Zostera muelleri) after an extreme flood event in Moreton Bay, Queensland, Australia. Sites were grouped into high and low disturbance categories, in which seagrass showed high and low resilience, respectively, as determined by measuring rates of key feedback processes , and physiological and morphological traits. Theoretically, meadows with higher genotypic diversity would be expected to have greater resilience. However, because the more resilient meadows occur in areas historically exposed to high disturbance, the alternative is also possible, that selection will have resulted in a narrower, less diverse subset of genotypes than in less disturbed meadows. Levels of genotypic and genetic diversity  based on 11 microsatellite loci, were positively related (R2 = 0.58). Genotypic diversity was significantly lower at highly disturbed sites (R = 0.49) than at less disturbed sites (R = 0.61). Genotypic diversity also showed a negative trend with two morphological characteristics known to confer resilience on seagrass in Moreton Bay, leaf chlorophyll concentrations and seagrass biomass. Genetic diversity did not differ between disturbed and undisturbed sites. We postulate that the explanation for these results is historical selection for genotypes that confer protection against disturbance, reducing diversity in meadows that contemporarily show greater resilience.The response of seagrass systems to a severe disturbance provides an opportunity to quantify the degree of resilience in different meadows, and subsequently to test whether there is a genetic basis to resilience. We used existing data on levels of long-standing disturbance from poor water quality, and the responses of seagrass ( The resilience of ecosystems to natural and anthropogenic disturbances is key to continued provision of services to humanity and support for biodiversity . The goaSeveral studies have used genotypic diversity when looking for positive effects of genetic diversity on ecosystem structure and functioning. This is because genotypes reflect genetically distinct individuals that may vary in ecologically important ways . However, neutral genetic diversity is not always strongly correlated with adaptive diversity, and therefore nor with ecological processes, but may become associated indirectly through random genetic drift and migration acting at a location .Posidonia oceanica showed that genetic diversity  was weakly, but significantly, associated with the extents of several disturbance types, namely shipping, pollution, and cumulative impact  for which the disturbance regime and measures of seagrass resilience were previously established by Figure 1 for map and site numbering):This study examines levels of genotypic and genetic variability within and among (1)n = 6 meadows); andhigh disturbance sites had poor and fluctuating water quality during summer months (wet season) over the previous 10 years due to riverine influence, quantified as high turbidity and nutrient concentrations and low salinity ((2)n = 6 meadows).low disturbance sites had historically better water quality during summer months with less riverine influence  and higher concentrations of chlorophyll pigments .Applying the measure of ecological resilience conceptualized in Figure 1). Twelve sites are the same as those shown in Figure 1 of  in Moreton Bay  following the manufacturer’s instructions. All samples were genotyped using nine polymorphic microsatellite markers previous developed for this species; NSWZos02, NSWZos15, NSWZos18, NSWZos19, NSWZos23, NSWZos25, NSWZos29, NSWZos38, and NSWZos46 . MicrosaE) and observed heterozygosity (HO). Estimates of genetic diversity were calculated using only unique multi-locus genotypes as clonal reproduction may influence estimates of population structure. Allelic richness was calculated per locus and population, and standardized based on the smallest sample size at a site (n = 13) in the program FSTAT 2.9.3.2 , expected heterozygosity  2.9.3.2 . Observe 2.9.3.2 .R) was calculated as the number of unique multi-locus genotypes (GN) relative to the number of samples collected (N) and expressed as: ∗ was used to determine clonal heterogeneity (D∗ = 1 - Σπ2), where π is the frequency of the multi-locus genotype detected in the sample. This index describes the probability of encountering distinct multi-locus genotypes when randomly taking two individuals from the sample. Multi-locus genotypes can occur through resampling of the same clone , the recombination of the same alleles in different individuals, somatic mutation or scoring error. The probability of identical multilocus genotypes arising from different sexual reproductive events was assessed by calculating the probability of identity, PID, for each site across all loci  calculated for each site was low, ranging from PID = 8 × 10-5 at Site 6 to PID = 9.8 × 10-7 at Site 8, indicating the marker system had a high degree of power to identify unique multilocus genotypes.Genotypic diversity (−1)(N−1)  using th−1)(N−1) . Simpsonall loci  using thall loci . PID calWe first used regression analysis to test the relationship between genotypic (R) and genetic diversity (AR/He/Ho). We then tested for differences between high and low disturbance sites on each dependent variable separately. We used a hierarchical ANOVA with the main factor Disturbance having two, fixed levels (High and Low). The 2 sites with no disturbance were included in Low disturbance. The random factor, Site, was nested within Disturbance, with 3 quadrats in each Site. Because R varied significantly between levels of disturbance, we also regressed seagrass morphological data against R, for the twelve sites for which morphological measurements were available. This analysis was done in two steps. First, we tested all morphological variables together, using a multivariate linear regression. We then analyzed each individual morphological variable in separate regressions.Z. muelleri samples from 14 sites across 11 loci. Overall, we ascertained high levels of genotypic diversity with a total of 397 unique multilocus genotypes detected across all sites. A total of 116 replicate genotypes was identified. The largest clone at any site showed a single genotype sampled 16 times. Numbers of clones per site ranged from 5 to 10. Global genotypic diversity was relatively low (R = 0.55), and ranged between 0.30 and 0.82 (Table 1). Levels of clonal heterogeneity across sites varied from 0.99 to 0.83.We genotyped 630 Table 1). Mean number of alleles per locus across all sites was 3.01 (1.50 Standard Error), and ranged between 2.21 (0.44) and 3.62 (0.94). Unbiased expected heterozygosity across all sites was 0.42 (0.03), varying between 0.37 (0.12) and 0.48 (0.10). Observed heterozygosities were similar to expected heterozygosities. Overall observed heterozygosity was 0.42 (0.03), ranging from 0.36 (0.26) to 0.46 (0.10). There was no evidence of inbreeding within any site with FIS values ranging between slight heterozygous excess  and slight deficit .Levels of genetic diversity were similar across all sites , but genotypic diversity was not significantly related to expected (He) or observed (Ho) heterozygosity (p > 0.05).Genetic diversity (AR) was significantly positively related to genotypic diversity (R) was significantly lower at highly disturbed sites (R = 0.49) than at less disturbed sites . R also varied among sites within disturbance categories . For genetic diversity, neither AR nor expected heterozygosity (He) differed between disturbed and undisturbed sites , but AR varied significantly among sites within disturbance categories .Genotypic diversity . Overall, these relationships were close to being significant (multivariate linear regression: p = 0.053). When tested individually, one was significant at the critical level of 0.05 , but the consistent negative relationship is important to note.Genotypic diversity declined with two morphological characteristics reported to be associated with resilience of seagrasses in Moreton Bay in The main finding of this study is that in Moreton Bay, seagrass meadows subject to long-term disturbance show greater resilience to a severe perturbation yet have lower genotypic, but not genetic diversity. We interpret this to be a case of highly disturbed sites having selected for a narrow range of genotypes that are able to cope with the harsher conditions. The trend of low genotypic diversity being associated with several morphological fitness parameters supports the conclusion that intense selection in disturbed populations has resulted in more resilient genotypes.P. oceanica exposed to pollution from fish farms was found to be correlated with lower genotypic diversity (P. oceanica subject to a range of different stressors showed the same trend of low genotypic diversity at more resilient sites (P. oceanica studies, the lower genotypic diversity went hand-in-hand with larger clone size (Z. muelleri in Moreton Bay is a topic worthy of further testing.Although a majority of previous studies examining seagrass responses to stressors show a positive trend of higher diversity providing a greater capacity to resist or recover , there aiversity . A broadnt sites . These cone size , and theone size . The posMany other studies show a positive relationship between genetic diversity and resilience of seagrasses to disturbances ranging from warming water  to graziZ. muelleri or any other seagrass species in Moreton Bay. The only study of dispersal in Z. muelleri from a resilience perspective is a test of recovery in small patches, 300 mm in diameter, from which seagrass had been removed; no recovery from sexual reproduction occurred over the year-long experiment (Z. muelleri in Moreton Bay remains an open question.One limitation of our large-scale natural experiment in Moreton Bay is the lack of information on levels of genotypic diversity prior to disturbance. The greatly increased disturbance from deposition of riverine sediment since European settlement is well cataloged , but witperiment . Sexual Z. muelleri (Our measure of genetic diversity, as for previous studies of genetic diversity and disturbance in seagrass, uses a small number of neutral markers. This technique cannot be expected to capture the full adaptive potential of plants . With thmuelleri  and the muelleri , it is nmuelleri .The responses of seagrass in Moreton Bay have provided an informative test of the role of genotypic diversity. Our interpretation of how genetic structure is affected by disturbance benefitted substantially from past studies of morphometric and physiological responses of seagrass to stressors in the bay. In terms of understanding the mechanisms through which genotypic diversity influences resilience , MoretonRC, TS, PSM, AO, PIM, and CS conceived, conceptualized, and interpreted the project, and revised and edited the manuscript. PSM and AO collected the field data. TS and CS ran the laboratory analyses. RC, CS, TS, and PSM performed the statistical analyses. RC and CS wrote the manuscript. RC and CS acquired funding.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor is currently co-organizing a Research Topic with one of the authors PIM, and confirms the absence of any other collaboration.


Hemarthria compressa L.) clones and assess the genetic diversity and population structure among these genotypes. The informativeness of markers was also estimated using various parameters. Using 25 highly reproducible primer sets, 368 discernible fragments were generated. Of these, 282 (77.21%) were polymorphic. The number of alleles per locus ranged from five to 21, and the genetic variation indices varied. The polymorphism information content (PIC) was 0.358, the Shannon diversity index (H) was 0.534, the marker index (MI) was 4.040, the resolving power (RP) was 6.108, and the genotype index (GI) was 0.782. Genetic similarity coefficients (GS) between the accessions ranged from 0.563 to 0.872, with a mean of 0.685. Their patterns observed in a dendrogram constructed using the unweighted pair group method with arithmetic mean analysis (UPGMA) based on GS largely confirmed the results of principal coordinate analysis (PCoA). PCoA was further confirmed by Bayesian model-based STRUCTURE analysis, which revealed no direct association between genetic relationship and geographical origins as validated by Mantel’s test . In addition, high-level genetic variation within geographical groups was significantly greater than that between groups, as determined by Shannon diversity analysis, analysis of molecular variance (AMOVA) and Bayesian analysis. Overall, SCoT analysis is a simple, effective and reliable technique for characterizing and maintaining germplasm collections of whipgrass and related species.Start codon targeted polymorphism (SCoT) analysis was employed to distinguish 37 whipgrass ( Hemarthria compressa L.) is a warm-season perennial creeping grass of the tribe Andropogoneae in the family Poaceae. Whipgrass is one of the most important and widely utilized forages plants in southern China due to its high forage yield, regeneration, fast growth and adaptability to hot, humid conditions [Whipgrass . The number of bands varied from 5 (SCoT87) to 24 (SCoT21 and SCoT84) per primer, corresponding to an average of 8.4 amplicons and 11.28 polymorphisms per primer, respectively. The percentage of polymorphic bands (PPB) ranged from 52.94% for primer SCoT60 to 100% for the following primers: SCoT5, SCoT7, and SCoT87 . None ofp < 0.01) was observed between GI and Rp (r = 0.860), between Rp and MI (r = 0.993) and between GI and MI based on average diversity (r = 0.870). However, these three parameters were found no clear correlation with PIC. Nevertheless, a positive linear relationship (p < 0.01) linear relationship was observed between PIC and H (r = 0.998).A positive linear relationship ( vs. H056) to 0.872 (H046 vs. H047). These results indicate relatively high genetic variability among the examined whipgrass clones. In the unrooted dendrogram made on the basis of similarity index, all 37 accessions grouped into five clusters  with a mean similarity threshold of 0.685, whereas the corresponding bootstrapping values of branches were relatively low (Nei-Li similarity coefficients (GS) ranged from 0.563  did not show a clear mode for the true K. This result is expected when factors such as departures from Hardy–Weinberg equilibrium are present. Therefore, an ad hoc quantity (ΔLnP(D)) was used to overcome the difficulty in interpreting real K values . Using tUsing the same initial conditions, independent repeats at the same inferred value K might yield a different individual membership coefficients matrix (Q-matrix). Of 37 accessions, 36 could be assigned to clusters based on a 60% membership threshold. Thus, more than 97.3% of the accessions were considered to have a relatively simple parentage . The Shannon diversity index of the four geographic groups ranged from 0.3964 to 0.5042, with an average of 0.4516. The genetic diversity (84.57%) was much higher within groups than between groups (15.43%). The results from the variance analysis indicated that there were significant differences in intra-group diversity in CQ and CDP; however, the intra-group diversity in CQ and CDP was significantly greater than the intra-group diversity in the GZ and YL groups . PT indicated a different degree of variation between the geographic groups, with values ranging from 0.048 (between CDP and YL) to 0.153 (between GZ and YL) , and only 6.66% could be attributed to differences between-groups . Pairwis and YL) . PT value was 0.067, confirming that geographic distribution had little effect on the diversity of whipgrass genotypes. The overall Φfull model, suggesting that it was the most suitable model for these data. For the four geographic groups, this model (DIC 3967.02) generated a mean Bθ of 0.0613 ± 0.0098  and a GST-B of 0.0464 ± 0.0073 . The Bayesian estimates of average panmictic heterozygosity (HS) are shown in S = 0.3614) and CQ group (HS = 0.3696), respectively. At the species level, genetic variation was 0.3841 for the total data. In total, these results demonstrate that the full model yielded estimates of FST in concordance with those previously described in the extension of the AMOVA (full model was 51 units lower than under the ƒ = 0 model (DIC = 4018). This result indicates deviation from Hardy-Weinberg equilibrium. The full model was also favored over the θ = 0 model (DIC = 4261), indicating significant population structure [The Bayesian approach provided the posterior mean, standard deviation, and 95% confidence interval for each of the four available models. The smallest mean DIC was obtained under the he AMOVA , supporttructure .Morinda tomentosa [Genetic diversity analysis is vital for the management of entire collections and breeding programs. The efficiency of a marker technique in discriminating genotype depends largely upon the polymorphism it can detect. The SCoT technique has been used successfully for the DNA fingerprinting, characterization of genetic variation and phylogenetic studies in some cultivated plants ,13,14,15omentosa , and tetomentosa . This suThe two different methods of multivariate analysis  applied to group individuals in this study sort data on different calculation bases; therefore, they can be used to elucidate relationships comparatively. The UPGMA-based phenogram and principal ordinate analysis displayed similar patterns. UPGMA analysis assumes a constant evolutionary rate among accessions. This assumption can be violated when comparing samples collected in the wild with those selected by humans. However, the purpose of the present study was to investigate the level of genetic variation rather than to determine which accession evolved from the others. Moreover, the STRUCTURE analysis grouped whipgrass accessions into two major clusters based on the Bayesian Method . The degr = 0.2268, p = 0.9999). These results correspond with studies of whipgrass collection from southwest China by using ISSR [Ecological and geographical conditions are important factors affecting plant growth, development and distribution. Most accessions with geographical proximity were grouped into different clusters, indicating that there was no direct association between genetic divergence and geographic origins, as validated by Mantel’s test /HW = 0.1543, respectively. The results from these three methods exhibited discrepancies due to the differing principles of the methods. Shannon analysis is based on amplified phenotypic frequency bands [S) and population genetic differentiation (GST-B) [Bayesian inference demonstrated that all geographic groups deviated from the genetic equilibrium state. Therefore, software based on Hardy-Weinberg equilibrium assumptions, such as POPGENE , should cy bands , and AMOcy bands . Bayesia (GST-B) . The thrH. compressa L.) germplasms, including three registered varieties, were analyzed in the present study (n = 6x = 54); only four accessions were tetraploid (2n = 4x = 36). A total of 37 whipgrass  protocol . The quaAll SCoT primers were synthesized by Shanghai Sangon Biological Engineering Technology and Services . Seventy-five primer sets were initially screened for polymorphism and reproducibility using three varieties of whipgrass. Each 20 μL amplification reaction consisted of 3.0 μL of template DNA (10 ng/μL), 0.8 μM primer (10 μM), 0.2 μL of Taq polymerase (2.5 U/μL), 6 μL of sterile distilled water and 10 μL of 2× Taq PCR MasterMix . PCR reactions were performed using a Bio-Rad icycler and the following procedure: an initial denaturation step at 94 °C for 5 min, followed by 35 cycles of 94 °C for 1 min, 51 °C for 1 min, and 72 °C for 2 min and a final extension at 72 °C for 5 min. The PCR amplifications for all primers were processed using the same procedure. All amplified products were resolved by electrophoresis on 1.5% agarose gels containing 0.5 μg/mL ethidium bromide in 0.5× TBE buffer. Images of the banding patterns were acquired under UV light using a Bio-Rad gel imaging system.No assumptions about the genetic nature of the SCoT DNA fragments/bands  were made due to the aneu-polyploid nature of whipgrass and the absence of segregation analysis . Therefo et al. [i = 2if(1 − if) where PICi is the polymorphic information content of marker i, if the frequency of the marker bands which were present, and (1 − if) the frequency of marker bands which were absent. PIC values for dominant marker bands such as SCoT markers have a maximum of 0.5 fori f = 0.5 [i|), where pi is the proportion of the varieties or genotypes containing the band [ifLnif, where “if” is the frequency of an amplified band across all samples. Pearson correlation coefficients were calculated between the last five parameters . The discriminatory power of the SCoT primer sets was evaluated based on the following eight parameters: the total number of bands (TNB), the number of polymorphic bands (NPB), the percentage of polymorphic bands (PPB), the polymorphism information content (PIC), the genotype index (GI), the resolving power (Rp), the marker index (MI) and Shannon’s diversity index (H). The polymorphism information content (PIC) for each SCoT marker was calculated with the formula described by Roldan-Ruiz et al. : PICi = fi = 0.5 . PIC valfi = 0.5 . The banthe band . Rp was the band . ShannonNei and Li’s GS  and the The Bayesian-based clustering method was applied to delineate the clusters of genetically similar accessions using STRUCTURE software version 2.3.3 ,43. An aW = −∑ifLnif, where “if” is the frequency of an amplified band across all samples. The Shannon index within a subset of data (a geographic group) can be calculated using the following equation: Hzone = −∑ifLnif, where “if” is the frequency of an amplified band within a subset. The average diversity between different groups was calculated using the following equation: HA = Hzone = ∑Hzone/n, where n is the number of groups. Thus, HA is the average group diversity over n groups. The intra- and inter-group diversity components were calculated as HA/HW and (HW − HA)/HW, respectively. To compare the levels of diversity detected by different primer sets, the total Shannon diversity was calculated separately for each primer set.The germplasm collections studied here were artificially grouped into four geographic groups. The geographical distances between accessions within the groups varied greatly from tens to hundreds of kilometers. It is difficult to analyze the variance components and their significance levels of genetic variation within and between geographic groups using common software under the Hardy-Weinberg equilibrium assumption. To avoid the complications mentioned above, genetic divergence was analyzed using Shannon’s diversity index, analysis of molecular variance (AMOVA) and Bayesian inference. The magnitude of the genetic variation was determined for each geographical group using Shannon’s diversity index . ShannonPT, an analogue of FST [AMOVA based on a Euclidean squared distance matrix was hierarchically calculated to estimate the allocation of genetic variation among and within regional groups using GenAlEx version 6.5 . AMOVA ce of FST . The sigBθ using dominant markers; HICKORY does not assume Hardy-Weinberg equilibrium. Its f and Bθ are equivalent to the inbreeding coefficient (FIS) and the fixation index (FST) of F-statistics, respectively. The Bayesian estimator of genetic diversity was calculated for each of the following four models: (1) the full model (with non-informative priors for f and Bθ); (2) f = 0 (assumes no inbreeding); (3) Bθ = 0 (assumes no population structure); and (4) f-free . We conducted several runs with default sampling parameters  to ensure model convergence. The deviance information criterion (DIC) was used to estimate the fit between the data and a particular model and to choose among models [We compared the Shannon index and AMOVA results for the population genetic structure with allele-frequency estimates calculated using HICKORY software 1.1 . HICKORY H. altissima and H. sibirica. Incorrect identification and the misplacement of labels remain serious problems in maintaining germplasm. This study demonstrates that SCoT analysis is a simple, effective and reliable technique for identifying and maintaining warm-season grass germplasm and could have applications in the assessment of genetic variability in collections of unknown origin, breeding stocks, and/or plant variety protection.In summary, the results of our study demonstrate a relatively high level of genetic diversity and low levels of genetic differentiation among geographical groups in whipgrass germplasm. The selected primer sets, which revealed high polymorphism among the accessions, might be applied to other closely related species such as


Magna Graecia is the ancient name for the modern geopolitical region of South Italy extensively populated by Greek colonizers, shown by archeological and historical evidence to be the oldest wine growing region of Italy, crucial for the spread of specialized viticulture around Mediterranean shores. Here, the genetic diversity of Magna Graecia grape germplasm was assessed and its role in grapevine propagation around the Mediterranean basin was underlined.Magna Graecia was compared with germplasm from Georgia to the Iberian Peninsula using the 18 K SNP array. A high level of genetic diversity of the analyzed germplasm was determined; clustering, structure analysis and DAPC  highlighted the genetic relationships among genotypes from South Italy and the Eastern Mediterranean (Greece). Gene flow from east (Georgia) to west (Iberian Peninsula) was identified throughout the large number of detected admixed samples. Pedigree analysis showed a complex and well-structured network of first degree relationships, where the cultivars from Magna Graecia were mainly involved.A large collection of grapevines from Magna Graecia germplasm was shaped by historical events that occurred in the area due to the robust link between South Italian and Greek genotypes, as well as, by the availability of different thermal resources for cultivars growing in such different winegrowing areas. The uniqueness of this ampelographic platform was mainly an outcome of complex natural or human-driven crosses involving elite cultivars.This study provided evidence that The online version of this article (10.1186/s12870-018-1576-y) contains supplementary material, which is available to authorized users. Vitis vinifera L. subsp. sativa) is one of the most important economic fruit species in the modern world; of West Asiatic origin, it is cultivated in a wide area from Trans-Caucasus to Western Europe and around the Mediterranean Basin [sylvestris) is suggested to have first appeared about 65 million years ago and its domestication was closely related to winemaking [Grapevine . In the National Register of Italian Grapevines more than 650 varieties are included , representing a slice of grapevine diversity, as assessed by genetic analysis of Italian germplasm collections, including both local varieties and the most widely known national varieties [Among European countries, Italy is one of the major wine producers. As stated in the OIV Statistical Report on World Vitiviniculture, the area under vines cultivation in Italy reached 690,000 ha in 2016, with 7.9 million tons of grape production for both wine and table grape markets and 50.9 million hectoliters of wine , referring to the Middle bronze age [According to archaeobotanical data, Italian grapevine domestication was mainly determined by local grape populations, sharing a genetic pool with varieties from the Hellenic world, where viticulture had already reached a high level of specialization , 29. As onze age . The fironze age .The biodiversity of southern Italian grapevine germplasm has been widely investigated by SSR (Simple Sequence Repeat) to study genetic variability , 14, 32,V. vinifera and 7 wild Vitis species) was developed by Myles et al. [V. vinifera and Vitis spp. genotypes [Recently, programmes of sequencing and re-sequencing of the grape genome have generated a database including an extensive number of single nucleotide polymorphisms (SNP), useful for setting up different genotyping SNP-panels developed for both SNPlex™ and chip array strategies , 34–36. enotypes . Finallyenotypes  describeV. vinifera populations, between wild and cultivated compartments of V. vinifera and among wild Vitis species [The usefulness of SNP sets has been demonstrated despite the bi-allelic nature of this kind of molecular marker. They were proven adequate to investigate genetic variability, discriminating among  species , 37–41.  species , 38 and  species , 38, 42.Magna Graecia is the ancient name of southern Italy  colonized by the Greeks during the eighth century BC. This area was influenced by Greek civilization, in terms of customs and traditions, such as language, religious rites and agriculture, including viticulture and its varieties [Magna Graecia were genotyped by Vitis18kSNP chip array to identify synonymies/homonymies, to investigate genetic diversity, population structure and parentage. To highlight the east-to-west gene-flow between the primary (Caucasus region) and the secondary domestication centers, a SNP dataset with genotypes from Georgia up to the Iberian Peninsula were included in the analysis.arieties . Here, aA total of 140 grapevine accessions, originating from southern Italy, Greece and the Eastern Mediterranean Sea  were genotyped by Vitis18kSNP chip array  was added with SNP profiles already available. This second sample set (#2) included genotypes from sample set #1, SNP profile of 42 varieties coming from Georgia already described in De Lorenzis et al. [To investigate the role of s et al.  and a sus et al.  , following the manufacture’s instructions. The quality (260/230 and 260/280 ratios) and quantity of DNA extracted were checked using the NanoDrop Spectrophotometer  and the Quant-iT dsDNA HS assay kit for Qubit 3.0 Fluorometer (Thermo Fisher Scientific), respectively.The Vitis18kSNP array , containing 18,071 SNPs, were used to genotype the 195 samples. The amplifications were performed on 200 ng of genomic DNA by the laboratory of Fondazione Edmund Much . SNP calls were scored with Genotyping Module 1.9.4 of the GenomeStudio Data Analysis V2011.1 software (Illumina Inc.). SNP loci showing call quality values (p50GC) lower than 0.54 were removed from the final dataset, as well as loci having GenTrain (GT) score values lower than 0.6.  and thoso), expected heterozygosity (He) [poppr [On the first sample set (#1), genetic distances and variability were assessed. The genetic variability was estimated by observed heterozygosity (Hity (He) , the minity (He) , and inbity (He) . The gen) [poppr  package ) [poppr  and Unwe) [poppr .e, Ho, MAF and F indexes and cluster analysis were performed as described for sample set #1. The dendrogram was displayed as topology in MEGA 5.0 software [On sample set #2, Hsoftware , where ttess3 R package [The genetic structure was investigated in more detail with a Bayesian model-based clustering algorithm implemented in  package . Based oadegenet of R software [e values were calculated and the genetic differentiation among DAPC clusters was validated by pairwise Nei’s standard genetic distance [nei.dist function of Poppr package [pp.fst function of HierFstat package [adegenet [Z-score indicated gene flow between A,B and C,D. Higher values showed evidence of gene flow in the tree.A Discriminant Analysis of Principal Components  was persoftware . The maxdistance , 56 and distance , perform package  and pp.f package , respectadegenet  to calcuadegenet  was used2 of linkage disequilibrium value at 0.05. The relationships among pair of genotypes were assigned taking into account four parameters: Z0 , Z1 , Z2 , and PI-HAT [(the relatedness measure measured as PI-HAT = P (IBD = 2) + 0.5 × P (IBD = 1)). A first-degree relationship (parent-offspring) was assigned to those pair of genotypes showing Z0 and Z2 values similar to 0, Z1 similar to 1 and PI-HAT to 0.5, a second-degree relationship when Z0 and Z2 showed values similar to 0.25, Z1 and PI-HAT to 0.5. An overview of first- and second-degree relationships among samples was displayed by circular visualization, obtained by Circlize R package [In order to infer relationships among non-redundant individuals, the identity-by-descent (IBD) index was calculated by PLINK 1.07 software . IBD ind package . The net package .o values were lower than the expected values from total germplasm within the sample set #1, ranging from 0.2873 (eastern Mediterranean Sea population) to 0.3060 . Total He value was 0.3179 and the range varied from 0.3017 (Georgia) to 0.3471  , the so called sample set #1, were investigated by a high-throughput genotyping system based on SNP chip array, the Vitis18kSNP array. The final dataset counted 11,023 loci  with that originating from neighboring winegrowing areas, from Georgia to the Iberian Peninsula, a second dataset called sample set #2 was built, including already available data from De Lorenzis et al. [e, Ho, MAF and F indexes were measured, subdividing samples based on geographic area and historical influences in, from east to west, Georgia, eastern Mediterranean Sea Countries , South Italy, North Italy, France and Iberian Peninsula ; the values per each population are reported in Table o (0.2819 and 0.3262) and He (0.3067 and 0.3406). Georgian population showed the lowest MAF value (0.2157), while North Italy population the highest (0.2368). About F index, the lowest value was detected for Georgia (0.1044) and the highest for South Italy (0.1830).In order to compare the genetic relationship between s et al.  and Laucs et al. . This laMagna Graecia germplasm”. Here three main clusters were highlighted, the largest included samples from Eastern Mediterranean Sea, South Italy and Iberian Peninsula (C1). The second included only samples from Georgia (C2), while the third cluster was mainly comprised of samples from North Italy and France (C3)  and UPGMA algorithm  showed a percentage of membership > 80%  within the germplasm from sample set #2 was investigated by e values of DAPC ranged from 0.2716 (Cluster 5) to 0.3073 (Cluster 1) (Table DAPC identified five clusters: Cluster 1 included mainly French genotypes 78%); Cluster 2 was the most assorted cluster, grouping together mainly samples from eastern Mediterranean Sea countries (97%) and South Italy (59%), together with Georgian, French, North Italy and Iberian Peninsula genotypes; Cluster 3 comprised only Georgian samples (87%); Cluster 4, 65% of Iberian Peninsula accessions, while Cluster 5 comprised only samples coming from the South Italy (31%)  and geographical distance matrices was found  tests whether population A and B vs. population D and C can be considered distinct clades in a population tree. The groups showing the highest non-zero Z-score values, suggesting gene flow, were Cluster 1 and 5 vs. Cluster 2 and 3 (Z-score = − 7.2797) and Cluster 1 and 4 vs. Cluster 2 and 3 (Z-score = − 9.2147)  and second-degree  relationships were detected among 709 genotypes included in sample set #2 to define a proposed pedigree of southern Italian samples. Z0, Z1, Z2 and PI-HAT values for PO and second-degree relationships detected among Southern Italian samples were reported in Additional file The cultivars with the highest number of relationships were Sangiovese (13) and Mantonico Bianco (10) Fig. . SangiovOther genotypes showing a well-defined pedigree-network were Bombino bianco (7 PO relationships) and Aglianico (6). Bombino bianco exhibited Z0, Z1, Z2 and PI-HAT values similar to PO relationship with Bombino n., Cococciola, Colatamurro, Impigno b., Montepulciano, Sanguinella b. and Uva di Troia  were similar to He values (0.3179 across populations), suggesting that the populations can be affected by frequent inbreeding events  and Ho values higher than He values , geographic origin and usage . InRegno d’Italia  was established, an united kingdom encompassing the entire Italian Peninsula. Apart the periods when the Italian territory was politically unified, such as under the domination of the Roman Empire or the Ostrogoths, different peoples have alternately occupied Italian territory over the course of the centuries. During classical age, Phoenicians and Greeks established settlements in the South Italy and the Celts inhabited the North Italy, while in the 8-9th century the Frankish Empire and Normans defeated North and South Italy, respectively, proclaiming the end of Italian political unity for the next 1300 years. Thus, rather than a division by geographic area, our data suggested a division by historical events.In attempting to back-track the origin of this differentiation, this result can be explained by the different historical and demographical events occurred in the two areas. Italy was fragmented country until 1861 when the Another aspect that might have been influenced the differentiation between northern and southern Italian germplasm is the different availability of thermal resources for cultivars growing in such different environments, characterized by different latitudes. Indeed, the thermal resources observed in the South Italy are similar to South-East Europe and the these observed in the North Italy are similar to North-East Europe .V. vinifera subsp. sativa can be considered an invasive species, though the grapevine dissemination was mainly mediated by human migrations, taking into account the first center of domestication as the origin of introduction.In invasive species, genetic diversity is expected to decrease from the origin of introduction to newly invaded areas . V. vinie values decreased from Cluster 1  to Cluster 2 . He increased to a maximum for Cluster 1 (0.3073), including samples from France and North Italy  reached Europe from North America and devastated European vineyards. The introduction of North America non-vinifera cultivars as rootstock safeguarded the future of European V. vinifera. At the same time, together with the ever more increasing practice of non-vinifera rootstocks the selection and vegetative propagation of elite cultivars occurred, resulting in the further reduction of grapevine genetic diversity [The cultivars showing the highest PO relationships were Sangiovese, Mantonico Bianco, Bombino bianco and Aglianico. The genetic diversity of grape was dramatically threatened when at the end of nineteenth century phylloxera  of Uva di Troia, Bombino nero and Impigno . In the Aglianico is an ancient grapevine cultivar mainly cultivated in Campania (biotypes Taburno and Taurasi) and Basilicata (biotype Vulture), and strongly related with the establishment of Greek colonies in Campania . Here, AAnother interesting cultivar was Gloria, sampled in Campania and showing a PO relationship with genotypes coming from Greece, Italy, France and Spain. This admixed progeny by geographic origin mirrors the shuffling and exchanges of cuttings that occurred over the centuries following migration routes in Europe and confirms the route from Greece to France and the Iberian Peninsula through South Italy.The name Malvasia was used to identify a group of cultivars diverse for both genetic and phenotypic profiles, cultivated in many Mediterranean countries –93. In tvia Greece, in at least two separate steps. In this study, an extensive genetic characterization of Magna Graecia grapevine germplasm, from Georgia to the Iberian Peninsula, was carried out by 18 K SNP loci. Based on genetic analysis, Magna Graecia germplasm showed a high level of heterozygosity and distinctive traits, such as a common genetic background and a complex pedigree. Nevertheless, a significant degree of gene flow was observed in agreement with historical and socio-eco-political events that occurred in the Mediterranean Basin. These results highlighted the central role of Magna Graecia in the spread of grapevine through western Europe, supporting the hypothesis of an intense exchange of plant material as well as a close relationship among southern Italian cultivars and genotypes from eastern and western countries. Moreover, the genetic diversity of Magna Graecia germplasm has been shaped by the historical events that occurred in this area and by the variability of atmospheric driving variables  that trigger selective pressures and determine productivity, quality and territorial specificity of agroforestry productions.Archaeological and historical data suggested that grapevine primary domestication took place in the Caucasus and from there spread to South Italy Additional file 1:a Passport data of 187 grapevine genotypes coming from Italy , Eastern Europe  and Greece, the so-called sample set #1, and allele profiles at 9 SSR loci (when available). b List of genotypes added to the sample set #1 to obtain the sample set #2. The genotypes included belong to Georgia, Bosnia and Herzegovina, Cyprus, Spain, France, Greece, Italy, Portugal and Turkey . , genotyped at 18 K loci. Original dataset was filtered based on SNP call quality and GenTrain score: samples with low SNP call quality (p50GC < 0.54) were removed from the analysis and only SNPs with a GenTrain score higher than 0.6 were retained. “A”: homozygous for dominant allele; “B”: heterozygous for recessive allele; “?”: missing data. (XLSX 7910 kb)Additional file 3:UPGMA dendrogram of 187 grapevine genotypes analyzed by 18 K SNP array. The samples were marked based on their geographical origin. South Italy: blue dots; Eastern Mediterranean Sea: red dots; Reference: black dots. (TIFF 1630 kb)Additional file 4:Magna Graecia germplasm (sample set #1) analyzed by 18 K SNP array. (DOCX 13 kb)List of synonymies identified among the Additional file 5:Topology of UPGMA dendrogram of 709 grapevine accessions genotyped with 18 K SNP array. Georgia: violet branch lines; Eastern Mediterranean Sea: red branch lines; South Italy: blue branch lines; North Italy: dark green branch lines; France: light green branch lines; Iberian Peninsula: cyan branch lines. C1, C2, C3: main clusters identified. (TIF 2662 kb)Additional file 6:Ancestry values at K = 3 inferred by structure analysis on 709 grapevine accessions (sample set #2) coming from Georgia, Eastern Mediterranean Sea Countries , Italy (North and South), France and Iberian Peninsula  genotyped at 18 K loci. (XLSX 56 kb)Additional file 7:List of five cluster inferred by DAPC on 709 grapevine accessions of sample set #2 genotyped at 18 K loci. (XLSX 27 kb)Additional file 8:Nei’s standard genetic distance  and Fst index  calculated on five clusters inferred by DAPC. (DOCX 12 kb)Additional file 9:Graphical representation of correlations between genetic distances and geographical distance. A: Nei’s genetic distance; B: Fst genetic distance. Red: high correlation between genetic and geographical distances; Yellow: medium correlation between genetic and geographical distances; Blue: low correlation between genetic and geographical distances. Dots represent distance values between two populations detected by DAPC. (TIFF 1753 kb)Additional file 10:Summary of four-population tests on five clusters inferred by DAPC. (DOCX 13 kb)Additional file 11:Parent-offspring (PO) and second degree (2°) relationships identified for South Italy cultivars genotyped at 18 K SNP loci. Z0: probability to share 0 allele; Z1: probability to share 1 allele; Z2: probability to share 2 alleles; PI HAT: probability to be identical by descendent. (XLSX 18 kb)Additional file 12:Circular representation of first (red links) degree relationships identified for South Italy cultivars genotyped at 18 K SNP loci. The samples are arranged based on their geographic origin. Italy: blue scale (based on sub-populations); Eastern Mediterranean Sea Countries: red; genotypes from Laucou et al. : grey; R


Lepidochelys kempii) is the world's most endangered sea turtle species and is primarily distributed in the Gulf of Mexico. In the United States, South Padre Island, Texas serves as a key nesting ground for the species. Genetic studies of the Kemp's ridley have been used to aid in conservation and management practices, with the mitochondrial control region as the most commonly used marker due to its perceived hypervariability and ease of sequencing. However, with the advent of next generation sequencing technology, targeting complete mitochondrial genomes is now feasible. Here, we describe a more complete mitochondrial genome for the Kemp's ridley than has been previously published in literature and demonstrate a cost‐effective and efficient method for obtaining complete mitochondrial genomes from sea turtles. We compare the genetic diversity and taxonomic resolution obtained from whole mitochondrial genomes to that obtained from the mitochondrial control region alone. We compare current genetic diversity with previous records. Furthermore, we evaluate the genetic structure between the breeding stock in South Padre Island and that of deceased Kemp's ridleys recovered on the Northern coast of the Gulf of Mexico after the 2010 BP Deepwater Horizon oil spill, and of Kemp's ridleys stranded on the East Coast of the United States. Our results show that complete mitochondrial genomes provide greater resolution than the control region alone. They also show that the genetic diversity of the Kemp's ridley has remained stable, despite large population declines, and that the genetic makeup of deceased turtles stranded after the Deepwater Horizon oil spill is indistinguishable from the breeding stock in South Padre Island, Texas.The Kemp's ridley  in the northwestern Gulf of Mexico is relatively stable despite recent and severe population decline, and the genetic makeup of deceased turtles from the Deepwater Horizon oil spill is virtually identical to that of the breeding stock in South Padre Island, TX. Using the mitochondrial control region to study genetic patterns of this sea turtle is necessary to compare the data to older records, but it should be replaced by targeting complete mitochondrial genomes. The complete mitochondrial region results in more genetic resolution while still providing the sequence for the control region for historical comparisons.The genetic diversity of Kemp's ridley sea turtle ( We compare the genetic diversity and taxonomic resolution obtained from whole mitochondrial genomes to that obtained from the mitochondrial control region alone, by evaluating a sampling of Kemp's ridleys in South Padre Island, Texas. Furthermore, using the control region, we evaluate the genetic structure between the breeding stock in South Padre Island and that of deceased Kemp's ridleys recovered on the Northern GoM coast after the 2010 BP 22.1From 2015 to 2016, opportunistic DNA tissue samples were collected from nesting and stranded Kemp's ridleys  n = 58) and Western GoM (n = 42). Samples collected on the Northern GoM coast (n = 11) were labeled as DWH samples due to uncertainty whether the sampled individuals were transients or residents of the area where they stranded. Samples collected from Kemp's ridleys in rehabilitation facilities (n = 2) were labeled as captive, due to their nonreleasable status  was obtained from all samples after standard extraction with Thermo Fisher Scientific's Purelink Genomic DNA extraction kit , following the manufacture's protocol for mouse tissue. Samples were fully digested before extraction for 2–8 hr with Proteinase K. Once the DNA was obtained using the Genomic DNA extraction kit, the concentration of DNA was measured using a Life Technologies Qubit fluorometer (Life Technologies Inc). Gel electrophoresis in a 2% agarose gel stained with ethidium bromide ensured the extracted DNA of all samples was of high quality and high molecular weight. Extracted DNA was stored at −20°C.2.3Taq Reaction Buffer, 2.0 µl of 10 mM dNTPs, 1.0 µl of 10 µM Forward Primer, 1.0 µl of 10 µM Reverse Primer, 0.125 µl of DreamTaq DNA Polymerase, and 1.0 µl of DNA was run on an Eppendorf Mastercycler pro thermocycler. The following parameters were used: (a) 2 min of initial denaturation at 94°C, (b) 50 s of DNA denaturation at 94°C for 36 cycles, (c) 2 min of primer annealing at 52°C, (d) 90 s of primer extension at 72°C, and (e) 5 min of primer extension at 72°C  and H950g (5′ GTCTCGGATTTAGGGGTTTG 3′)  and with a minimum Phred score of 20.2.4The genomic DNA extraction of ten individuals was used to prepare an indexed library following standard procedures with the Nextera X2 kit. These 10 libraries, along with 86 libraries from other projects, were multiplexed and sequenced on a 100 bp paired‐end lane of Illumina HiSeq 2500 at Harvard's Biopolymers facility. The sequences were de‐multiplexed according to their indices. De novo assemblies were conducted using the software CLC Genomics Workbench. Default settings were used with reads mapped back to contigs . The sequences obtained from the assemblies included the full mitochondrial genome for each specimen with an average read coverage of over 100 and a minimum coverage of 35. The assembled genomes were annotated using Qiagen CLC Genomics Workbench software, referencing the two previously published partial Kemp's ridley genomes on GenBank (JX454981 and JX454982).2.5Two separate datasets were analyzed, one only using complete mitochondrial genomes and the other only using the mitochondrial control region. All available sequences from GenBank were added to these two datasets Table . HaplotyPhylogenetic analyses of the haplotypes identified from the control region were performed with MEGA7 . Furthermore, the ΦST values are very low . Values close to zero indicate samples are homogenous.The control region was sequenced for 113 samples, resulting in ten unique haplotypes within the dataset. Eight of these haplotypes match those identified by Frey et al.  Lk 1.1, π) varied from 0.00134 ± 0.0002 (Western Gulf) to 0.00100 ± 0.00023 (DWH)  and Fu's F  statistics  varied from 0.685 ± 0.0029 (Western Gulf) to 0.626 ± 0.052 (East Coast)  Table . The tenn = 31), but a lower haplotype diversity (Hd = 0.4545) compared to the control region (Hd = 0.5909) and COX3 (Hd = 0.5909)  and the highest level of haplotypic diversity (Hd = 1) Table . Gene 169) Table .The ten mitochondrial genomes recovered match three previously defined haplotypes (based on the control region): Lk 2.1, 4.1, 6.1 , and the captive Kemp's ridley from the U.S. East Coast matches Haplotype 2 (Lk 6.1). Both these haplotypes are found on either coast; however, the U.S. East Coast samples in this study typically belonged to Haplotype 1 (Lk 4.1).After complete assembly of ten mitochondrial genomes, it was determined that most of the variation in the Kemp's ridley mitochondrial DNA occurs in the hypervariable control region, evidenced by the number of haplotypes present, haplotypic diversity, and sequence length compared to the other markers Table . RibosomTargeting solely the control region limits the detection of variation between individuals. This is apparent from the phylogenetic reconstruction where the tree based on complete mitochondrial genomes shows greater bootstrap support and more resolved branches. Though patterns can be seen within the control region, this study indicates that full genomes convey a more robust analysis. Targeting solely the control region is adequate for assigning individuals into haplotype groups; however, specimens still contain differences from each other within the rest of the mitochondrial genome. Analysis of full mitochondrial genomes results in more haplotypes and provides greater resolution for phylogenetic reconstruction. Though it must also be considered that the poor resolution in the phylogenetic reconstruction based solely in the control region is partly due to the multifurcation shown by the star‐shaped haplotype network which cannot be represented well by bifurcation as required in phylogenetic reconstruction  in the human genome is a read depth of 30. This number is based on Bentley et al.  research5Deepwater Horizon oil spill is indistinguishable from the breeding stock in South Padre Island, Texas, indicating that the stranded turtles likely originated from nesting beaches along the Western GoM coast. Our results demonstrate that the genetic diversity of the critically endangered Kemp's ridley, despite large population declines, is stable. Routine genetic analyses of populations, such as ours, provide vital information for managers establishing recovery priorities and developing conservation strategies for critically endangered species.Congruent with previously published research, our results indicate that the control region is the most variable region in the Kemp's ridley mitochondrial genome. We demonstrate a cost‐efficient method of obtaining mitochondrial genomes in sea turtles and show that analysis of Kemp's ridley complete mitochondrial genomes will result in greater genetic resolution than analysis of the control region alone. Future studies with marine turtles should utilize full genomes for a greater understanding of genetic diversity of within populations. We show that the genetic makeup of deceased turtles stranded after the The authors declare they have no conflicts of interest. Sea Turtle, Inc. is a nonprofit organization focused on sea turtle rehabilitation, conservation, and public education and is not a competing interest.DF conceptualized the study; DF, HRF, and JAG designed the methodology; DF, JAG, and HRF acquired funding; DF, JAG, and HRF provided resources and acquired the data; DF and HRF analyzed and interpreted the data; DF and HRF drafted the manuscript; and DF, HRF, and JAG revised and approved the manuscript to be submitted.https://www.ncbi.nlm.nih.gov/genbank/.This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at


Ipomoea batatas) plays a critical role in food security and is the most important root crop worldwide following potatoes and cassava. In the United States (US), it is valued at over $700 million USD. There are two sweetpotato germplasm collections (Plant Genetic Resources Conservation Unit and US Vegetable Laboratory) maintained by the USDA, ARS for sweetpotato crop improvement. To date, no genome-wide assessment of genetic diversity within these collections has been reported in the published literature. In our study, population structure and genetic diversity of 417 USDA sweetpotato accessions originating from 8 broad geographical regions  were determined using single nucleotide polymorphisms (SNPs) identified with a genotyping-by-sequencing (GBS) protocol, GBSpoly, optimized for highly heterozygous and polyploid species. Population structure using Bayesian clustering analyses (STRUCTURE) with 32,784 segregating SNPs grouped the accessions into four genetic groups and indicated a high degree of mixed ancestry. A neighbor-joining cladogram and principal components analysis based on a pairwise genetic distance matrix of the accessions supported the population structure analysis. Pairwise FST values between broad geographical regions based on the origin of accessions ranged from 0.017 (Far East – Pacific Islands) to 0.110  and supported the clustering of accessions based on genetic distance. The markers developed for use with this collection of accessions provide an important genomic resource for the sweetpotato community, and contribute to our understanding of the genetic diversity present within the US sweetpotato collection and the species.Sweetpotato ( Ipomoea batatas (L.) Lam. (Convolvulaceae), is the sixth most important food crop worldwide, following rice, wheat, potatoes, maize, and cassava , with a B1B1B2B2B2B2 genome composition resulting from an initial crossing between a tetraploid ancestor and a diploid progenitor followed by a whole genome duplication event  and aligned RNAseq data is available via the Genomic Tools for Sweetpotato Improvement Project, Regardless of its origin, the sweetpotato genome is hexaploid and highly heterozygous, and this genetic complexity has slowed genome sequencing, assembly, and annotation over the past 10 years. Nevertheless, the available molecular resources for sweetpotato are rapidly expanding and include elatives , microsaelatives , recentlelatives  and wholelatives . DiploidGenetic improvement of sweetpotato through traditional plant breeding is difficult due to its polyploid nature, genetic complexity, and high variability with regard to flower production and incompatibility . GeneratIpomoea spp. and provides clonal propagules of sweetpotato that are maintained as in vitro cultures. Available clones of hexaploid I. batatas were acquired over many decades, often in collaboration with various national and international programs and organizations. The collection provides the genetic foundation that supports ongoing research in breeding and genetics programs for the improvement of sweetpotato.The US sweetpotato germplasm collection is maintained by the USDA, ARS, Plant Genetic Resources Conservation Unit (PGRCU) in Griffin, Georgia, United States. This genebank maintains a diverse collection of The USDA sweetpotato breeding program was initiated more than 45 years ago with the goal of developing germplasm resistant to soil insect pests while maintaining good horticultural characteristics. The germplasm used for improvement is primarily from materials developed and maintained at the USDA, ARS, US Vegetable Laboratory (USVL) in Charleston, South Carolina, United States and secondarily from the PGCRU collection. In general, resistance to insects is not associated with undesirable root quality traits in sweetpotato . The proThe objective of our study was to provide information on the level of genetic diversity contained within the combined USDA (PGRCU and USVL) sweetpotato collections. A major obstacle to utilizing the recently developed genomics resources for sweetpotato and other polyploid crops has been the lack of bioinformatics tools designed specifically to handle polyploid genetic data. Here, we take advantage of recently developed resources for polyploid genotyping and analysis at the genomic level. GBSpoly, an optimized genotyping-by-sequencing protocol for highly heterozygous and polyploid genomes, and GBSapp, a SNP calling and filtering bioinformatics pipeline, were used to identify 32,784 segregating SNP markers within a collection of 417 accessions from the combined collections. The markers developed for the analysis of this collection of accessions will provide an important genomic resource to the sweetpotato community.Ipomoea batatas accessions randomly selected from the PGCRU and USVL were examined in this study. Of the 417 accessions, 303 were from PGRCU and 114 accessions from USVL (Table 1 and Supplementary Table S1). Eleven cultivars recommended for production in the southeastern US were part of the set of accessions, including Beauregard, Bayou Belle, Bellevue, Bonita, Burgundy, Carolina Ruby, Hayman White, Hernandez, Jewel, O’Henry, and Orleans. These materials originated from over 30 countries in 8 broad geographical regions . Accessions were planted in field plots at the USVL and phenotyped for percent dry weight, periderm color, and stele color according to the methods of ® style bag, stored on ice during the field collection process, and then immediately freeze-dried. Freeze-dried leaf tissue was stored at -20°C until used.A total of 417 CviAII at 25°C for 3 h and then with 5 units of TseI at 65°C for 3 h in NEB CutSmart buffer (New England Biolabs). The digested DNA samples were purified with AMPure XP magnetic beads (ThermoFisher), quantified using a picogreen assay and then diluted to a concentration of 10 ng/μl. The resulting fragments were ligated to barcoded adapters which were designed to contain an 8-bp buffer sequence positioned upstream of the variable length (6–9 bp) barcode sequence (multiplexed for 96 pooled samples). Barcode design accounted for substitution and indel errors using the levenshtein/edit distance metric (CviAII and TseI (same enzymes and reactions conditions above) was performed to eliminate chimeric sequence ligations. The pools were again purified with AMPure XP magnetic beads and size-selected for 300–400 bp fragments using the Blue Pippin Prep system (Sage Science). PCR amplifications were performed (18 cycles) using NEB Phusion high-fidelity polymerase (New England Biolabs). The resulting libraries were size-selected again and then sequenced on an Illumina HiSeq 2500 system.Total genomic DNA was isolated from freeze dried leaf tissue using the DNeasy Plant Mini Kit (Qiagen). The integrity, purity, and concentration of the isolated genomic DNA was determined by 2% agarose gel electrophoresis and a NanoDrop 2000 spectrophotometer (ThermoFisher). A modified genotyping-by-sequencing (GBSpoly) protocol optimized for highly heterozygous and polyploid genomes was implemented . A comple metric . The bufRaw Fastq files were processed by the following steps within the GBSapp pipeline, which integrates various software tools. The steps within the pipeline included:(i)1, QC plots of the raw reads were generated to ensure that the base calls within the barcodes had high quality scores.Using the FASTx-Toolkit(ii)Supplementary Figure S1).Using the FASTx-Toolkit, the buffer sequence and any base position with low quality scores at the proximal end of the reads were trimmed to a quality score of at least Q36 for the lower whisker (minimum) in the boxplot Demultiplexing of samples was performed with the FASTX-Toolkit.(iv)I. trifida and I. triloba; I. trifida and I. triloba, respectively, produced the 4x (tetraploid) and 2x (diploid) genotypes, while reads aligned to both genomes produced 6x (hexaploid) genotypes. On average, 90, 3.7, and 3.8% of the reads produced 6x, 4x, and 2x genotype calls, respectively.Using BWA-MEM , reads d(v)2.Additional processing of alignment files was performed with SAMtools  and Pica(vi)The GATK (version 3.7) HaplotypeCaller was used to call SNP, copy number variation (CNV), and Indel variants . Using V(vii)Data filtering in R was performed to identify high quality variants and bi-allelic variants. Thresholds were set for minor allele frequency (MAF) at 0.05 and missing data at no more than 20% missing. The optimal read depth for calling 2x (diploid), 4x (tetraploid), and 6x (hexaploid) genotype calls were empirically determined using a diagnostic tool written in R . This war2 of 0.5, window size of 50, and window increment of 5. To determine the extent of LD decay in sweetpotato, LD was assessed by estimating r2 values (r2) against marker intervals  was implemented with ggplot2, a R package (N = 417). To examine the population structure of sweetpotato accessions, they were clustered into populations with the program STRUCTURE v2.3.4 using the admixture model with correlated allele frequencies  using the ad hoc statistic delta K . The markers were filtered by linkage disequilibrium to generate a set of unlinked SNPs to meet assumptions for population structure analyses in STRUCTURE. Linkage disequilibrium was calculated via the expectation-maximum method  using th2 values  for all  package . The anaquencies  using tharvester , which i delta K . This isp-distance model . The quality scores were empirically determined based on aligning reads to the Illumina’s PhiX control. All the bases within the barcode region and the genomic insert had a high quality score of 38 or approximately 99.984% accuracy , except for the last base call which had a minimum score of 28 and was trimmed off . This high accuracy in the base calls of the barcode sequence is particularly important for accurate de-multiplexing of the reads derived from pooled samples. The first 5–6 bp of this 8 bp buffer sequence had slightly lower quality score (32–34), while the next 2–3 base calls had a quality score of 38. These 8 bases were trimmed off before de-multiplexing.The raw fastq files generated on Illumina HiSeq2500 platform were processed to evaluate the distribution of quality scores at each base position along the sequence reads , and 7.2% were specific to the I. triloba subgenome . Reads specific to the I. triloba subgenome tended to have higher proportions than those specific to I. trifida-specific . Even though more subgenome specific reads matched I. triloba, the I. trifida produced more unfiltered SNPs . This suggests more reads specific to the I. triloba subgenome might be enriched and derived from repetitive sequences. Distribution of read depth was relatively uniformly distributed across the sweetpotato accessions and across genomic loci, except for a few individuals that were underrepresented in the library .After aligning the raw reads to the two ancestral reference sub-genomes, on average, 96.05% of the raw reads mapped to both reference genomes. On average, 85.7% of reads matched to both sub-genomes , 3.1% were specific to the a priori, a read threshold of 45 was used. A read depth threshold of 45 indicates almost 100% accuracy for nulliplex genotypes, over 95% accuracy for simplex genotypes and about 85% accuracy for multi-dose genotypes . Most of the genotypes were nulliplex and simplex .To empirically determine the optimal read depth threshold for accurately calling the dose-dependent genotypes, four individuals with high sequencing coverage/read depth were resampled (as described in the methods). The accuracy (or dose-dependent genotype stability) associated with the genotypic classes was determined by this resampling method. To achieve a 95% accuracy in 6x genotypes, the read depth threshold required for simplex and multi-dose genotypes was 35 and 100, respectively, while an 85% accuracy required a read depth threshold of 20 and 45. Since the data set is comprised of different dose-dependent genotypic classes that were not known I. batatas accessions from 8 broad geographical regions  were genotyped at 43,105 diploidized SNPs derived from GBSpoly . Pruning by linkage disequilibrium resulted in a set of 32,784 segregating markers used for all analyses (Supplementary Dataset S1). Genetic distances between accessions  ranged from 0.035 to 0.41, with a mean of 0.31 and a standard deviation of 0.028 (Supplementary Table S2). The distribution of pairwise genetic distances had a narrow spread with only 0.1% of the comparisons less than 0.1 and 0.02% greater than or equal to 0.4. The cultivars (n = 11) recommended for production in the southeastern US had lower genetic diversity with a mean genetic distance of 0.25. The most similar cultivars, Bayou Belle and Burgundy, only differed at 4.5% of their alleles. Jewel was the most genetically distinct cultivar and was present in all of the top 10 most genetically distant pairwise comparisons within the cultivars (0.32–0.34). The remaining cultivar comparisons did not exceed 0.30. The overall mean MAF was 0.219 and ranged from 0.204 (South America) – 0.237  by geographical regions (Table 2). Allele frequencies and Hardy-Weinberg equilibrium values for each SNP by region are provided in Supplementary Table S3.A total of 417 K peak was observed at K = 2 and a smaller peak was seen at K = 4 . We adopted the grouping for K = 4, because this represents a more accurate estimate of the gene pools given the historical movement of sweetpotato germplasm and that developed through crop improvement programs. There was a high degree of admixture observed in cluster assignments of the accessions with both population numbers but it was higher for K = 4 . When K = 2, the accessions clustered into two groups . Alternatively, when K = 4 is considered, there is a much higher degree of admixture and assignment of accessions to clusters into four gene pools . Despite the high degree of admixture within the accessions, our analyses supported dividing the 417 PIs into four clusters using a q-value threshold of 0.65 , where C1 is red, C2 is green, C3 is blue, C4 is yellow, and gray is admixed . In total, 247 out of 417 accessions (60%) were assigned to 1 of the 4 clusters. Clusters 1 through 4 consisted of 96 (23%), 49 (12%), 29 (7%), and 73 (18%) accessions, respectively. The remaining 170 accessions (40%) were categorized as having admixed ancestry from the clusters (Supplementary Table S1). Cluster 1 consisted mostly of accessions from Central America (n = 19), the Far East (n = 26), and the Pacific Islands (n = 25); cluster 2 from North America (n = 26) and the Pacific Islands (n = 9); cluster 3 from South America (n = 20); and cluster 4 consisted only of accessions from the North America [United States (n = 73)]. Accessions with mixed ancestry were found from all the studied regions: Africa (53.3%), Australia (50%), Caribbean (77.8%), Central America (15.4%), Far East (44.7%), North America (46.7%), Pacific Islands (26.2%), and South America (29.6%).To determine population structure, STRUCTURE 2.3.4 was used. The largest delta FST values between geographical regions (Table 3), supported the cluster assignments for K = 4. The greatest differentiation was between South American and other regional groups, as all values were ≥ 0.063 and ranged from 0.063 (Far East) to 0.110 . Values differentiating the North American group were the greatest between it and the South American (0.103) and Central American (0.074) groups and the lowest between it and the Caribbean group (0.031). In general, differentiation was low between regions, as 17 of the 28 pairwise FST values were ≤ 0.05. The lowest differentiation was between the Far East and Pacific Islands (0.017) and the Far East and Australia (0.018). Further corroboration for the Bayesian clustering method was observed in the neighbor-joining (NJ) cluster analysis  and the principal components analysis [PCA ]. The NJ cladogram clearly separated the accessions into four groups, primarily based on region of collection . The PCA also separated the accessions into four groups and was consistent with the individual assignments made with STRUCTURE . There was no apparent pattern of the accessions in the PCA by dry weight or periderm (skin) color, but there was clustering by stele (flesh) color .The results from the Bayesian clustering method (STRUCTURE) were further supported by population differentiation and genetic diversity analyses. Population differentiation, measured as pairwise r2 values for all marker pairs within and between chromosomes in order to empirically determine LD decay. LD analysis based on the 417 sweetpotato accessions revealed that linkage disequilibrium blocks  are small enough for fine-mapping to the gene-level . Genotypes with allelic dosage information and the diploidized genotype data set both revealed very similar trends.Knowing the extent of linkage disequilibrium decay in a crop is crucial for determining if genome-wide association analysis in diverse germplasm can be used for fine-mapping QTL to genic resolution. We estimated Sweetpotato is widely produced throughout the tropical regions of the world and plays a critical role in food security. Within the US, increased consumption and exports of sweetpotato have spurred substantial economic growth for producers as the value of the crop has increased by over $500 million USD between 2000 to 2015 . The US n = 178) and found high levels of genetic diversity and support for 3 groups. Our mean minor allele frequency (0.219) was similar to their reported value (0.216). We were unable to compare the degree of admixture as it was not discussed by Our analyses are the first to report the use of SNPs to characterize US sweetpotato germplasm collections and the second reported for any sweetpotato collection. K = 3 that was reported by Figure 4 and Supplementary Figure S4, green cluster). The existence of a third gene pool would account for the subset of accessions not developed by the USVL that were clustered within the materials from North America. The majority of the accessions from the Far East (55%) have probability assignments in STRUCTURE similar to the Central American accessions . We speculate that this third gene pool from tropical America . ‘Porto Rico’ was imported into the US from Puerto Rico in 1906 and the sweetpotato industry was subsequently built around it  and introgression of these are evident within the North American accessions. We also found support of a second gene pool within the North American accessions . We suspect that this gene pool is the result of the heavy selection pressure that has been used in the development of this germplasm (USVL collection). The W-lines and cultivars developed by the USDA sweetpotato breeding program are the result of mass selection using parents of diverse origins and this is reflected in the high level of mixed ancestry within these materials (75%). These individuals were selected for multiple insect, nematode, and disease resistance traits in combination with other desirable production and market quality traits  show a less diverse genetic base due to this material being developed through recurrent selection for insect resistance where the most resistant selections were used as parents in subsequent breeding cycles as compared to the mass selection techniques used in the development of the USVL W-lines  exhibit a significantly different background than the other USVL-lines. We believe that this is due to incomplete or erroneous information regarding the origins of the parents of those lines.Molecular markers (AFLPs and RAPDs) have been used to examine the diversity of sweetpotato in Oceania . These s America  is the sround it . Additioy traits . We susp W-lines , 1991. WGermplasm collections are critical for providing genetic materials needed to ensure a continued global supply of food. Plant breeding and the associated disciplines require well characterized and readily available germplasm resources to develop crops with resistance/tolerance to pests, disease, and environmental stress. Our results indicate that there is high genetic diversity within the US sweetpotato collection and now there is the potential to utilize genotype data from our study and corresponding phenotype data  for selePW, BO, SB, RJ, GY, and DJ: conceived and designed the experiments. PW, BO, and SB: performed the experiments. PW, BO, SB, RJ, GY, and DJ: analyzed the data. PW, BO, SB, RJ, and GY: contributed reagents, materials, and analysis tools. PW, BO, and SB: wrote the paper. PW, BO, SB, RJ, GY, and DJ: read and approved the manuscript. PW, BO, and SB: contributed equally to this project.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


IcarisideII (ICAII) could promote the differentiation of adipose tissue-derived stem cells (ADSCs) to Schwann cells (SCs), leading to improvement of erectile function (EF) and providing a realistic therapeutic option for the treatment of erectile dysfunction (ED). However, the underlying molecular mechanisms of ADSCs and ICAII in this process remain largely unclear.ADSCs were treated with different concentrations of ICAII. Cell proliferation was determined by MTT assay. qRT-PCR and western blot were performed to detect expressions of SCs markers, signal transducer and activator of transcription-3 (STAT3), and microRNA-let-7i (let-7i). Luciferase reporter assay was conducted to verify the regulatory relationship between let-7i and STAT3. The detection of intracavernosal pressure (ICP) and the ratio of ICP/mean arterial pressure (MAP) were used to evaluate the EF in bilateral cavernous nerve injury (BCNI) rat models.ICAII promoted cell proliferation of ADSCs in a dose-dependent manner. The mRNA and protein levels of SCs markers were increased by ICAII treatment in a dose-dependent manner in ADSCs. Moreover, let-7i was significantly decreased in ICAII-treated ADSCs and upregulation of let-7i attenuated ICAII-induced promotion of SCs markers. In addition, STAT3 was a direct target of let-7i and upregulated in ICAII-treated ADSCs. Interestingly, overexpression of STAT3 abated the let-7i-mediated inhibition effect on differentiation of ADSCs to SCs and rescued the ICAII-mediated promotion effect on it. Besides, combination treatment of ADSCs and ICAII preserved the EF of BCNI rat models, which was undermined by let-7i overexpression.ICAII was effective for preserving EF by promoting the differentiation of ADSCs to SCs via modulating let-7i/STAT3 pathway. Prostate cancer is the second leading cause of cancer-related death in men worldwide , 2. ErecMicroRNAs (MiRNAs) have been reported to be associated with EF . For exaIn this study, we sought to explore the roles of let-7i and STAT3 in the ICAII-mediated neuronal differentiation of ADSCs, as well as the effect of combination of ADSCs and ICAII on ED treatment.2. In addition, the ADSCs separated from rats differentiated into Schwann cells (SCs) as descripted in previous study [The experiments were approved by the Animal care and Experiments committee of the First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine. The SD male rats  were obtained from the Kaixue Biotechnology , following sacrificed. ADSCs were isolated from SD rats as descripted in previous study , subsequ−9–10−5 mol/L), The cells without ICAII treatment was as a control. In addition, let-7i mimic (let-7i), negative control mimic (miR-NC), let-7i inhibitor (in-let-7i), negative control inhibitor (in-miR-NC), pcDNA3.0 vector (pcDNA), or STAT3 overexpression plasmid (STAT3) were also transfected into ADSCs with or without ICAII (10−7 mol/L) treatment. The treated or transfected cells were used for following experiments.ADSCs (at 80% confluence) were treated with ICAII in different concentrations  according to the manufacturer’s instructions. For let-7i expression, total RNA was reversely transcribed into cDNA using One Step Prime Script miRNA cDNA Synthesis kit . To quantify the level of STAT3, nerve growth factor (NGF), neurotrophin-3 (NT-3), S100β, and P75 neurotrophin receptor (P75), total RNA was reversely transcribed into cDNA using the prime-script reagent kit . qRT-PCR was performed using SYBR green . The primers were listed as follows: STAT3 forward, 5′-CACCCATAGTGAGCCCTTGGA-3′, and reverse, 5′-TGAGTGCAGTGACCAGGACAGA-3′; NGF forward, 5′-CCAAGGACGCAGCTTTCTATC-3′, and reverse, 5′-CTGTGTCAAGGGAATGCTGAAG-3′; NT-3 forward, 5′-TGTGGGTAGCCGACAAGTC-3′, and reverse, 5′-GAGTTCCAGTGTTTGTCATC-3′; S100β, forward 5′-TCACTGAGGGACGAAATCAACAC-3′, and reverse, 5′-GGTGCTATTGGTAGTCTGCCTTG-3′; P75 forward, 5′-GAGGGCACATACTCAGACGA-3′, and reverse, 5′-CTCTTCGCATTCAGCATCAG-3′; β-actin forward, 5′-ATGGATGACGATATCGCTGC-3′, and reverse, 5′- CTTCTGACCCATACCCACCA-3′; let-7i forward, 5′-GGGGTGAGGTAGTAGTTTGT-3′, and reverse, 5′-TGCGTGTCGTGGAGTC-3′; U6 forward, 5′-CTCGCTTCGGCAGCACA-3′, and reverse, 5′-AACGCTTCACGAATTTGCGT-3′. U6 and β-actin were used as internal controls for miRNAs or mRNAs, respectively. Relative expression was calculated using the 2Protein form treated or transfected cells were collected and subsequently measured using BCA Protein Assay Kit . Then, each protein sample was separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and then transferred onto polyvinylidene difluoride membranes . After that, the membranes were blocked with 5% BSA for 1 h and then incubated with the primary antibody solution  at 4 °C overnight. The blots were probed by horseradish peroxidase-conjugated secondary antibody solution (Abcam) for 1 h. Finally, protein bands were visualized using PierceTM ECL western blotting substrate (Thermo Fisher Scientific). Captured the signals using films in dark room.2 for 4 h. The optical density (OD) was detected at a wavelength of 450 nm using an Elx800 reader .Cell proliferation was measured by MTT assay. Briefly, the treated or transfected cells were seeded into 96-well plates and cultured for 48 h. MTT reagent solution (Thermo Fisher Scientific) was added to each well and incubated at 37 °C in 5% COThe 3′-UTR of STAT3 containing let-7i binding sites were amplified by PCR and inserted into pMIR-REPORT™ (Thermo Fisher Scientific) to construct STAT3 wild-type reporter vector (STAT3-WT). The mutant type (STAT3-MUT) was made with GeneArt™ Site-Directed Mutagenesis PLUS System (Thermo Fisher Scientific). STAT3-WT or STAT3-MUT and let-7i mimic, inhibitor or their negative controls were transfected into ADSCs using Lipofectamine 3000 (Thermo Fisher Scientific). The luciferase activity was examined using the Dual Luciferase Reporter Assay System  according the protocols. The transfections were performed three in independent experiments.Adult male SD rats  were divided into six groups (n = 6): sham, BCNI, PBS, ADSCs, ICAII, and ADSCs + ICAII. The BCNI rat models were established as descripted in previous study [Mean arterial pressure (MAP) and intracavernosal pressure (ICP) were applied to evaluate the EF of the rats at 4 weeks after the surgery and injection as descripted in previous study . EF was t test or one-way ANOVA. P < 0.05 was considered to be statistically significant.Data were presented as the mean ± standard deviation. The significance of the in vitro data and in vivo data between experimental groups was determined by Student’s Firstly, ADSCs were isolated from adipose tissues of SD rats. We found that cell proliferation of ADSCs was promoted by ICAII in a dose-dependent manner Fig. a. To furThen, we explored whether let-7i is implicated in the ICAII-mediated SCs differentiation of ADSCs. As presented in Fig. We next dissected the potential molecular mechanism by which let-7i repressed the differentiation of ADSCs to SCs. miRNAs regulate gene expression by posttranscriptional in a sequence-specific manner . TargetsGiven the above results, we next sought to evaluate the role of STAT3 in the differentiation of ADSCs to SCs. Also, following transfection efficiency demonstrated that the transfection of let-7i mimic was efficient to induce the lower expression of STAT3, while the introduction of STAT3 overexpression plasmid strikingly rescued STAT3 expression in ADSCs Fig. a, b. MorFinally, BCNI rat models were established to identify the roles of ICAII and ADSCs. As shown in Fig. ICAII, an active component of Herba Epimedii, has been proven to improve cardiovascular function and facilitate the differentiation of ADSCs to cardiomyocytes , 23. In miRNAs play critical roles in regulating the self-renewal and differentiation of ADSCs . PreviouProteins of the signal transducer and activator of transcription (STAT) family mediate cellular responses to cytokines and growth factors . STAT3 iIn conclusion, our results demonstrate for the first time that ICAII facilitates the differentiation of ADSCs to SCs via let-7i/STAT3 axis to preserve erectile function. However, there are some limitations in this report. Our study only shed light on the roles of let-7i and STAT3 in the treatment of ICAII and ADSCs for ED, further research is needed to study the underlying signal pathways.In summary, we found that ICAII promoted differentiation of ADSCs to SCs and ICAII was responsible for preserving EF after CNI by promoting the differentiation of ADSCs to SCs via modulating let-7i/STAT3 axis. Moreover, let-7i suppressed the therapeutic effect of dADSCs on preserving EF in BCNI rat models. Our findings provided novel insights into the let-7i/STAT3 axis in ICAII-mediated promotion of differentiation of ADSCs to SCs and a significant theoretical basis for the utilization of ICAII for ED treatment.


Schistosoma mansoni.A key component of schistosomiasis control is mass drug administration with praziquantel. While control interventions have been successful in several endemic regions, mass drug administration has been less effective in others. Here we focus on the impact of repeated praziquantel treatment on the population structure and genetic diversity of S. mansoni epidemiology, population genetics, and variation in praziquantel susceptibility in parasites isolated from children across three primary schools in a high endemicity region at the onset of the Ugandan National Control Programme. Children were sampled at 11 timepoints over two years, including one week and four weeks post-praziquantel treatment to evaluate short-term impacts on clearance and evidence of natural variation in susceptibility to praziquantel.We examined S. mansoni was 85% at baseline. A total of 3576 miracidia larval parasites, isolated from 203 individual children, were genotyped at seven loci. Overall, genetic diversity was high and there was low genetic differentiation, indicating high rates of parasite gene flow. Schistosome siblings were found both pre-treatment and four weeks post-treatment, demonstrating adult worms surviving treatment and natural praziquantel susceptibility variation in these populations at the beginning of mass drug administration. However, we did not find evidence for selection on these parasites. While genetic diversity decreased in the short-term (four weeks post-treatment), diversity did not decrease over the entire period despite four rounds of mass treatment. Furthermore, within-host genetic diversity was affected by host age, host sex, infection intensity and recent praziquantel treatment.Prevalence of Our findings suggest that praziquantel treatments have short-term impacts on these parasite populations but impacts were transient and no long-term reduction in genetic diversity was observed. High gene flow reduces the likelihood of local adaptation, so even though parasites surviving treatment were observed, these were likely to be diluted at the beginning of the Ugandan National Control Programme. Together, these results suggest that MDA in isolation may be insufficient to reduce schistosome populations in regions with high genetic diversity and gene flow. Schistosoma mansoni sexually reproduces (predominantly) in humans and eggs are excreted in faeces. In high endemicity areas, worm burdens can be very heavy, producing as many as 9600 eggs per gram (epg) of stool [Schistosoma species remain [Schistosomiasis is a neglected tropical disease that infects over 240 million people across 78 countries, predominantly within the developing world . Adult Sof stool . Cercariof stool –7, persiSchistosoma populations and their response to MDAs have the ability to quantify the impact and potential limitations of MDAs [Studies investigating the genetic structure of  of MDAs , 11. The of MDAs , 13, and of MDAs . Drug se of MDAs  and can  of MDAs . In the  of MDAs , 18, but of MDAs –21. Howe of MDAs –25. TherSchistosoma species at relatively small scales, from within villages to between sites up to 60 km apart [S. mansoni in East Africa relative to South America may contribute to these differences in population structure, but studies explicitly evaluating these hypotheses are lacking.Several studies in Africa support a lack of genetic population structure in km apart –30. Highkm apart . Howeverkm apart . Human mkm apart . Higher S. mansoni and drug resistance can be selected for in as few as six generations [S. mansoni genetic diversity in the field also offers conflicting results. Reduction of genetic diversity has been observed six months after a single praziquantel treatment in two schools in Tanzania [At least under laboratory conditions, praziquantel reduces the diversity of erations , 35. Rederations , 36, thoTanzania , 37. In Tanzania  and anotTanzania . SimilarTanzania .S. mansoni at the beginning of the MDA in Uganda, the first schistosomiasis MDA programme in sub-Saharan Africa [Schistosoma genetic diversity over short (less than one month) and medium-term (six months or more) follow-ups after praziquantel treatment. In this study, we use a unique longitudinal dataset to examine how repeated praziquantel treatments can affect schistosome populations. We examine evidence for adult worms surviving treatment, suggesting natural variation in tolerance or resistance to praziquantel treatment. We hypothesize that mean genetic diversity would decline immediately following praziquantel treatment but expect diversity to recover at longer time-scales because of high gene flow and high genetic diversity at the population level. We expect clearance of parasites to be high, as host populations were praziquantel-naïve and parasites had not undergone repeated rounds of praziquantel selection.Here we focus on the structure and genetic diversity of n Africa . Few fieChildren aged 6–12 years were initially recruited for this study in 2004 with an equal sex ratio from three primary schools on the shores of Lake Victoria in eastern Uganda  (a standard 41.2 mg template was used to prepare). After Kato-Katz slides were prepared, the remainder of each stool sample was filtered through a Pitchford funnel to collect and hatch miracidia from eggs [TM cards for cell lysis and DNA storage [At each timepoint, stool samples were collected for three consecutive days to measure infection intensity by duplicate Kato-Katz thick smears . The numrom eggs . Filtere storage . Cards w storage . The cumS. mansoni eggs detected in any Kato-Katz slides. Children were treated with 40 mg/kg praziquantel, determined by weight. At all timepoints, observed treatment was recorded for each child.The entire cohort was treated with praziquantel after each medium-term timepoint (no praziquantel treatment in the previous four weeks), indicated by red arrows in Figs. in vitro assay that measured phenotypic praziquantel susceptibility of miracidia. This in vitro assay exposes miracidia to praziquantel and uses changes in shape, behaviour and activity levels as a proxy for susceptibility and has been validated in prior laboratory and field studies [in vitro exposure to 2 × 10−6 M praziquantel. Resistant genotypes have fewer changes in shape and behavioural responses to praziquantel exposure and thus a higher proportion of these miracidia are still behaving normally at seven minutes [A randomly selected subset of miracidia from 11 children at 26 and 27 weeks were used for an  studies , 45. Her minutes , 45. BecS. mansoni from Lake Albert, Uganda. Allele sizes were determined using ABI PRISM Genescan v2.7 and Genotyper v2.7 software .DNA extraction and microsatellite analysis followed established protocols . BrieflyWhile we aimed to amplify all microsatellites from 30 miracidia per infrapopulation per timepoint, one quarter of timepoints were represented by fewer than ten miracidia. This disparity in terms of sample size could affect the statistical power of the models and the accuracy of genetic diversity measures. However, a simulation study using similar microsatellite markers reported that more robust measures of genetic diversity are obtained when increasing the number of hosts rather than the number of miracidia per host . AdditioAll analyses were carried out in R v3.5.1 . SpecifiIndividual schistosome infection intensities were calculated as an arithmetic mean of epg estimates from daily Kato-Katz slides examined at that timepoint. Paired rank sum Wilcoxon tests were used to test differences between pre- and post-treatment infection intensities. Population prevalence was calculated at each timepoint for each school and 95% confidence intervals (CI) intervals were calculated with Agresti-Coull approximations .pegas v0.11 [hw.test with 1000 permutations. At each sampling point, infrapopulation schistosome diversity observed heterozygosity (Ho) and expected heterozygosity (He) were calculated in poppr v2.8.1 [hierfstat package v0.04-22 [Departure from Hardy–Weinberg equilibrium (HWE) was quantified in as v0.11 , implemer v2.8.1 . Allelicv0.04-22 .lm. Child ID  was included as a random effect in a linear mixed effect model using lme4 [To identify potential factors that affected the observed infrapopulation genetic diversity of parasites at a given timepoint, we constructed linear models using the function ing lme4  to accouing lme4 .poppr v2.8.1 [Some infrapopulations were sampled for miracidia at more than one timepoint. To further examine within-host dynamics, the genetic dataset was subset to include infrapopulations that were sampled at more than one timepoint, particularly pre-treatment and one week and four weeks post-treatment. Trees of infrapopulations over time were constructed using Nei’s distances in r v2.8.1 . COLONY r v2.8.1 . Becauseamova in poppr [P-values were calculated by 1000 random permutations. Population structure was also investigated using the discriminant analysis of principal components (DAPC) method [adegenet v2.1.1 [hierfstat v0.04-22 with the neighbour-joining method implemented in ape v5.2 [in vitro praziquantel data to elucidate whether infrapopulations with more drug-resistant phenotypes at that timepoint were genetically distinct from those infrapopulations which were more susceptible.To determine the levels of gene flow, we used several methods to quantify structure of populations. Analysis of molecular variance (AMOVA), which detects population differentiation, was conducted using the function in poppr . An AMOV) method  implement v2.1.1 , and by ape v5.2 . PhylogeS. mansoni over 11 timepoints during the two-year study . This supports the hypothesis that genetic diversity of S. mansoni is resilient to praziquantel, at least within this observation period and setting.Although these short-term effects were significant, prevalence, intensity and genetic diversity recovered over timescales greater than four weeks. Genetic diversity declined from baseline (week 0) to subsequent pre-treatment samples , but this decline was not significant ; however, in male hosts the interaction between age and sex reduced genetic diversity .The best-fit model to explain infrapopulation genetic diversity, measured by AR at a given timepoint, contained infection intensity, short-term treatment, and an interaction between age and sex as significant predictor variables Fig. . The meain vitro drug-resistant phenotypes were not genetically distinct from infrapopulations with lower measures of drug-resistant phenotypes . Although these microsatellites do not seem to serve as resistance markers, they are useful for parentage analysis and identifying worms that survive treatment. Despite no evidence for selection in this study, concerted drug treatment across the area for several years may have selected for these resistant worms over longer timescales and resulted in the low cure rates currently observed in the region more recently [Despite evidence of resistant/tolerant parasites in this population, there was no evidence for selection for these parasites in the timeframe observed. Parasites found four weeks after treatment did not cluster, nor did phenotypically resistant parasite populations. High rates of transmission and high rates of gene flow likely prevent population bottlenecking and could reduce the likelihood of resistance developing at a local level at the coverage levels and short- to medium- (under two years) time scales studied here . Our genrecently .Interestingly, there was very little difference in genetic diversity between pre- and one week post-treatment. We expect this is because eggs were still being excreted from adult worms that had produced the eggs before treatment, but which may have then died with treatment. Because genetic diversity and infection intensity were significantly lower at four weeks post-treatment, we expect most eggs of susceptible worms to be expelled by four weeks post-treatment. It is thought that eggs only survive up to three weeks after expulsion from the female . This isThere was no strong evidence for effects of praziquantel on genetic diversity over the medium-term. This is supported by other studies across sub-Saharan Africa , 60, 62.S. mansoni occurs at the human host level, rather than village or district level [S. mansoni populations in Uganda [S. mansoni and larger scale surveys have reported the area to have the highest levels of genetic diversity across several markers [Many studies, including ours, find that the majority of the genetic diversity in ct level , 31, 33.n Uganda , and hign Uganda  reported markers , 67. Thi markers , 13. HowS. haematobium in Mali also found significant impacts of host demographics; males had more unique genotypes and these private alleles increased with age [We found that infrapopulation genetic diversity is also significantly related to host age and sex. We interpret infrapopulation genetic diversity as the combined outcome of the genetic diversity of parasites circulating in the environment, variation in behaviour , and establishment probability (dependent on host susceptibility and immune history and parasite infectiousness). We found that males had higher parasite genetic diversity than females. We expect this higher genetic diversity, particularly at younger ages, to reflect a difference in behaviour as young males have reported to play in the water more often than females of a similar age . This efwith age . NeitherThis study highlights the resilience of schistosome populations to repeated drug treatments in a high endemicity setting in Uganda. We found evidence for adult worms surviving treatment at the beginning of the national control programme, suggesting natural variation in resistance/tolerance. These may have been selected on with subsequent round of MDA and led to low cure rates observed a decade later. In settings with similar epidemiology and genetic diversity as observed here, MDA alone is unlikely to be sufficient for elimination and could even lead to long-term issues if drug resistance is selected.Additional file 1: Text S1. Additional methods. Table S1. Travel distances between schools. Table S2. Summary of infrapopulations  sampled at each timepoint (listed as weeks since start of study). Table S3. Microsatellite loci utilized in this study. Text S2. Additional results. Table S4. Comparison of genetic diversity at the seven microsatellite loci used in the study. Table S5. Proportion infected at pre- and post-treatment. Table S6. Infection intensity between pre- and post-treatment timepoints. Table S7. Summary of infrapopulation phenotyped. Table S8. Frequency of miracidia isolated per individual from relative timepoints. Table S9. Frequency of full-sibling miracidia belonging to a family structure (including singletons) per individual. Table S10. Analysis of molecular variance (AMOVA) for Schistosoma mansoni. Figure S1. Parasite structure through time. Figure S2. Trees of phenotype data. Figure S3. Phylogenies of infrapopulations from individual children sampled over time .