et al. (2014). Asterisks and dots correspond to the mean of the total fluorescence intensity (F) over the averaged baseline fluorescence intensity (F 0 ) of GFP and RFP, which gives a proxy for the amount of proteins in the spine and its volume, respectively. Errors bars denote SEM. B, Markers: normalized concentrations calculated as GFP/RFP from (A). Lines: evolution of the normalized concentration of proteins in the model used to fit the influx, efflux, and decaying rate parameters (squared norm of the residual = 0.0538). C, Evolution of the non-spatial version of the model in Equations 10, 14, and 15. Note that the left y-axis shows the units of barbed ends per μm 3 and the right y-axis the units of Arp2/3 and cofilin concentration. D, Normalized change in concentration of the quantities on (C) over time obtained by dividing the variable value over its value before the stimulation. Asterisks correspond to the β-actin data points in (B).
to the solutions of Equation 19 with the lsqcurvefit function in Matlab to obtain the values of the efflux rate constants: k b , k A , and k C ; and the influxes upon LTP: I S,b (t), I S,A (t), and I S,C (t).
Figure 5 .
Figure 5. Spatio-temporal evolution of proteins upon LTP induction. A, Configuration of the dendritic spine before stimulus. B, Dendritic spine at the end of the simulation. C, Snapshots of the dendritic spine slide at x = 0 μm showing the number of barbed ends per unit volume and the Arp2/3 and cofilin concentration at different times. See Movie 1. D, Sum of the value of the number of barbed ends per unit volume, and Arp2/3 and cofilin concentration in the cubic domain. Vertical black lines signal the times when the simulation changes: at the start, the membrane is fixed, then the membrane is allowed to move until the simulation stabilizes before the stimulus is given (horizontal blue line). E, Spine volume and surface area evolution over time. F, Spine head and neck radius evolution over time. G, Normalized (to t = 0 min) change in the number of barbed ends and concentration of Arp2/3 and cofilin in the spine (concentration/volume). H, Normalized (to t = 0 min) volume, surface area, spine neck, and head radius.
Movie 1 .
Protein temporal and spatial evolution upon LTP. Cross section of the dendritic spine at x = 0 μm showing the value of the number of barbed ends (left), Arp2/3 (center), and cofilin (right) concentrations. Volume, surface area evolution, and head radius. Movie corresponding to Figure 5. [View online]
Figure 6 .
Figure6. Quantification of spine forces evolution over time. A, Temporal evolution of the forces generated by the membrane, actin polymerization, or both, color-coded for the norm of the force vectors. B, Sum of the norm of the forces F = ||F|| generated by the membrane and actin polymerization over the mesh vertices. C, Normalized (to t = 0 min) total force F. D-F, Plot of the protrusion velocity over force generated by the membrane, actin polymerization, and both, respectively. The protrusion velocity was calculated as the horizontal displacement of the membrane at the middle of the spine head. The speed with negative directions was set to zero. The force was taken locally, i.e., the force corresponding to the triangular face intersecting the displacement vector.
Figure6. Quantification of spine forces evolution over time. A, Temporal evolution of the forces generated by the membrane, actin polymerization, or both, color-coded for the norm of the force vectors. B, Sum of the norm of the forces F = ||F|| generated by the membrane and actin polymerization over the mesh vertices. C, Normalized (to t = 0 min) total force F. D-F, Plot of the protrusion velocity over force generated by the membrane, actin polymerization, and both, respectively. The protrusion velocity was calculated as the horizontal displacement of the membrane at the middle of the spine head. The speed with negative directions was set to zero. The force was taken locally, i.e., the force corresponding to the triangular face intersecting the displacement vector.
Figure 7 .
Figure 7. Influence of barbed ends, Arp2/3, and cofilin in spine volume. A, Normalized spine volume at different times for various setups where either the stimulus-triggered influx of Arp2/3, cofilin, or actin are excluded, or the stimulus-triggered influx of Arp2/3 and cofilin are enhanced 50%. The stimulus is delivered during the first minute of the simulation. B-D, Normalized number of barbed ends per unit volume, Arp2/3, and cofilin concentration at different times.
Figure 8 .
Figure 8. Effect of membrane bending stiffness on dendritic spine expansion. A, Dendritic spines after stimulation (top) and at the end of the simulation (bottom), color-coded for the norm of the force vector F = ||F|| at each node of the membrane mesh. The membrane stiffness k was increased by 25% its value. B, Same as (A) but decreasing the value of the membrane stiffness set to 75% its value. C, Normalized (to t = 0 min) volume for different values of k at different times. D-F, Normalized (to t = 0 min) number of barbed ends per unit volume, concentration of Arp2/3 and cofilin in the spine for different values of k at different times. G-I, Normalized (to t = 0 min) total force F generated by the membrane, actin polymerization, and both, for different values of k at different times.
Figure 9 .
Figure 9. Influence of perisynaptic elements in dendritic spines. A, Schematic depiction of coupling between F-actin and ECM and neighboring cells by shootin1a (red arrows). Protease (MMP-9) activity in the ECM. Image created with BioRender.com. B, Snapshots of the dendritic spine mesh at different times with control conditions (top) and including the interaction with perisynaptic elements (40% enhancement of the polymerization velocity n and 40% reduction of the drag coefficient v). Edges are color-coded for the combination of the forces generated by the membrane and actin polymerization. For full evolution, see Movie 2. C, Normalized spine volume evolution at different times. Different colors correspond to different parameter values in the model. D, Normalized number of barbed ends per unit volume at different times. E, Normalized spine volume evolution over time. F, Normalized sum of forces over time F = ||F mem + F actin ||.
Movie 2 .
Influence of perisynaptic elements in dendritic spines. Cross section of the dendritic spine at x = 0 μm showing the value of the number of barbed ends (left), Arp2/3 (center), and cofilin (right) concentrations. Volume, surface area evolution, and head radius. Movie corresponding to Figure 9 with increased polymerization velocity 140%n and decreased drag 60%v. [View online]
Figure 10 .
Figure 10. Effect of repeated LTP inductions in protein spatial distribution. A, Snapshots of a slide of a dendritic spine at x = 0 μm showing the spatiotemporal distribution of the number of barbed ends, Arp2/3, and cofilin. See Movie 3 for full evolution. B, Normalized volume evolution over time. Different stimulation windows are marked with a blue horizontal line. C, Normalized number of barbed ends per unit volume and Arp2/3 and cofilin concentration in the spine. D, Temporal evolution of the integral of the force generated by the membrane, actin polymerization, or both, normalized to the value at the start of each stimulus. E, Normalized volume and number of barbed ends per unit volume at different times, corresponding to (B) and (C), respectively. Numbers at the top of the bars indicate the percentage change between consecutive times.
Movie 3 .
Effect of repeated LTP inductions in protein spatial distribution. Cross section of the dendritic spine at x = 0 μm showing the value of the number of barbed ends (left), Arp2/3 (center), and cofilin (right) concentrations. Volume, surface area evolution, and head radius. Movie corresponding to Figure 10. [View online]
Figure 11 .
Figure 11. Evolution of the dendritic spine enlargement upon LTP. Summary of the main predictions from our model for the change in spine volume and how it may be regulated by different components. Created with BioRender.com.
Table 1 .
Model parameters for further information about parameter fitting (see "Constraining kinetic parameters to experimental measurements" section)
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Domain: Biology
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Revision of the Dicranotropis hamata group (Auchenorrhyncha, Delphacidae) and remarks on the implication of chiral dimorphism in its history
A new species, Dicranotropis remaniaca, is described. Morphological differences between the new species and the closely related D. hamata (Boheman) and D. zenata Logvinenko are summarized. Chirality is discussed in view of hypothetical implications in the history of the D. hamata group and of the presence of the phenomenon in a supposed hybrid area between D. hamata and D. remaniaca in southwestern France. Zoogeographic and phylogenetic aspects are discussed using D. sagata Logvinenko as outgroup.
Introduction
The genus Dicranotropis was established by Fieber (1866) for the type species Delphax hamata Boheman, 1847. It is widely distributed in the Palearctic region. Some species are recorded also from tropical Africa, Australia and the Neotropic region, but these records concern probably other genera with double or bifurcate carinae on the frons. No species of Dicranotropis is recorded from North America. Kirkaldy (1907) established for Dicranotropis beckeri Fieber, 1866 the genus Leimonodite on the base of the morphology of the frontal carinae. Nowadays this taxon is considered a subgenus of Dicranotropis. In Europe are recorded: Dicranotropis (Dicranotropis) hamata (Boheman, 1847), D. (Leimonodite) beckeri Fieber, 1866, D. (Leimonodite) divergens Kirschbaum, 1868, D. (Leimonodite) montana (Horváth, 1897). D. carpath-Academic editor: Dominique Zimmermann ica Horváth, 1884 is considered a synonym of D. divergens Kirschbaum (Wagner 1963).
D. hamata is recorded from vast parts of Europe, Turkey, Siberia and perhaps North Africa, D. divergens from most parts of Europe (except for Fennoskandia and the Iberian Peninsula) and some regions of Central Asia, D. beckeri primarily from the Balkan region and Eastern Europe with some relictary area in France and Spain, Turkey and Central Asia, and D. montana from some alpine regions (Germany, Austria, Italy) and Romania.
D. hamata is closely related to D. (s.str.) zenata Logvinenko, 1969, described from the Caucasus region (Georgia), and shares with it a similar genital morphology. D. (s.str.) sagata Logvinenko, 1976, a further species from the Caucasus region described from Georgia, displays a quite different morphology of pygofer and styles, but has some other characters in common with D. hamata concerning for example the aedeagus shape in males and the shape of the genital scale in females.
The existence of a taxon slightly different from D. hamata and replacing it in Italy was discovered by Remane and independently by D'Urso already many years ago, but difficulties to obtain material also from the closely related species described by Logvinenko from the Caucasus delayed the publication of these data until today.
The aim of the present paper is to describe the new taxon, D. remaniaca sp.n., to outline the distribution of D. hamata and D. remaniaca, respectively, and to discuss the relationships between both taxa and D. zenata, and to debate the relevance of aedeagal chiral dimorphism in the history of this species group.
Material and methods
Measurements were made by using a Zeiss Stemi SV 11 Stereomicroscope with ocular micrometer. A camera lucida attachment was used for the drawings; pencil sketches were subsequently copied on cardboard by means of a light table and elaborated with drawing ink. Photographs were prepared with a digital camera Canon Eos70D supplied with lens 105 mm f/2,8 Macro Canon, extension tube 25 mm Canon, and ring flash Nikon SM-2.
We examined specimens of the following collections: -Institute of Zoology, National Academy of Sciences of The material of the Servadei collection is presently not available, but was checked and listed by our colleague Manfred Asche (Berlin) some years ago.
The locality numbers in parentheses in the examined material of the collection Guglielmino (CG) coincide with the locality number system used in our faunistic and zoogeographical papers. For a detailed list of the material please see Suppl.material 1.
Description. In size, coloration and shape very similar to D. hamata: Median carina of frons forked below junction with vertex (Figs 15,16); lateral carinae of pronotum not reaching hind margin; wings of brachypterous specimens between 1.5 and 2 × longer than wide, apically rounded (Figs 9,14).
Coloration. Males 15): Face with carinae white and areas between carinae black or light brown bordered with black; vertex light brown, pronotum light brown with carinae white; mesonotum light brown or more or less dark with white central longitudinal stripe extending onto scutellum; upper side of abdomen black, often with central part and some spots on lateral parts more or less light brown; pygofer black with more or less extended light brown areas; anal tube white; anal style black; forewings (brachypterous) hyaline brown, in brachypterous specimens apical half of suture black with adjacent area of wing dark, basal half of suture and hind margin with adjacent veins white; in macropterous specimens forewings hyaline with apical half of clavus and narrow adjacent area dark; underside mostly black; legs black with knees, tibiae and tarsi light or dark brown, third tarsomere generally dark. Females (Figs 13,14,16): similar to males but generally lighter: areas between frontal carinae light brown narrowly bordered with black; dark spot on wing suture small; upper side of abdomen in great part light brown; ovipositor sheath light; femura often in part light brown.
Genital morphology. Males (Figs 17-26): Pygofer with distinctly protruding dorsocaudal protuberance on each side; protuberances apically with small and short spine in medioventral position ; anal tube on each side with small tooth of variable size near the base in subbasal position (Figs 25,26); styles subbasally on the mediocaudal side with scabrous surface and acute spine shaped process, in the middle distinctly curved mediocaudad and provided with preapical tooth (Fig. 21); aedeagus laterally depressed, ventrally bent, with phallotreme on the right side, only in rare exceptions on the left side; on its dorsal margin in central position with carina comprised of varying number of fused teeth and in preapical position with large single tooth, both bent towards right side; on right side, close to ventral margin, with group of about three small teeth in preapical position and, basally of them, single large tooth curved somewhat dorsad; on left side very close to ventral margin with one or more series of small teeth, varying in size and number, and with group of about three teeth more apically and quite distant from each other and from ventral aedeagus margin . Females: Gonocoxae VIII wide, median margin equally convex (Fig. 46); genital scale distinct, ± triangular, with narrow deep apical incision reaching about half length of genital scale (Figs 44,45).
Diagnosis. Main differences to D. hamata consist in the shape of the genital styles and the aedeagus. The genital styles are stout, curved and provided with a preapical tooth in D. remaniaca while they are slender, straight, devoid of preapical tooth in D. hamata (Fig. 5). The aedeagus has its phallotreme on the right side, only in rare exceptions on the left side, while it is typically on the left side in D. hamata, and also in all other characters of the aedeagus D. remaniaca is the mirror image to D. hamata (Figs 1-4). Other differences lie in the shape of the pygofer which is in D. remaniaca generally with a less protruding dorsocaudal portion and further caudally and dorsally located preapical teeth, therefore these are often visible in lateral view , while D. hamata has a more protruding dorsal portion of the pygofer and the preapical teeth are not visible in lateral view (Figs 6-8). However, the pygofer characters are rather variable and can be misleading in some cases.
Ecology. D. remaniaca shares its ecological characteristics with D. hamata and is found generally on not too dry meadows, often near forest margins or groups of bushes, from low to medium high altitude until about 1600m. Host plants are different species of Poaceae.
Biology. The species was mostly found from beginning of June until end of August, but one record from April (340m) indicates that the taxon may be bivoltine in lowlands. In mountain regions it has apparently only one generation.
We examined specimens from Finland (Fig. 55 (For further material of this taxon see Suppl.material 1).
Remark: The record of D. hamata from Caucasus (Georgia: Kodžori, two males) by Dlabola (1958, Figs 43, 44) refers apparently to this taxon. The figures of the styles show a long preapical tooth as is typical for this taxon (the lack of the subbasal thorn in these figures is probably due to the fact that Dlabola apparently did not the left, in others on the right side (Figs 27,28,30,31,(144)(145)(146)(147)(150)(151)(152)(153)(154)(155)(156)(157)(158). One specimen is found also in a more northwestern region (Dep. Saone-et-Loire) (Figs 76,148,149).40 specimens with intermediate characters were examined on the whole: 22 had an aedeagus with phallotreme on the left side and 18 an aedeagus with phallotreme on the right side.31 specimens were from the same locality (St. Béat), 17 of which had an aedeagus with phallotreme on the left side and 14 with phallotreme on the right side.
Dicranotropis sagata Logv.: small and short styles without preapical tooth and without basal spine shaped protuberance (Fig. 42), and a small aedeagus with low number of teeth on both sides and phallotreme on the right side (Figs 40, 41, see also Figs 25-27 in Logvinenko 1976). The species lacks the large dorsocaudally protruding pygofer protuberances present in reverse specimens were from localities peripheric within the area of that species and not far from the area of D. remaniaca.
For the explanation of this situation we may go back to a period when the areas of the ancestors of both recent taxa were separated and speciation was in progress.
But before we have to make some considerations: The asymmetry of the aedeagus in Delphacidae (as in most of the other insect groups with asymmetric genital structures) is in most cases directional, i.e.only one of the two mirror symmetric possibilities is observed (rare exceptions are found in many taxa). There are, however, several cases in delphacids where the aedeagus asymmetry is not directional and both possible aedeagus types are present in a proportion of 50:50. This phenomenon was recorded for example for Stiroma affinis Fieber (De Jong 1985) and Chloriona vasconica Ribaut (Guglielmino and Bückle 2010). It is called chiral dimorphism, mirror image dimorphism or antisymmetry, and the two possible mirror images are termed enantiomorphs (Schilthuizen 2013). Apparently, this situation provides no disadvantage for the species and has no impact for the mating ability. In several taxa of insects, species of the same genus differ in the direction of chirality (Schilthuizen 2007(Schilthuizen , 2013;;Huber et al. 2007). This implies that intermediate D. hamata and D. zenata, but shares with these taxa the small tooth on the inner side of the dorsocaudal pygofer margin. Females display a small elongate genital scale with very long apical incision (Fig. 48). The gonocoxae VIII are narrowed basad and basally abruptly protruding mediad (Fig. 49).
Unfortunately, our knowledge on both taxa from the Caucasus region is based only on very few specimens, thus the range of variability in these taxa is unknown.
Chiral dimorphism (antisymmetry)
Besides the different shape of the genital styles in D. hamata and D. remaniaca, the most distinct difference between both taxa consists in their aedeagus morphology with this structure in one taxon being the mirror image of the other (Figs 1, 18). Exceptions, i.e. aedeagi with phallotreme on the right side in D. hamata (Fig. 118,119,138,139) or with phallotreme on the left side in D. remaniaca (Figs 183,184), are found in both taxa but they are not very common. It makes approximately 1% in D. remaniaca, and ca.4% for D. hamata, but for the latter species should be considered that most of the their area was distinctly more limited than now due to climate constraints.
A striking parallel case is to be mentioned in another delphacid genus, Chlorionidea Löw. In central and eastern Europe and central Asia occurs C. flava Löw, on the Apennines C. apenninica Guglielmino and Bückle. Both species differ mostly by differences in the morphology of their anal tube and in their aedeagus morphology with this organ being in one species the mirror image in respect to the other (Guglielmino and Bückle 2010).stages of chiral dimorphism must have existed, either during cladogenesis or during anagenesis.
The aedeagus morphology in D. hamata and D. remaniaca with one species representing the mirror image to the other may be interpreted in the same way, with a transitional stage of antisymmetry and a subsequent return to a directional asymmetry opposite to the original one. As such processes are more likely to occur in small populations, possibly this happened in the ancestor populations of one of the two taxa during a situation where Chiral dimorphism is observed also in other groups of insects as in the mantid genus Ciulfina. Populations of four species belonging to this genus were investigated. In one of them a proportion near 50% between both enantiomorphs was observed, in a second one only one enantiomorph was present (directional asymmetry). For two species, however, the proportions of both enantiomorphs were far from 50:50 and unequal among the populations of the same species (Holwell and Herberstein 2010). A completely different situation is found in the snail Partula suturalis Pfeiffer. This species is polymorphic for the direction of coiling. Populations with directional asymmetry are prevailing. Mixed populations are generally small and unstable. As mating between snails of opposite coil is difficult there is apparently a strong selection against chirally dimorphic populations which exist only under special conditions (Johnson et al. 1990).morphology or a mixture of both possible enantiomorphic aedeagus types. In those areas, the species show no signs of hybridisation.
Hybrid area in southwestern France?
Preliminary remark: The existence of supposed hybrids between Dicranotropis hamata and D. remaniaca taxa north of the Pyrenees may imply to describe them on a subspecies level. However, in other contact regions between both taxa (South Germany, Slovenia, Switzerland) to date no specimens were observed that present unequivocably intermediate characters in their genital ulations on both sides of these mountains. The contact between southern D. remaniaca and northern D. hamata populations may have been hindered for a long time, until D. remaniaca populations from the South succeeded in surmounting this barrier and mixed with D. hamata mata populations on the other (southeastern and central France, Figs 52-54). Of course, the Pyrenees were an interface between populations of numerous species which expanded from separate glacial refugia. During postglacial expansions, the Pyrenees formed a barrier for pop- populations from the North. Therefore, we interpret the intermediate characters in the genital morphology of the populations immediately north of the Pyrenees as due to hybridization of populations of both species.
In these supposed hybrid populations, aedeagi with phallotreme on the left side (Fig. 30, as in D. hamata) and with phallotreme on the right side (Fig. 28, as in D. remaniaca) are present. Both aedeagus types are not rare. The proportion, based on 40 specimens, is not far from 50:50 (the phallotreme on the left side, i. e. the "hamata-type", is slightly prevailing). Two scenarios are possible: (1) the fixation of directional asymmetry is lost and the supposed hybridisation resulted in a real antisymmetry (i.e. a not fixed direction of the (asymmetric) aedeagus shape and consequently a 50:50 proportion of both aedeagus types); (2) each specimen has its individual aedeagus orientation not by chance, as in true antisymmetry, but due to special genetic constraints based on the combination of its genetic heritage as the result of hybridisation between populations each of which had their fixed aedeagus directionality. Thus, the hybrid populations consist of a mixture of specimens with different directionally asymmetric aedeagi. In this case, the proportion of the two aedeagus types may be different from the 50:50 proportion, moreover it may be varying between different areas of the hybrid area. This condition may be named "pseudo-antisymmetry".
Biogeographical aspects (Fig. 257)
There is little doubt that the division of D. hamata and D. remaniaca from each other happened not long ago, probably during the last glaciation. The two taxa have a nearly identical aedeagus shape (except for the opposite orientation of one taxon in respect of the other, see above), with only some barely discernable differences in the pygofer morphology and the different shape of the central and apical parts of the genital styles, i.e. differences that certainly need a relatively short time to evolve.
We suppose that the area of the common ancestor of both taxa was restricted during a cold climate period, and finally divided in two separate areas, which was the basal situation for a speciation process towards the presently observed two taxa. During a following warmer period both groups may have extended their areas, and developed a hybrid area where they got in contact with each other.
The present disjunct distribution of D. remaniaca, occuring on parts of the Iberian Peninsula on the one and continental Italy with some adjacent areas on the other hand requires further explanation.
One scenario is the colonisation of the Iberian Peninsula directly from Italian mainland or, less probably, viceversa via drifted macropterous specimens crossing the Mediterranean Sea. Generally macropterous specimens are found quite frequently within D. hamata and D. remaniaca populations, even though brachypterous ones prevail by far. Thus, this possibility cannot be completely excluded. On the other side it is noticeable that for D. remaniaca, in spite of the flight ability of macropterous specimens, there are no records from Sicily and Sardinia, though it is present on the entire peninsular Italy until Calabria.
In our opinion another scenario is more probable: we suppose that the taxon in former times had a continuous distribution in the Westmediterranean region (and possibly not only there) including at least southern France. A following restriction of its area due to climatic changes may have resulted in the division in two separated areas on the two Peninsulas, respectively.
Finally, D. hamata populations might have extended their area in southwestern direction, filled in southeastern France the gap between D. remaniaca populations in Italy and Spain and hybridized with D. remaniaca north of the Pyrenees. In the central part of the Alps D. remaniaca apparently passed the barrier of the main Alpine chains and established itself in a small part of south Germany (probably it is present also in the western parts of Austria: Tirol and Vorarlberg). On the other side D. hamata occurs in a small part of the southern Alps in northern Friuli-Venezia Giulia; north of this area in Carinthia D. hamata is found as well, whereas in western Slovenia D. remaniaca occurs.
Phylogenetic aspects
It is quite evident that D. sagata differs distinctly from the other taxa treated in this study. The large protruding dorsocaudal protuberances of the pygofer are less developed, the styles (Fig. 42) are small and devoid of a basal spine shaped process, the gonocoxae VIII (Fig. 49) have a distinct basal protuberance. Nevertheless it shares with the three other taxa the general morphology of the aedeagus (even though in a smaller and more simple version, Figs 40, 41), the deep caudal incision in the genital scale of females (Fig. 48) and a small thorn near the caudolateral part of the pygofer. These features might represent a synapomorphy of all four taxa. D. hamata, D. remaniaca and D. zenata are very closely related taxa. They share with each other (1) the general shape of their pygofer (Figs 7,23,38) with its dorsolateral parts distinctly protruding caudad, (2) their aedeagus shape (Figs 1,2,17,18,33,34) including the arrangement of spines and teeth on both sides, and (3) the subbasal thorn on their genital styles (Figs 5,21,36).
D. zenata differs from D. remaniaca only slightly in the more robust aedeagus (Figs 33,17), the longer tooth on the genital style (Figs 36,21), and in the shape of the genital scale (Figs 47,45). Both species have as a common character an aedeagus with its phallotreme on the right side. They share this aedeagus directionality with D. sagata (Fig. 40), what suggests that this is the plesiomorphic condition and the phallotreme on the left side of the aedeagus in D. hamata is apomorphic. Furthermore, they have generally a shorter pygofer (Figs 38,23) than D. hamata (Fig. 7), but this character is quite variable. The genital styles with their distinctly bent central part and the robust preapical tooth is structurally similar in D. zenata and D. remaniaca as well (Figs 36,21), even if this tooth is distinctly longer in D. zenata. Possibly this preapical tooth is a synapomorphic character of both taxa, and D. zenata and D. remaniaca are sister species, and together the sister group to D. hamata. Alternatively, it may represent an apomorphic character of the common ancestor of the three hamata group taxa, which is lost in D. hamata. In this case it is a plesiomorphic character of the three hamata group species and does not support monophyly of D. remaniaca + D. zenata.
The small preapical tooth in two D. hamata specimens from northern Poland (Fig. 74) can be interpreted as a residue of the preapical tooth which is generally lost in D. hamata but was possibly present in its ancestor populations, or it may be a result of hybridisation in the past. Presently these populations are apparently surrounded exclusively by areas with pure D. hamata populations.
Further research
For a better understanding of the distribution of D. hamata and D. remaniaca it would be necessary to collect more material above all from the region where the areas of both taxa are adjacent to each other, specifically in the Alpine region, Slovenia, southern Germany, western Alps and southeastern France, but also in Spain, northeastern Europe, and, of course, around the supposed hybrid area in southwestern France.
Furthermore, it would be interesting to compare morphological data, gathered in the presented paper and in future studies, with bioacoustic and molecular data, in order to get further hints on how the present disjunct area of D. remaniaca may be explained, and to assess the hypothesis of a hybrid area in southwestern France.
Crossing experiments between populations from the latter region, and the examination of the offspring of left side phallotreme and right side phallotreme males would as well furnish interesting results.
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Domain: Biology
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D R A F T Signatures of non-neutral processes within the population structure of Streptococcus pneumoniae
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M any bacterial pathogen populations contain a number of co-circulating lineages bearing unique signatures of alleles at selected housekeeping loci [1] and also at a whole genome level [2][3][4]. The maintenance of these discrete lineages is hard to ascribe to purely neutral processes, given the high rate of genetic exchange in these pathogen populations [5]. We have previously proposed that extensive co-adaptation between loci may give rise to these patterns, as even small fitness differences among different combinations of alleles can lead to the loss of less 'fit' lineages under intense competition for resources [6]. Bacterial populations may also segregate into a set of successful 'metabolic types' which are able to co-circulate by virtue of exploiting separate metabolic niches and thereby avoiding direct resource competition and immune pressures [7]. As an example, specific differences in the ability to absorb particular carbohydrate resources have been observed in functional genomics studies of Streptococcus pneumoniae [8], and these may reflect specialization upon different resources within the same environment as a means of avoiding competition.
Several bacterial pathogens are also antigenically diverse: S. pneumoniae, for example, can exist in over 90 different serologically distinguishable states or 'serotypes' [9]. Many bacterial populations -including S. pneumoniae -exhibit strong associations between antigenic type and lineage, at least at the level of MLST [7,10]. Such associations may have arisen through neutral processes; alternatively, as we have previously demonstrated, they may represent the outcome of a combination of immune selection acting upon antigen genes and direct resource competition acting upon metabolic genes and virulence factors [6,10]. Distinguishing between these two hypotheses is complicated by the high levels of linkage disequilibrium observed across the whole genome [10,11]. However, the alternative hypotheses make very different predictions about how the system would respond to perturbation by vaccination, particularly when only a subset of antigenic types are included in the vaccine, as is the case for S. pneumoniae. Under these circumstances, antigenic types that are not included in the vaccine may be expected to increase in frequency but, under the neutral model, it would be highly unlikely that they would do so in association with the genotypes previously associated with vaccine serotypes. By contrast, if the associations were primarily generated by selection, one would expect non-vaccine serotypes to become associated with genotypes that were previously commonly associated with vaccine serotypes [10]. Evidence for this phenomenon of Vaccine
Significance Statement
Populations of Streptococcus pneumoniae (the pneumococcus) appear to form stable clusters of closely related organisms despite the fact that they frequently exchange genetic material. In this paper we show that, rather than emerging by chance, these clusters have evolved to maintain their differences so that they may avoid competing with each other. Our work suggests that these clusters are fundamentally determined by variation within a set of genes which encode "chaperones" that help other pneumococcal proteins fold correctly under changes in the physical environment they would encounter while trying to infect their vertebrate host. These chaperone proteins are also targets of immunity and therefore may have originally diverged to minimise immunological interference between pneumococci, thereby necessitating changes across the whole genome.
S. G., J. L. and E. W. designed the study; S. G. and J. L. conducted the study. All authors were involved in analysis and interpretation of results and available data and in writing the paper.
We declare we have no competing interests. 1 To whom correspondence should be addressed. E-mailEstablishing the contribution of co-adaptation and metabolic competition in the maintenance of lineage structure is thus important as the outcome of certain interventions, such as vaccination, depends crucially on these underlying determinants of population structure. Here, we assess the potential to achieve this by machine learning techniques, which work by attempting to identify relevant features based on information supplied on a set of potential predictor variables for each individual genetic sample. Random forests (RF) are one of such methods currently witnessing a surge of attention, owing to its unique advantages in dealing with large datasets of both numerical and categorical data, as well as having low computational overhead, a nonparametric nature and a well defined probabilistic output [16]. A random forest algorithm (RFA) is an ensemble method that combines the information of multiple, regression or classification trees built around predictor variables towards a response variable. The output of an RFA is composed both of the classification success rates of the response variable and a ranking of the predictor variables (scores) quantifying their relative role in the classification process. RFA-based methods are widely applied in genome-wide association studies of cancer and chronic disease risk [17], drug resistance [18], species classification [19], and in the analysis of microarray data [20]. In the context of host-pathogen systems, machine learning techniques have been shown to be able to successfully ascertain host tropism, for instance by identifying the key sites that determine host specificity of zoonotic viruses [21], by analyzing the probability of Escherichia coli cattle strains more likely to be virulent to humans [22], and by selecting the clear genetic distinctions in both avian and human proteins of Influenza viruses [23,24].
In this paper, we undertake a feature selection analysis of a dataset containing 616 whole genomes of S. pneumoniae collected in Massachusetts (USA), including 133 samples from 2001, 203 from 2004 and 280 from 2007 [3,25], thus representing the bacterial population at the point of PCV7 introduction in year 2000, and any changes that may have followed. These data have been used in numerous studies, including analysis of post-vaccine epidemiological and genetic changes [3,10,26], maintenance of population structure [2], beta-lactam resistance [27], determinants of colonization [28] and constraints on serotype switching [29]. Each isolate in this dataset contains information on its capsular serotype (determined by serological means), and had also been assigned to one of a number of monophyletic Sequence Clusters (SC) using a phylogenetic and clustering analysis on a core genome built from all putative protein-coding sequences that were present in a single copy in all genomes [3]. Using a machine learning technique and a previous allelic annotation of 2135 genes among these isolates (using ATCC 700669 serotype 23F as reference [10], table S1), we attempt to identify the relative contribution of each gene in maintaining the observed population structure in terms of (i) capsular serotype and (ii) Sequence Cluster (SC). We find a clear distinction between the sets and functions of genes highly informative for serotype versus SC, suggesting that different selective processes have led to the emergence and maintenance of S. pneumoniae's population structure.
Results
Genes which predict serotype do not perform well in predicting Sequence Cluster. We first assessed the success of the combined variation in 2135 genes of known and unknown function in identifying the Sequence Cluster (SC) to which isolates belonged, this being a measure of shared ancestry (as per [3]). Classification of SC by RFA was accurate ( Fig. S1B) with all SC types being predicted with success close to 100%. By contrast, the success rate in identifying the capsular serotypes of the 616 whole genomes, although also very high, was not perfect. None of housekeeping genes included in MLST classification performed better than average in predicting serotype or SC (Fig. 1).
As might be expected, genes within the capsular locus (defined as being within but not including the genes dexB and aliA) achieved high scores in predicting serotype but these did not score above average in predicting SC (Fig. 1). We noted that many of these genes contained what appeared to be a high proportion of deletions but, in fact, had simply eluded allelic notation on account of their high diversity at the level of the population. For certain genes, such as those encoding the polysaccharide polymerase Wzy and the flippase Wzx, the allelic notation process failed at least 50% of the time for over 90% of the isolates, essentially working only for 23F (the reference genome) and the closely related 23A and 23B serotypes. In general, the degree of success in allelic notation of each gene was closely linked to the potential for alignment with its counterpart in the 23F reference genome (Fig. S4). Nonetheless, the same shift towards lower RFA scores of capsule associated genes in predicting SC rather than serotype was observed upon performing these classification exercises after excluding all genes which contain > 50% (Fig. S2) or > 10% (Fig. S3) of gene mismatches/deletions. When imposing an exclusion criterion of > 10% we retained only the genes wze, wzg and wzh (in addition to two pseudogenes), and these could also clearly be seen to shift from above the upper 97.5% limit into the neutral expectation of RFA scores when predicting SC (Fig. S3).
Finally, we performed the same analysis excluding all genes which showed mismatches or deletions above a threshold of 1%. This eliminated all of the genes considered above as belonging within the capsular locus, although many flanking genes were retained and a number of these achieved the top 2.5% of RFA scores in predicting serotype ( Fig. 2A, Table 1): 38% of the top genes occurred within 10 genes downstream and upstream of the capsular locus, and 66% were situated within 60 genes (a distance amounting to 2.8% of the genome). None of the genes achieving the top 2.5% of RFA scores in predicting serotype (shown in red in Fig. 2) remained in the top 2.5% category when asked to predict SC. Similarly, all genes which achieved top scores in predicting SC ( Table 2) were only of average importance in elucidating serotype (shown in green in Fig. 2). Interestingly, the MLST gene spi gained a place among the top-scoring genes for SC (Table 2) under this stringent cutoff.
Top-scoring genes for serotype classification mediate competitive interactions. Genomic position for each gene in the dataset against their normalised RFA score. The circular genome is presented in a linear form, with the first gene being dnaA and the last gene parB. MLST genes are marked in yellow diamonds (spi, xpt, glkA, aroE, ddlA, tkt) and genes within the capsular locus with blue diamonds (pseudogenes tagged with 'x'). (B) RFA for sequence cluster classification; figure details the same as in A. Blue shared areas mark the capsular locus (genes within aliA and dexB).
D R A F T
were associated with serotype to be involved in metabolic functions linked to resource competition, at least in related streptococcal species. The top-scoring gene trpF, for example, is known to be essential for the biosynthesis of tryptophan for S. pneumoniae [30], and more generally of the biosynthesis of aromatic amino acids in at least 9 species of bacteria [31]. Another top-scoring gene, fabG, encodes the β-ketoacyl-ACP reductase, the only known keto-acid reductase in bacterial fatty acid biosynthesis [32]. The gene lysC codes for an aspartokinase involved in lysine production and aminoethyl cysteine resistance in Corynebacterium glutamicum [33]. We also found two genes, mvaD and mvaK2, of the mevalonate pathway scoring highly for serotype prediction. This pathway, also known as the HMG-CoA reductase pathway, can be found in bacteria, eukaryotes and archaea [34]. One of its main products, the isopentenyl pyrophosphate (IPP), is used to make isoprenoids, a diverse class of over 30,000 biomolecules. In bacteria, the principal products of IPP include the lipid carrier undecaprenol (involved in wall biosynthesis), plus a range of menaquinones and ubiquinones both involved in electron transport, and the latter also in aerobic cellular respiration [34][35][36]. In S. pneumoniae, these two genes are essential for growth and are proposed to be part of a single operon [35].
ATP-binding cassette (ABC) transporter genes, which are critical for intake and metabolism, were found 5 times more frequently in the top genes classifying serotype compared to those determining SC ( Table 1). As part of one such transporter, the SpuA protein is involved in α-glucan metabolism, whose main substrate is glycogen (polysaccharide of glucose), an abundant resource in human lung epithelial cells [37,38]. The gene patB also encodes part of an ABC efflux pump in S. pneumoniae, responsible for resistance to fluoroquinolenes [39][40][41]. Among other ABC transporters, two other genes were located within the pit operon which is involved in iron uptake. In line with our findings, the pit operon has previously been shown to exhibit strain-specific variation [42]. In contrast, our approach did not select 2 other operons involved in iron uptake (piu and pia), which are conserved between S. pneumoniae strains [42] and therefore unlikely to be predictors of serotype. Another important regulator of iron transport among the topscoring genes is gnd [43]. The latter is transcriptionally linked to another top gene, ritR, which is orthologous to the streptococcal global regulator covR, for which there is conclusive evidence from S. pyogenes, S. suis and S. agalactiae of regulatory functions on capsular biosynthesis [44][45][46]. Another ABC transporter known as Ecs is represented in the top list by one of its two genes, ecsA. The substrate of Ecs is so far unknown, but obligatory anaerobes or microaerophilic bacteria do not carry the Ecs transporter, and its function is therefore argued to be related to respiration [47]. Finally, transport can be achieved by a multitude of systems alternative to ABC transporters, such as 'passive' channels like the top-scoring sodium symporter GlyP [48]. Sodium is one of the main electrolytes in human saliva, existing there at a higher concentration than in blood plasma, and differentiation in sodium transport, similarly to iron or glucose transport, could potentially be under selection for niche specialization.
High RFA scores for serotype were also found among a number of genes flanking the capsular locus which are involved in the cell wall peptidoglycan biosynthesis pathway [9]. These include the penicillin-binding protein genes pbpX and pbp1A, the 16S rRNA cytosine-methyltransferase gene mraW and the phospho-N-acetylmuramoyl-pentapeptide-transferase gene mraY. Mutations in these genes can lead to penicillin resistance, and single-nucleotide positions in all three genes have been shown to associate strongly with S. pneumoniae βlactam resistance in genome-wide association studies (GWAS) performed on the dataset used in this study [3], in a Thai study containing 3,085 isolates [49], and in a Canadian study on 11,083 isolates [50]. It is of relevance to note that in S. Pneumoniae, pbp1A is also involved in the formation of the septum during cell division [51] and is associated in a two-gene operon with the top-scoring gene recU, coding for the Holliday junction resolvase, required for homologous DNA recombination, repair and chromosome segregation [52,53]. Finally, resistance to various classes of cell wall-inhibitory antibiotics (ex. methicillin, vancomycin, daptomycin) in S. Aureus is regulated via the vra operon, by up or downregulation of a set of genes commonly designated as the cell wall stimulon [54]. We find this operon represented by two entries, the vraT and vraT genes.
D R A F T
In addition to genes clearly related to critical resource functions, transport and antibiotic resistance, we also found some of the top-scoring entries to be involved in functions associated with direct inter-and intra-species competition, either through factors related to immune escape or warfare. For instance, blpH is part of the BlpABCSRH pathway [55], which regulates production of class II bacteriocins and related immunity proteins [56,57]. In related species, the aminotransferase GlmS is also known to upregulate the production of ammonia thereby increasing acid tolerance and survival [58]. The capsular flanking gene luxS is also a good example, as it is part of a Staphylococcus epidermidis quorum-sensing system in biofilm formation, and linked to pneumolysin expression, a key player in interference with the host immune response [59,60]. Finally, the top-scoring lytC gene encodes a lysozyme (or glycoside hydrolase) which can be found in a number of secretions, such as tears, saliva and mucus, with the potential to damage (interspecies) bacterial cell walls by catalyzing hydrolysis of linkages and residues in peptidoglycans and chitodextrins [61, 62].
Several top-scoring genes for SC classification are also key determinants of phenotype.
A number of top scoring genes (ex. sodA, groEL, groES, lmb) in predicting SC have previously been demonstrated to be powerful discriminators of genealogy in a range of bacterial species. For instance, sodA, encoding for the manganese superoxide dismutase, critical against oxidative stress and linked to both survival and virulence, has been highlighted in numerous studies for its relevance in identification of rare clones of pneumococci [63, 64] and Streptococci at the species level [65,66]. Also, the lmb gene encodes for an extracellular protein with a key role in physiology and pathogenicity [67,68], and homologs of this protein have been documented to be present and discriminatory of at least 25 groups of the Streptococcus genus with possible similar functions [69, 70].
Certain top-scoring genes were strongly associated with phenotype such as cell-shape, virulence or invasiveness. For instance, glycolytic enzymes (GE) such as the one encoded by the top-scoring gene pdhB have long been regarded as virulence factors [71] and are involved in cytosol-located metabolic D R A F T processes. When transported to the surface, the PdhB proteincomplex is known to interact with host factors such as the extracellular matrix and fibrinolysis system [72]. Critically, Mycoplasma pneumoniae's pdhB is involved in the degradation of human fibrinogen and is also able to bind human fibronectin [72,73]. Fibronectin is commonly found in human saliva, presenting a vast set of functions, from prevention to colonization of the oral cavity and pharynx, to involvement in adhesion and wound healing [74]. Another top gene, pclA, encodes for the pneumococcal collagen-like protein A, a top candidate for human collagen mimicry [75], involved in host-cell adherence and invasion [76]. Binding to fibronectin and collagen are common strategies employed by various invading bacterial pathogens to colonize or disseminate within the host [77,78].
In ovococcus bacteria such as S. pneumoniae the function of the top-scoring protein MreD (the Rod shape-determining protein) is unknown. It is therefore down to speculation on why this protein is a good predictor of SC, but since the depletion of MreD protein can cause cells to stop growing, become spherical, form chains and lyse, its selection hints on the possibility that variation in this gene may dictate specific lineage differences in cell-shape phenotype [79]. We also find the genes designated as SPN23F11320 and SPN23F09460 to be relevant for SC classification, which in our dataset represent about 13% of all non-putative GCN5-related, N-acetyltransferases of the (GNAT) family. These are key proteins involved in acetylation, and there is growing evidence in the literature of their role in regulation of central carbon metabolism and phenotype through epigenetics [80,81].
Overall, the characteristics of these top-scoring genes differed significantly from those which were successful in predicting serotype and, contrary to expectations from a population structured mainly by neutral evolution, we found the top-scoring genes for SC (ancestry) not to be uniformly distributed across the genome. Most strikingly, 25% of the top scoring genes for SC were contiguous and contained the groESL operon, which includes the GroEL and GroES chaperon proteins (Table 2). Other studies have reported the power of the groESL operon and its proteins to ascertain phylogeny and classification within the Streptococcus genus [82] and between species of the Viridans and Mutans Streptococci groups [83,84]. We also noted the top-scoring gene recX is in close proximity to the groESL operon, which encodes a regulatory protein that inhibits the RecA recombinase in multiple species of bacteria [85-88].
Discussion
We have presented a novel technique for attempting to distinguish the effects of selection from neutral processes giving rise to population structure by applying a machine learning algorithm to genomic data. Our strategy involves applying a Random Forest Algorithm (RFA) to predict particular features (serotype or Sequence Cluster) of each isolate from information on the allelic composition of all isolates. By comparing the contribution of different genes as reflected in their RFA scores in predicting serotype or Sequence Cluster, inferences can be made concerning the evolutionary processes underlying their formation, relationship and maintenance at the population level. We performed this analysis on a dataset containing 616 whole genomes of S. pneumoniae collected in Massachusetts (USA) [3] , for each of which we had obtained allelic profiles Letters a to h in the second column denote groups of contiguous genes.
Classification success of Sequence Cluster (SC) to which each isolate belonged was achieved almost perfectly by the RFA. This is a reflection of the strong correspondence between taxonomy and classification trees based on genetic information, as explored in recent studies [19], and demonstrated by Austerlitz and colleagues when comparing the success of RFA, neighbour-joining and maximum-likelihood (PhyML) methodologies on simulated and empirical genetic data [89]. Classification of serotype by the RFA was more variable and, most importantly, there was no overlap between the genes which appeared to be most important in determining serotype and those which scored highly in identifying SC. As might be expected, genes of the capsular locus (cps) and many of those flanking it achieved high RFA scores in predicting serotype but did not perform better than average in predicting SC. Interestingly, none of the genes among the MLST loci showed a consistently strong association with SC across sensitivity Lourenço et al . 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 experiments and all performed no better at predicting SC than serotype. We encountered difficulties in using the entire dataset due to the large number of putative deletions recorded. Some of these proved to be a result of the extreme diversity of genes (such as wzx and wzy within the capsular locus) which interfered with their alignment to the reference serotype 23F genome (ATCC 700669). In the entire dataset, around 7% of the genes had over > 80% deletions/mismatches recorded, 10% had > 50% deletions/mismatches recorded, and just over 25% of the data had to be discarded if we rejected all genes with an excess of > 1% of deletions/mismatches. Given these limitations, we repeated the RFA analysis under various cutoffs for percentage of gene deletions/mismatches in a series of sensitivity exercises. While this did not affect the trend of genes within and flanking the cps locus to shift to lower RFA scores when comparing prediction of serotype against prediction of SC, it thwarted Conceptual representation of phylogenetic relationships between serotypes and Sequence Clusters (SC), where the former are defined by variation at the cps locus (arbitrarily designated X, W, Y, Z, M, and L, respectively coloured yellow, purple, green, orange, cyan and pink) and the latter are linked to variation in the groESL operon (arbitrarily designated A and B and respectively coloured red and blue). Circles symbolize genotypes, with size relative to their prevalence. Inner genome arcs represent epistatic links: those with the groESL operon extend across the genome, while links with the cps locus are more local. Within our framework and according to observed patterns [3], most SCs will be dominantly associated with a single serotype. Current vaccine strategies (white area) that target a selection of capsular serotypes can lead to the expansion of non-vaccine serotypes (VISR, [26,29]), potentially within the same sequence cluster (VIMS [10]). Vaccine strategies based on groESL variants (grey area) would target entire SCs instead, including all uncommon serotypes within and thereby preventing their expansion. efforts to ascertain whether any specific associations with serotype existed among other highly variable surface proteins of interest: PspA, choline binding protein CpbA/PspC, the IgA proteases or the histidine triad proteins. Future work of this methodology will rely on the development of more robust methods of allele classification for this category of genes, an area still lacking adequate approaches.
D R A F T
By eliminating all genes with > 1% of deletions/mismatches, we were left with 1581 genes which likely corresponds to the 1500 'core' cluster of orthologous genes (COGs) identified by Croucher et al [2] in their recent analysis of the same dataset. Within this more restricted set, we also observed a clear disjunction between genes that score highly in predicting serotype and the top-scoring genes for predicting SC. Not surprisingly, a significant proportion of genes that were good markers for serotype were found to flank the capsular locus (shown in bold in Table 1), although there were a number which were distal to it. A high proportion of genes scoring highly for serotype prediction were associated with key functions in metabolism and very likely defined unique 'metabolic types', but since most were in proximity to the cps locus, it was not possible to determine whether these had become segregated through resource competition [10] or by physical and/or functional associations with this locus. The presence of several co-functional, co-transcribed or co-localizing sets of genes (eg. the gnd and ritR genes, the pit, mva and vra operons, and the penicillinbinding genes) on this list (Table 1) argues, however, that the evolution of these serotype-associated traits may best be understood within a modular framework in which different serotypes are characterized by particular combinations of these units. 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744
D R A F T
Genes that were highly informative for SC classification were also not uniformly distributed across the genome, with around a quarter of them co-localizing within and around the groESL operon (shown in bold in Table 2), encoding a chaperonin system which remains a paradigm of macromolecular machinery for protein folding [90]. Apart from assisting protein folding by preventing inappropriate interactions between non-native polypeptides [90], this system may also buffer deleterious effects of mutations on protein foldability and stability [91], with important consequences for protein evolution. The protein GroEL is also highly immunogenic for different bacterial species and has been shown to provide strain-specific protection in vaccine studies [92][93][94]. This raises the radically alternative possibility that sequence clustering may have arisen from immune selection operating on these genes in conjunction with epistatic interactions between the relevant heat shock proteins and the loci encoding the proteins they are chaperoning. The associations between serotype and SC may thus be primarily driven by immune selection operating on multiple immunogenic loci (in this case, cps and groESL) causing them to be organized into non-overlapping combinations, as predicted by strain theory of host-pathogen systems [95,96]. It has previously been proposed that immune selection acting jointly on capsular and sub-capsular antigens could account for the maintenance of these associations [29]. Immunological selection of unique combinations of cps and groESL, however, has the additional advantage of consolidating the link with a range of other genes across the genome through essential epistatic and highly specific (chaperoning) interactions with GroEL and GroES [90].
Our results are in broad agreement with the framework proposed by Croucher and colleagues [2], based on their analysis of the same dataset, in which lineage structure is maintained by infrequent transfer of modular elements ("macroevolution") and provides a stable backdrop for more frequent, and often transient, "microevolutionary" changes (see Figure 3). The differentiation of the groESL operon is potentially a striking example of "macroevolution", being specific not only to S. pneumoniae sequence clusters but also serving to genealogically distinguish closely related bacterial species [82][83][84]. We propose that this is the evolutionary outcome of a combination of immune selection and epistasis operating on specific modules, such as groESL, rather than neutral processes. Selection would also operate at a "microevolutionary" level in creating (more transient) associations between SC and serotype as means of avoiding immunological and direct resource competition [6,10,29]. We note that genes belonging to the Rec family are positioned in close proximity to both the contiguous clusters of top-scoring genes for SC and serotype (Tables 1 and 2) and would argue that these endorse the role of restrictionmodification systems (RMS) in protecting the modularity of the genome [2], and that population structure arises through selection favouring particular combinations of variants of these modules. Our analyses support the hypothesis that lineage structure in maintained by co-adaptation and competition [6,10] and show, unexpectedly, that these selection pressures converge upon the same locus, namely the groESL operon, and strongly endorse the development of vaccines targeting the associated chaperone protein GroEL to avoid vaccine induced changes in the population structure such as Vaccine Induced Serotype Replacement (VISR, [26,29]) or Vaccine Induced Metabolic Shift (VIMS, [10]) which have the potential of greatly reducing the benefits of capsular serotype targeted interventions. [3] for collection details). In summary, allelic notation was carried out using the BIGSdb software with an automated BLAST process [97], and the genomes were analysed using the Genome Comparator tool (with ATCC 700669, serotype 23F, accession number FM211187, as the reference). Alleles identical to the reference were classified as '1', with subsequent sequences, differing at least by one base, labelled in increasing order. Genes were further classified as allele 'X' when genetic data present had no match to the genome of interest, or were found to be truncated or non-coding (see S1 Dataset of [10] for a visual representation of allele annotation and diversity). The allelic matrix as obtained by this approach and used in the RFA analysis is herein made available in supplementary Table S1, which also includes the Accession Numbers, gene name, gene product, gene position in reference genome, and year of collection, Sequence Cluster and serotype of each sample.
Random Forest Approach.
We implement a machine learning approach based on a Random Forest Algorithm (RFA) to predict particular features (serotype or Sequence Cluster) of each pneumococci isolate from information on the allelic composition of 2135 genes [16]. In summary, the RFA process takes the following pseudosteps: (I) the response variable and predictor variables are chosen by the user; (II) a predefined number of independent bootstrap samples are drawn from the dataset with replacement, and a classification tree is fit to each sample containing roughly 2/3 of the data, for which predictor variable selection on each node split in the tree is conducted using only a small random subset of predictor variables; (III) the complete set of trees, one for each bootstrap sample, composes the random forest, from which the status (classification) of the response variable is predicted as an average (majority vote) of the predictions of all trees. Compared to single classification tress, RFs increase prediction accuracy, since the ensemble of slight different classification results adjusts for the instability of the individual trees and avoids data overfitting [98].
Here we use randomForest: Breiman and Cutler's Random Forests for Classification and Regression, a software package for the R-statistical environment [99]. Predictor variables are set to be each gene in our genome samples and the response variable is set to the serotype or Sequence Cluster classification of each genome (as per [3]). We use the Mean Decrease Accuracy (MDA), or Breiman-Cutler importance, as a measure of predictor variable importance, for which classification accuracy after data permutation of a predictor variable is subtracted from the accuracy without permutation, and averaged over all trees in the RF to give an importance value [98]. For the results presented in the main text, we assume the predictor variables to be numerical (as opposed to categorical). This assumption is known to introduce RF biases, as classification is effectively made by regression and artificial correlations between allele numbering and the features being selected (serotype and Sequence Cluster) may be present. The assumption is herein necessary since the RFA R-based implementation (version 3.6.12) has an upper limit of 53 categories per predictor variable and we find some genes to present up to 6 times this limit in allele diversity. The categorical constraint is a common feature of RFA implementations, as predictor variables with N categories imply 2 N possible (binary) combinations for an internal node split, making 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 D R A F T the RFA method computationally impractical. Given this inherent RFA limitation, we implemented an input shuffling strategy to minimize potential bias. For this, M random permutations of each gene's allelic numbering in the original dataset is performed, effectively creating M independent input matrices. The RFA is run over the input matrices and in the main results we present each gene's average MDA score. A sensitivity analysis was performed by comparing RFA results between two independent sets of M = 50 input matrices (effectively comparing 100 independent runs) (Fig. S5). Results suggest that the existing biases in independent runs of the RFA due to the assumption of numerical predictors are virtually mitigated with our shuffling approach, specially for experiments classifying serotype.
ACKNOWLEDGMENTS.
The authors acknowledge the sequence data and constructive comments by Angela Brueggemann and Andries van Tonder. 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q
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Domain: Biology
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Unusual chromosome polymorphism and heterochromatin variation in the Argentinean population of the necrophagous fly Lucilia sericata (Diptera: Calliphoridae), comparison with other populations and evolutionary aspects
Heterochromatin may vary qualitatively, quantitatively, and in its location both in and between species. There were differences recorded in sex chromosomes, satellite sizes and location of C-bands in specimens of Lucilia sericata (Meigen, 1826) from three localities in the Buenos Aires region, Argentina (Bernal, Castelar, and Buenos Aires City). Mitotic analyses revealed the same diploid chromosome number of 2n = 10 + XY / XX (male / female) and the same size, morphology, and C-banding patterns on their autosomes. However, there are different morphotypes of X and Y chromosomes with a distinctive heterochromatin content in both arms. Four morphotypes of X (X1, X2, X3, and X4) and three of Y (Y1, Y2, and Y3) were recorded. The X metacentric and Y subtelocentric chromosomes were identifi ed based on their very large and small sizes, respectively, and both had large heterochromatic blocks. These intraspecifi c differences are mainly due to quantitative variations in non-coding repetitive sequences located in the arms of both X and Y sex chromosomes. The specimens were very polymorphic both between and within the three localities sampled. Cytogenetic studies on specimens of L. sericata from the Nearctic, Palaearctic and Ethiopian regions also report differences in the morphology of the sex chromosomes and their C-banding patterns, some of which may have evolved independently. Our cytogenetic observations on L. sericata indicate that the accumulation of heterochromatin in the genome could be involved in chromosomal divergence and karyotype evolution of this species, as demonstrated in other Diptera. The polymorphic sex chromosomes are therefore important for understanding the evolution within species as well as speciation.
INTRODUCTION
The cosmopolitan and geographically widespread blowfl ies, the Calliphoridae, are a group of great medical, forensic, and veterinary signifi cance (Greenberg, 1991;Byrd & Castner, 2001;Opletalová et al., 2012). Lucilia sericata (Meigen, 1826) is one of the fi rst insects to arrive at a corpse (Centeno et al., 2002;Ames & Turner, 2003) and, therefore, is the primary and most accurate indicator of the time of death (Centeno et al., 2002).
Based on morphological differences, 32 species of Lucilia are described (Stevens & Wall, 1996;Whitworth, 2010Whitworth, , 2014, with 23 species occurring in the Neotropical Region (South America, Central America, the West Indies and portions of southern Mexico along the east and west coasts).
To determine the karyotype, we measured at least twenty wellspread mitotic metaphases for each female and male larva of L. sericata from CABA, Bernal, and Castelar. The total chromo-es in the constitutive heterochromatin in several isolated populations of different species are reported (White, 1973;Baimai, 1998;Rafael & Tadei, 2000;Selivon et al., 2005;Ullerich & Schöttke, 2006;Cáceres et al., 2009;Agrawal et al., 2010;Acuña-Morera et al., 2011;Chirino et al., 2015). Moreover, DNA studies on L. sericata from North America detected that there are three subpopulations there based on AFLP genotype analyses (Picard & Wells, 2010) whereas a random mating occurs in Eastern European fl ies and there are no subpopulations based on the chaetotaxy of the thorax and eight microsatellite loci (Diakova et al., 2018). Thus, the population genetic structure and morphological features of L. sericata may depend on geographic origin and associated differences in life-history traits. The processes of genetic differentiation may include chromosomal rearrangements, leading to diverse chromosome morphologies and variation in inter-and intra-specifi c constitutive heterochromatin, resulting in quantitative changes in repetitive DNA sequences (Grewal & Jia, 2007).
The karyotype and C-banding pattern of L. sericata from Argentina are reported in a previous study (Chirino et al., 2015). Here, we determine the C-banding on the autosomes and sex chromosomes in specimens of L. sericata from three localities in the Buenos Aires region (Fig. 1) because the X chromosomes had two distinctive morphologies ( Fig. 1c in Chirino et al., 2015). We measured their relative lengths, size of satellites, centromeric index, arm ratio, and distribution and percentage of heterochromatin.
Insects
Females of Lucilia sericata from three localities in the Buenos Aires region, Argentina ( Fig. 1) were collected using beef meat as bait, between October 2016 and May 2017. Flies were sampled in some length measurements (TCL; mean ± SE) of all bivalents and sex chromosomes, relative average length (μm; length of an autosomal bivalent as a percentage of the TCL) and relative chromosomal percentage (%) were obtained using MicroMeasure software for Windows, version 3.3 (Reeves & Tear, 2000). Measurements were of chromosomes at metaphase. Differences in the lengths of chromosomes of males and females from the three localities were analysed using Kruskal-Wallis ANOVAs on ranks for global comparisons (P < 0.05), followed by Kruskal-Wallis all-pairwise comparison test for contrasts between treatments because the data were not normally distributed and did not show homoscedasticity. Statistical analyses were performed using In-foStat software (Di Rienzo et al., 2015). The constitutive heterochromatin percentage as the C-positive area divided by the TCL was calculated. The C-banded region in the short and long arms of pairs II and III and in both sex chromosomes was calculated as the ratio between the C-positive area and the arm length. The heterochromatic blocks in the satellites of the X and Y chromosome were determined as the C-positive area of the satellite concerning its total length. Idiograms illustrating the C-band patterns were based on the results obtained from several individuals.
Microscopy and image processing
Preparations were observed under a Leica DM2000 epifl uorescence microscope equipped with a Leica DFC345 FX CCD camera and Leica Application Suite (LAS), version 3.8 (Leica Microsystems Imaging Solutions Ltd., Cambridge, UK). Blackand-white images of chromosomes were recorded, pseudo-coloured and processed with Adobe Photoshop CS6 Version 6.1 software. Morphotypes of sex chromosomes and patterns on the 2 nd and 3 rd chromosomal pairs. A − female X 1 X 1 , DD, Bb, Bernal; B − female X 1 X 2 , DD, BB, CABA; C − female X 1 X 4 , SS, Bb, CABA; D − female X 3 X 4 , DS, Bb, CABA; E − female X 4 X 4 , SS, bb, Castelar; F − male X 4 Y 1 , SS, Bb, Castelar; G − male X 1 Y 2 , SS, BB, Bernal; H − male X 4 Y 3 , SS, Bb, CABA. Heteromorphic sex pair in detail (A, C-D). Very large Cpositive blocks are observed in the pairs of autosomes II and III, and both X and Y sex chromosomes. X, Y -sex chromosomes. Arrows indicate the double C+ heterochromatin band (DD) near the secondary constriction on chromosome 2. Arrowheads show the single C+ heterochromatin band (SS) near the secondary constriction on chromosome 2. Open arrowheads indicate the heterozygous C-band pattern (DS) on the secondary constriction on chromosome 2. Double lines indicate the C-block pattern (BB) on the secondary constriction on chromosome 3. Single lines indicate the C-band pattern (bb) on the secondary constriction on chromosome 3. Asterisks indicate the presence of one C-block (B) and one C-band (b), heterozygous state (Bb), on the secondary constriction on chromosome 3. Bar = 10 μm.
RESULTS
The autosomes from specimens collected from the three localities (CABA, Bernal, and Castelar) were very similar in morphology and size (H = 2.49, P = 0.2884) and gradually decreased in size (H = 153.95, P < 0.0001; Table 1). Four types of X (X 1 , X 2 , X 3 , X 4 ) and three types of Y chromosomes (Y 1 , Y 2 , Y 3 ) were distinguished, characterized by differences in the location of the secondary constriction, the size of their satellites and C-banding patterns (Figs 2, 3; Tables 1, 2). The X was larger than the Y chromosome (H = 216.91, P < 0.0001). No statistical differences in sizes were detected between the four X chromosomes or between the three Ys of individuals from the same or different localities. Data for all specimens are shown together (Figs 2, 3; Tables 1, 2). Centromeric C-bands were found on all chromosomes. The variation in the amount of constitutive heterochromatin in the pericentromeric regions (bands and blocks) of autosome pairs II and III and X and Y chromosomes are shown in Figs 2 and 3 and Table 2.
In specimens from all three sites, the autosome pair II had one or two well-defi ned interstitial C-bands on the short arm associated with the secondary constriction. According to the number of these interstitial C-bands, there were three different patterns in females and males: homozygous for the presence of two C-bands on each short arm (double banding pattern = DD) ( Fig. 2A, B), homozygous for the presence of one C-band on each short arm (single banding pattern = SS) (Fig. 2C, E-H) and heterozygous for the presence of one C-band (S) on the short arm of one homologue and two C-bands (D) on the short arm of the other ( Fig. 2D; Table 3). The fi rst two C-banding patterns were predominant in specimens from all three sites, while only one female from each locality showed the DS pattern (Table 3). Furthermore, we observed differences in the size of the heterochromatic blocks around the secondary constriction on the short arms of autosome pair III in both females and males from all three sites. This autosome pair can exhibit one C-block on each short arm (block pattern = BB) (Fig. 2B, G), one C-band on each short arm (band pattern = bb) (Fig. 2E) or it can be heteromorphic for the size of the band, i.e., the C-band has a different size in each homologue (Bb; Fig. 2A, C, D, F, H). The BB and Bb patterns were common in both sexes from all three sites, whereas the bb pattern was rare and not detected in specimens from CABA (Table 3).
Sex chromosomes showed greater variation in the content and distribution of heterochromatic blocks than the autosomes (Figs 2, 3; Table 2). The X 1 and X 2 chromosomes were very similar in size, relation of arms, small size of their satellites, and percentage of heterochromatin. The X 1 chromosome differed from X 2 in the content and distribution of the heterochromatic block, with about 75% of the X 1 satellite but only about 50% of the X 2 satellite heterochromatic (Figs 2B, 3). The X 3 and X 4 chromosomes had larger satellites than X 1 and X 2 . The satellite of X 3 was mainly euchromatic (Fig. 2D), with only a small terminal area of heterochromatin, whereas the satellite of X 4 was mainly heterochromatic (Fig. 2C-F, H). The morphotypes of Y also differed in the relative content of heterochromatin Morphology of chromosomes. m -metacentric, m-sm -metacentric-submetacentric, sm -submetacentric, st -subtelocentric. * Statistical comparisons were analysed using Kruskal-Wallis tests. Different letters indicate signifi cant differences in post-hoc analyses (P < 0.05). ** Distance of the secondary constriction concerning the centromere in the short arm of pairs II, III, and X chromosomes, and in the long arm of Y chromosomes. "-" -lack secondary constriction. 38.54 -78.81 -* Differences in C-positive heterochromatin percentage in the total length (TL) of the centromeric and pericentromeric areas of each pair of chromosomes. "-" -lack heterochromatin. ** C-positive heterochromatin percentage in short arm of pairs II and III, and in the heterochromatic blocks in short and long arms of sex chromosomes (including each satellite). # C-positive heterochromatin percentage exclusively recorded in the satellites of X and Y chromosomes.
in the satellite. The Y 1 chromosome had the largest fully heterochromatic satellite (Fig. 2F); Y 2 had a medium-sized heterochromatic block covering nearly 50% of its satellite (Fig. 2G), and Y 3 had an euchromatic satellite (Fig. 2H). The polymorphism of sex chromosomes varied both within and between collecting sites. However, the X 1 and Y 1 chromosomes were the commonest morphotypes recorded at all the sites studied (Table 4). Specimens from Bernal were only of the X 1 morphotype (X 1 X 1 in females, X 1 Y 1 or X 1 Y 2 in males). Samples from Castelar were of all four X morphotypes; with females of four cytotypes (X 1 X 4 , X 2 X 4 , X 3 X 4 , X 4 X 4 ), with the X 4 being present in all plates examined, males were of two Y morphotypes (Y 1 and Y 2 ), with Y 2 being less common. Females from CABA were also of all four X morphotypes in several combinations, whereas males displayed only of the X 1 , X 3 and X 4 (with a predominance of the latter) and a high proportion of the Y 3 morphotype, which was only detected at this locality.
DISCUSSION
This study revealed a surprisingly high chromosome variability in terms of the amount, distribution, and location of the constitutive heterochromatin in the pericentromeric regions of autosome pairs II and III, and in the X and Y chromosomes of L. sericata from the three sites sampled in Argentina. There is remarkable conservation of autosomal banding patterns, in contrast to the variety of X and Y chromosomes in specimens from all three sites, including four metacentric and three subtelocentric morphologies, with different distributions of pericentromeric heterochromatic blocks. Polymorphisms involving heterochromatin are frequently found in other dipteran insects, mammals, and plants (Baimai, 1998;Rafael & Tadei, 2000;Rudra & Bahadur, 2013;Steiner et al., 2014;Siljak-Yakovlev et al., 2017). However, it is not common to fi nd heterochromatic polymorphisms in the same population, as in L. sericata from Argentina. The small number of females used in this study undoubtedly infl uenced our results and underestimated or skewed the real cytogenetic pattern. Adults arriving together at baits could be related and indicate a higher level of inbreeding than would be expected from a random sample, although for the three Argentinean sites sampled for L. sericata there is no evidence of a geographic barrier to gene fl ow, isolation by distance or a population genetic structure, as has been detected in the United States and Europe (Picard & Wells, 2010;Diakova et al., 2018). Therefore, it is diffi cult to interpret the recorded polymorphism in terms of population genetics (genetic drift, differential selection, migration, etc.).
Cytogenetic studies on L. sericata from several biogeographical regions have revealed intraspecifi c variability in the morphologies and sizes of X and Y, in the extent of the C segments they contain and that the sex chromosomes are more likely to rearrangements by the accumulation of constitutive heterochromatin than the autosomes (Boyes, 1961;El-Bassiony, 2006;Ullerich & Schöttke, 2006;Acuña-Morera et al., 2011;Chirino et al., 2015;this study). Concerning the content and distribution of heterochromatic blocks on the sex chromosomes, only the Argentinean population is known to have different morphotypes of X and Y sex chromosomes. The X chromosomes of individuals from Argentina, Colombia, and Egypt are (629) Abbreviations: F -female, M -male, DD -double banding pattern, SS -single banding pattern, DS -heterozygous for the presence of one C-band D (S) and two C-bands (D), BB -C-block pattern, bb -C-band pattern, Bb -heterozygous for the presence of one C-block (B) and one C-band (b). * Number of adults (and cells) examined of L. sericata for each locality and sex. Kingdom are telocentric. The long and short arms of the Y chromosome in Colombian populations are heterochromatic and euchromatic, respectively, whereas those in German populations are heterochromatic, but lack a secondary constriction as in the Argentinean population. Therefore, the chromosomes of L. sericata blowfl ies from different regions differ in their C-banding patterns. The processes of genetic differentiation include chromosomal rearrangements giving rise to diverse chromosome morphologies and variation in intra-specifi c constitutive heterochromatin, which results in quantitative changes of repetitive DNA sequences that lead to karyotype evolution (Baimai, 1998;Rafael & Tadei, 2000;He et al., 2003;this study). In L. sericata, the sex chromosomes are very variable in the content and distribution of heterochromatic blocks compared with the autosomes. An intra-specifi c variation in sex chromosomes through the accumulation of constitutive heterochromatin is a common phenomenon in reproductively isolated populations of other widely distributed species of Diptera (Baimai, 1998;Rafael & Tadei, 2000;He et al., 2003). In addition, chromosome polymorphisms were also detected in the three Argentinean population studied and in one Uruguayan population of Hematobia irritans (Linnaeus, 1758) (Diptera: Muscidae) (Forneris et al., 2015). In this species, there are seven karyotypes with different frequencies in populations, which also differ in chromosome banding, chromosome rearrangements (polymorphisms), and chromosome number. Therefore, qualitative and quantitative variations in heterochromatinization associated with changes in its amount and location both within and between species, appear to be an important step in the morphological differentiation of simple sex chromosome systems.
The karyotype data described herein demonstrate that L. sericata is undergoing chromosomal differentiation and karyotype evolution through the accumulation of heterochromatin in the genome. Genetic differentiation in several populations of a species, such as described here, could lead to evolutionary divergence at the chromosomal level and consequently, to speciation. Cryptic and sibling species, or incipient speciation, are frequently detected fi rst at the cytological level in species of Diptera (Baimai, 1998;He et al., 2003). Future studies using a greater number of females could reveal the evolutionary mechanisms behind the observed cytogenetic differentiation. Karyological studies may also help elucidate the high plasticity of genomes at the chromosomal level, potentially leading to adap tation and speciation.
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Domain: Biology
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EVALUATION OF THE NUTRITIONAL QUALITY OF CHAETOCEROS MUELLERI SCHÜTT (CHAETOCEROTALES: CHAETOCEROTACEAE) AND ISOCHRYSIS SP. (ISOCHRYSIDALES: ISOCHRYSIDACEAE) GROWN OUTDOORS FOR THE LARVAL DEVELOPMENT OF LITOPENAEUS VANNAMEI (BOONE, 1931) (DECAPODA: PENAEIDAE )
The biomass, proximal composition and fatty acid profile of Isochrysis sp., Chaetoceros muelleri and their mixture, grown under greenhouse conditions, were evaluated. The nutritional value of both species supplied as the monoalgal (Chaetoceros muelleri: Diet I, and Isochrysis sp. Diet II) and mixed diet (Diet III) for larval Litopenaeus vannamei was also assessed on the basis of the development and biochemical composition of the larvae. The highest protein levels were obtained in Diets I and II (40% and 35%, respectively). No significant differences in larval survival were found among the diets; however, larvae fed on Diet II had the lowest mean larval length.
INTRODUCTION
The production and distribution of farmed, high quality, disease-free shrimp postlarvae is one of the most important issues for sustainable shrimp aquaculture. In commercial hatcheries, biosecurity for the disease control of broodstock and larvae, as well as quality diets for farmed organisms, are the two most important aspects to consider for this purpose.
It is extremely important to account for the nutritional requirements of larvae, especially during larval development. Larvae have specific energy requirements, particularly to progress through certain stages of development, such as metamorphosis (Müller-Fegua et al. 2003). The feeding protocols of larvae in commercial shrimp hatcheries include a wide range of balanced feed and nutritional supplements, particularly in the early stages. However, live feed continues to be the principal nutritional basis for culture of larvae (Aguirre-Hinojosa et al. 1999, Voltolina and López-Elías 2002, Richmond 2004).
For Zoea larvae, and to a lesser degree for Mysis, phytoplankton is the main source of proteins, carbohydrates, lipids and other nutritional compounds. It has been proven that the proximal composition and growth rate of shrimp larvae are associated with the biochemical composition of microalgae used as feed (D'Souza and Loneragan 1999). The biochemical composition of microalgae varies depending on culture conditions, and is affected by factors such as light, pH, temperature and nutrients (López-Elías et al. 1999).
For the commercial production of postlarvae in northwest Mexico, the species of microalgae most commonly fed to shrimp larvae are: Chaetoceros muelleri Shütt; Isochrysis sp.; Tetraselmis suecica (Kylin) Butch; and Dunaliella tertiolecta Butcher. All of these species can be produced in open environment and in different types of containers where culture conditions vary widely. Variable culture conditions alter the biochemical composition of the microalgae and affect their quality as live feed (López-Elías et al. 2003). It is very important to regularly evaluate the effect of the composition of microalgae production on the composition of the farmed larvae and overall. This was an objective of the present study.
MATERIALS AND METHODS
The study was conducted using the facilities of the Peñasco Experimental Unit, University of Sonora, Puerto Peñasco, Sonora, Mexico.
Three treatments were fed to Litopenaeus vannamei (Boone, 1931) larvae in the Zoea I to Zoea III stages. There were two monoalgal diets (Diet I, Chaetoceros muelleri, Diet II, Isochrysis sp), and a mixture of both species (Diet III).
Microalgae were obtained from the AREMAR S. A. DE C. V. shrimp production hatchery. Each algal species was grown under greenhouse conditions in 800 L opaque conical cylinders, using a batch system with f/2 medium (Guillard and Ryther 1962) in exponential phase of growth, at temperatures between 20 and 34°C, and light from 450 to 516 µmol•m -1 •s -1 .
The feeding of bioassays was done twice during the study, in quadruplicate each time, in 8 L experimental units, at a stocking density of 100 nauplii . L -1 , with a daily water exchange of between 20 to 50%. A concentration of 100,000 cells .mL -1 for monoalgal diets, and 50,000 cells .mL -1 of each of the species for the mixed diet were used.
The dry weight and the protein, carbohydrate, lipid, and fatty acid content of the microalgae were quantified from samples collected from the conical cultivation cylinders. The dry weight was quantified gravimetrically by filtering 100 to 300 mL of the cultivated microalgae through a 47 mm diameter Whatman GFC glass fiber filters. For evaluation of proteins and carbohydrates, 10 to 30 mL, for lipids 30 to 50 mL, and for fatty acids 100 to 500 mL of the cultivated microalgae were filtered. All samples were evaluated in quadruplicate.
Proteins were extracted with NaOH 0.1 N (López-Elías et al. 1999), according to the Lowry method (1951) and modified by Malara and Charra (1972a). The Dubois et al. method (1956) modified by Malara and Charra (1972b) was used for carbohydrate extraction. Lipids were extracted with a mixture of methanol chloroform and water (Bligh and Dyer 1959), for colorimetric determination pursuant to the Pande et al. method (1963).
Lipid extraction to quantify fatty acids was performed according to Bligh and Dyer (1959); thereafter, the sample was evaporated in a R-Buchi CH 9230 vacuum rotovapor, followed by methyl esterification (AOAC 1993). The sample was analyzed in a Model Varian gas chromatograph in an Omegawax 250 silica column (0.25 mm inside diameter x 30 cm long). The standard used was PUFA-1 (marine origin) No. 4-7033 Supleco Inc.
Larval development was evaluated by daily microscopic observation. The total length of the larvae at each developmental stage was measured, and at the end of the experiment, percentage survival was quantified. The biochemical composition of the shrimp larvae was also determined at the end of each experiment by the same methods used for microalgae.
To evaluate the effect of the diet treatments on organic matter, proteins, carbohydrates and lipids, as well as on the growth survival and biochemical composition of the larvae, a one way ANOVA and Tukey test for post comparison were performed (Zar, 1984).
RESULTS
The dry organic biomass (g•L -1 ) supplied to the organisms was equal among the diets in both experiments (F = 0.18, p >0.83; F = 0.01, p > 0.98, respectively), with a mean of 0.0414 g•L -1 . The microalgae biomass decreased over time, despite efforts to maintain a constant number of cells. The decrease was not significant for Chaetoceros muelleri (F = 0.18, p >0.84), but it was for Isochrysis sp. and the mixed diet (on average, 43 and 31%, respectively, less than the initial value) during the feeding of Zoea I to III (F = 15.79,p < 0.0001; F = 5.56, p< 0.01, respectively) (Table I).
The proximal composition of the diets supplied in both experiments was different. Protein level was highest in Diet I (Chaetoceros) (40.39 %) and Diet III (mixture) (34.93 %) compared with Diet II (Isochrysis) (28.83 %). The carbohydrate level was higher in Diets II (Isochrysis) and III (mixed) in the first experiment, whereas in the second, it was equal among the diets. The percentage of lipids was variable in all treatments, although high values were always recorded in Diet II (Table II).
The profile of fatty acids in Diet I (Chaetoceros muelleri), Diet II (Isochrysis sp.) and Diet 3 (mixture) was similar in both experiments, with high ratios of saturated fatty acids (between 62.7% and 76.9% on average), followed by monounsaturated (18.9% and 26.6%) and polyunsaturated (4.3% and 10.8%) (Table III). The most abundant polyunsaturated fatty acids in Diet I were 20:5w3 and 22:5w3. In Diet II, the highest fatty acid was 22:6w3; Diet III (mixed) provided a more complete fatty acid composition, according to the established ratios for both microalgae in the diet.
The survival of shrimp larvae was equal among treatments in both experiments, but survival was significantly higher in the second experiment compared with the first (Table IV).
The proximal composition of nauplii was relatively similar in both experiments, with a large ratio of proteins, followed by carbohydrates and lipids. For the zoeas, the pattern was the same. At the end of the experiments, zoea larvae had a proximal composition similar to nauplii with the three diets. The protein and lipid levels of Zoea III were not significantly different among the diets in any of the experiments, in spite of the lipid level being significantly higher in Diet III in the first experiment (Table VI).
The profile of fatty acids of larvae fed with the different diets was different to that recorded for the corresponding microalgae (Table VII). The high content of linoleic acid was evident in larvae fed with Diet II. As regards polyunsaturated fatty acids, larvae fed Diet I had acids 20:5w3 and 22:5w3; larvae fed Diet II, had mostly 20:5w3, 22:5w3 and 22:6w3; while larvae fed the mixed diet had acids 20:5w3 and 22:5w3.
DISCUSSION
The amount of organic matter provided to shrimp larvae with the same cell concentration was similar among the diets. In all cases, it was enough for the survival of shrimp larvae, despite the slight decrease in organic matter over the course of the experiment.
Overall, the chemical composition of the diets was similar in both experiments. The protein level of microalgae used in the two experiments varied from 23.9 to 43.9%. However, all values within this range promoted adequate larval growth. The mean protein level from the two experiments was lower for Isochrysis (Diet II), although this diet contributed the largest percentage of carbohydrates and lipids. Similar values were found by López Elías et al. (1999) for the same two species: percentage of protein was 39.3The profile of fatty acids was different among the species. For polyunsaturated fatty acids, a larger ratio of EPA was found for Chaetoceros and a greater ratio of DHA was found for Isochrysis. The mixture represented the composition of both species with all the polyunsaturated fatty acids present. It has been reported that DHA is the most abundant polyunsaturated fatty acid in Isochrysis, whereas for Chaetoceros muelleri, it is EPA (Brown et al. 1997, D´Souza andLoneragan 1999). In this research, EPA was not detected in Isochrysis sp. The high variability of lipid composition in this microalgae has been documented for both laboratory and greenhouse conditions (Pernet et al. 2003, Piña, et al. 2006), and coincidently, Liu and Lin's (2001) research on the lipid formation of Isochrysis sp.found DHA present but did not detect EPA. López-Elías et al. (2003) reported values of DHA for Chaetoceros between 0.03 to 5.23 %, but this acid was not detected in our study. It is possible that under greenhouse culture the production of DHA was very low and therefore undetectable.
In general, the proximal composition of nauplii and larvae fed with monoalgal diets and a mixed diet were similar. The major constituents in the larvae were proteins, which is consistent with research by Rodríguez et al. (1994), followed by carbohydrates and lipids. Saturated fatty acids were the main lipid components of larvae fed with the monoalgal diets, followed by polyunsaturated and monounsaturated fatty acids. These results are similar to those previously reported by D´Souza and Loneragan (1999) with Penaeus spp.larvae fed with Isochrysis sp. and Chaetoceros muelleri.
Cultivated larvae present a larger proportion of monounsaturated and highly unsaturated fatty acids as compared to the diet they were fed. This implies that larvae bio-convert fatty acids as reported by Teshima et al. (1992) and Lim et al. (1997).
The content of essential fatty acids in shrimp larvae depends on their availability in the diet and the remnant from nauplii (Jones et al. 1997).
The average growth of Litopenaeus vannamei larvae from Zoea I to III recorded in this study, was similar to the size range described by Treece and Yates (1990). The survival obtained was similar between the monospecific treatments (Chaetoceros and Isochrysis) and the mixed treatment. This result differs from the results reported by D´Souza and Loneragan (1999), who found that a monoalgal diet based on Isochrysis was unsatisfactory for shrimp larvae nutrition. In addition, Piña et al. (2006) found that a monoalgal diet with Isochrysis sp. did not improve the survival rate and rate of development in L. vannamei protozoa larvae.
In this study however, Isochrysis was cultivated in a greenhouse and was able to synthesize a sufficient amount of DHA and other important cell constituents, which had a positive effect on the larval culture.
The use of Chaetoceros as a food for larval L. vannamei zoeas was more than adequate with respect to growth and survival. Although Isochrysis sp. had the lowest ratio of proteins, its carbohydrate and lipid ratio was high, and it also had the highest percentage of total polyunsaturated fatty acids. The size and survival of larvae fed with Isochrysis sp. was equal for both diets. The mixed diet was more complete, with regard to the major constituents and fatty acid profile, with survival and growth comparable to monospecific diets. In this research, the fatty acid profile was rich in the mixed diet, more than in the monospecific diets. Although some authors considered that mixed algal diets are better in order to improve the nutritional quality to sustain the growth and development of shrimp larvae, most of the commercial laboratories for L. vannamei larvae production used monospecific diets (Piña et al. 2006). The results of this research indicate that Isochrysis sp. could be included in the diets used in commercial hatcheries.
Table 1 .
Average amount of organic matter provided as food to Litopenaeus vannamei larvae with Diet I (Chaetoceros muelleri), Diet II (Isochrysis sp.) and Diet III (mixture) in two experimental runs. Letters different indicate significant differences, a<b<c.
Table 2 .
Average percentage and standard deviation (s.d.) of the protein, carbohydrate and lipid composition of Diet I (Chaetoceros muelleri), Diet II (Isochrysis sp.) and Diet III (mixture) during their use as L. vannamei larvae feed in two experimental runs. Letters different indicate significant differences, a<b<c.
Table 4 .
Average percentage of survival of Litopenaeus vannamei larvae fed with Diet I (Chaetoceros muelleri), Diet II (Isochrysis sp.) and Diet III (mixture). Letters different indicate significant differences, a<b<c.
Table 5 .
Larval length (mm) in Zoea I, II and III stages of Litopenaeus vannamei fed with Diet I (Chaetoceros muelleri), Diet II (Isochrysis sp.) and Diet III (mixture). Letters different indicate significant differences, a<b<c.
Table 6 .
Average percentage and standard deviation (s.d.) of the protein, carbohydrate and lipid composition of L. vannamei larvae fed with monospecific diets (Chaetoceros muelleri and Isochrysis sp.) and the mixture of both in two experimental runs. Letters different indicate significant differences, a<b<c.
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Domain: Biology
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The Enigma of Identifying New Cattle Tick Vaccine Antigens
Several reviews have summarised cattle tick Rhipicephalus (Boophilus) microplus vaccine candidate discoveries by comparing efficacies and localisation characteristics. However, few have re-analysed all the reported proteins using modern bioinformatics tools. Bm86 was developed as a successful vaccine in the 1980s; however, global efficacies vary from 45 to 100%. Subsequent vaccines, including four published patents, were discovered by targeting enzymes important for blood digestion and/or metabolism or by targeting genes shown to disrupt tick survival following RNA interference experiments. This chapter analyses published vaccine candidates using InterPro, BLASTP, SignalP, TMHMM and PredGPI tools to confirm whether each reported protein is likely to be secreted, membrane associated or intracellular. Conversely, these proteins are considered as ‘exposed’, ‘exposed’ and ‘concealed’ or ‘concealed’, respectively. Bm86 was always described as a ‘concealed’ antigen; however, the protein has a confirmed signal peptide and GPI anchor which suggests it is anchored to the cell membrane and exposed on the surface of gut cells. It is the only tick vaccine with a GPI anchor. Secreted vaccine candidates appear to have promise and exhibit higher efficacies if delivered with an ‘intracellular’/‘concealed’ antigen. Improvements in tick genomics and bovine immunomic resources will assist to identify robust new cattle tick vaccines.
Introduction
. It has been noted that more crossing studies need to be undertaken using geographically diverse wild strains Ticks and Tick-Borne Pathogens 2 and preferably not 'inbred' colony isolates of R. (B.) microplus before conclusions on clades and species relationships can be confirmed. Publications and sequences reviewed here are most likely to be from different R. (B). microplus clades and R. (B.) australis but will be referred to collectively as Rhipicephalus microplus.
Regardless of the above seemingly complicated taxonomic status, the treatment of cattle tick infestations is either addressed by vaccination using Bm86-based vaccines: TickGARD PLUS (now discontinued) or GAVAC™ and most commonly through the application of chemical acaricides [5]. Bm86 vaccines have diverse efficacies reported worldwide (45-100%), but in a few isolated countries, the vaccines have worked well apart from the need for multiple annual boosts to achieve adequate efficacies [1,5,6]. Ticks are also quite capable of developing resistance to acaricides; thus vaccine research continues globally [7] to identify conserved and immunogenic alternatives to Bm86.
The first notion that tick guts could be the source of viable tick vaccines was reported in 1979 [8] where native tick gut and organ extracts protected guinea pigs and cattle from Dermacentor andersoni ticks. The authors also suggested that this vaccine would affect tick feeding and reproduction and would be ideal for 'Boophilus microplus' as all tick stages feed on the same host [8]. A gut protein named Bm86 was discovered in the 1980s as a protective antigen isolated from R. microplus in Australia [9]. The most notable characteristics at this time was the presence of epidermal growth factor (EGF) domains which are highly conserved extracellular domains associated with membrane-bound or secreted proteins ( [URL]86 is also a glycosylphosphatidylinositol (GPI)-anchored protein and as such is modified post-translationally [10]. It has been proposed that Bm86 is secreted and anchored to gut digestive cells through its C terminus [11]. Using immunogold labelling Bm86 was found to be located on the microvilli of gut digest cells [12]. The immune response induced by Bm86 was hypothesised to be mediated through host complement and anti-Bm86 antibodies which damage the tick gut surface affecting egg viability [13,14]. However, the actual function of this tick protein has never been determined. Nonetheless, the early successes of Bm86 vaccines such as TickGARD PLUS in Australia and GAVAC™ in Cuba provided researchers with the necessary fervour to identify alternative vaccine candidates to potentially be either 'broad spectrum' (i.e. cross protective for different tick species) or with a longer duration of immunity compared to Bm86-based vaccines.
Methods
Previously reviewed antigen types were summarised as 'secreted' , 'intracellular' or 'membrane associated' [1]. In this review, each antigen was analysed in silico to confirm previously described localisations. Each ORF was submitted to InterPro to determine if the candidate antigen had domains or motifs representative of conserved protein families including the predicted GO Terms associated with 'biological process' , 'molecular function' and 'cellular component' ( [URL]. ebi.ac.uk/interpro/) [15]. InterPro also predicts the presence of signal peptides and transmembrane helices; however these were examined separately using the SignalP 4.1 server ( [URL]/) [16,17] and the TMHMM server v. 2 ( [URL]/). GPI anchor predictions were undertaken using PredGPI ( [URL]) [18]. The BLASTP server was employed to confirm published sequence identities ( [URL]). This analysis was limited to vaccine candidates reported as screened against R. microplus ticks in cattle challenge trials. Table 2 summarises localisations of these vaccine candidates analysed through SignalP, TMHMM and PredGPI and provides known trial data, references and patents (if applicable).
Secreted antigens
Most tested antigens are predicted to be secreted with no membrane-associated moieties (transmembrane helices or GPI anchors) ( Table 2). The idea of selecting secreted proteins may have been cultivated to identify putative antigens that are more immunogenic in comparison to Bm86 and therefore boosted by natural tick challenge. The latter is usually associated with the injection of proteins by tick salivary glands. Studies have also shown that tick gut proteins also elicit host antibody responses; however perhaps gut protein-based vaccines are less immunogenic, that is, Bm86, which requires multiple annual boosts.
Two secreted proteins were also isolated from salivary gland and gut fractions similarly to how Bm86 was originally derived: 5′ nucleotidase [19] and Bm91 angiotensin converting enzyme-like protein [20,21]. However, neither demonstrated notable vaccine efficacies to warrant further development ( Table 2).
In other studies, successful vaccine candidates were identified in other tick species, that is, Ixodes ricinus (sheep tick) Ferritin-2 at 96% efficacy [22]. The researchers subsequently mined the R. microplus (BmGI) database for a R. microplus IrFerritin-2 homologue [22,23], and RmFerritin-2 was patented at 64% vaccination efficacy [24]. Ferritin-2 was discovered in the sheep tick when studying iron homeostasis and it was found to be required for optimal tick feeding. In addition, unlike other tick ferritins, it was found to be unique without functional orthologs in vertebrate hosts [25].
Metalloproteases were targeted as vaccine candidates as these proteins were considered crucial for the maintenance of blood meal-related functions in other tick species [26,27]. After an examination of five R. microplus metalloprotease GenBank sequences (AAZ39657.1-AAZ39661.1; Untulan et al., 2005, unpublished), it was found that Bmi-MP4 (AAZ39660.1) was expressed in female organs and male ticks and exhibited potential antigenic properties in comparison to other R. microplus metalloproteases [28]. A Bmi-MP4 metalloprotease vaccination study in Brazil yielded 60% efficacy as reported in 2015 [29], with no patent published ( Table 2). A different Brazil-based study identified an unrelated metalloprotease Rm239/Sequence 82 (31% identity with Bmi-MP4, data not shown) as a component of a cocktail vaccine of four proteins achieving 73% protection in a tick challenge trial [30]. These proteins were identified through a salivary gland transcriptome study; thus in this instance the researchers were targeting secreted salivary proteins. Interestingly, the proteins selected were highly up-regulated in male ticks found on tick susceptible cattle which were not known to induce antibodies in naturally infected bovines [30]. Note that these two metalloproteases (Bmi-MP4 and Rm239/ Sequence 82) and the Bm91 angiotensin converting enzyme-like protein described above all possess the GO:0008237 pertaining to 'metallopeptidase activity' ( Table 1). As metalloproteases are members of a large protein family [31], this may lead to differences between strains or clades of R. microplus causing variable vaccination responses. Metalloproteases have been considered as vaccine candidates for other parasite species such as hookworm and human amebiasis, but no commercial products have emerged [32,33]. Four proteins conform a cocktail vaccination [30]; see Table 2 for vaccine efficacies. 2 Sourced from the BmGI database [23]. 3 Nil predictions denoted by a dash. The second protein in the above-described cocktail with Rm239/Sequence 82 metalloproteinase was Rm180/Sequence 79 which has a proteinase inhibitor domain (IPR002223: pancreatic trypsin inhibitor Kunitz domain) similar to a trypsin inhibitor on the 'intracellular' list (Tables 1 and 2), also tested in Brazil. Rm180/Sequence 79 in contrast is likely to have a signal peptide based on its top BLAST hit, and this new proteinase inhibitor does not appear to have any homology with known tick proteins (data not shown). Trypsin inhibitors are serine protease inhibitors potentially involved with tick blood meal digestion through the inhibition of trypsin (a serine protease which hydrolyses proteins).
The third protein within the cocktail was Rm76/Sequence 76 (also secreted) which is an immunoglobulin G (IgG)-binding protein C possessing domain IPR036846 ganglioside GM2 activator associated with lipid recognition function ( Table 1). The top BLASTP hit for this protein is AAB68803.1 Rhipicephalus appendiculatus IgG-binding protein C at 88% identity. Tick immunoglobulin-binding proteins have been examined previously in several other tick species including R. appendiculatus, Rhipicephalus haemaphysaloides and Ixodes scapularis [34][35][36] and are thought to function as tick defences against host antibodies. Rm 239/Sequence 82 (metalloprotease) and Rm76/Sequence 76 (IgG-binding C) were shown to be the most immunogenic proteins in the cocktail vaccine based on antibody titres, predicted T cell epitopes and antibody boosting during tick challenge [30]. The fourth protein in this cocktail Rm39/Sequence 81 did not return any significant hits using BLAST or InterPro thus could not be examined using the parameters in the tables. The vaccine cocktail consisting of the trypsin inhibitor (Sequence 79), IgG-binding protein C (Sequence 76), metalloprotease (Sequence 82) and the unknown protein (Sequence 81) has been patented [37]. All sequences were published in the associated patent [37] without signal peptide regions.
'SILK' protein was predicted from an expressed sequence tag (EST) library prepared from male R. microplus ticks in response to Anaplasma marginale infection, and it was thought to be similar to arachnid flagelliform silk proteins [38]. However, no significant hits of the R. microplus EST to a 'SILK protein' sequence could be confirmed in this study. The protein has not been exploited further as an anti-tick or anti-Anaplasma transmission vaccine; however, at 62% efficacy [39] perhaps further study is warranted. No patent has been published.
Membrane-bound antigens
Apart from Bm86, the only other published antigen with a membrane association was aquaporin. Aquaporin does not have a GPI anchor as Bm86 but has four transmembrane helices predicted by TMHMM ( Table 2). A reported 73% trial efficacy has been published and the data patented [40,41]. The protein was identified in tick gut transcriptome studies and predictably functions as a waterconducting channel. An aquaporin was previously suggested as vaccine candidate for the human blood fluke Schistosoma japonicum with six predicted immunogenic epitopes and an integral membrane structure [42]. No further testing has been reported which is common for many human vaccine candidates. Perhaps the tick aquaporin vaccine will inspire further investigations of similar orthologs in human parasite infections.
Bm86 is thus the only protein with a confirmed GPI anchor that has been examined as a tick vaccine candidate. GPI-anchored proteins are conserved in eukaryotes and are luminal secretory cargo proteins with several functions across mammals and parasites [10,43]. Notably, the R. microplus 5′ nucleotidase (listed as a 'secreted protein') was predicted to have a 'weakly probable GPI anchor' , and it is known that mammalian 5′ nucleotidases possess GPI anchors [10]. In terms of vaccine candidates, GPI-anchored proteins have been investigated in several parasite species such as Leishmania amazonensis [44], Plasmodium falciparum [45], Schistosoma mansoni [46], Theileria annulata [47] and Babesia bovis [48] and have appeared to be associated with host invasion. In mammals, certain GPI-anchored proteins are cytokines with complement regulation functions [10]. Further studies pertaining to the discovery of tick salivary or gut proteins with GPI anchors have not been reported.
Intracellular antigens
Although Bm86 is cited as a 'concealed antigen' [49,50], it appears to be a combination of 'exposed' and 'concealed' based on localisation predictions including a signal peptide ( Table 2). Antigens in the 'intracellular' category do not have predicted signal peptides, GPI anchors or transmembrane helices and thus perhaps should be considered as truly 'concealed' . Several intracellular antigens have been investigated as tick vaccine antigens; however, results have been variable and seemingly dependent on delivery mechanisms as host antibodies need to target the protein that resides intracellularly.
Subolesin from the akirin protein family ( Table 1) has been investigated in several tick species as a putative vaccine candidate [51] with the first R. microplus ORF described in GenBank as accession ABZ89745.1 (Shao et al. 2008, unpublished). Studies have confirmed that subolesin is involved in blood ingestion and reproduction in 2006 [52]; however, no predicted GO terms or other localisation predictions were identified in this study to confirm any of these putative functions (Tables 1 and 2). Subolesin was recently patented with Bm86 as a dual vaccine emulsion at a reported patented efficacy of 100% [53]. This dual vaccine is currently being testing by the CATVAC consortium in Morocco [7]. It is unknown if the varied efficacies of Bm86 will affect the activity of this dual vaccine or whether the short duration of immunity will continue to be an issue as for Bm86-based vaccines. Previously, a strong phenotypic knockdown of Rhipicephalus sanguineus ticks was observed using RNA interference through the silencing of subolesin and Rs86 (R. sanguineus Bm86 homologue) [54].
The 60S acidic ribosomal protein P0 has demonstrated 96% efficacy using a peptide fragment in cattle tick challenge trials in Cuba [55]. This is otherwise a conserved ribosomal protein, and the peptide region selected had significant sequence differences from the host ortholog. This vaccine has been patented and is under further trial testing also through CATVAC [7,56]. Previously, gene silencing of this intracellular protein was found to be lethal to Haemaphysalis longicornis ticks [57]. Ubiquitin (also an intracellular protein) when silenced is also found to be lethal to R. microplus ticks [58] but was not found to be an effective vaccine candidate [59].
Haemaphysalis longicornis glutathione S-transferase (GST) showed some cross protection against R. microplus in a cattle trial [60]; however, further investigation as a tick vaccine candidate has not been reported. GSTs have been examined by several researchers as candidate parasite vaccines, for example, for hookworm, schistosomiasis and trichinellosis [61][62][63], at varying degrees of efficacy. GST proteins are considered as common 'housekeeping' genes forming a large protein superfamily present in eukaryotes and prokaryotes [64]. They function as detoxifying enzymes and thus in ticks may function in response to acaricides or in response to tick-borne pathogens and or stress [65,66].
Trypsin inhibitors are serine protease inhibitors potentially involved with blood meal digestion as described above. A BmTI-6 sequence was identified in the BmGI database [23] and while native protein vaccine efficacies were high (73%), the corresponding recombinant protein efficacy was poor at 32% [67,68] ( Table 2). This particular trypsin inhibitor is not predicted to be secreted (Table 2) thus may have a function different from gut digestion. The protein sequence reported by Andreotti et al. [67] is identical to BmTI-6 P83606.2 [69]. Alternatively, a 'secreted' trypsin Inhibitor showed promise within the cocktail vaccine described above [37]. As stated for metalloproteases, trypsin inhibitors are also members of large dynamic protein families which may circumvent host immune responses if administered as vaccines.
Vitellin was investigated as a native vaccine candidate showing some promise in sheep trials through a reduction in female ticks and their weights and a reduction in tick oviposition [70]. However, the recombinant form had no vaccine effect ( Table 2), and no further studies were conducted. Vitellin is a high molecular weight yolk lipoglycoprotein, and in ticks and insects, it is synthesised in female fat bodies as a large precursor polypeptide-vitellogenin [70]. In insects, vitellogenin is processed into vitellin polypeptides by specific proteolytic cleavages during passage into haemolymph and/or upon receptor-mediated endocytosis by the developing oocyte [71,72]. Tick vitellogenins are crucial for egg development and oviposition as demonstrated when silencing of three H. longicornis vitellogenin genes [73]. There are no reports of vitellin or vitellogenin as vaccine candidates in other species to date; however, this could be because they exist in arthropods (ticks and insects) rather than other 'pathogenic' species of parasites.
The investigation of intracellular vaccine candidates appears to less likely lead to a successful outcome. Perhaps some of these proteins could be delivered in dual emulsions as shown above for Bm86 and subolesin for a strong vaccination effect. It seems prudent to suggest that an intracellularly localised vaccine candidate requires a mechanism whereby host antibodies are able to access cells internally in order to be active against feeding ticks.
Other potential protein features
G protein-coupled receptors (GPCRs), also known as 'seven-(pass)-transmembrane domain receptors' are associated with many diseases and as such are the targets of several treatments. They are receptors for pheromones, hormones and neurotransmitters and could potentially be targeted as tick vaccine candidates [78]. Most literature associated with GPCR studies in ticks to date are acaricide-related and not associated with vaccines.
Protective immune response
The identification of tick vaccine candidates since the discovery of Bm86 appears to be haphazard in that selection has involved either targeting an enzyme involved with feeding or metabolism or to target a gene that showed diminished tick survival following RNA interference silencing. Neither of these approaches is directly linked to the development of a protective immune response which is fundamental for a protective vaccine. Many different experiments have been undertaken describing effective tick immune responses in different breeds of cattle including different mixtures of Bos indicus (naturally tick resistant) and Bos taurus (innately tick susceptible) cattle. These studies have also been undertaken in many different geographic regions with the use of highly divergent tick infestation protocols. The latter is particularly problematic where in some instances tick-naïve cattle cannot be sourced, and researchers treat the cattle for ticks prior to artificial tick infestations and subsequent immune studies. This topic has been reviewed in detail elsewhere and will not be repeated here [79]. The latter review summarised that there are different immune responses in tick-susceptible and tick-resistant breeds of cattle.
Perhaps different R. microplus tick vaccine candidates will need to be developed for different cattle breeds and crosses? Is the tick host response in tick-resistant breeds of cattle a result of superior immune function or genetic differences or a combination of both? One theory is that naïve tick-resistant breeds are readily primed with epithelial γδ T cells able to respond to larval ticks, whereas susceptible breeds need to recruit these T cells to the larval bite sites [80,81]. This immune cell recruitment phase seems to manifest in an inefficient immune response in susceptible breeds. It has been a challenge to demonstrate this phenomenon in all immune studies due to the common practise of studying previously exposed cattle in several published experiments, reviewed previously [79].
Further research
Reverse vaccinology or genome-based approaches have been reviewed elsewhere, and promise in this approach has been reported [1]. Studies have used EST and transcriptome sequence databases to mine for potential tick antigens using a variety of approaches [1,30]. Tick genomics has only recently become possible due to the availability of new 'long read' sequencing technologies and a dramatic decrease in the cost of sequencing large repetitive genomes [82,83]. Bovine-specific immunology resources are also increasing [84,85] with earlier research relying on human models for the major histocompatibility complex predictions. In combination with new genome sequences and bovine immunomic resources, a modern approach to identify robust tick candidates could perhaps finally be developed.
Conclusions
Although several approaches have been examined, one way to determine the true significance of a particular antigen or protein is to examine the current-published patents associated with cattle tick (R. microplus) vaccines. Upon examination of all patents and publications with cattle trial data to date, there are mixed features for R. microplus vaccine candidates with either secreted, membrane-bound or intracellular localisations which can also be described as 'exposed' , 'a combination of exposed and concealed' and 'concealed, respectively. Intracellularly localised antigens are truly 'concealed' and in comparison to 'secreted' antigen types have highly variable outcomes. The key to identifying efficacious vaccine candidate(s) is to determine how best to stimulate a long-term protective immune response. This may also be feasible through new vaccine delivery options such as nanotechnologies or liposomes which may enhance the immunity to previously identified vaccine candidates.
© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License ( [URL]/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Domain: Biology
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