Sturnus vulgaris), we artificially simulated dispersal of a subset of individuals; we term this ‘Simulated Disperser Analysis’. We then ran analyses for diminishing numbers of loci, to assess at which point simulated dispersers could no longer be correctly identified. Not surprisingly, the correct identification of dispersers varied significantly depending on the individual chosen to ‘disperse’, the number of loci used, whether loci had high or low Polymorphic Information Content and the location to which the dispersers were moved. A review of the literature revealed that studies that have implemented first-generation migrant detection to date have used on average 10 microsatellite loci. Our results suggest at least 27 loci are required to accurately identify dispersers in the study system evaluated here. We suggest that future studies use the approach we describe to determine the appropriate number of markers needed to accurately identify dispersers in their study system; the unique nature of natural systems means that the number of markers required for each study system will vary. Future studies can use Simulated Disperser Analysis on pilot data to test marker panels for robustness to contemporary dispersal identification, providing a powerful tool in the efficient and accurate design of studies using genetic data to estimate dispersal.Empirical genetic datasets used for estimating contemporary dispersal in wild populations and to correctly identify dispersers are rarely tested to determine if they are capable of providing accurate results. Here we test whether a genetic dataset provides sufficient information to accurately identify first-generation dispersers. Using microsatellite data from three wild populations of common starlings ( Dispersal is a major contributor to population processes, and can strongly influence genetic diversity, local adaptation, speciation, levels of inbreeding, sexual selection and many other biologically important processes . KnowledOligosoma grande) populations by directly comparing genetically identified dispersers with mark-recapture data collected over a seven year period. Ideally, estimates of dispersal between populations should be carried out using multiple approaches to confirm congruence between estimation methods; however, most ecological studies are restricted to using a single approach either due to logistical or budget constraints (or both). Genetic data may permit the identification of dispersers and assignment to their source populations, but also provide the ability to estimate a range of other population parameters often of interest to ecological researchers  populations to test the power of a marker system to accurately identify ‘dispersers’  using genetic assignment tests and genetic data only. This approach simulates dispersal between populations and then tests a number of parameters  on the ability to identify dispersers and to correctly assign these to their collection locality.Here we perform a Simulated Disperser Analysis, using microsatellite data from three Australian common starling  approved by The University of New South Wales ethics committee.Sturnus vulgaris), a highly vagile species that maintains an extensive distribution across southeast Australia. Samples were taken from three populations previously shown to have low levels of genetic differentiation (FST = 0.02–0.07): Orange ; Mallala ; and Munglinup .We investigated dispersal in Australian invasive starlings (DNA was extracted using Gentra PureGene DNA extraction kit (Qiagen) following the manufacturer’s instructions. Microsatellites were developed using next generation sequencing on the GS-FLX 454 platform  following methods described by FST values. Pairwise values for Shannon’s mutual information index  and were removed from further analyses  treatments . Each treatment was analysed using 57 genetic datasets. The first analysis started with the locus with the highest PIC and then consecutively adding the next highest PIC locus until all loci were added . The second analysis started with the locus with the lowest PIC and then consecutively adding the next lowest PIC locus until all loci were added .Loci were ranked by their Polymorphic Information Content (PIC) , or, when they fail to be identified as a disperser when they are one . Once identified, GeneClass2.0 assigns dispersers to the most likely source population.The partial Bayesian method  implemenputation . IndividIn our assessment of the ability of GeneClass2.0 to detect simulated migrants, assignment tests were repeated for treatments and individuals, resulting in the non-independence of data. To account for this, we used Generalised Linear Mixed Models (GLMMs), specifying treatment and individual as random effects, and predictor variables  as fixed effects . The resFST value, and general article meta-data such as author(s), year published, common name and species name. Of the 160 papers that were assessed, 132 matched the selection criteria, including a total of 136 datasets  we used Google Scholar to conduct a literature search (on 19/12/2016) for articles that cited the publication that originally presented the GeneClass2 analysis . We useddatasets .FST values of studies using genetic assignment tests and the number of loci that were used by these studies to identified dispersers. The number of loci used was specified as a continuous response variable, with a Poisson distribution, which was determined graphically and the global FST value was a fixed effect.Generalised Linear Models (GLMs) were used to determine the relationship between the global To demonstrate the continuing relevancy of this work see , we searRegression models and graphics were generated using the statistical programming environment R , while the population from Munglinup  displayed higher levels of genetic differentiation from Orange  and Mallala  indicating that Munglinup is the most genetically distinct area sampled.Populations from Orange  and Mallala  showed the lowest levels of differentiation from each other , where global FST was available, the average global FST was, 0.120 ±0.119. There was no relationship between the global FST of these studies and the number of loci used when conducting genetic assignment tests . Although spurious results might occur with any combination of loci, it is reasonable to assume that such results would generally become less likely as more discriminatory information becomes available with an increased number of loci.FST ) and the number of loci required and the ability to correctly identify dispersers, studies that use genetic assignment tests should use more loci when their global FST is lower. As shown by our analysis of the literature  a ‘worst-case scenario’ is necessary. This study showed that a worst-case scenario can be expected to occur when individuals are moved to the most genetically similar population and the lowest PIC loci are used. Therefore, for future studies to validate their disperser analysis, they could move simulated dispersers to the most genetically similar population, start with the lowest PIC locus and continue by adding the next lowest PIC locus . Such a her away . If spurIn studies where some information on levels of genetic differentiation and levels of loci polymorphism are known, a preliminary “true” simulation study  can potentially provide guidance on sample sizes needed to correctly identify dispersers. However, a simulated dispersal approach is applicable in cases where there is no prior information on levels of genetic differentiation and levels of loci polymorphism. It is also important to note that a simulated dispersal approach will only be valuable when estimates of the underlying allele frequencies are gained from a robust sampling protocol.FST) between a disperser’s collection locality and its final destination increases the accuracy of genetically assigning the disperser  including locus name, GenBank accession number, primer sequences, repeat motif, number of alleles and allele size range.Characterisation of microsatellite loci in Click here for additional data file.10.7717/peerj.4573/supp-2Table S2Click here for additional data file.10.7717/peerj.4573/supp-3Table S3Hardy-Weinberg Equilibrium testing for the 20 markers described in this study . Loci inClick here for additional data file.10.7717/peerj.4573/supp-4Table S4Visual order of simulated movement treatments that correspond to the individual heat maps of each simulated disperser. The ‘heat map row’ corresponds to the visual row (1–27) on each heat map, while the number under Plot A, B and C  specifies which treatment results are shown in the row. For example, heat map row 2 for simulated disperser A (A plot) corresponds to treatment 12 in Click here for additional data file.10.7717/peerj.4573/supp-5Supplemental Information 1Click here for additional data file.10.7717/peerj.4573/supp-6Supplemental Information 2Click here for additional data file.


Lynx canadensis) across eastern Canada, including the islands of Cape Breton and Newfoundland. Specifically, we assessed whether we could identify a genetic component to the observed morphological differentiation that has been reported across insular and mainland lynx. We used a dinucleotide repeat within the promoter region of a functional gene that has been linked to mammalian body size, insulin‐like growth factor‐1 (IGF‐1). We found high genetic differentiation at neutral molecular markers but convergence of allele frequencies at the IGF‐1 locus. Thus, we showed that while genetic drift has influenced the observed genetic structure of lynx at neutral molecular markers, natural selection has also played a role in the observed patterns of genetic diversity at the IGF‐1 locus of insular lynx.Island populations have long been important for understanding the dynamics and mechanisms of evolution in natural systems. While genetic drift is often strong on islands due to founder events and population bottlenecks, the strength of selection can also be strong enough to counteract the effects of drift. Here, we used several analyses to identify the roles of genetic drift and selection on genetic differentiation and diversity of Canada lynx ( Canada lynx also inhabit the islands of Newfoundland and Cape Breton Island, Canada. Little is known, however, about the origin and subsequent isolation of these populations in comparison with the mainland population  to evaluate the influence of genetic drift and natural selection on insular and mainland populations of Canada lynx in eastern Canada. First, we were interested in determining whether lynx on Cape Breton Island are genetically differentiated from lynx on mainland Canada. Genetic distinctness is likely for this population, given: (1) genetic differentiation at neutral molecular markers is proportional to time since divergence under an island model when it can be assumed that the population has descended from an ancestral population and since diverged without gene flow  genetic marker , and it is speculated that lynx are using sea ice to traverse the Strait of Canso (Nova Scotia Lynx Recovery Team 2.2N = 493), Labrador (N = 21), and Newfoundland (N = 27) between 2008 and 2011. We obtained tissue samples from lynx that were incidentally harvested in New Brunswick (N = 15), in 2010. We obtained samples of Cape Breton Island lynx (N = 46) by extracting DNA from bone specimens that we obtained from the Canadian Museum of Nature in Ottawa, Ontario, Canada in 2013 (Supplement 1); these specimens were dated between 1977 and 1980.We sampled Canada lynx from Quebec (both north and south of the St. Lawrence River), New Brunswick, Labrador, and the islands of Newfoundland and Cape Breton Figure . With th2.3Fca031, Fca035, Fca043, Fca077, Fca090, Fca096, Fca441, Fca391, Fca559, Lc106, Lc109, Lc110, Lc111, and Lc118) by Koen, Bowman, Lalor, and Wilson , 1X PCR Reaction Buffer (Invitrogen), 2 mmol/L MgCl2 (Invitrogen), 0.2 mmol/L dNTP solution (Invitrogen), 0.2 mg/ml BSA, 0.4 μmol/L forward and reverse primers (forward primer labeled with the fluorescent dye HEX) (Integrated DNA Technologies), 0.025U Taq DNA Polymerase (Invitrogen), and 5 ng of DNA. We ran the PCR in a Bio‐Rad DNA Engine Dyad Disciple thermocycler under the following conditions: 95°C for 10 min followed by 30 cycles of 94°C for 30 s, 62°C for 1 min, and 72°C for 1 min, and completed with a step of 65°C for 15 min.All lynx samples (excluding those obtained from Cape Breton Island) were amplified and genotyped at 14 presumably neutral microsatellite markers  into 1 ml of HiDi Formamide (Applied Biosystems), and we added 9.5 μl of this product to 0.5 μl of each amplified sample.For genotyping of the IGF‐1 locus with Softgenetics LLC GeneMarker AFLP/Genotyping Software Version 1.91 . We used manual scores from Koen et al. , lynx sampled south of the St. Lawrence River in Quebec (N = 165), Labrador (N = 19), Newfoundland (N = 27), New Brunswick (N = 14), and Cape Breton Island (N = 38).For analyses, we grouped samples into 6 sites based on their geographic location in eastern Canada; lynx sampled north of the St. Lawrence River in Quebec (HE) and observed (HO) heterozygosity for each locus, and conducted a t‐test to evaluate whether average expected and observed heterozygosity were significantly different across loci. We also used this package to conduct Bonferroni‐corrected (α = .05) chi‐squared tests to detect loci that departed from Hardy–Weinberg equilibrium (HWE). We used the software GENEPOP . We used the R package diversity  on each estimate (999 bootstraps). We estimated AR, genetic differentiation, and FIS separately for our set of 14 neutral microsatellites and the IGF‐1 locus, due to the prospect that IGF‐1 was under selection or linked to a nearby marker under selection. The IGF‐1 data set consisted of lynx sampled from north of the St. Lawrence River in Quebec (N = 297), south of the St. Lawrence River in Quebec (N = 141), Labrador (N = 19), Newfoundland (N = 24), New Brunswick (N = 14), and Cape Breton Island (N = 38). All of these samples were from the same individuals that we sampled for neutral markers.We used the R  as wellIGF‐1 locus). We tested scenarios of K = 1–9 and ran 10 repetitions for each value of K. We used a burn‐in length of 500,000 Markov chain Monte Carlo iterations, followed by a run length of 1,000,000 iterations. We selected an admixture model without prior location information and used a correlated allele frequency model. We selected the most likely number of genetic clusters using the Evanno method  with the R package diversity , a history of population bottlenecks, and/or reduced genetic diversity across loci in island but not mainland populations. Alternatively, insular populations could be experiencing strong directional or positive selection, where few alleles are driven to high frequencies on the islands  due to differences in environmental conditions, prey base and/or inter and intraspecific competition. In this case, we would expect values of genetic differentiation that are greater at the loci under selection  in comparison with neutral markers , or a correlation of alleles with environmental or other variables that differ between the examined mainland and insular populations . To differentiate between these hypotheses, we conducted multiple analyzes.There are two alternative hypotheses explaining genotypic and allelic variation in insular lynx populations. First, allelic distributions may be largely influenced by genetic drift. If this were the case, we would expect to find evidence of small effective population sizes (HO) and expected (HE) heterozygosity, and allelic richness (AR). We estimated these metrics using the same approaches as for our regionally defined populations (AR was corrected for our lowest sample size [N = 26] using rarefaction). We also estimated Ne for each of our genetic clusters using the molecular co‐ancestry method of Nomura  for noninbred populations, and its narrower confidence intervals are more practical for interpretation  within our 4 genetic clusters, and average measures of within‐population genetic diversity  at our 14 neutral microsatellite markers. As gene flow with mainland populations is unlikely to have significantly contributed to within‐population genetic diversity of the islands  are dependent on, and can be biased by within‐population heterozygosity, especially in cases when migration rates are expected to be low .As significant morphological divergence has been documented in insular lynx in comparison with mainland populations . Individuals with missing measurements were removed. We controlled for sex by including it as our second factor in each analysis, but were unable to control for age due to sample size limitations. Thus, to indirectly assess the influence of age on our measured traits, we estimated correlations between age and each trait using Pearson's correlation coefficients , or Spearman's rank correlation coefficients .We obtained seven morphological measures for each of our individual lynx genotyped on Cape Breton Island: total length, length of tail, length of hind foot, weight of skinned carcass, total length of skull, zygomatic width, and mandible length. We conducted 2‐way ANOVAs on each of these measured traits to determine whether mean trait values differed significantly by 33.1p < .001) across our set of 15 neutral and functional loci, suggesting deviations from HWE , whereas Quebec lynx north of the St. Lawrence River departed at only one locus . Insular lynx also showed deviations from HWE, with Newfoundland and Cape Breton Island deviating at 4 and 2 loci, respectively . Lynx from Labrador and New Brunswick were within HWE at all loci. We identified five pairs of loci that showed evidence of LD . Across all of our 14 neutral microsatellite loci, we detected only two sites (Quebec north and south of the St. Lawrence River) that significantly indicated inbreeding (95% CIs did not overlap 0), but the range of values of FIS was small , and thus inbreeding is likely not prevalent in these populations . Following Keenan et al.  supporting four versus two genetic clusters. In concert with published evidence that Newfoundland  were small for the insular populations, whereas we estimated Ne of mainland sites as infinite, and thus likely to be large (Table p < .05), while there was slightly higher evidence for bottlenecks in the two mainland clusters (p < .01) than the island clusters (p < .05), there was also greater power in our analyses of the mainland sites as they had much larger sample sizes overall. Linear regressions between pairwise genetic differentiation and each of our within‐population measures of genetic diversity  were significant, regardless of the measure of genetic differentiation we used , and the most common allele found on Cape Breton Island , which was also found at a relatively high frequency on Newfoundland , was not found in any of the mainland clusters in high frequency .Island populations of Canada lynx had lower genetic diversity than mainland clusters at the s Figure ; Table 2IGF‐1 locus was under positive selection. There was some evidence that microsatellite locus Fca43 was under balancing selection, however, the locus consistently fell very close to the range of neutrality. In contrast to our LOSITAN results, our Jost's D plot comparing neutral microsatellites and IGF‐1 indicated that the IGF‐1 locus was potentially under selection in eastern populations of lynx. Specifically, genetic differentiation at the IGF‐1 locus between the Cape Breton Island cluster and both of the mainland clusters fell outside of the standard error intervals estimated from the mean of our 14 neutral microsatellites  were lower than neutral expectations, whereas the mainland group comparison was approximately equivalent to neutral expectations, and pairwise comparisons with Cape Breton Island were higher than neutral expectations  excluding some comparisons involving two mainland sites . When we conducted this estimation across genetic clusters identified using STRUCTURE, genotypic distributions were significantly different across all pairwise comparisons (p < .01).Our Fisher's exact tests indicated that the distribution of genotypes at the F = 14.75, df = 1, p = .0005; length of hind foot, F = 11.12, df = 1, p = .0022); however, the factor “IGF‐1 genotype” was not significant for any of our traits following Bonferroni correction .All seven datasets used in our ANOVA analyses, had homogeneous variance. Sex was found to be a significant factor in two of our ANOVA analyses  and were consistent with expected patterns of gene flow for lynx . Importantly, however, Cape Breton Island lynx were collected approximately 30 years (~10–15 generations) prior to all other samples, which could be contributing to the high levels of genetic differentiation reported here. Nonetheless, as the closest mainland population of lynx in Nova Scotia was extirpated long before the sampling of our Cape Breton lynx individuals, the substantial isolation (based on the Canso causeway and the considerable geographic distance to the nearest mainland population) of this population since the early 1900s supports high levels of contemporary genetic differentiation despite the gap in our sampling period.We found that lynx on Cape Breton Island were genetically distinct, supporting the hypothesis that the Strait of Canso is a barrier to gene flow for this insular population. Supported by previously published evidence of the Strait of Belle Isle isolating Newfoundland lynx  and within‐population genetic diversity . Combined, these results suggest that genetic drift has affected the genetic variation observed in island populations of Canada lynx. Interestingly, however, while AR of both neutral markers and the IGF‐1 locus were lower in island versus mainland sites as predicted, private allelic richness of our neutral markers was lower in sites south of the St. Lawrence River, compared to sites north of the river. This result is consistent with the work of Koen, Bowman, Murray, and Wilson  lower genetic diversity of insular populations in comparison with mainland groups; (2) lower effective population sizes of island versus mainland groups; (3) signatures of bottlenecks; and (4) negative relationships between pairwise genetic differentiation  allele frequency differences; (2) contrasting patterns of genetic differentiation at neutral markers and IGF‐1; and (3) outlier tests using Jost's D. Combined, these results suggest that selection has also contributed to the genetic differentiation and diversity observed in island lynx populations. While it is true that the lower genetic diversity  of the IGF‐1 locus on island compared to mainland groups of lynx could be the result of genetic drift, selection for fewer, but more favorable alleles on islands are better supported by our cumulative data. This is because both islands have retained the same two alleles despite likely being founded by genetically divergent populations on the mainland (north versus south of the St. Lawrence River). Further, the more common of the two alleles on Cape Breton Island, which is also found at a relatively high frequency in Newfoundland, was not found at high frequencies in either of the mainland sites of eastern Canada that we sampled, and there is likely a lack of gene flow between the two islands (supported by high estimates of FST and Jost's D). Thus, the unlikely prospect that an uncommon allele on the mainland would be present in the founding populations of both Cape Breton and Newfoundland and subsequently drifted to high frequencies on both islands lends support to the hypothesis that convergent selection has caused the increase in frequency of this allele in both populations of insular lynx simultaneously.In addition to genetic drift, we were able to identify a functional locus that has likely been influenced by natural selection in insular lynx, IGF‐1 genotypes in our insular lynx populations. Specifically, while Newfoundland was more highly differentiated than Cape Breton Island with neutral genetic markers, the opposite trend was observed at the IGF‐1 locus, suggesting that different forces are influencing genetic differentiation at these markers . Greater neutral genetic differentiation of Newfoundland is consistent with the premise that gene flow with the mainland is less prevalent in Newfoundland versus Cape Breton Island, which exchanges more migrants with the mainland via the Canso causeway , and increasing sample sizes within groups. Indeed, the probability of detecting significant associations between morphological traits and IGF‐1 alleles would be greater if a larger variation of morphologically distinct phenotypes were compared across island and mainland sites; however, we did not have access to morphological measurements for the mainland lynx samples used in our study. Coupled with the fact that the Cape Breton Island population has low levels of variation in morphological trait measurements and high levels of gene flow, our analysis likely lacked power to associate common IGF‐1 alleles with phenotype. Ultimately, addressing these limitations may allow for a better understanding of the genetic basis of the island rule in mammals. Taken together, our analyses of the IGF‐1 locus indicated that while genetic drift may be strong in island populations, the strength of selection may also be strong enough to allow for the adaptive divergence of important traits, including body size.Although we did not find associations between morphological trait measurements and Disentangling the contributions of genetic drift and natural selection in island populations is an important conservation goal with relevance to species management. The low genetic diversity of island Canada lynx populations via genetic drift reported here may suggest that lynx are limited in their capabilities to track changes in the environment, especially at a time‐scale that is consistent with climate change. Specifically, the ability of insular populations to adapt to environmental stressors is largely dependent on the population's standing adaptive genetic variation (Barrett & Schluter, Although extinction risk is more likely driven by genetic drift than selection in small populations (Whitlock, 5The protection of island populations is often a conservation goal due to their unique genetic contribution to the species. Integral to this goal is understanding the relative roles of genetic drift and natural selection in shaping the genetic diversity and differentiation of insular populations. The protection of adaptive genetic diversity within species is critical for ensuring species persistence in the face of both short‐term environmental stressors and long‐term climate change. Additionally, understanding the impact of genetic drift in isolated and often small island populations is a critical step toward understanding whether the loss of genetic variation imposes a significant threat to the long‐term persistence of populations via inbreeding depression. We showed that while genetic drift has played a role in shaping the observed genetic structure of insular Canada lynx populations, adaptive differentiation also seems to contribute to their unique gene pool. Extending the analysis of genes putatively under selection (Kirk & Freeland, Facilitated sample collection: JB, PJW, DM, and KK. Conceived and designed the experiments: MBP, JB, PJW, and KK. Performed the laboratory work and analyzed the data: MBP. Wrote the manuscript: MBP. Critically reviewed the manuscript: MBP, JB, KK, ELK, JR, DLM, and PJW.None declared.http://dx.doi.org/10.5061/dryad.8ff46.Data available from the Dryad Digital Repository:  Click here for additional data file.


Triticum turgidum var. durum) is an important crop in Europe, particularly in the Mediterranean countries. Fusarium head blight (FHB) is considered as one of the most damaging diseases, resulting in yield and quality reduction as well as contamination of grain with mycotoxins. Three winter durum wheat cultivars originating from Austria, Slovakia, and Poland were analyzed during 2012–2014 seasons for FHB incidence and Fusarium mycotoxin accumulation in harvested grain. Moreover, the effects of sowing density and delayed sowing date were evaluated in the climatic conditions of Southern Poland. Low disease severity was observed in 2011/2012 in all durum wheat cultivars analyzed, and high FHB occurrence was recorded in 2012/2013 and 2013/2014 seasons. Fusarium graminearum was the most abundant pathogen, followed by Fusarium avenaceum. Through all three seasons, cultivar Komnata was the most susceptible to FHB and to mycotoxin accumulation, while cultivars Auradur and IS Pentadur showed less symptoms. High susceptibility of cv. Komnata was reflected by the number of Fusarium isolates and elevated mycotoxin  content in the grain of this cultivar across all three seasons. Nivalenol was identified in the samples of cv. Komnata only. Genotype-dependent differences in FHB susceptibility were observed for the plants sown at optimal date but not at delayed sowing date. It can be hypothesized that cultivars bred in Austria and Slovakia show less susceptibility towards FHB than the cultivar from Poland because of the environmental conditions allowing for more efficient selection of breeding materials.Durum wheat (The online version of this article (10.1007/s00114-017-1528-7) contains supplementary material, which is available to authorized users. Triticum turgidum var. durum) is an important crop in EU member countries, where it is cultivated on the area of nearly 3 mln ha, and according to the European Commission reports (2014), the top yields are between 5 and 6 t ha−1. The main areas of cultivation include four traditional regions—Italy, Greece, Spain, and France. Outside the Mediterranean area, lower productivities are recorded and breeders’ efforts are focused on improving the yield-forming potential as well as crop quality. Important features of wheat cultivar evaluation are drought tolerance, resilience to low temperatures, and other environmental stresses, as well as considerable level of resistance to diseases and pests  being the most damaging. Besides decreasing the grain quality and yield, it results in massive accumulation of mycotoxins with deoxynivalenol (DON) and its derivatives as prevailing metabolites, followed by zearalenone (ZON) and moniliformin (MON)  to identify Fusarium species present in the infected heads.The main scientific aims of the study were (i) to evaluate the effect of three sowing densities and two sowing dates on the FHB incidence and severity on three winter durum wheat cultivars of different origin, (ii) to assess the accumulation of the most important 2 each, with three replications.The field experiments  were conducted near Kraków  in randomized block design, plots of 10 mThree cultivars of winter durum wheat: Komnata (Poland), Auradur (Austria), and IS Pentadur (Slovakia).Sowing dates—optimum (25–30 September) and delayed (15–20 October).Sowing densities—400, 500, and 600 germinated seeds on square meter.Experimental factors were−1; active ingredients triasulfuron and dicamba), fungicides , and a growth regulator .The pre-crop was potato or oilseed rape. After harvesting the previous crop, full soil tillage was performed. A standard chemical protection was applied according to the general recommendations, i.e., seed treatment, herbicide .Ammonium nitrate 34% N in three doses  located near the field experiments. The three seasons’ data on monthly average temperatures and total precipitation are presented on Fig. ni denotes the number of plants within the category i .Evaluation of FHB infection was performed in the grain maturation phase in 8° scale where 1° = healthy heads, 2° ≤ 15%, 3° = 15–30%, 4° = 30–45%, 5° = 45–60%, 6° = 60–75%, 7° = 75–90%, and 8° = 90–100% area of the head with disease symptoms. All diseased heads per plot were recorded to evaluate the FHB incidence. The evaluation scale was converted to a disease index (DI) factor according to the formula proposed by Pierre and Regnault :\documenFusarium fungi  were plated aseptically on the potato dextrose agar  medium and cultured for 5–7 days at 20–25 °C and 12-h photoperiod in triplicate. Multiple species infecting the same head were observed frequently; they were all isolated independently. Specifically, more than one Fusarium species could be isolated from a single kernel. Other fungal genera  were also present (results not shown).Durum wheat cultivars were harvested at full plant maturity. Diseased heads were randomly chosen for pathogen isolation and identification, regardless of the DI recorded for the plot or cultivar. Grains from diseased heads exposed to natural infection by Fusarium strains were isolated using Leslie and Summerell manual  to identify F. culmorum, Fg16 marker (282 bp) specific for F. graminearum, and Fa marker (900 bp) to determine F. avenaceum  as described by Stępień et al. (Genomic DNA was extracted using a modified CTAB (hexadecyltrimethylammonium bromide) method described earlier  and FastAP alkaline phosphatase (Thermo Scientific) using the following program: 30 min at 37 °C and 15 min at 80 °C. Both DNA strands were labeled according to Stępień et al.  using thZON, deoxynivalenol (DON), nivalenol (NIV), and MON standards were purchased with a standard grade certificate from Sigma-Aldrich . Organic solvents (HPLC grade) and all the other chemicals were also purchased from Sigma-Aldrich . Water for the HPLC mobile phase was purified using a Milli-Q system .Ten grams of ground kernels were subjected to mycotoxin extraction as described previously  and MON (λ = 229 nm) analysis and (ii) Waters 2475 Multi λ Fluorescence Detector  and Waters 2996 Photodiode Array Detector with Nova Pak C-18 column (150 × 3.9 mm) for ZON analysis. Mycotoxins were re-dissolved and separated according to Wiśniewska et al.  coupled with (i) Waters 2996 Photodiode Array Detector with Nova Pak C-18 column (300 × 3.9 mm) for DON and NIV . All calculations were carried out using the STATISTICA 10.0  software package.Data regarding DI were analyzed by three-way analysis of variance (ANOVA). Graphs were plotted using the means and standard errors (SE) for each data point. A post hoc comparison was conducted using Tukey’s multiple range test (Weather conditions (mean temperatures and precipitation) throughout the three seasons of the study were monitored and summarized Fig. . The 201DI was measured independently for each cultivar in each season. Significant variance was observed in FHB incidence during this 3-year study among the three cultivars tested Fig. . In geneFusarium spp. abundance was observed in the 2011/2012 in all durum wheat cultivars analyzed .The abundance of individual FHB-related species varied among seasons, particularly in the 2011/2012, when lower number of isolates was observed. Some of the species identified were exclusive for this season, e.g., Two different sowing dates were analyzed: optimal and delayed . In the 2013/2014 season, an increase of disease symptoms was observed for the delayed sowing date  resulted in lower FHB incidence in all cultivars and across the three seasons; however, the differences between the densities  were statistically not significant  concentrations .NIV was identified in the samples of cv. Komnata only, from which both DON and NIV chemotypes of Fusarium strains isolated, as well as by low mycotoxin contamination of the grain. Komnata was the most susceptible cultivar to the disease progress and mycotoxin accumulation through all three seasons, while cvs. Auradur and IS Pentadur were less susceptible. Studies conducted in various climatic conditions have proven a strong correlation between FHB epidemics and favorable temperatures and high humidity before and during flowering  resulted in higher infection level  in the present study, but no similar correlation was observed for ZON. In the case of the most susceptible cultivar (Komnata), this was valid for all mycotoxins analyzed. It can be hypothesized that the environmental conditions present in the Southern Europe are more suitable for selection and breeding of less susceptible materials than those bred in Poland.ESM 1(PDF 161 kb)


Differences in genetic ancestry and socioeconomic status (SES) among Latin American populations have been linked to health disparities for a number of complex diseases, such as diabetes. We used a population genomic approach to investigate the role that genetic ancestry and socioeconomic status (SES) play in the epidemiology of type 2 diabetes (T2D) for two Colombian populations: Chocó (Afro-Latino) and Antioquia (Mestizo). Chocó has significantly higher predicted genetic risk for T2D compared to Antioquia, and the elevated predicted risk for T2D in Chocó is correlated with higher African ancestry. Despite its elevated predicted genetic risk, the population of Chocó has a three-times lower observed T2D prevalence than Antioquia, indicating that environmental factors better explain differences in T2D outcomes for Colombia. Chocó has substantially lower SES than Antioquia, suggesting that low SES in Chocó serves as a protective factor against T2D. The combination of lower prevalence of T2D and lower SES in Chocó may seem surprising given the protective nature of elevated SES in many populations in developed countries. However, low SES has also been documented to be a protective factor in rural populations in less developed countries, and this appears to be the case when comparing Chocó to Antioquia. In fact, complex common diseases such as heart disease, cancer and diabetes already account for the majority of the morbidity and mortality in the region2. Complex multifactorial diseases of this kind are associated with the effects of multiple genetic loci combined with a variety of environmental factors, such as diet, lifestyle and exposure to toxins. The burden of complex disease is not evenly distributed within or between countries in Latin America; genetic and environmental differences among Latino populations often lead to pronounced health disparities3. Furthermore, population health disparities in Latin America tend to have a disproportionate impact on vulnerable Native American and Afro-Latino communities4.With ongoing economic development, and the lifestyle changes that accompany increased standards of living, the primary disease burden in Latin America is shifting from infectious to non-communicable, complex diseases10. The higher prevalence of T2D in these populations has been associated with both genetic and environmental factors. Genetic risk for T2D is correlated with both increased Native American and African ancestry14, and low socioeconomic status (SES) has also been widely associated with increased T2D prevalence in Native American and African-American populations17.Diabetes mellitus is a complex multifactorial disease characterized by both a very high disease burden and strikingly disparate impacts among distinct populations in the Americas. For example, type 2 diabetes (T2D) has substantially higher prevalence in both Native Americans and African-Americans compared to European-Americans in the United States (US)18. Colombia has among the highest levels of three-way genetic admixture seen for any Latin American country20 and is home to a large population of Afro-descendants23. Estimates for the size of the Afro-Colombian population range from 9–20 million, making it the second largest population of its kind in Latin America after Brazil. The collaborative ChocoGen research project was conceived to study the genetic heritage of the Afro-Colombian population from the administrative department  of Chocó, located along Colombia’s Pacific coast (http://www.chocogen.com)22. The ChocoGen project has the joint aims of (1) characterizing the genetic ancestry of the population of Chocó, and (2) exploring the relationship between genetic ancestry and determinants of health and disease in the region.Latin American populations are characterized by substantial genetic admixture – with predominant ancestry contributions from Europe, the Americas and Africa – owing to historical patterns of migration, conquest and slavery24. The population of Chocó is considered to be particularly vulnerable, with high levels of poverty and low measures of economic development across a number of indices compared to Antioquia. We chose to focus our comparative study of genetic and health differences between Chocó and Antioquia on T2D for several reasons: (1) its high disease burden, (2) its known contribution to population health disparities, and (3) the relative wealth of knowledge regarding its underlying genetic architecture. We set out to assess whether and how genetic and environmental differences between these two very distinct regions may manifest themselves with respect to population-specific levels of T2D genetic risk and/or differences in observed prevalence for the disease.The objective of this study was to evaluate the contributions of genetic ancestry and environmental factors to population health disparities in Chocó, and we addressed this issue here via a population genomic analysis of the genetic risk and the observed prevalence of T2D. Our efforts towards this end involve a comparison between the populations of Chocó and the neighboring state of Antioquia, which borders Chocó to the east  as previously described22. Sample donors from the ChocoGen project signed informed consent documents indicating their understanding of the potential risks of the project along with how their data would be handled and how their identity would be protected. Collection, genotyping and comparative analyses of human DNA samples were conducted with the approval of the ethics committee of the Universidad Tecnológica del Chocó22. All methods were performed in accordance with the journal’s relevant guidelines on the use of human participants. Names and other HIPAA identifiers are removed from all sections of the manuscript, including the supplemental information. No other information that could lead to the identification of study participants is provided.Whole genome sequence data and whole genome genotype data were analyzed in order to infer the genetic ancestry and admixture profiles for the Colombian populations of Chocó and Antioquia Table . Whole g25. Whole genome sequences from several additional admixed American populations were taken from the 1KGP for analysis: Utah residents with European ancestry (n = 99), African ancestry individuals from the Southwest US (n = 61), and a Peruvian population from Lima, Peru (n = 85).All of the other data used for the analysis described here corresponds to publicly available and de-identified genome sequences or genotypes. Publicly available whole genome sequences for 94 individuals from Medellín, Antioquia were characterized as part of the 1000 Genomes Project (1KGP)23: Africa, Europe and the Americas. African ancestry was inferred using whole genome sequences for a Yoruba population from Ibadan, Nigeria (n = 108), and European ancestry was inferred using whole genome sequences for an Iberian population from Spain (n = 107), both of which were characterized as part of the 1KGP. Whole genome genotypes for three Native American populations – Embera from Colombia (n = 5), Quecha from Peru (n = 40) and Zapotec from Mexico (n = 43) – were taken from a dataset collected as part of a previous study on Native American genetic ancestry26.Genome sequence and genotype data were also sampled from putative ancestral populations corresponding to the three major continental regions that are known to contribute to genetic admixture in Colombia27, and the resulting merged single nucleotide polymorphism (SNP) dataset was pruned in order to remove SNPs that are in linkage disequilibrium (r ≥ 0.05). This resulted in a final dataset of 220,724 SNPs across 736 individual genome samples. Pairwise genomic distances between individuals were calculated as allele sharing distances between all pairs of merged/pruned SNP sets, also using PLINK. The pairwise allele sharing distance matrix was reduced to two-dimensions with principal component analysis (PCA) using the prcomp function from the R package for statistical computing28  the SNP association was uncovered via a case-control study based on at least 100,000 genotyped SNPs, (2) the SNP had the strongest association seen for its genomic locus, and (3) the SNP showed a genome-wide T2D association P-value < 1.0e−5. This yielded a set of 165 T2D-associated SNPs, and for each SNP the identity of the risk allele  linked to T2D was taken from the study where it was reported.The underlying genetic architecture of type 2 diabetes (T2D) was assayed from a series of 29 case-control genome-wide association studies (GWAS). T2D SNP association data from these studies were taken from the NHGRI-EBI GWAS catalog32 using the 1KGP phase 3 haplotype reference panel. The phased whole genome genotypes from Chocó, consisting of 522,458 SNPs per individual, were then imputed using the program IMPUTE235 with the 1KGP phase 3 haplotype reference panel32. This process resulted in the imputation of 35,056,488 additional SNPs across all samples. The accuracy of the imputation process was evaluated by comparing the genetic ancestry relationships between individuals from Chocó, computed before and after imputation, and a panel of global reference populations. The observed genetic ancestry relationships for the individuals from Chocó are virtually identical before and after imputation, in support of the accuracy of the imputation process  and Antioquia (genome sequences). Prior to imputation, the whole genome genotypes of individuals from Chocó were phased using the program SHAPEITOR) was used to compute the relative genetic risk of T2D for Chocó compared to Antioquia . A meta-analysis was conducted to evaluate the joint effect of all 165 T2D-associated SNPs on the relative genetic risk of T2D in Chocó versus Antioquia using the metafor package in R36. 95% confidence intervals for the individual SNP and meta-analysis OR values were computed using both fixed- and random-effects models. The fixed- and random-effects models were both computed with moderators via linear (mixed-effects) models.For each T2D-associated SNP, a log odds ratio  for individual genomes were computed as the unweighted, normalized sum of the number of risk alleles for all 165 T2D-associated SNPs  disease-associated SNP sets from the NHGRI-EBI GWAS catalog was used to create 500,000 replicate SNP sets, each of which was used for genetic risk OR calculation and meta-analysis as described above. The resulting distribution of random disease-associated SNP set meta-analysis OR values was compared to the observed value to evaluate whether systematic biases in disease-associated allele frequencies between populations may affect the comparison of genetic risk , as described above for T2D, for 324 diseases between Chocó and Antioquia in order to assess whether there is any systematic bias in disease genetic risk score computation between the two populations  Bootstrap: random sampling with replacement from the 165 T2D-associated SNPs was used to create 10,000 replicate SNP sets, each of which was used for genetic risk ons Fig. . (2) Rank Figure . (3) Disi.e., states) was taken from three database sources: (1) Cuenta de Alto Costo , (2) Observatorio de Diabetes de Colombia (http://www.odc.org.co/), and (3) the Sistema Integral de Información de la Protección Social databases   Fig. . Data ono.org/),  ObservatGenome sequence variant data are available from the project resources listed in Table 22, we characterized the genetic heritage of Chocó and Antioquia with respect to their populations’ ancestry proportions derived from Africa, Europe and the Americas. To do so, whole genome genotypes characterized for donors from Chocó, along with publicly available whole genome sequences from Antioquia, were compared to genomes from putative ancestral source populations collected from a variety of sources (Table Here and elsewherees Table . Detailses Table . NeverthOR) that expresses the relative genetic risk of T2D for the two populations: Chocó/Antioquia. Log odds ratios were used to provide a statistical framework to measure the T2D risk contributions of individual SNPs and to allow for a meta-analysis that considers the additive genetic risk contribution of all SNPs together. Details of this approach are provided in the Methods section. The majority of T2D associated SNPs show higher risk allele frequencies in Chocó compared to Antioquia, pointing to a relatively higher genetic risk of T2D in the population of Chocó  were mined from a collection of 29 T2D genome-wide association studies (GWAS) . The results of the bootstrap analysis are consistent with greater T2D genetic risk in Chocó and indicate that the signal in the data, based on the individual SNP OR values, is robust to sampling noise.We performed a series of controls in an effort to ensure that the difference observed for T2D genetic risk between Chocó and Antioquia cannot be attributed to any systematic bias in the SNP allele frequencies of the two populations (Methods). First, we used bootstrap analysis of the T2D SNP set to evaluate the signal-to-noise ratio in the data. In particular, we wanted to assess whether the observed difference in T2D genetic risk between Chocó and Antioquia may be due to a few outlier SNPs  was performed, yielding a random meta-analysis log odds ratio value (OR). The null distribution of the resulting random meta-analysis OR values was then compared to the observed T2D relative genetic risk OR value for Chocó/Antioquia. Contrary to the expectations of the demographic bias model, Antioquia shows a higher overall relative genetic risk when ensembles of randomly sampled disease-associated SNP sets are analyzed .We developed a simulation-based approach in order to control for the possible effects of demographic history on estimates of population-specific T2D genetic risk for Chocó and Antioquia. If the apparent elevated genetic risk for T2D in Chocó reflects a bias in the relative frequencies of disease-associated SNPs, perhaps owing to increased African ancestry of the population, then we would expect to see an overall shift to higher estimated disease risk for Chocó compared to Antioquia. To evaluate this possibility, 500,000 SNP sets of the same size as the set of T2D-associated SNPs were randomly simulated from a collection of disease-associated SNPs taken from the NHGRI-EBI GWAS catalogzed Fig. . In addiz = −0.1, P = 0.92). Taken together, these three controls suggest that the observed difference in T2D genetic risk for Chocó versus Antioquia cannot be attributed to any systematic bias in disease-associated allele frequencies between the two populations.In addition to the simulation-based approach described above, we also used disease-associated SNPs from the NHGRI-EBI GWAS catalog to compute the relative genetic risk between Chocó and Antioquia for 324 additional diseases. In this case, a systematic bias in the population-specific allele frequencies of disease-associated SNPs would be expected to reveal an overall elevation of disease genetic risk in one of the two populations. However, the distribution of the differences in predicted genetic risk for these diseases is centered very close to 0 and more or less symmetrical Fig. ; the mea12. We asked whether elevated genetic risk of T2D in Chocó may also be related to greater African ancestry, and conversely lower European ancestry, in Chocó compared to Antioquia. To do this, we computed polygenic T2D risk scores for individuals from Chocó and Antioquia along with individuals from their most closely related putative ancestral populations in Europe (Spain) and Africa (Nigeria). We applied a widely used approach that computes polygenic risk scores for individual genomes, or whole genome genotypes, based on the sum of risk alleles present across all associated SNPs43 (Methods). The Antioquia population has the lowest T2D genetic risk measured this way followed by the Spanish population; however, the T2D genetic risk score distributions between these two populations are not significantly different , and the Nigerian population has the highest overall risk . These results lend additional support to the association of African genetic ancestry with elevated T2D genetic risk.We further evaluated the relationship between genetic ancestry and T2D genetic risk worldwide by comparing five European populations to seven African populations Fig. . All of 10. However, we were surprised to find that the reported prevalence of diabetes is in fact more than three-times higher in Antioquia than in Chocó (Fig. th highest out of 32 states in the country, compared to 0.6% for Chocó, which is ranked 27th. The large difference in diabetes prevalence observed for Chocó versus Antioquia is highly consistent across the three different Colombian epidemiological databases that we sourced (Fig. Given the elevated genetic risk for T2D in the Afro-Colombian population of Chocó, along with its association with African ancestry, we expected to see a substantially higher prevalence of diabetes in Chocó compared to Antioquia. Indeed, numerous studies report that African-Americans in the US have far higher prevalence of T2D than European-Americansocó Fig. . Averagiced Fig. .46, and the populations of Chocó and Antioquia do indeed occupy very distinct environments. In particular, as previously stated, the population of Chocó has far lower overall SES compared to Antioquia (Table st out of 32 Colombian states, compared to an HDI of 0.85 for Antioquia, which ranks 4th in the country. Thus, it appears that even though low SES has been associated with the risk for T2D in numerous studies47, in Chocó low SES somehow serves as a protective factor against T2D. This unexpected finding suggests that poverty may play a very different role in the etiology of complex disease, particularly for diabetes and perhaps other metabolic syndrome disorders, in Colombia compared to more developed countries in the Global North.The far lower prevalence of diabetes in Chocó versus Antioquia, compared to what may be expected based on the genetic profiles of their populations, strongly suggests that environmental factors predominantly shape diabetes outcomes in the region. This would be consistent with several large cohort studies showing that environmental factors contribute substantially more to T2D than genetic factors49. However, poverty in Chocó, which is generally more extreme than what is found in the US, is actually associated with a diet that is protective against T2D, particularly when compared to Antioquia. The dietary staples of Chocó are fish, plantains, yucca and rice; fish are readily available from the Atrato River and its tributaries, and plantains and yucca are cultivated along the banks of this vast river system51. Thus, the typical diet of Chocó is high in polyunsaturated lipids, such as omega-3 and omega-6 fatty acids, and fiber, both of which are known to mitigate T2D risk. In Antioquia, the main sources of protein are beef and pork, which are rich in both cholesterol and triglycerides formed by saturated fatty acids, known risk factors for T2D. In addition to the ready availability of fish in the region, SES in Chocó also impacts dietary choices in a way that is protective against T2D. In Quibdó, the capital of Chocó, one kilogram of meat costs $9000 Colombian pesos, or approximately $3 US dollars; 10 kg of fish from the Atrato River can be bought for the same amount, providing a week’s worth of protein.Our study of the contributions of genetic ancestry and environmental factors to T2D prevalence in two divergent Colombian populations suggests that poverty can serve as a T2D protective factor in Colombia. The possibility that poverty in Chocó is an environmental protective factor against T2D, as opposed to a strong risk factor as seen for African-Americans in the US, may be attributed to the differing nature of poverty in developed countries compared to some parts of the developing world. Poverty in the US is associated with poor diet and other lifestyle factors that elevate T2D prevalence52. Since moderate alcohol consumption has been linked to reduced risk for the onset of T2D55, this could represent an additional protective factor associated with the lifestyle in Chocó. Conversely, Antioquia was found to have higher tobacco use in the same survey, with 14.1% use over the last month compared to 6.6% for Chocó. Smoking is a known risk factor for T2D58, pointing to yet another possible advantage of the lifestyle in Chocó with respect to T2D prevalence.We also found Chocó and Antioquia to be distinct with respect to the prevalence of alcohol consumption and tobacco use, both of which have been implicated as environmental factors that influence T2D outcomes. A 2013 government survey on the consumption of psychoactive substances in Colombia found that Chocó had the highest prevalence of alcohol consumption for the country, with 44.6% of respondents reporting alcohol consumption over the past 30 days compared to 36.6% for Antioquiaper se. While the notion that poverty in Chocó serves as a protective factor against diabetes was certainly unexpected to us, if we consider Chocó to be under-developed relative to Antioquia, then the environmental protective effect may not be as surprising. Indeed, as previously stated, the HDI for Chocó points to substantial under-development compared to the rest of the country, and the pyramid shaped age distribution of Chocó is more consistent with what is seen in less developed countries; the narrower age distribution of Antioquia, on the other hand, resembles those of more developed countries (Supplementary Figure 59. In fact, studies have shown precipitous increases in T2D prevalence in populations that have undergone rapid transitions to more developed economies60. The comparison of Chocó versus Antioquia may underscore the public health relevance of stark differences in economic development within a single country, albeit in way that counterintuitively favors the less developed region.Another way to consider the discordant results that we observed for population-specific genetic risk versus the observed prevalence of diabetes in Colombia is through the lens of economic development as opposed to poverty 2 compared to 99 per km2 for Antioquia. The rural setting of Chocó, along with the overall challenging conditions of its environment, are associated with a more physically active lifestyle compared to more modernized parts of the country51, highlighting yet another potentially protective factor against T2D. Interestingly, a recent study in India showed that low SES is simultaneously a risk factor for T2D in cities and a protective factor for T2D in more rural areas61. Thus, it may be the case that urban poverty in developing countries is more reminiscent of overall poverty in the developed world, in terms of risk for T2D, whereas the features of rural poverty in the developing world are distinctive and protective for T2D.It is also worth noting that Chocó is more rural, and less urbanized, than Antioquia. Chocó is relatively underpopulated with a population density of 11 individuals per kmWe explored the relationship between economic development and T2D for the entire country by comparing HDI levels to T2D prevalence estimates for all states. We observe a strong positive correlation between HDI and T2D prevalence across Colombia, with more developed regions of the country showing higher T2D prevalence estimates (Supplementary Figure 64, to developing countries in Latin America. For instance, a commonly accepted environmental risk factor for many common diseases, such as SES, may have very different implications in Latin America compared to the US. In addition, the public health value of dietary and lifestyle choices, which may have been historically dictated by poverty, should be recognized and incorporated into public health campaigns as countries in Latin America continue to experience rapid economic development and urbanization. A corollary of this suggestion would be to strategically avoid pitfalls of urbanization in the developed world, such as the increasingly sedentary lifestyle, the reliance on processed and fast foods as well as the emergence of so-called ‘food deserts’ in poor neighborhoods where it is exceedingly difficult, if not impossible, to access fresh and whole foods.Genomic approaches to health care, while still in their infancy in the region, hold great promise for Latin America, especially as the public health burden continues to shift toward common complex diseases with at least partial genetic etiology. The distinction that we observe between population-specific genetic risk and observed prevalence of T2D provides lessons for the implementation of genomic approaches to personalized and precision medicine in Latin American countries such as Colombia. Caution should be taken when extrapolating results from studies in the Global North, where the vast majority of this kind of research is still conducted39. Genetic associations discovered in one population may not replicate in a different population, and ancestry and admixture can have additional confounding effects on the expression of genetic variants. Nevertheless, as we have endeavored to show here, exploration of disease associated variants in understudied populations can provide valuable insight into the joint contributions of genetics and environment to common complex diseases, which are an increasing public health threat to the developing economies of the Global South.Finally, caution also needs to be exercised when extrapolating the results of studies on the genetic architecture of complex diseases between populations with distinct ancestry profilesSupplementary Information


While traditional forensic genetics has been oriented towards using human DNA in criminal investigation and civil court cases, it currently presents a much wider application range, including not only legal situations sensu stricto but also and, increasingly often, to preemptively avoid judicial processes. Despite some difficulties, current forensic genetics is progressively incorporating the analysis of nonhuman genetic material to a greater extent. The analysis of this material—including other animal species, plants, or microorganisms—is now broadly used, providing ancillary evidence in criminalistics in cases such as animal attacks, trafficking of species, bioterrorism and biocrimes, and identification of fraudulent food composition, among many others. Here, we explore how nonhuman forensic genetics is being revolutionized by the increasing variety of genetic markers, the establishment of faster, less error-burdened and cheaper sequencing technologies, and the emergence and improvement of models, methods, and bioinformatics facilities. Forensic genetics derives from a late offshoot of the big tree resulting from the conjunction between legal medicine and criminalistics . Its Forensic Science International: Genetics), when defining it as “The application of genetics to human and nonhuman material  for the resolution of legal conflicts” [It is surprising that most of the life span of the discipline has been devoted to human genetics e.g., , since anflicts” . Consequnflicts” . ImportaBelow, we begin by describing the commonly used methodologies, including genotyping and sequencing strategies, evolutionary frameworks, and statistical approaches. Next, we broadly describe applications of NHFG based on diverse biological sources. Finally, we discuss the future of the discipline, including needs and recommendations.The techniques of forensic genetics originally developed for humans were rapidly adapted to other sources of genetic material. The experimental pipeline used in NHFG  starts wCare must be taken when extracting and storing the genetic material to maintain integrity. Storage of nonhuman evidence does not create any specific problem (except required space) that is not common to HFG, and it must be handled in the same way as human material, following the same rules in labelling, chain of custody recording, etc. However, reproducibility in NHFG is clearly a major issue, especially when dealing with wildlife and environmental materials, due to inherent sampling difficulties. Therefore, validation studies cannot be performed in the same strict sense as they are in HFG .The selection of the genetic test depends on the question to be addressed (see next subsection). For instance, the sequencing of a PCR-amplified genetic region  is often used for species identification. The identification of individuals can be achieved using a number of markers sufficient to provide high power of discrimination. In any case, it is important to check the quality of profiles or DNA sequences before analysing the results. The genetic information obtained is then compared with other genetic information  , Inter SHowever, due to their limitations, these techniques were rapidly replaced by Simple Sequence Repeats  and Single Nucleotide Polymorphisms (SNPs) e.g., . The devAdvances in NHFG are also caused by the progress of bioinformatics and statistical tools. A clear example is the emergence and evolution of the bioinformatics pipeline for the assembly of reads generated in MPS [see for a review, Here, we overview the use of nonhuman genetic material (NHGM) as ancillary evidence to solve classical forensic problems and in cases that fall outside the civil and criminal human authorship or responsibility.NHGM can play a crucial role in the investigation of diverse criminal disputes with the aim of identifying the human individual(s) who committed a crime or is/are responsible for some liability or damage. Historically, the first contributions correspond to situations in which NHGM is used as a silent witness resulting from involuntary transfer and leading to the so-called transfer or associative evidence. This type of NHGM usage is best illustrated in criminalistics where it is increasingly important, as perpetrators are progressively avoiding carefully leaving their biological traces in the crime scene. However, they can, for example, inadvertently leave their pets’ hairs at the crime scene or, inversely, to carry the victims’ pet biological material e.g., . AlthougThere are several cases for which the expert evidence is used to deal with a law/regulation infringement, irrespectively of the human authorship or responsibility (which may be investigated separately). Here, a comprehensive classification is complex, given the dynamic evolution of the applications and the diversity of ever-growing fields for which laws are constantly being issued. Nevertheless, we must note a clear difference between the applications in which the litigation treats the nonhuman in a framework similar to human cases  and a plethora of other applications.i) an individual living being  is the direct causation of an injury to another living being or causes property damages [e.g., ii) the genetic relationship  of a living being to another one is unsettled [e.g., iii) the identity of the donor of a sample is under dispute  [e.g., Concerning the former, both theoretical frameworks and technological platforms developed for humans can be almost directly translated. This category includes several scenarios:  e.g.,  . Nevertheless, several difficulties can arise in practice, especially when dealing with small sized and/or poorly studied populations, as in endangered species. The lack of knowledge in the population structure and sampling errors obviously has a serious impact on the confidence of the parameter estimates. The software developed in HFG for kinship and identification can be used in this scenario. For instance, the computer programs GDA  and GeneRegarding scenario B, the traditional procedure consists of comparing sequences that are highly variable among species but highly conserved within species, in the so-called DNA barcoding . An alteRegarding scenario C, the selection of the genetic marker depends on the investigated species. For metazoan, the most used markers are regions of the mitochondrial genome (and plastid for Plantae) that can provide accurate distinction between subspecies e.g.,  and autoDiverse organisms involved in forensic studies present short generation times and belong to large populations (as most pathogens). Therefore, relationships between queried and control samples are usually obtained under the light of evolutionary analyses since those samples most likely belong to distant generations . The evoThe computational pipeline for the evolutionary analysis of genetic data in NHFG follows well-established methodologies ; howeverAs noted above, the estimated time of internal nodes of the inferred phylogeny can be useful in forensic litigations by revealing the timing of infections . These tNHFG is expanding to more and more biological areas due to the increasing emergence of forensic cases based on NHGM. In this section, we revise the most relevant areas of NHFG, including zoology, botany, microbiology, and food analysis and traceability.The relevance and close presence of animals in a variety of human activities explain why they are among the first targets of NHFG , 68–70. The preferential DNA markers used for individual identification in animals are autosomal STRs, as established in HFG. For example, STR kits have been developed for individual identification and kinship testing in dogs –78, catsPioneer works endured tremendous efforts trying to reach the quality standards of human genetic testing. Difficulties in developing a new genotyping system for animals are various, including the collection of representative samples , the access to high-quality genomic sequences  and obtaining funding for the experiments (often focused on human research). Therefore, some of these STR kits are still a few steps behind those developed for human identification. For example, dinucleotide repeats are still used in nonhuman DNA testing e.g., , 91, 94,A few panels of autosomal SNPs have also been developed for individual identification in different animal species –117. TheForensic zoology often has to deal with degraded samples. In such cases, mtDNA may be the only source of genetic information that can be used. The high copy number of mtDNA in cells increases the probability of obtaining results from degraded/low-copy DNA samples such as hair, bones, and scat , 120. ImWhile the genetic identification of an individual or a species is not problematic in most situations, defining animal breeds or geographical populations has been considerably more difficult. Most breeds had a recent origin and are often defined by a few morphological features arbitrarily defined. For instance, cat breeds are defined by phenotypic characteristics  that are single-gene traits. Nevertheless, most cats can be assigned to their proper breed or population of origin using genetic data .A crucial aspect for some forensic cases  is the identification of the origin of the sample. This identification depends on the existence of genetic data in different regions , enough genetic differentiation among regions and the quality of the analytical method. The recent origin, intensive inbreeding, and genetic drift make difficult-to-use neutral genetic markers for rigorous identification of breed or populations. In such ambiguous cases, genetic tests should assess the genetic variants of the morphological traits that define the breed. However, our understanding of the genetics underlying such complex traits is still very limited, although significant progress is expected . A famouPlant evidence can provide crucial information for the reconstruction of forensically relevant events or in cases where the crime scene and autopsy reports are not compelling .Conventional taxonomic identification  has a reduced application since botanical forensic evidences are often very fragmented  limiting the use of dichotomous keys. However, molecular markers can be applied to identify samples, regardless of their state, morphology, and development phase. In this concern, in the last couple of decades, diverse molecular markers have been applied for the forensic identification of species and individuals  e.g., , 154. ImForensic botany presents numerous applications such as the identification of the origin of seized illegal drugs  is still a relatively recent scientific field , 169.MF aims to identify a target microorganism and its source. Although culture in selective growth media remains as the preferred standard for characterization of microbial agents at the resolution of genus/species level, complementary detection methods based on diverse molecular markers are increasingly applied . Indeed,Applications of MF involve diverse areas such as biocrimes, bioterrorism, frauds, outbreaks and transmission of pathogens, or accidental release of a biological agent or a toxin e.g., , 172. AdBacillus anthracis spores were sent through the U.S. Postal Service to several media offices in New York and Florida and to U.S. senators in Washington [Some biological agents can be used as weapons or threats. The best well-known example is the Amerithrax case (2001), where letters laden with shington , 174. InEscherichia coli O104:H4; 2011) [Under the scope of epidemiological investigation, MF also helps to determine whether a pathogen outbreak was natural or human-driven. Therefore, MF is intimately associated with epidemiological surveys, allowing studying and following disease outbreak dynamics, mainly concerning the identification of the agent or toxin, origin and natural reservoirs, genetic diversity and evolution, and possible transmission routes. Some well-known cases of the epidemiological studies are the swine-origin influenza A virus  , the Hai4; 2011) , the Cor4; 2011) , the avi4; 2011) , the Wes4; 2011) , the Mid4; 2011) , the Por4; 2011) , and the4; 2011) . Note thApplications of MF in biocrimes also include the tracking of sexually transmitted diseases and healthcare malpractice linked to the transmission of HIV e.g., , 53, 184The human microbiome is starting to be a focus of interest for identification purposes. The rational is to trace human microbiomes on our skin on the surfaces and objects we interact with the potential to supplement the use of human DNA for associating people with evidence and environments. The Human Microbiome Project has significantly improved the scientific knowledge in the field , 190. NoOne of the main constraints associated with the use of MF is the lack of standards and guidelines, although phylogenetic analyses have supported associations and have successfully been admitted as evidence in legal criminal cases . AnotherThe investigation of the biological composition of food products regarding the species, variety or cultivar, and geographic origin is of major forensic interest. Such investigations are relevant for guaranteeing consumer choices according to health concerns , dietary preferences , religious beliefs , and to detect fraudulent substitution of a given species by a similar one with lower economic value , 199. LaThe methodology used in food forensics is similar to that used in classical crime investigations, facing the same demands of dealing with potentially degraded DNA samples . Severali) identification of cultivars of basmati rice [ii) patent misappropriation of strawberry cultivars [iii) confirmation of Protected Designation of Origin (PDO), Protected Geographical Indication (PGI), or Traditional Speciality Guaranteed (TSG) in olive [iv) adulteration of traditional medicines [v) insufficient and erroneous food labelling, including the presence of some hidden allergens [These genetic markers have been applied to perform identification in a variety of food products such as olive oil e.g., , 210, grati rice , pome [2ati rice  and stonati rice , leguminati rice , 228, coati rice , and teaati rice ; ii) patultivars ; iii) coin olive  and grapin olive , 233 proedicines , 235 andedicines ; v) insullergens , 238 or llergens   or undercooked foods  [Over the last 2 decades, the prevalence of foodborne diseases has drastically increased, becoming a worldwide major public health concern. Foodborne diseases are often triggered by the consumption of food or water contaminated either by pathogens  or derived toxins. The most common pathogens responsible for foodborne disease outbreaks are poultry) . To overpoultry) .An area of growing interest is the detection of GMOs. The number of genetically modified plants has been growing in recent years despite the intense discussion about the benefits or damage that these organisms may have on humans and ecosystems. The detection of a GMO is carried out by targeting the genetic elements  that have been introduced artificially in the genome of the transgenic organism in order to improve a particular trait . A curathttp://www.ibol.org/) on forensics has been much less than desired and several difficulties have been raised on its power, limitations, and governance [Within the enormous variety of applications, methods, and sources of NHFG, the forensic use of NHGM is still limited and faces enormous difficulties due to diverse causes. Among them, and perhaps the most important, is the sheer amount of biodiversity and the current poor knowledge about it, with an impact not only on the species identification problem but also at the intraspecific level where, for most wildlife organisms, population genetics data are nonexistent or extremely poor. This makes relevant parameters difficult to estimate with acceptable accuracy and thus inhibits solid statistical evaluation of the evidence . In thisvernance , 257. Invernance  and ISAGvernance ). Given On the other hand, less error-burdened, cheaper, and faster MPS, together with progress in bioinformatics frameworks and computational resources, now allow the analysis of complex samples  with more accurate and reliable results e.g., , 186. WiIn this regard, it is clear that genetic analyses based on very large datasets  can provide high statistical confidence that can be useful for forensic cases . HoweverThe future of NHFG is dependent on the progress in removing current limitations Click here for additional data file.S2 Table(PDF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.


Signal transducers and activators of transcription 3 (STAT3) is known to participate in various cardiovascular signal transduction pathways, including those responsible for cardiac hypertrophy and cytoprotection. However, the role of STAT3 signaling in cardiomyocyte autophagy remains unclear. We tested the hypothesis that Angiotensin II (Ang II)-induced cardiomyocyte hypertrophy is effected, at least in part, through STAT3-mediated inhibition of cellular autophagy. In H9c2 cells, Ang II treatment resulted in STAT3 activation and cellular hypertrophy in a dose-dependent manner. Ang II enhanced autophagy, albeit without impacting AMPKα/mTOR signaling or cellular ADP/ATP ratio. Pharmacologic inhibition of STAT3 with WP1066 suppressed Ang II-induced myocyte hypertrophy and mRNA expression of hypertrophy-related genes ANP and β-MHC. These molecular events were recapitulated in cells with STAT3 knockdown. Genetic or pharmacologic inhibition of STAT3 significantly increased myocyte ADP/ATP ratio and enhanced autophagy through AMPKα/mTOR signaling. Pharmacologic activation and inhibition of AMPKα attenuated and exaggerated, respectively, the effects of Ang II on ANP and β-MHC gene expression, while concomitant inhibition of STAT3 accentuated the inhibition of hypertrophy. Together, these data indicate that novel nongenomic effects of STAT3 influence myocyte energy status and modulate AMPKα/mTOR signaling and autophagy to balance the transcriptional hypertrophic response to Ang II stimulation. These findings may have significant relevance for various cardiovascular pathological processes mediated by Ang II signaling. Pathological hypertrophy of cardiac muscle is one of the most common causes of heart failure in the United States . The halAngiotensin II (Ang II), an endogenous peptide, is a key component of the renin-angiotensin system (RAS) that plays important roles in fluid and electrolyte homeostasis, regulation of blood pressure and pathogenesis of heart failure. Inhibition of RAS has proven highly efficacious not only for blood pressure control, but also for the attenuation of postinfarct ventricular remodeling. Although complex signaling initiated by Ang II plays an important role in pathological cardiac hypertrophy , the molIn hypertrophic hearts, the source of energy in the form of ATP switches from fatty acid oxidation to increased glucose utilization with decrease in ATP levels. Activated by increased AMP/ATP or ADP/ATP ratio, AMPKα is an important mediator of metabolic adaptation that promotes ATP production. AMPKα activation results in inhibition of mammalian target of rapamycin (mTOR), which is important for both energy usage and inhibition of autophagy ,9. AlthoWe hypothesized that Ang II-induced myocyte hypertrophy results, at least in part, from STAT3-mediated suppression of myocyte autophagy. This hypothesis was tested in H9c2 cells using both pharmacologic and genetic inhibition of STAT3 as well as activation and inhibition of AMPKα/mTOR signaling axis. Our results indicate that although STAT3 activation is necessary for Ang II-induced myocyte hypertrophy, its inhibition tilts the balance toward autophagy promotion and amelioration of hypertrophy through activation of AMPKα.Media and reagents were acquired from the following sources: DMEM and Trypsin from Corning; FBS from Gibco; cell lysis buffer from Cell Signaling Technology; angiotensin II, WP1066, AICAR and compound C from EMD Millipore; (GFP)-fused LC3B (pEGFP-LC3) from Addgene; and ADP/ATP assay kit from Sigma. Antibodies were acquired from the following sources: rabbit anti-p-STAT3 (Tyr705), rabbit anti-p-STAT3 (Ser727), rabbit anti-STAT3, rabbit anti-p-mTOR (Ser2448), anti-mTOR, rabbit anti-p-AMPKα (T172), rabbit anti-AMPKα, rabbit anti-p-JAK2 (Tyr1007/1008) and rabbit anti-p-4E-BP1 (T37/46) from Cell Signaling; rabbit anti-LC3Bfrom Novus Biologicals; rabbit anti-β-Actin and rabbit anti-p62 from Santa Cruz Biotechnology.®CRL-1446™) and maintained in DMEM with 4.5g/L glucose supplemented with 10% (v/v) FBS. Cells were expanded in cultured to 80% confluence prior to passage and harvest for experiments following standard protocols. Before experiments, cells were plated in growth media for 24 h. Cells were exposed to Ang II alone or in combination with other agents , which were added to the culture media at various concentrations for 48 h.H9c2 cells were purchased from ATCC  and the resulting medium was used to infect H9c2 cells for 24 h. This was followed by puromycin selection for 7 d. Single colonies were then picked and amplified on puromycin selective media.HEK293 cells were transfected with piLenti-shRNA-STAT3 vector  or 5 μM of Compound C (specific AMPKα inhibitor) and co-treated with Ang II (5 μM) and/or WP1066 (4 μM). All experiments were performed in triplicate.H9c2 cells were fixed with 4% paraformaldehyde and stained with 1% crystal violet (Fisher Scientific). The cross-sectional surface area of at least 100 cells in each experimental group was measured by a blinded investigator in microscopic images acquired from at least 10 random fields using the Image-Pro Plus software (Media Cybernetics).Western immunoblotting analysis was performed using standard sodium dodecyl sulfate-polyacrylamide gel electrophoresis techniques to determine levels of p-mTOR rabbit polyclonal antibody , mTOR rabbit polyclonal antibody , p-STAT3 (Tyr705)rabbit monoclonal antibody , p-STAT3 (Ser727)rabbit polyclonal antibody , STAT3 rabbit monoclonal antibody , p-AMPKα (T172)rabbit monoclonal antibody , AMPKαrabbit polyclonal antibody , p-JAK2rabbit monoclonal antibody , p-4E-BP1 Rabbit monoclonal antibody  ,LC3-I and -II rabbit polyclonal Ab  , β-Actin goat polyclonal antibody , anti-p62 rabbit polyclonal antibody  (Santa Cruz Biotechnology). Incubation of infrared dye-labeled secondary antibodies (LI-COR Biotechnology) was performed in darkness for 1 h at room temperature. After extensive washing, membranes were scanned and analyzed on an Odyssey™ scanner (LI-COR Biotechnology).H9c2 cells were transfected with plasmid encoding EGFP-LC3 (EGFP-labeled microtubule-associated protein 1A/1B-light chain 3)  and seleH9c2 cells transfected with scramble control or STAT3 shRNA, H9c2 cells were fixed with 2.5% glutaraldehyde , dehydrated in a graded series of ethanol, and critical point dried in EMS 850 Critical Point Drier. Samples were mounted on Al mounts and sputter-coated with gold in the EMS 150 RES sputter coater and examined using Hitachi S-2700 scanning electron microscope.The ADP/ATP ratio was measured to evaluate the energy status of cultured H9c2 cells using a commercially available kit (Sigma). Briefly, after exposure to various agents for specified times, working reagent was added to culture medium to lyze cells and release ADP and ATP. ATP levels were measured in relative light units following interaction between ATP and substrate (D-luciferin) in presence of luciferase. After 10 min, ADP is converted to ATP and the total ATP levels are measured under the same wavelength of plate reader . The ADP/ATP ratio was calculated from these numbers following standard protocol.5’-ATGGGAAGCTGGTCATCAAC-3’ (forward) and 5’-GTGGTTCACACCCATCACAA-3’ (reverse); ANP, 5’-ATACAGTGCGGTGTCCAACA-3’ (forward) and 5’-AGCCCTCAGTTTG CTTTTCA-3’ (reverse); β-MHC, 5’-GGAGAAAGAGAAGAGCGAGTTC-3’ (forward) and 5’-GGCACATCTTCTCCAGGTTAG-3’ (reverse). PCR reactions were performed using the following conditions: denaturation at 95°C (5 min) and 45 cycles for GAPDH, ANP and β-MHC consisting of 3s (95°C), 30s (60°C), and 30s (72°C). All data were normalized to GAPDH and quantitative measurements were obtained using the ΔΔ-CT method.Following exposure to various agents in culture, cellular total RNA was isolated using TRIzol Reagent (Invitrogen). qRT-PCR was performed following standard methods using SYBR green (Applied Biosystems). The following primer sequences were used: GAPDH, t-test was used; for comparison among multiple groups (≥3groups) one-way ANOVA with Bonferroni’s post hoc test was used. A P value less than 0.05 was considered statistically significant. All statistical analyses were performed using the SPSS software version 22.0 .Data were expressed as mean ± SEM. For comparison between 2 groups, 2-tailed Student’s As shown in In order to block endogenous STAT3 expression, STAT3-specific shRNA was used to knock down STAT3 expression. After lentiviral transfection and puromycin selection, single-cell colonies were selected and amplified. Western immunoblotting showed marked reduction in STAT3 protein expression levels in cells transfected with STAT3 shRNA compared with scramble RNA-transfected cells . These cP<0.05,As shown in P<0.05 for both comparisons, Using quantitative real-time RT-PCR, we further examined the mRNA expression levels of ANP and β-MHC, molecular markers associated with cardiac hypertrophy. As shown in Next, we tested whether pharmacological inhibition of STAT3 would recapitulate the above findings with genetic inhibition of STAT3 expression. As shown in The measurement of ANP and β-MHC mRNA levels provided further evidence indicating that STAT3 regulates Ang II-induced hypertrophy. As shown in Next we examined whether Ang II induced autophagy in H9c2 cells and whether STAT3 activation played a role in this process. The subcellular localization of EGFP-LC3 protein in H9c2 cells treated with Ang II and/or STAT3 inhibitor WP1066 was analyzed. After stable expression with GFP-LC3, exposure to Ang II alone resulted in a modest increase in EGFP-LC3 puncta , a nonsiTo further elucidate the induction of autophagy, the expression of p62 and LC3-II, molecular markers of autophagy, were examined. The protein levels of p62 decreased significantly in H9c2 cells treated with STAT3 inhibitor WP1066, with further reduction with concomitant exposure to Ang II, indicating marked increase in autophagy . FurtherTo identify the molecular cascade by which STAT3 modulates Ang II-induced hypertrophic response, the AMPKα/mTOR pathway was examined as it has considerable importance in cardiac hypertrophy as well as autophagy. Both pharmacologic (WP1066) and genetic approaches (STAT3-KD cells) were used to inhibit STAT3 in H9c2 cells. As shown in In contrast, Western immunoblot analysis following STAT3 inhibition with WP1066 revealed significant increase in p-AMPKα levels and significant decrease in p-mTOR levels, consistent with autophagy induction. Densitometric analysis of the immunoreactive bands demonstrated that p-AMPKα levels increased by 1.78-, 3.09- and 4.08-fold with 2 μM, 4 μM, and 8 μM concentrations of WP1066, respectively, compared with control . In addiThe effects of Ang II and STAT3 on AMPKα and mTOR activation were further investigated by the use of the stable STAT3-KD cell line. Knocking down STAT3 significantly activated AMPKα and inhibited mTOR activation . To furtIn order to elucidate the molecular underpinnings of STAT3-induced AMPKα/mTOR signaling, the ADP/ATP levels were measured. Consistent with our observations with p-AMPKα and p-mTOR levels, STAT3 suppression with WP1066 treatment  or STAT3To further elucidate the role of STAT3/AMPKα/mTOR signaling pathway during Ang II-induced myocyte hypertrophy, a specific activator (AICAR) and inhibitor (Compound C) of AMPKα were used in co-treatment experiments with Ang II and WP1066. The mRNA expression of ANP and β-MHC was used as readouts. Exposure to Ang II alone induced a 8.9-fold increase in ANP and 3.8-fold increase in β-MHC mRNA expression levels . This Anin vitro are as follows: i) Ang II activates STAT3 and induces hypertrophy in H9c2 cells; ii) concomitantly, Ang II also enhances autophagy, however, without any significant impact on AMPKα/mTOR pathway and without altering cellular ADP/ATP ratio; iii) inhibition of STAT3 results in marked increase in autophagy and attenuation of myocyte hypertrophy; and iv) STAT3 inhibition increases myocyte ADP/ATP ratio and activates AMPKα/mTOR pathway. Together, these results identify STAT3 as a key determinant of myocyte hypertrophy vis-à-vis autophagy following Ang II stimulation, through modulation of AMPKα/mTOR signaling.Although Ang II is well known to induce cardiomyocyte hypertrophy, the details of molecular signaling remains incompletely understood. The primary findings of our studies using Ang II stimulation Signaling initiated by Ang II is complex and leads to numerous physiological and pathological outcomes depending on the context . AlthougP = 0.091) the number of EGFP-LC3 puncta, indicating increased myocyte autophagy concomitant with myocyte hypertrophy. However, and interestingly, Ang II stimulation alone did not affect the activation of AMPKα and mTOR, nor did it alter the ADP/ATP ratio significantly. These observations suggest that Ang II-induced increase in autophagy occurs largely independent of cellular energy status and possibly involves pathways other than the classical AMPKα/mTOR axis.Since cellular growth during hypertrophy requires accelerated protein synthesis, the attendant derangements of protein quality control may lead to additional pathologies. Indeed, cardiac hypertrophy may exacerbate ventricular functional decline by producing misfolded and toxic proteins . Consistin vivo [With regard to the above findings, it is important to consider that Ang II is known to activate a broad array of molecular pathways , often iin vivo . In H9c2in vivo , similarAnother key finding from this investigation demonstrates that inhibition of STAT3 is associated with induction of autophagy in Ang II-treated H9c2 cells. Pharmacologic blockade or genetic silencing of STAT3 in effect converted the myocyte response to Ang II from predominantly hypertrophic to largely autophagic. These observations indicate that STAT3 signaling is a critical determinant of the overall structural outcomes of Ang II stimulation in myocytes. These results are also consistent with the prior evidence that induction of autophagy prevents the development of cardiac hypertrophy or regresses established hypertrophy ,7. Enhanin vivo studies of hypothalamus with regard to food intake have suggested the possibility that AMPK might function downstream of or in a pathway parallel to STAT3 [Furthermore, our results demonstrate that inhibition of STAT3 results in enhanced autophagy through activation of AMPKα and consequent inhibition of mTOR signaling. mTOR is known to be an important regulator of protein synthesis, and mTOR inhibition is associated with induction of autophagy . The curto STAT3 . ImportaBoth pharmacologic and genetic inhibition of STAT3 resulted in significant increase in cellular ADP/ATP ratio, indicating myocyte ATP depletion. Since AMPKα is activated by reduction in cellular energy source, this alteration in cellular ATP levels may represent the underlying mechanism for the activation of AMPKα with STAT3 inhibition. In the myocardium, AMPKα activation boosts ATP production by stimulating both fatty acid oxidation and glycolysis, while slowing down ATP-consumption processes ,34. In aSTAT3 participates in numerous signaling pathways in the cardiovascular system culminating in transcriptional responses that are largely protective, reparative, and prosurvival. These genomic actions of STAT3 in the heart have been shown to upregulate antiapoptotic, antioxidant and proangiogenic genes ,19,22. IIn summary, our results unravel complex signaling details with regard to the role of STAT3 in Ang II-induced myocyte hypertrophy. While Ang II stimulation led to STAT3 activation, myocyte hypertrophy, and enhanced autophagy, inhibition of STAT3 triggered autophagy and attenuated hypertrophy through increased ADP/ATP ratio and activation of AMPKα. These results suggest interdependence of genomic and nongenomic actions of STAT3 with regard to myocyte protein and energy homeostasis and myocyte growth. Thus, modulation of STAT3/AMPKα/mTOR axis may prove useful for treatment of RAS-mediated cardiovascular pathologies.S1 FigP< 0.05 vs. Scramble control.Representative electron microscopic images of autophagolysosomes (arrows) in H9c2 cells treated with scramble peptide (A) and STAT3 shRNA . (C) Western immunoblots showing LC3-I and -II expression in H9c2 cells treated with scramble peptide or STAT3 shRNA. (D) Densitometric quantitation of LC3-I and -II levels. Data represent mean ± SEM (n = 3). *(TIF)Click here for additional data file.S2 FigP<0.05 vs. control.(A) Representative Western immunoblots showing p-AMPKα, AMPKα, p-mTOR, mTOR, p-4E-BP1, LC3-I, LC3-II, and β-actin protein expression in H9c2 cells treated with increasing concentration of WP1066 for 48 h. Densitometric quantitation of protein levels of autophagy marker LC3-II (B) and p-AMPKα and p-mTOR (C). Data represent mean ± SEM (n = 3). *(TIF)Click here for additional data file.


Gracilaria species for the exploitation of agarophytes and the development of the agar industry were conducted before the prevalence of molecular tools, resulting in the description of many species based solely on their morphology. Gracilaria firma and G. changii are among the commercially important agarophytes from the western Pacific; both feature branches with basal constrictions that taper toward acute apices. In this study, we contrasted the morpho-anatomical circumscriptions of the two traditionally described species with molecular data from samples that included representatives of G. changii collected from its type locality. Concerted molecular analyses using the rbcL and cox1 gene sequences, coupled with morphological observations of the collections from the western Pacific, revealed no inherent differences to support the treatment of the two entities as distinct taxa. We propose merging G. changii (a later synonym) into G. firma and recognize G. firma based on thallus branches with abrupt basal constrictions that gradually taper toward acute (or sometimes broken) apices, cystocarps consisting of small gonimoblast cells and inconspicuous multinucleate tubular nutritive cells issuing from gonimoblasts extending into the inner pericarp at the cystocarp floor, as well as deep spermatangial conceptacles of the verrucosa-type. The validation of specimens under different names as a single genetic species is useful to allow communication and knowledge transfer among groups from different fields. This study also revealed considerably low number of haplotypes and nucleotide diversity with apparent phylogeographic patterns for G. firma in the region. Populations from the Philippines and Taiwan were divergent from each other as well as from the populations from Malaysia, Thailand, Singapore and Vietnam. Establishment of baseline data on the genetic diversity of this commercially important agarophyte is relevant in the context of cultivation, as limited genetic diversity may jeopardize the potential for its genetic improvement over time.Many studies classifying  Gracilaria Greville by pre-treating the agarophyte with alkali before agar extraction, the food grade agar industry experienced rapid expansion that resulted in the uncontrolled harvesting of various Gracilaria species from natural stands  sensu emend. P.-K. Ng, S.-M. Lin, P.-E. Lim, A.Q. Hurtado, S.-M. Phang, Y.-Y. Yow et al.Holotype: AST 66–450 Type locality: Xindi, Xuwen County, Guangdong Province, ChinaProposed synonyms: Polycavernosa changii Xia & Abbott ; Hydropuntia changii (Xia & Abbott) Wynne ; Gracilaria changii (Xia & Abbott) Abbott, Zhang & Xia  .Distribution: Widely distributed in the tropical and subtropical regions in the western Pacific, including China [G. firma and G. changii in [G. changii in [G. firma in [G. firma and G. changii in [G. firma in [G. firma and G. changii in [G. changii in [G. firma in [G. verrucosa, G. fisheri and G. blodgettii in [G. confervoides in [G. verrucosa in [G. blodgettii in [tenuistipitata var. liui [G. firma in [ng China , 51–52, angii in , 16; G. angii in –33; and firma in ), the Phangii in ; G. firmfirma in ), Thailaangii in , 55; G. angii in ), Vietnafirma in ; G. verrettii in ), Japan ettii in , Taiwan oides in ; G. verrucosa in ; G. blodettii in ; G. tenuar. liui ; and G. firma in )S1 TableVoucher number in boldface indicates sample used for morphological analyses; asterisk after voucher number indicates sample collected from type locality.(DOCX)Click here for additional data file.