Allotoca diazi, Allotoca meeki and Allotoca catarinae occur in areas of biological, cultural and economic importance in central Mexico: Pátzcuaro basin, Zirahuén basin, and the Cupatitzio River, respectively. The species are closely related genetically and morphologically, and hypotheses have attempted to explain their systematics and biogeography. Mitochondrial DNA and microsatellite markers were used to investigate the evolutionary history of the complex. The species complex shows minimal genetic differentiation. The separation of A. diazi and A. meeki was dated to 400–7000 years ago, explained by geological and climate events. A bottleneck and reduction of genetic diversity in Allotoca diazi was detected, attributed to recent climate fluctuations and anthropogenic activity. The isolation of A. catarinae occurred ~1900 years ago. No geological events are documented in the area during this period, but the date is contemporary with P’urhépecha culture settlements. This founder effect represents the first evidence of fish species translocation by a pre-Hispanic culture of Mexico. The response of the complex to climate fluctuation, geological changes and human activity in the past and the future according to the ecological niches predictions indicates areas of vulnerability and important information for conservation. The new genetic information showed that the Allotoca diazi complex consist of two genetic groups with an incomplete lineage sorting pattern: Pátzcuaro and Zirahuén lakes, and an introduced population in the Cupatitzio River.The evolutionary history of Mexican ichthyofauna has been strongly linked to natural events, and the impact of pre-Hispanic cultures is little known. The live-bearing fish species  The diversity and distribution of freshwater ichthyofauna has been strongly linked to natural events, mainly geological process and historical climatic fluctuations. It is therefore that, taking into account the biogeographic history of each region in the world; the freshwater fish have been used as a model for the study of biogeography, speciation, paleohydrology and evolution. However, translocation and introduction of fish species is well documented in many ancient cultures having an important role in their current distribution, misunderstanding the evolutionary history of the species, In America, particularly in Mexico, the impact of pre-Hispanic cultures in the current distribution of the biota, is little known and mostly documented for birds –3. In faThe region in West-central Mexico, particularly between the Lerma-Santiago and Balsas rivers, is characterized by varied and rugged physiography, a product of intense tectonic and volcanic activity in the area since the Miocene . Also, tApproximately 100 fish species are in the West-central Mexico, 70% of which are endemic and one of the most representative endemic fish groups of this region is the subfamily Goodeinae . The groAllotoca is the most diverse within the Goodeinae, and is the subject of taxonomic controversy, with the need for taxonomic revision to validate species [A. diazi complex comprises three recognized species [Allotoca diazi [A. meeki [Allotoca catarinae [Allotoca diazi was extirpated from Pátzcuaro Lake and is currently restricted to the Chapultepec Spring, a tributary of Pátzcuaro Lake, while A. meeki was extirpated from Zirahuén Lake and currently restricted to Opopeo Spring, a tributary of the Zirahuén Lake [The genus  species ,10,15–17ca diazi , endemicA. meeki , endemicuén Lake  around 7uén Lake . The secuén Lake .Additionally, the application of modern biogeographic, genetic, systematic, and bioinformatics methodologies in the study of evolutionary processes within the Goodeinae has questioned recent connections of the Lerma and Cupatitzio Rivers with the Zirahuén, Pátzcuaro, Cuitzeo, and Zacapu basins ,12,22,23Allotoca diazi complex using two different molecular markers described for the Goodeinae Subfamily [b, Cytb gene) and a less conserved microsatellite nuclear markers. We obtained these molecular markers to explore the genetic differentiation at intraspecific and interspecific level applying phylogeographic and phylogenetic approaches. The results of the genetic study were analyzed based on geological, paleoclimatic and anthropogenic records from central Mexico to access how natural and artificial historical processes are involved in the evolutionary history of A. diazi complex. In addition, we used all the genetic information and ecological niche predictions for identify operational conservation units.In order to elucidate and analyze the two biogeographic hypotheses about the connections or disconnections between the Zirahuén, Pátzcuaro and Cupatitzio basins, and the possibility of a species translocation, we examined the evolutionary history of ubfamily ,25,26: aThis research was a part of the multidisciplinary work for restoration and conservation of the Cupatitzio River, and the complete work, field and laboratory protocols were approved by the Mexican Government, SEMARNAT  and CONACyT  (Permit No. FOMIX-MICH-2009-C02-115897). All individuals were anesthetized using Tricaine mesylate (MS-222) to minimize suffering.A. diazi or A. meeki from Pátzcuaro Lake and Zirahuén lacustrine basin, respectively. This is consistent with reports that these species were extirpated from these basins as a result of the introduction of exotic species [2 of tissue was used for DNA extraction. Digestion was performed with ATL QIAGEN Buffer and Proteinase K and purifying by BioSprint DNA Blood Kit QIAGEN according to the manufacturer’s instructions.One hundred and twenty specimens were collected from six populations throughout the distribution range of the species complex . Our sam species . Captureb gene, we used the primers GLuDG [2, and 1 unit of Taq DNA Polymerase. The PCR procedure consisted of 2 min at 94°C followed by 35 cycles of 45 s at the 94°C for DNA denaturation, 1 min at 46°C for primer alignment, 1.5 min at 72°C for synthesis, and a final extension of 5 min at 72°C. The PCR products were checked by electrophoresis in agarose gel 1.5% and submitted to Macrogen Inc. (Korea) for sequencing.Polymerase Chain Reaction (PCR) was performed to amplify the molecular markers. To amplify the Cytrs GLuDG  and H164rs GLuDG . The PCRA. diazi complex using 20 primers designed for Goodeinae [For microsatellites, we searched polymorphic loci of the oodeinae ,26. SeveD and Fu’s FS) to Cytb were implemented using Arlequin v3.5.1.3 [P-distances separating the three species were obtained (Dp) with Mega v5.2, and a bootstrapping process was implemented with 1000 repetitions. We conducted an analysis of geographic correspondence by network haplotype reconstruction using Network v4.6.1.0 [Manual alignment was implemented in Mega v5.2  and examv3.5.1.3 . The genv4.6.1.0 , applyinAllotoca zacapuesis, a sister group of the species complex [p with bootstrapping with 1000 replicates. Maximum likelihood reconstruction was conducted with RAxMLGUI v1.3 [Tree-building algorithms were used for phylogenetic reconstruction using  complex , as outgGUI v1.3 ,34, usinGUI v1.3  and 1000GUI v1.3  + invariGUI v1.3  and seleGUI v1.3 , using ta), effective number of alleles (Nae), null allele proportion, and linkage disequilibrium were obtained using Arlequin v3.5.1.3 and Genepop v4.1.2 [o) and expected (He) heterozygosity was determined, and the endogamy coefficient was obtained based on Wright’s fixation index (FIS) [Quality of the samples genotyped and polymorphism in microsatellite loci was analyzed using GeneMapper v 4. Allele number by loci and species (Np v4.1.2 . Observeex (FIS)  to calcuA. diazi complex was estimated with paired fixation indices (FST). Significance was assessed by AMOVA with 20 000 permutations implemented in the software Arlequin v 3.5.1.3 using multiple hypothetical arrangements based on phylogenetic and biogeographic information. A Bonferroni correction [P-value obtained in the paired test, HW analysis and linkage disequilibrium estimation. Bayesian clustering was conducted with the software Structure v2.3.3 [et al. 2005 [The genetic structure level among the three species of the rrection  was apple v2.3.3 . We perfe v2.3.3  with 10 al. 2005 , ΔK was ST values to assess deviation of equilibrium between RST and FST values. RST values greater than FST imply influence of the size differences of alleles on interpopulation genetic differentiation; thus we can assume that loci mutate faster by multi-step changes [ST estimation was performed with SPAGeDi v1.3a [ST and FST values.To determine whether the difference in allele size contributed to genetic structure and if each locus is consistent with a strict Stepwise Mutation model, we estimated the R changes –47. The Di v1.3a  without A. diazi complex were calculated using three analyses in IMa2 software [This approach allows determination of gene flow before and after isolation within a population and assesses phylogenetic relationship scenarios in recent isolation events within closely related populations or species. Divergence times of populations of members of the software , which hb estimated for teleosts of 0.76–2.2%/million years (4.33*10-6-1.25*10-5subst/locus/generation) [-4-1.5*10–4 locus/gamete/generation, estimation obtained for the Zebra fish Danio rerio [Pairwise analysis was conducted to find the confidence limits of the time to divergence among the three species, combining the molecular markers to substantiate and support the exchange rates within Markov chains. A molecular clock was calibrated using the mutation rate for Cyteration) –52 and teration) . The mutio rerio  and congio rerio ,56. The b marker. Chains were run for 70 000 000 steps sampling each 1000 and 10% was discarded as burn-in under HKY substitution model, a relaxed molecular clock calibrated with the information of Cytb mentioned above. All operators were optimized automatically and the results were analyses and visualized using Tracer 1.5 [The demographic history of the members of the species complex was obtained constructing Bayesian skyline plots (BSP) using Beast 1.8.0  for Cytbacer 1.5 . EffectiA test to identify recent bottlenecks was conducted using Bottleneck v1.2.0.2  with thrhttp://www.worldclim.org/bioclim), bootstrapping of 100 replicates to obtain 95% confidence intervals, regularization multiplier (RM) values from 0.01 to 10, evaluating area under the ROC curve (AUC) values and using the omission rates to discard identical AUC of different RM values. Collinear climatic variables were discarded  were obtained for the sampled populations of the three species (GenBank accession numbers: KJ776467-KJ776575) , followed by A. meeki with three haplotypes (n = 22), of their respective and unique populations  . Overallulations . No signing them . SpatialA. diazi complex, we obtained a total of 96 genotypes. The average number of alleles per locus was 10.4±3.5; the number of genetic variants contributing to heterozygosity in each locus of the species complex was 5.8±2.2; and the proportion of null alleles was 0.06±0.03 (P > 0.05). Significant deviation from linkage equilibrium was not found at most loci, LD = 0.06±0.03 (P > 0.05). Significant linkage disequilibrium was observed in two loci of A. diazi (A. diazi and A. catarinae (P < 0.05) (For the 7 microsatellites loci amplified in populations of the A. diazi . Signifi < 0.05) .ST values for all loci were not significantly different from expected (Ho:RST = pRST) or from the FST values (A. diazi complex. Low but significant genetic differentiation was detected (FST = 0.113 P < 0.05) via AMOVA analysis (A. diazi+A.meeki—A. meeki) with a high proportion of individuals assigned to other groups (0.3–0.7) , while genetic differentiation of A. catarinae from A. diazi and A. meeki was 0.133 and 0.109 (P<0.05), respectively  . Howeverectively . The numA. catarinae + A. diazi/A.meeki) with high proportion of individuals assigned to another group (0.3±0.7)  Figs .A. diazi complex divergence from its common ancestor A. zacapuensis was estimated to have occurred in the past million years. The isolation event of A. diazi from A. meeki occurred in the past 7000–400 years (HPD≥95%) with the peak of probability estimated as 1700 years ago   , and recAllotoca diazi and A. meeki. The species Allotoca catarinae was excluded of the analysis because could be result of translocation. The predictions were performed taking into account that the gene flow between species will be null.We included in the analysis the species considered as natural members of the species complex: Allotoca diazi and A. meeki is bigger than their real current distribution. In the case of the ecological niche models for two periods in the future, high AUC values for were obtained for both species  [The isolation of the z (1972)  with resz (1972) .Allotoca diazi complex consists of a genetic group with low genetic structure and its isolation within the members was more recent, during the Holocene. The isolation of A. diazi, A. meeki, and A. catarinae is reflected in the low genetic differences, non-monophyletic patterns, shared haplotypes, and genetic groups assignment, and we considered that the species complex consist in an incomplete lineage sorting pattern [b are smaller than reported to sister Goodeinae species (1.7–11%) [b gene is attributed to genetic drift and mutation . Low genetic variation was detected as a genetic pool arrangement through AMOVA analysis. However, BI detected this minimum mark of genetic structure as two genetic groups. The results obtained are supported by the minimal morphological differences previously reported [The  pattern –75. The 1.7–11%)  and sist1.7–11%) –79. The reported ,17,80–84Chirostoma estor, Chirostoma pátzcuaro (Atherinopsidae), Algansea lacustris (Cypronidae), Alloophorus robustus, Skiffia lermae, Allotoca dugesii, and Goodea atripinnis (Goodeidae) [The hypothesis of connection and disconnection of Pátzcuaro and Zirahuén lakes during the last 700 000 years  is suppoodeidae) ; and genodeidae) ,89.A. diazi and A. meeki and the high proportion of individuals assigned to other species (0.5 to 0.7) by microsatellite data, suggest recent gene flow. Microsatellite information showed low level of genetic differentiation (FST = 0.084). Low genetic diversity was estimated for A. diazi in the Cytb gene  and even lower for A. meeki . The migration rate between the two species is not significant any time.Shared haplotypes of A. diazi from A. meeki occurred in the past 400–7000 years, which is consistent with geological, climatic, and anthropogenic events involving Pátzcuaro and Zirahuén lakes. Tecto-volcanic events giving rise to the separation of Pátzcuaro and Zirahuén lakes began 8000 years ago with the formation and activity of the La Tasa volcano southwest of Lake Pátzcuaro and climate fluctuations causing decline in water level and drying of streams [A. diazi and A. meeki. These demographic changes were reflected in genetic diversity including the positive DT value, which was found to be low in A. diazi, and associated with recent bottlenecks that promoted inbreeding (FIS = 0.141), reduced heterozygosity, and the loss of allelic richness. In contrast, a bottleneck identified in A. meeki is attributed to the rapid loss of allelic diversity; however, the RST value and the BSP analysis (in the HPD range) indicates that the bottleneck detected by IAM were overestimated. The population dynamics of A. meeki can be explained by the variations in nucleotide diversity and proportion of segregating sites (π < Θs), which is interpreted as a recent population reduction followed by expansion, as demonstrated by the estimated value of DT = -0.86 and non-significant positive values of Fu’s FS estimates [We estimated that the isolation of  streams ,90–93. F streams ,94–99, a streams  could bestimates –103.A. diazi from A. meeki during the Holocene, not enough time to diverge and showing an incomplete lineages sorting.Based in our results, we conclude that natural processes, such as geologic events and climate fluctuations, are the main factors responsible for the isolation of A. catarinae from A. diazi and A. meeki was calculated to be within the past 8000–700 years and 8000–800 years, respectively. The peak of probability for isolation of A. caratinae was 1900 years ago. The low number of native species fish shared [Divergence times and isolation of h shared ,88, and h shared ,105, or h shared ,107, andh shared ,93,108, h shared .b haplotype in populations, along with a loss of genetic diversity. The lack of shared haplotypes between Cupatitzio populations and Pátzcuaro-Zirahuén populations cloud this interpretation, but the specimens sampled in the Pátzcuaro basin were from a small spring 10 km east of the lake and represent the only known local population of the species, which has been extirpated form the main basin [2 approximately) were isolated from the main basin, and the genetic drift fixed rare haplotypes. In addition, null genetic diversity was found in A. catarinae, associated with recent bottlenecks. Allotoca catarinae is the only one in which bottlenecks were detected in several models of evolution, and also supported with the BSP analysis. These bottlenecks drive in all cases to low heterozygosity (FIS = 0.151).We propose that the existence of this taxon in the Cupatitzio River could be the result of a pre-Hispanic introduction, causing a founder effect, which resulted in rapid fixation of a single Cytin basin . We specRelevant anthropogenic events in the region of Lakes Pátzcuaro and Zirahuén and the Cupatitzio River are congruent with our estimated divergence times. There is evidence that fishing was a major activity among the P'urhépecha. Fish, specifically goodeines and atherinopsids, had importance as food, even today goodeids are important part of the food supply for natives in the region, as well as culturally, in all socioeconomic strata, especially for Cazonci, the supreme ruler of the P’urhépecha state. Offerings of fish found in tombs at the archaeological site Uricho southwest of Lake Pátzcuaro, dating to the Late and Epi-Classic periods (~500–900 A.D.) ,8.A.diazi translocation may have occurred. We conclude that the biogeographic history of A. catarinae was possibly determined by a founder effect mediated by pre-Hispanics in the past 3000 years, and taking into account this inference we cannot rule out species translocations in Pátzcuaro and Zirahuén lakes.The “islander”, “Chichimeca” and the “Coringuaro”, considered as first human groups to settle in the region of Uruapan and south of Michoacán, probably had influence on the management of aquatic resources and translocation of species may have occurred ,110. HowA. diazi and A. meeki, were not only the founder effect (A. meeki) and climate fluctuations (A. diazi), but also anthropogenic activity [The factors involved in the genetic diversity and demographic history of activity –99.A. diazi from the main Pátzcuaro basin and A. meeki from Zirahuén Lake. Neither Pátzcuaro nor Zirahuén are designated protected areas. In addition, the results of the future ecological niche modeling for A. diazi and A. meeki show a reduction of potential habitats questioning the survival of the species, taking into account its genetic vulnerability to climate fluctuations and human activity, which indicates the necessity of the implementation of effective conservation and management strategies of this micro-endemic species with a long history of economic and cultural importance to the lacustrine settlements.The two OCU’s proposals (Pátzcuaro and Zirahuén lakes) are areas with a high degree of alteration, including eutrophication, introduction of exotic species, drought, and overfishing ,113–115,S1 Fig(EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 FigModified from .(TIF)Click here for additional data file.S4 FigDotted lines represent the location of the upper bound (Max), the mean ((EPS)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table-lnL = log likelihood, Ti = Transitions, Tv = Transversions.(DOC)Click here for additional data file.S4 TableRM = Regularization multiplier value selected according to the of AUC value.(DOC)Click here for additional data file.S5 Tablea = mean of alleles, Nae = mean number of effective alleles, Pan = null allele proportion, LD = linkage disequilibrium test with significant value after Bonferroni correction (* = P > 0.05).n = sample size, N(DOC)Click here for additional data file.S6 Tableo RST = pRST, ns = non-significant, *significant (P < 0.05) after Bonferroni correction, RST = observed values, pRST = expected values, IC = 95% confidence interval.H(DOC)Click here for additional data file.S7 Table(DOC)Click here for additional data file.


The transcription factor STAT3 regulates genes that control critical cellular processes such as proliferation, survival, pluripotency, and motility. Thus, under physiological conditions, the transcriptional function of STAT3 is tightly regulated as one part of a complex signaling matrix. When these processes are subverted through mutation or epigenetic events, STAT3 becomes highly active and drives elevated expression of genes underlying these phenotypes, leading to malignant cellular behavior. However, even in the presence of activated STAT3, other cellular modulators can have a major impact on the biological properties of a cancer cell, which is reflected in the clinical behavior of a tumor. Recent evidence has suggested that two such key modulators are the activation status of other STAT family members, particularly STAT5, and the expression of STAT3-regulated genes that are part of negative feedback circuits, including microRNAs such as miR-146b. With attention to these newly emerging areas, we will gain greater insight into the consequence of STAT3 activation in the biology of human cancers. In addition, understanding these subtleties of STAT3 signaling in cancer pathogenesis will allow the development of more rational molecular approaches to cancer therapy. Signal transducers and activators of transcription (STATs) are a family of transcription factors that regulate a range of important cellular functions . Under bSTAT3, which was initially identified as a mediator of the inflammation-associated acute phase response, has been of particular interest in cancer biology ,5 Figur. The tarSubsequently, a number of well-performed studies have suggested that the activation of STAT3 might not be uniformly associated with increased malignancy, and that in some circumstances STAT3 might act as a functional tumor suppressor or its activation may be associated with an improved prognosis ,8,9,10. STAT3 is activated commonly  in a wide spectrum of human cancers, including cancers of the breast, prostate, ovary, and pancreas ,12,13. TAlthough STAT3 is activated inappropriately in a majority of breast cancers, an understanding of the physiological roles of STAT transcription factor in mammary epithelium actually began with STAT5. Originally identified as “mammary gland factor”, STAT5 mediates the effect of prolactin in stimulating milk production in mammary epithelium ,15,16. TA number of studies have examined the activation state of STAT3 in primary breast cancers. Using a variety of approaches, including biochemical techniques such as immunoblots and electrophoretic mobility shift assays (EMSAs) as well as immunohistochemistry (IHC), a number of investigators have found evidence of constitutive activation of STAT3 in breast cancer cells but not in normal mammary tissue . SimilarThese findings then raised the question of whether an individual breast cancer could display activation of both STAT3 and STAT5, or whether these were mutually exclusive events. To address this question, tissue microarrays were stained with antibodies that could recognize the activated, tyrosine-phosphorylated form of either STAT3 or STAT5 . The firA number of investigators had found that a majority of primary breast cancers display STAT3 activation. However, when analysis of both STAT3 and STAT5 activation was performed on the same tumors, it became clear that approximately 40% of the tumors with STAT3 activation also showed activation of STAT5 . This raOne potential explanation for this finding is that the activation of STAT5 along with STAT3 reflects a different cell of origin from tumors with activation of STAT3 alone, and this may underlie the differing biology and outcomes. To address this possibility, a cell line with constitutive activation of STAT3, MDA-MB-468, was transduced with an expression vector for a constitutively activated form of STAT5 or, as a control, the empty vector. The cells with activation of both STAT3 and STAT5 displayed changes in gene expression that paralleled the differences seen in the primary cancers displaying co-activation of STAT3 and STAT5 . FurtherIn vitro studies have shown that STAT3 and STAT5 have essentially identical preferences for binding nine base pair DNA sequences of the form TTCNNNGAA. However, STAT3 and STAT5 mediate disparate biological effects and appear to bind distinct regions in intact chromatin. Gene expression analyses have revealed that some genes are oppositely regulated by STAT3 and STAT5, including the transcriptional repressor BCL6, which can be oncogenic in B cell lymphomas. In breast cancer cells, STAT3 leads to increased expression of BCL6 and STAT5 mediates repression of this gene [his gene . When bohis gene . Furtherhis gene . This suversus STAT5 alone [Another notable finding from these studies concerns the relative proportion of tumors displaying activation of STAT3 alone T5 alone . While 4Taken together, these findings indicate that while activated STAT3 can promote malignant cell behavior in breast cancer, important aspects of the biology of tumors characterized by activated STAT3 can be modulated by the presence of activated STAT5. While similar effects have not yet been found in other cancer types, these findings suggest that it is important to understand the status of STAT5 (and perhaps other modifying transcription factors) before one can understand the functional effects of activated STAT3 in a cancer cell.Since the genes regulated by STAT3 control cellular processes such as proliferation, survival, self-renewal, and invasion, it is not surprising that the activation of STAT3 is tightly regulated by several layers of negative feedback regulation. Thus, under physiological conditions, cytokine-induced STAT3 activation reaches maximal levels within 15 to 30 min, and then returns to basal levels within one to two hours. For the constitutive activation of STAT3 to occur, as is seen in cancer, there needs to be activation of some upstream process driving the activity of a kinase that can phosphorylate Tyr-705 of STAT3. However, there likely also needs to be a loss of activity of one or more negative regulators such as phosphatases or SOCS family members, which also occurs commonly in cancer ,25,26,27One group of mediators of negative feedback regulation are microRNAs (miRNAs), which can bind to sequences in mRNA and block protein translation and or/promote the degradation of target mRNAs . AlthougMiR-146b can downregulate components of the NF-κB signaling cascade, including the upstream mediators TRAF6 and IRAK1 , and intin vivo, primary breast cancers were analyzed for expression of this microRNA. Increased expression of miR-146b was found to be associated with longer survival in patients with tumors that do not express the estrogen receptor, the subtype most likely to display STAT3 activation. This finding suggests that miR-146b may well function in vivo as an endogenous negative feedback regulator with tumor suppressive properties. Thus, once again, the activation state of STAT3 reveals only part of the biology of a tumor. The expression of a key target of STAT3, miR-146b, which can be repressed by DNA methylation, is an important factor in understanding the biology and prognosis of a tumor.To determine whether miR-146b acts as a tumor suppressor It is natural to ask whether activated STAT3 is a good or bad prognostic marker in a tumor. However, as we gain more insight into the complexity of the signaling matrix of a cancer cell, it is clear that the answer to that question depends on the status of other signaling and regulatory molecules, including other STATs (such as STAT5) or microRNAs (such as miR-146b). Currently, this ambiguity makes it difficult to answer clinically relevant questions about a patient’s tumor. However, understanding these subtleties reflects our progress in dissecting the signaling events underlying cancer pathogenesis driven by activated STAT3. It also indicates that we are getting closer to appreciating the key biological modulators of STAT3 in a cell, and being able to use this information to design rational molecular therapeutic strategies.


Due to human variations in genetic susceptibility, vaccination often triggers adverse events in a small population of vaccinees. Based on our previous work on ontological modeling of genetic susceptibility to disease, we developed an Ontology of Genetic Susceptibility Factors (OGSF), a biomedical ontology in the domain of genetic susceptibility and genetic susceptibility factors. The OGSF framework was then applied in the area of vaccine adverse events (VAEs).e.g., SNP and gene). A general OGSF design pattern was developed to represent genetic susceptibility to VAE and associated genetic susceptibility factors using experimental results in genetic association studies. To test and validate the design pattern, two case studies were populated in OGSF. In the first case study, human gene allele DBR*15:01 is susceptible to influenza vaccine Pandemrix-induced Multiple Sclerosis. The second case study reports genetic susceptibility polymorphisms associated with systemic smallpox VAEs. After the data of the Case Study 2 were represented using OGSF-based axioms, SPARQL was successfully developed to retrieve the susceptibility factors stored in the populated OGSF. A network of data from the Case Study 2 was constructed by using ontology terms and individuals as nodes and ontology relations as edges. Different social network analysis (SNA) methods were then applied to verify core OGSF terms. Interestingly, a SNA hub analysis verified all susceptibility alleles of SNPs and a SNA closeness analysis verified the susceptibility genes in Case Study 2. These results validated the proper OGSF structure identified different ontology aspects with SNA methods.OGSF aligns with the Basic Formal Ontology (BFO). OGSF defines ‘genetic susceptibility’ as a subclass of BFO:disposition and has a material basis ‘genetic susceptibility factor’. The ‘genetic susceptibility to pathological bodily process’ is a subclasses of ‘genetic susceptibility’. A VAE is a type of pathological bodily process. OGSF represents different types of genetic susceptibility factors including various susceptibility alleles (OGSF provides a verified and robust framework for representing various genetic susceptibility types and genetic susceptibility factors annotated from experimental VAE genetic association studies. The RDF/OWL formulated ontology data can be queried using SPARQL and analyzed using centrality-based network analysis methods. These various genetic variant entities are named ‘genetic susceptibility factors’ by the authors. The allele that confers increased susceptibility may be inherited; whereas the disease itself will not. The single locus genotype is usually insufficient to cause a disease. A disease often appears when impaired expressions of alleles at other gene loci and/or environmental factors co-exist [Various kinds of genetic variations bear susceptibilities, co-exist . Geneticco-exist . Furtherco-exist ,12. Manyco-exist . The sope.g., VAEs) has been termed vaccinomics [The general methodology to identify the genetic susceptibility to complex disease is a combination of linkage and association studies in biological experimental science. At first, the family-based studies identify a linkage region contains several mega bases of DNA. To narrow down such a region to a susceptible gene (or genes), population-based case–control studies identify variants in linkage disequilibrium with the susceptibility locus, which lead to define the genomic region responsible for the original linkage signal . Althougcinomics . The notcinomics . Since tet al. have previously developed an Ontology of Genetic Susceptibility Factors to Diabetes Mellitus (OGSF-DM) intended to provide a framework for genetic susceptibility to diseases [TCF7L2 gene and its susceptibility to Type 2 Diabetes (T2D) as an example, OGSF-DM formalizes the basic definitions of ‘genetic susceptibility’ and ‘genetic susceptibility factor’. The ontology OGSF-DM is a virtual ontology composed of three ontologies: the Ontology of Genetic Disease Investigation (OGDI), which imports other two ontologies: the Ontology of Glucose Metabolism Disorders (OGMD) and the Ontology of Geographical Regions (OGR). The previous study found out that essential variables impacting genetic susceptibility to diseases include: genetic polymorphism, the population and geographical location, the disease entities, and related statistical values  [A formal ontology is a set of computer- and human- interpretable terms and relations that represent entities in a specific domain and how these entities relate to each other. Ontological terms are expressed in formal logic to support automated reasoning. Lin diseases . By usinp-value) .The Open Biological and Biomedical Ontologies (OBO) Foundry community  has recei.e., diabetes mellitus) [While the OGSF-DM modeled the genetic susceptibility to a disease (ellitus) , geneticellitus) -21. The ellitus) . TherefoIn this paper, we introduce our development of a new version of genetic susceptibility-focused ontology: the Ontology of Genetic Susceptibility Factors (OGSF) by using BFO 2.0 as its upper ontology. To illustrate the ontology and verify our ontology design patterns, two vaccine adverse event-related genetic susceptibility case studies were specifically analyzed. Our studies demonstrate that the OGSF successfully provides an ontological framework for systematically representing genetic susceptibility, genetic susceptibility factors, associated entities and relations.Italics are used to indicate the axioms or properties defined in the ontology.In what follows, single quotes are used to refer to a specific term within OGSF where appropriate. The numerical ID following the prefix of ontology is given after the term is mentioned, which gives the indication of the term’s resource. The development of OGSF follows the OBO Foundry principles, including openness, collaboration, and use of a common shared syntax . To aligi.e., they are dispositions to acquire other dispositions. The realization of such a predisposition consists in processes which change the physical makeup of its bearer in such a way that parts of this bearer serve as the material basis for a disease [In addition to the reuse of existing ontology terms, over 60 OGSF-specific class and property terms exist. The two OGSF core terms are: ‘genetic susceptibility’ and ‘genetic susceptibility factor’. The OGSF term ‘genetic susceptibility’ (OGSF_0000000) is a subclass of ‘BFO:disposition’ (BFO_0000016). The alternative term for ‘genetic susceptibility’ is ‘genetic predisposition’. In BFO 2.0, the genetic and other risk factors for specific diseases are considered as predispositions,  disease . Since tAnother OGSF core term ‘genetic susceptibility factor’ (OGSF_0000004) is a subclass of ‘material entity’ (BFO_0000040). Any allele, gene, genotype, or haplotype may be a genetic susceptibility factor if a genetic association study supports the association between any of those entities and a phenotype. The relation ‘material basis of at some time’ (BFO_0000127) is formalized in BFO 2.0 to represent the relation between a material entity and a disposition . BFO 2.0OGSF represents different types of genetic susceptibility factors, including haplotypes, genes, single nucleotide polymorphisms (SNPs), and alleles. A haplotype is a combination of DNA sequences at adjacent locations (loci) on a chromosome that can be inherited together from a single parent. A haplotype can describe a pair of genes on one chromosome or all genes on a chromosome from a parent. A haplotype can also refer to an inherited cluster of SNPs that are variations at single positions in the DNA sequence among individuals. An allele is an alternative form of the same gene or other genetic material that occupies a specific location on a chromosome. The Ontology for Genetic Interval (OGI)  defines http://www.ontobee.org/ontostat.php?ontology=OGSF. In our VAE susceptibility use case studies, we have also generated many OGSF instances as introduced later in this paper.In total, OGSF contains over 600 class and property ontology terms as shown on e.g., susceptibility alleles) to vaccine adverse event outcomes, the results of these studies cannot be automatically processed by computers. Our OGSF presentation aims to create a machine-interpretable ontological representation of these studies in order to analyze the results across studies and search for possible causal associations.As defined in the Vaccine Adverse Event Reporting System (VAERS) and Ontology for Adverse Event (OAE), a vaccine adverse event is an adverse event following vaccination and does not necessarily assume a causal association ,20,21. HFigure et al. to simplify the complex axioms involving nested class expressions to make it ‘triple-friendly’ for complex OWL ontologies [The direct linkage from susceptibility-related terms to VAE terms is often required in our OGSF modeling. For example, in OGSF, we need to link ‘human vaccinee carrying susceptibility allele for adverse event’ (OGSF_0000029) to a vaccine. An object property term  reflecting such linkage is not available in existing ontologies. However, VO defines a shortcut relation ‘vaccine immunization for host’, which relates a vaccine with a vaccinee . The strtologies . In our The term ‘genetic association investigation’ (OGSF_0000016) is defined as: an investigation that aims to test whether single-locus alleles or genotype frequencies  differ between two groups of individuals . Different types of those studies exist. For example, a ‘case control genetic association study’ (OGSF_0000017) is a genetic association study that contains two types of human study subject groups: ‘case group’ and ‘control group’. The control group provides a background control in order to properly assess the results identified from the case group study. In contrast, a ‘case-only genetic association study’ (OGSF_0000036) includes a case group and does not have a control group to compare. The results obtained from a case-only genetic association study provide sufficient evidence to detect an association . HoweverA reported genetic susceptibility study typically includes a conclusion of the association between a genetic factor and a disease (or pathologic bodily process) under specific conditions. Such conclusion is required to be represented ontologically. To represent the results from individual genetic association studies as reported in different papers, we have added an OGSF term ‘textual conclusion of genetic susceptibility’ to represent the textual conclusion of a genetic susceptibility study. Ontologically, a ‘textual conclusion of genetic susceptibility’ is asserted as a ‘specified output of’ a ‘genetic association investigation’. There are three types of ‘textual conclusion of genetic susceptibility’: ‘positive textual conclusion of genetic susceptibility’ (OGSF_0000031), ‘negative textual conclusion of genetic susceptibility’ (OGSF_0000032) and ‘neutral textual conclusion of genetic susceptibility’ (OGSF_0000033). Using the vaccine adverse event example, a ‘positive textual conclusion of genetic susceptibility’ means that a positive conclusion is drawn based on a significant statistical association of a genetic factor and a vaccine adverse event as identified in a published paper. A ‘negative textual conclusion of genetic susceptibility’ denies such a possible association between a genetic factor and an adverse event as declared in a published paper. Sometimes, depending on the data, an investigator might not be able to draw a definitive positive or negative conclusion on a genetic susceptibility association. This situation is captured using ‘neutral textual conclusion of genetic susceptibility’. In addition, OGSF also provides several datatype properties, such as ‘hasOddsRatio’ and ‘hasPvalue’, to allow the representation of digital data for statistical evaluation of the textual conclusion of genetic susceptibility  to validate the modeling, 2) to test possible applications of the ontology. Below we represent two case studies reported from peer-reviewed journal articles using the OGSF framework.et al. reported the occurrence of severe Multiple Sclerosis (MS) in a previously healthy young male in association with the vaccination of Pandemrix [Pandemrix is an influenza pandemics vaccine that is developed by the company GlaxoSmithKline. The vaccine Pandemrix is represented in the Vaccine Ontology (VO) with the VO ID: VO_0000410. Vrethem andemrix . In thisandemrix ,36.This genetic susceptibility case was represented in Figure • ‘DRB1*15:01’ is subclass of ‘allele of gene’.• ‘DRB1*15:01’ is subclass of .In addition to the above basic logical definitions, genetic susceptibility related to ‘DBR1*15:01’ can be identified based on different studies. Case Study 1 is such a study, which is represented as ‘genetic association study_1’ Figure . This st• ‘DRB1*15:01’ is subclass of  some Pandemrix)))• ‘DRB1*15:01’ is subclass of • ‘DRB1*15:01’ is subclass of ‘susceptibility allele’This case study indicates that OGSF provides necessary elements to represent genetic susceptibility and genetic susceptibility factors associated with vaccine adverse events.et al. reported that genetic polymorphisms in several genes encoding important immune factors, including enzyme methylenetetrahydrofolate reductase (MTHFR), an immunological transcription factor (IRF1), and interleukin-4 (IL-4), were associated with adverse events after smallpox vaccination [e.g., the A allele of rs839 SNP in the gene irf1), their associated genes, and the Odds Ratio and p-value from two clinical trials [Reif cination . In thisactor IRF, and intl trials .The OGSF design pattern was applied to represent the information from these clinical trial studies Figure . This fiSince OGSF provides a framework to ontologically represent the complex data structure (including different variables and relations among these variables), the representation of the knowledge and data using OGSF supports computer-assisted data integration and reasoning. Such data sets can be queried efficiently using SPARQL as described below.The SPARQL Protocol and RDF Query Language (SPARQL) is the query language and protocol for the Resource Description Framework (RDF) data. RDF decomposes any knowledge into triples. Each RDF triple contains three components: subject, predicate, and object . OGSF isFrom the OGSF supported knowledge system, our questions are focused on: 1) the list of susceptibility factors to a certain disease or pathological bodily process; 2) the evidences, either supportive or negative, supporting those susceptibilities. Using Case Study 2 as an example, we designed a SPARQL query to identify the genetic susceptibility factors to systemic adverse event of smallpox vaccination and related statistical evidences. The SPARQL script developed to query against the OGSF ontology is provided as follows:http://www.ontobee.org/sparql/index.php) in Ontobee [This query was executed in the SPARQL plugin embedded with Protégé 4.3, build 304, and it could also be performed using the SPARQL endpoint  to q . Therefore, the methods used for social network analyses may be potentially used for identifying key ontology terms as hubs or clusters of ontology terms [After an ontology is generated, it is often valuable but challenging to determine which ontology terms are more central and carry more information than other terms in the ontology. As an ontology defines terms and relations (object properties) between terms, an ontology can be viewed a social network. Specifically, the terms and relations of an ontology can be viewed as a directed hyperlinked graph G =  with nodes v∈V and edges e∈E, where the nodes correspond to the terms or entities in an ontology, and a directed edge  ∈ E indicates the relation that links from gy terms . In thisSocial Network Analysis (SNA) is the sum of the tools and methodologies of graph theory to analyze and thus describe structures of social networks . Many SNThe first method of our network analysis was based on the calculation of the degree centrality Figure A. The degenetic susceptibility’ and ‘vaccine adverse event’, the variants link to ‘genes’. It is interesting that all the three genes were identified together in this study.Secondly, we used the closeness centrality for network exploration Figure B. The clThe third network analysis was based on the calculation of the authority and hub scores ,48 Figu. The terIn summary, different network characteristics calculations reflect different dimensions of the ontology knowledge. The closeness and authority centrality analyses verified the core terms of the OGSF dataset in case study 2 are ‘systemic adverse event of smallpox vaccination’ and ‘genetic susceptibility of vaccine adverse event’. Interestingly, the hub analysis identified all the alleles of SNPs, and the closeness analysis detected all three hidden genes that are related to those alleles of SNPs. It is noted that the genes instead of the alleles of SNPs are usually found by direct literature searching. Based on these observations, our network analyses accurately identified ontology terms essential for representing genetic susceptibility and genetic susceptibility factors.i.e., vaccine adverse event). Two vaccine adverse event use cases were represented and evaluated. The SPARQL and social network analyses were implemented to evaluate and analyze the OGSF contents and structure. Different social network analysis methods identified ontology terms with different types of importance in the ontology.In this paper, we have introduced the development of the new version of the Ontology of Genetic Susceptibility Factors (OGSF) and its usage for ontologically representing genetic susceptibility to vaccine adverse events. The new OGSF is aligned with the BFO 2.0. OGSF imports many terms from existing ontologies and also includes many new ontology terms. For the first time, we have ontologically represented the genetic susceptibility to a pathological bodily process  [In addition to the genetic susceptibility factors, many other variables may also contribute to the manifestation of a disease or a pathological bodily process outcome (e event) . For exaDifferent methods can be used for ontology evaluations . A use cet al. have applied SNA to analyze the structures of Suggested Upper Merged Ontology (SUMO) and SWRC ontology [et al. and Hogan et al. have been developing search strategies using network-based approaches to mine linked data in semantic web respectively [i.e., object properties) as edges. Our distinct treatment of ontology relations as edges makes senses since these relations are designed to link different classes and their instances. Our SNA study found that the visualization and social network analysis results using the Case Study 2 data provide better understanding of ontology designing and evaluation. Interestingly, our SNA hub and closeness analyses generated two distinct sets of results. The hub analysis identified all five susceptibility alleles of SNPs as top key terms while the closeness analysis detected all three susceptibility genes collected in the Case Study 2. The SNA hubs are terms directed to the high authority terms. Our identification of all the SNPs as hubs is consistent with the notion that these SNPs are essential for the authority terms such as ‘systemic adverse event of smallpox vaccination’ and ‘genetic susceptibility of vaccine adverse event’. The closeness centrality measures how a node can be easily reached by other nodes. As the genes have different susceptibility variants , it makes sense that the genes have better closeness centrality scores than their variants. Since these genes are not directly defined as genetic susceptibility factors, the genes appear to be hidden factors that can be mined from the OGSF data. When we consider the gene functions, the direct gene name extraction gives more biological meaningful information than the variants themselves. These distinct observations suggest that different SNA analysis methods may identify ontology terms essential from different aspects.Although social network analysis (SNA) has been widely used in the fields of web search and social studies, its application in ontology field is rare. SNA uses graph theories. Since ontologies can be considered as  graphs, graph analysis techniques are promising tools for evaluating ontologies in many dimensions. Hoser ontology . Harth eectively ,53. TheiOther than OGSF, many other research projects also focus on establishing and cataloging the relation between genotypes and phenotypes. For example, the Database of Genotypes and Phenotypes (dbGaP) is a repository for archiving, curating, and distributing the information obtained from studies investigating the interactions of genotypes and phenotypes . SNPediaOur study clearly shows that OGSF provides a robust platform to support logical representation and analysis of genetic susceptibility and genetic susceptibility factors. Such platform will allow us to logically organize the knowledge and data related to genetic susceptibility and genetic susceptibility factors. With the well-organized information, it is then possible to generate automatic reasoning programs to analyze the data, predict new knowledge on genetic susceptibility, and support personalized medicine research. However, while the use case studies out of the literature curation were meant for evaluating and validating the OGSF framework, it would be a huge effort to manually curate all the possible data available in the literature. To improve the study of genetic susceptibility factors, it might help to devote more programing effort to selectively integrate related data sources from openly accessible resources such as the SNPedia  as introOriginated from previous OGSF-DM research , the newhttp://www.w3.org/TR/owl-guide/). For this study, many new terms and logical definition were added into original OGSF [http://protege.stanford.edu/).The format of OGSF ontology is W3C standard Web Ontology Language (OWL2)  . To suppUse case studies were designed based on literature survey. SPARQL was performed using the SPARQL query plug-in embedded with Protégé 4.3.0 build 304.http://gephi.org) [Graphed data used for visualization was first extracted from OGSF using the OntoGraf plug-in . After mphi.org) . Gephi wphi.org) , and hubphi.org) .http://code.google.com/p/ogsf/. As an OBO Foundry library ontology, OGSF has been deposited by default in the Ontobee linked data server [http://www.ontobee.org/browser/index.php?o=OGSF. The source of the ontology is also deposited in the NCBO Bioportal: http://bioportal.bioontology.org/ontologies/3214.The website for OGSF project is available at a server . All OGSBFO: Basic formal ontology; FOAF: Friend of a friend project; HLA: Human leukocyte antigen; GAZ: Gazetteer; IAO: Information artifact ontology; LD: Linkage disequilibrium; OAE: Ontology of adverse event; OBI: Ontology for biomedical investigations; OBO: Open biological and biomedical ontologies; OGDI: Ontology of genetic disease investigation; OGI: Ontology for genetic interval; OGMD: Ontology of glucose metabolism disorders; OGMS: of General medical science; OGR: Ontology of geographical regions; OGSF: Ontology of genetic susceptibility factors; OGSF-DM: Ontology of genetic susceptibility factors to diabetes mellitus; OVAE: Ontology of vaccine adverse event; OWL: Web ontology language; REO: Reagent ontology; SKOS: Simple knowledge organization system; SNA: Social network analysis; SNP: Single polymorphism nucleotide; SPARQL: SPARQL protocol and RDF query language; SUMO: Suggested upper merged ontology; URI: Uniform resource identifier; VO: Vaccine ontology.The authors declare that they have no competing interests.YL: Primary OGSF developer, use case testing, social network analysis and visualizing, drafting of manuscript. YH: OGSF developer, vaccine adverse event domain expert, use case testing, and drafting of manuscript. Both authors read and approved the final manuscript.Screen shots of SPARQL queries. (A) SPARQL query used in Ontobee SPARQL query endpoint. The file includes the SPARQL query script used in the Ontobee SPARQL query endpoint (http://www.ontobee.org/sparql/index.php) and its results as returned by the Ontobee SPARQL query server. (B) Additional File 4.png. Screen shot of Protégé SPARQL query tab showing the SPARQL query result.Click here for fileNetwork characteristic measurements of each node in the use case 2 graph. The file includes in-degree, out-degree, degree, authority, hub, modularity, clustering, strength, local clustering coefficient, eigenvector centrality, PageRank, eccentricity, closeness centrality, betweenness centrality scores of the 24 nodes in the graph. The calculation was conducted by using Gephi software.Click here for fileThe input file used for network visualization analysis using Gephi software. The file includes the data of individuals and related classes of case study 2 extracted from OGSF ontology.Click here for file


Oeceoclades maculata, considered an invasive plant, is the only species of the genus throughout the Americas. We used 13 ISSR primers to assess the genetic diversity and structure of 152 individuals of O. maculata distributed in five sampled sites in Brazil. Low diversity was found within samples while most of the genetic diversity was found among localities. A substructure was observed in one of the sites, suggesting lack of gene flow even between very small distances. Results may be understood considering the interaction between the reproductive system, colonization history, genetic drift, selective pressures and multiple introductions.Originally from Africa, the terrestrial orchid  Oeceoclades maculata, considered an invasive plant, is the only species of the genus throughout the Americas. Considering the lack of information on population genetics of this species, the aim of this study was to evaluate the genetic diversity and structure of Brazilian populations of O. maculata. We used 13 inter-simple sequence repeat primers to assess the genetic diversity of 152 individuals of O. maculata distributed in five sampled sites from three Brazilian states . Low diversity was found within samples, with estimates of the Shannon index (H) ranging from 0.0094 to 0.1054 and estimates of Nei's gene diversity (He) ranging from 0.0054 to 0.0668. However, when evaluated together, the sampling locations showed substantially higher diversity estimates , and most of the genetic diversity was found among populations (ΦST = 0.933). Both clustering and principal coordinate analysis indicate the existence of five distinct groups, corresponding to the sampled localities, and which were also recovered in the Bayesian analysis. A substructure was observed in one of the localities, suggesting a lack of gene flow even between very small distances. The patterns of genetic structure found in this study may be understood considering the interaction of several probable reproductive strategies with its history of colonization involving possible genetic drift, selective pressures and multiple introductions.Despite the fact that invasive species are of great evolutionary interest because of their success in colonizing and spreading into new areas, the factors underlying this success often remain obscure. In this sense, studies on population genetics and phylogenetic relationships of invasive species could offer insights into mechanisms of invasions. Originally from Africa, the terrestrial orchid  Biological invasions represent one of the major threats to biodiversity, ecosystem integrity, agriculture, fisheries and public health . DespiteAlthough many species of Orchidaceae are endangered due to the destruction and fragmentation of their habitats and the predatory extraction of specimens in natural environments . There iGenetic and evolutionary processes are the main determinants of the establishment and spreading ability of invasive species . Thus, bThe Orchidaceae represents ∼7 % of angiosperms, and is considered one of the largest families of this group . AlthougMicrotis media R.Br, an invasive terrestrial orchid, support the idea that terrestrial invasive orchids can be associated with the occurrence of a great diversity of mycorrhizal fungi, increasing the chance of seed germination and tolerance to a wide range of habitats, providing, therefore, competitive advantage over native species of orchids , and their preference for ephemeral environments provide a degree of resilience to orchids that is often underestimated . EvaluatOeceoclades maculata was originated from the tropical regions of Africa, as all the other 38 species of the genus, but it is the only species in the genus that occurs in the Neotropics as well  markers show great potential for studies on genetic diversity and structuring of natural populations , Mato Grosso, in the municipality of Cáceres (CAC) and São Paulo, one in the city of Ribeirão Preto (RP) and two in the municipality of Piracicaba (PIR1 and PIR2) .Individual samples were collected in five icaba PIR and PIR2icaba PIR and PIR2® QIA PCR purification kit and resuspended in 50 µL of EB buffer (kit Qiagen®) following manufacturer's instructions.New and healthy leaves were sampled from 19 to 39 individuals per sampled site. The sampled leaves were stored at 1.5 mL microtubes containing CTAB/salt gel , and maintained under refrigeration at 4 °C for, at least, 7 days before DNA extraction. The protocol used for DNA extraction was that described by 2, depending on the primer; 0.2 mM dNTPs; 10 pmol of each primer; 1 unit of Taq DNA polymerase and 20 ng DNA with a final volume of 30 μL.A total of 20 ISSR primers  were tesThe reactions were performed in a MyCycler BioRad thermocycler, according to the following amplification conditions: 94 °C/90 s; 35 cycles of 94 °C/40 s, 44 °C/45 s; 72 °C/90 s; 94 °C/45 s; 44 °C/45 s and a final extension of 72 °C/5 min. The products resulting from the amplification reaction were subjected to electrophoresis on 2 % agarose gel in 1× TBE buffer for 90 min at 135 V. To assist the analysis of the bands stained with ethidium bromide, 3 μg of 100 bp DNA Ladder was used . The gels were then photographed on a transilluminator with a photo documentation system . The genotyping was performed manually by comparing the banding patterns on the agarose gels. We used only stable, unambiguous, high resolution and reproducible bands.He). However, since this concept is not applicable to dominant markers, it becomes a measure of genetic variability estimated by He = 1 − Σip2, where ip is the frequency of a given band in the population. The Shannon–Wiener index  were estimated using POPGENE  or absence (0) of bands, considering only the robust and unambiguous bands. Genetic diversity was estimated by the parameters: number of bands, number of polymorphic bands, percentage of polymorphic loci , number of private bands (which occurred in only one population), number of distinct banding patterns (different band combinations), Nei's genetic diversity  and Shaner index , which iTo evaluate the genetic structure of the populations on the sampled sites, the analysis of molecular variance (AMOVA) . ClusterK groups (with K ranging from 1 to 10) were performed. Each simulation consisted of 500 000 iterations of MCMC after an initial discard (burn-in) of 250 000. The analysis was performed using the no admixture ancestry model and the no correlated allele frequencies model, since the species is considered to be self-pollinated. The most probable number of groups was estimated by the ad hoc method based on ΔK . Considering each sampled site, the number of polymorphic bands ranged from 7 (PIR1 and RP) to 55 (PIR2) (Table H) estimates were low, ranging from 0.0094 (RP) to 0.1054 (PIR2), as well as the estimates of Nei's genetic diversity, ranging from 0.0054 (RP) to 0.0668 (PIR2). However, when evaluated together, the five sampling sites presented substantially higher estimates for the Shannon index and Nei's genetic diversity  . The existence of significant genetic diversity among sampled locations was also observed in the clustering analysis , with the presence of a possible migrant individual Supporting Information—Fig. S3] of Oeceoclades maculata with K = 5.Figure S3. Bayesian analysis of the genetic structure of 39 Oeceoclades maculata individuals from the ESALQ/USP population, with K = 2. Subpopulations are coded following Table


Mycosphaerella fijiensis, a wind-dispersed fungus for which a secondary contact zone had previously been detected using landscape genetics tools. By tracking the spatio-temporal frequency change of 15 microsatellite markers, we find that σ, the standard deviation of parent–offspring dispersal distances, is 1.2 km/generation1/2. The analysis is further shown robust to a large range of dispersal kernels. We conclude that combining landscape genetics approaches to detect breaks in allelic frequencies with analyses of changes in neutral genetic clines offers a powerful way to obtain ecologically relevant estimates of dispersal in many species.Dispersal is a key parameter of adaptation, invasion and persistence. Yet standard population genetics inference methods hardly distinguish it from drift and many species cannot be studied by direct mark-recapture methods. Here, we introduce a method using rates of change in cline shapes for neutral markers to estimate contemporary dispersal. We apply it to the devastating banana pest  Virtually every ecological and evolutionary process is influenced by dispersal. From an ecological perspective, dispersal influences population dynamics and persistence, species distribution and abundance, and community structure . Such neutral clines are not stable through time Endler . ProvideMycosphaerella fijiensis, a wind-dispersed plant pathogen causing black leaf streak disease (BLSD) of banana. BLSD is the most economically important disease of bananas   and Feb(2) Fig. . We esti(2) Fig. . Three t1) or from young lesions (T2). Genotyping was carried out using 15 microsatellites loci  and 244 individuals (T2) were genotyped.Total genomic DNA was either extracted from mycelial cultures  allowing K to vary from 1 to 10 in geneland and from 2 to 10 in tess. K was inferred from the modal value of the run highest posterior probability in geneland and from the mean value of the run lowest deviance information criterion (DIC) in tess. For the best K, posterior individual membership probability to each genetic group or parental population was averaged across best runs∼ outputs. We used the mba.surf function of R software  and the number of alleles (NA) using fstat2.9.3 , which we modified to account for 2D data. It allows fitting bi-allelic allele count data to geographical distance using various cline shapes. Several clines can be fitted to compare their slopes (concordance) and position (coincidence) by constraining shape parameters. Estimation is performed by maximum likelihood. Likelihood is computed considering that allelic counts in a site are drawn from a binomial distribution. It is maximised using a simulated annealing algorithm. Here, most genetic markers had only two alleles  were transformed into a single coordinate along the cline axis using:We first determined how to best describe the data by fitting three different 2D cline shapes to multilocus genetic data independently for Tx to allelic count data: two Logit shapes consistent with the neutral hypothesis and a stepped cline expected in tension zones involving strong indirect selection across the genome (details and full equations in 1 (non-zero slope) using likelihood ratio tests (LRT). Third, for each locus presenting a significant cline pattern at T1, we tested for a slope change between T1 and T2 using LRT again. Model choice was made at the 5% significance level using a chi-squared test at d.f. difference in freedom degrees between the compared models.where θ is the angle between cline axis and the parallel (East–West line). We then fitted three functions of w) is the ratio between the maximal difference in allele frequencies along the cline and maximal cline slope:Cline width (p(.) is allele frequency, p′(.) its derivative with respect to geographical distance and T )  thus depends on cline width at T1 (w1) and dispersal (σ):Cline width at T1 and T2 by a distance shift δ. As detailed in Because the contact zone may move with time, for example under asymmetric gene flow , as used in previous theoretical studies about kernel-mediated biases in cline approaches, and 11 distributions (Δ5 → Δ16) belonging to three different kernel families commonly used to model dispersal kernels: Exponential power, 2Dt and Geometric (full equations in 2), corresponding to the value inferred from the real data, but different kurtosis  from 1 to 185 generations after secondary contact. All simulations were performed with Mathematica 7.0 .where 1 and T2), geneland and tess clearly detected two genetic groups . The remaining 93 and 51 individuals were assigned to two other parental populations.In both datasets . No pair of loci showed significant linkage disequilibrium after FDR control . Genetic differentiation (FST) between the two clusters detected in geneland was significant in both samplings (FST = 0.091 at T1 and FST = 0.07 at T2) and greatly variable among microsatellite loci   was not significantly different between the two samplings Table  and was σ Table . The bes0) Table . From th–1586 m] .4, a mixture of Binomial distributions with extremely high kurtosis, combined with a very early sampling date (T1 < 8 generations) could generate a detectable bias (underestimation of σ). Under these conditions, clines showed non-logit shapes with discontinuities in allele frequencies . This suggests that moments of order higher than four (distribution tail) might be at the origin of the observed deviation with Δ4.Figure This study represents the first use of the decay of neutral clines to quantitatively infer dispersal. This approach basically requires genotyping individuals with neutral markers in a situation of secondary contact between differentiated populations in which drift can be considered as negligible in front of migration. It provided fairly accurate measure of contemporary gene flow and was robust to deviations from diffusion approximation.M. fijiensis. It is also among the first quantitative genetic-based estimates of dispersal at a landscape scale in fungal plant pathogens, a group that comprises most emerging infectious plant diseases  suggests very high dispersal abilities in M. fijiensis. Despite the absence of previous dispersal estimates in this species, this estimate is consistent with the biology of the fungus. Mycosphaerella fijiensis exhibits two natural modes of dispersal. The first relies on asexual spores (conidia), suspected to disperse at very short distances through rain splash . Both cline theory and empirical data have shown that linkage disequilibrium can build up easily in clines maintained by selection Barton . When thet al. M. fijiensis, dispersal has been shown to follow a leptokurtic kernel, that is with an excess of small- and long-distance dispersal in comparison to the Gaussian distribution  have low impact on the estimation. The only noticeable deviations were detected for Binomial mixture distributions, with exceptionally strong kurtosis (γ > 100) and only for early sampling dates. Such high kurtoses have never been reported from experimental studies  can be internally checked, so that the method should overall provide robust estimates.et al.  encompass individuals at both extremities of the contact zone so that cline plateaux can be properly estimated and (2) be intensified near the contact zone for cline centre and slope to be accurately inferred. If more than two populations meet, similar sampling advices apply as the method would allow taking advantage of such design by simultaneously fitting clines from several locations. Second, if dispersal truly occurs in 2D, sampling should also be performed in 2D, for cline main axis to be correctly inferred. Such precaution is necessary because if sampling is designed as a 1D transect along an inappropriate direction that forms an angle θ with cline main axis, inferred cline slopes will be multiplied by a factor Cos(θ ), resulting in overestimating σ by a factor 1/|Cos(θ )|. Here, secondary contact occurred in a shallow valley, where pathogen host plants are continuously distributed along roadsides. The valley is not a straight line and secondary axes exist .Our results show that combining genetic clustering approaches to detect breaks in allelic frequencies with neutral genetic cline analyses offers a convenient way to gain solid information on the rate of contemporary dispersal. It combines the desirable properties of both direct methods (e.g. estimation over a known and recent period) and classical indirect approaches . With the continuous development of both new sequencing technologies and spatial genetic clustering algorithms (Guillot


For species formed by hybridization or polyploidy, genetic diversity within the newly formed species is influenced by the number of independent evolutionary origins of the species. For recently formed species, an analysis of genetic structure can provide insight into the number of independent origin events involved in the formation of the species. For a putative invasive allopolyploid species, the number of origins involved in the species formation, the genetic diversity present within these origins, and the level of gene flow between independent origins determines the genetic composition of the neospecies. Here we analyze the genetic structure of the newly formed allopolyploid species, Salsola ryanii, a tumbleweed which evolved within the last 20–100 years in California. We utilize the genetic structure analysis to determine that this new species is the result of at least three independent allopolyplodization events followed by gene flow between the descendants of independent origins.It has recently become clear that many invasive species have evolved  The rate of introduction of non‐native species, including many invasive and weed species, continues to increase annually  (Ellstrand and Schierenbeck Gossypium including the wild polyploids) and in the allopolyploid invasive Spartina anglica   potential number of independent origins and (ii) potential early interlineage gene flow. This approach to identifying independent origin events is likely conservative in terms of the number detected because independent origins from parental types with similar genetic compositions are unlikely to be detected. However, this method is extremely unlikely to assign multiple origins when a single origin is the true history.Salsola ryanii (2n=54) is a newly formed weedy allohexaploid derivative of S. tragus (2n=36) and S. australis (2n=16), which formed in situ in California in the last 20–100 years . Salsola australis is a weed in California and Arizona and is likely native to Australia or South Africa  determine whether S. ryanii is the result of single or multiple origin events utilizing an analysis of its genetic structure and (ii) investigate patterns of dispersal and admixture that have occurred following its origin(s).This is a preliminary study of the origins of S. ryanii in California as described in  and all the remaining alleles were coded as missing data because in polyploid species it is not possible to know which allele is present in multiple copies or a single copy. For the diploid species , when only one allele was detected it was coded as homozygous locus. This does potentially code null alleles as a homozygous loci.DNA was extracted from each sample using a modified CTAB extraction method, with the incubation step following grinding with liquid nitrogen omitted Doyle . The speS. ryanii, S. tragus and S. australis) was estimated using STRUCTURE  was 3 for S. ryanii individuals appear to be admixed; of the nonadmixed individuals (64%) are assigned to one of the three genetic clusters. The green cluster (A) of Fig. We considered an individual admixed if its assignment to any single cluster did not exceed 90% Table , we usedS. tragus. We do not present a STRUCTURE plot for S. tragus because a STRUCTURE plot with K=1 is uninformative. For S. australis, the optimal number of genetic clusters was 2, with both clusters occurring in each of the floristic provinces . The morphological differences are consistent with the differences in population structure observed; we found that S. australis has some limited population structure but more structure than is observed in the closely related highly dispersing species S. tragus.The high‐level of dispersal in  Bohonak . SalsolaS. ryanii individuals detected, it appears that gene flow has occurred between descendants of the multiple independent origins. We also observe that multiple S. ryanii lineages are currently located within the same or nearby collection sites, creating the potential for additional gene flow in the future. The genetic structure of S. ryanii contrasts with what has been found for the multiple allopolyploid lineages of Tragopon (T. mirus and T. miscellus) which are the result of multiple origins but do not admix with each other , while fitness experiments between individuals derived from single versus multiple origins allows for a test of the role of postpolyploidization gene flow to the evolution of invasiveness.Future work should compare the fitness of http://dx.doi.org/10.5061/dryad.k28r8.Data available from the Dryad Digital Repository:


Listropsylla agrippinae, and Chiastopsylla rossi. The taxa differ in the time spent on the host (predominantly fur vs. nest) and level of host specificity.In parasitic taxa, life history traits such as microhabitat preference and host specificity can result in differential evolutionary responses to similar abiotic events. The present study investigates the influence of vicariance and host association on the genetic structure of two generalist flea species, L. agrippinae and C. rossi were determined by making use of 315 mitochondrial COII and 174 nuclear EF1-α sequences. Both parasites show significant genetic differentiation among the majority of the sampling sites confirming limited dispersal ability for fleas. The generalist fur flea with a narrower host range, L. agrippinae, displayed geographic mtDNA spatial genetic structure at the regional scale and this pattern is congruent with host vicariance. The dating of the divergence between the L. agrippinae geographic clades co-insides with paleoclimatic changes in the region approximately 5.27 Ma and this provides some evidence for a co-evolutionary scenario. In contrast, the more host opportunistic nest flea, C. rossi, showed a higher level of mtDNA and nDNA spatial genetic structure at the inter-populational scale, most likely attributed to comparatively higher restrictions to dispersal.A total of 1056 small mammals were brushed to collect 315 fleas originating from 20 geographically distinct localities in South Africa. Phylogeographic genetic structure of In the present study, the evolutionary history of the flea species could best be explained by the association between parasite and host (time spent on the host). The phylogeographic pattern of the fur flea with a narrower host range correspond to host spatial genetic structures, while the pattern in the host opportunistic nest flea correspond to higher genetic divergences between sampling localities that may also be associated with higher effective population sizes. These findings suggest that genetic exchange among localities are most likely explained by differences in the dispersal abilities and life histories of the flea species.The online version of this article (doi:10.1186/s12862-015-0389-y) contains supplementary material, which is available to authorized users. Paleoclimatic events have been put forward as one of the main mechanisms causing speciations and extinctions of evolutionary lineages –4. The dRhabdomys Listropsycumented , 35, 37.cumented –40. Furtcumented , 34, 36)cumented –40, we pR. pumilio and R. dilectus were trapped at a contact zone, Fort Beaufort   and O. irroratus (37.31 %) compared to the other 4 host taxa , and ressi Fig. . Listrop %) Fig. . Chiastoaxa Fig. . Individermedius 3.02 %   and when calculated for each cluster L1  and L2  separately. The time of divergence between the two main clusters within L. agrippinae date back to the late Miocene which was estimated to be 5.27 Ma .TCS statistical parsimony analysis based on the mitochondrial 80) Fig.  and c. Trk Figs.  and 4. TCOII data indicated that there were marked differences between some sampling localities when the distribution of genetic diversity found at each is compared across the landscape    Table . FixatioCOII data showed a remarkably different genetic pattern for C. rossi when compared to L. agrippinae. The majority of the localities sampled are characterized by unique divergent haplotypes . The spatial genetic pattern of the nuclear EF1-α network and the Neighbor-Net tree suggest more lineage sharing across the landscape when compared to the mitochondrial DNA COII data . The nuclear TCS analyses suggest a distinct clade comprising individuals from KP, MN and AF and this assemblage is supported as one of the six mtDNA BAPS subclusters. This subcluster is mainly found in the north eastern part of South Africa, and although it may have some biological meaning, the Neighbor-Net analysis .TCS analysis based on mitochondrial pes Fig. , and altpes Fig. , it was pes Fig. . Insteadpes Fig.  and b. Tpes Fig.  and moreta Figs. . Similarsis Fig.  show thasis Fig. . Mantel C. rossi is supported when the genetic landscape surface plot is considered. In contrast to L. agrippinae, C. rossi show several genetic discontinuities (peaks) throughout the total sampling distribution  which seems to have a narrower niche breath (i.e. narrower host range) .


The transmission of genes and culture between human populations has major implications for understanding potential correlations between history, biological, and cultural variation. Understanding such dynamics in 19th century, post-contact Native Americans on the western Great Plains is especially challenging given passage of time, complexity of known dynamics, and difficulties of determining genetic patterns in historical populations for whom, even today, genetic data for their descendants are rare. Here, biometric data collected under the direction of Franz Boas from communities penecontemporaneous with the classic bison-hunting societies, were used as a proxy for genetic variation and analyzed together with cultural data. We show that both gene flow and “culture flow” among populations on the High Plains were mediated by geography, fitting a model of isolation-by-distance. Moreover, demographic and cultural exchange among these communities largely overrode the visible signal of the prior millennia of cultural and genetic histories of these populations. It is evident that historical (long term) patterns of divergence and descent can be created in both the genetic and cultural systems of human communities given that both are based on systems of inheritance, which facilitates some degree of continuity1356Bison bison)13Native American communities that occupied the western portion of the Great Plains during the post-contact  period have come to be globally famous12These combined push and pull effects ensured that by the 1800s, communities from at least five different language families were occupying the western Great Plains , pursuin1016“linguistic diversity was much greater than the diversity of other aspects of their cultures and must be a retention from a time when they had less contact with each other and were culturally more distinct … The general picture that is suggested is of diverse peoples retaining their distinct ancestral languages while adopting new and to a large extent shared lifeways after coming into contact with each other.”Examining potential congruence between cultural diversity and genetic patterns and their pathways of transmission under such dynamic and historically contingent conditions obviously presents a major challenge. Moreover, in this specific case, additional analytical problems are present. Most obviously, there is the passage of time between the late 19th century and the present day, meaning we are dealing with historical rather than directly contemporary populations. Furthermore, there is currently an absence of contemporary genetic data for the living descendants of the equestrian bison-hunting populations of the pastcirca 1888–1903 under the direction of Franz Boas232425Here, we sought to overcome these challenges and simultaneously examine both biological and cultural pathways of transmission in nine ethno-linguistic tribes who occupied the western Great Plains during the 19th century . More spIn a situation such as the historical Great Plains, geographic distance between communities provides a primary hypothesis for predicting both cultural and biological similarities and differences between those groups. This is because under such circumstances, geographic distance is likely to mediate likelihood and intensity of between-group interactions, whereupon similarities and differences in cultural and/or genetic patterns are, most parsimoniously, simply a function of mutation (copying and transmission errors), drift (stochastic trait loss), migration (between group movements of people), and/or diffusion . In other words, under these conditions, what has long been referred to as “isolation by distance” in population genetics parlance28No statistical evidence was found for a correlation between the geographic distances among tribes and linguistic distances among them . This result thus accords with the known historical situation, in that many groups were recent migrants to the region and hence their geographic position, and the geographic relationships between tribes, had nothing to do with deeper historical factors as measured by linguistic affinity.With respect to tests of our central hypotheses, both the biological and cultural data sets independently showed a statistical fit to the isolation-by-distance model . That isWe also tested whether among-tribe cultural patterns of variation correlated with among-tribe biometric variation directly. We found that among-tribe cultural and biometric patterns of variation were not significantly correlated with each other .Here, we have examined the extent to which geographic distances among Native American communities occupying the western Great Plains during the 19th century mediated patterns of transmission in both their genetic and cultural inheritance systems. Our central results identify that among-tribe patterns of cultural and biometric variation correlate with the geographic distances among these tribes. In other words, our results identify that both gene flow and “culture flow” among these communities was mediated partly by the geographic distances between pairs of tribes in any given instance.Our results also show that deeper, historical patterns of cultural and biometric differentiation among these tribes, as measured by correspondence with their ancestral languages, were largely overridden by their more recent geographically mediated interactions, which led to genetic admixture and exchange of cultural ideas. Admittedly, use of direct genetic data for these historical populations rather than necessary use of proxy biometric data, may have revealed stronger evidence of deeper historical signal in terms of biological patterns of differentiation. Nevertheless, our results indicate that recent historical events of population exchange among these groups led to empirically recognizable effects in the available data in a relatively short time span. Indeed, there are two ways in which “culture flow” may occur: either via demographic exchange between populations or by transferal of ideas in the absence of demographic exchange31Linguistic differences among the various groups appear to have provided little barrier to either gene flow or culture flow among groups, at least such that these led to a deviation from patterns that can be predicted on the basis of geographic distances among tribes alone. Such a pattern is explicable, however, when it is considered the many of the cultural variants we examined here (moccasin decorative features and parfleche characteristics in particular) were the product of female craftswomen. Given that girls and women were taken as captives by tribes hostile toward one another, as well as married into allied tribes31and cultural patterns . In this sense, our analytical framework shares much in common with those who have previously advocated such an approach on the basis of identifying broad and deep historical correlations between genetic, linguistic, and/or other cultural dimensions of human variability  measurements for several thousand individuals from North American tribes, which were recorded on living individuals between 1888 and 1903353637383923242542n = 82 traits), moccasin decorative features (n = 10 traits), parfleche design features (n = 23 traits), and a single trait associated with tipi design (3 pole or 4 pole base). Traits were typically recorded on a presence/absence basis, although three trait variants relating to parfleche design were treated as categorical variables. An among-tribe cultural distance matrix  and alliance as they stood during the latter decades of the 19th century are reasonably well documented31Correlations between distance matrices were examined for statistical correlation. Since matrices violate the assumptions of traditional correlation statistics, matrices were analyzed using Mantel tests58How to cite this article: Lycett, S. J. and von Cramon-Taubadel, N. Transmission of biology and culture among post-contact Native Americans on the western Great Plains. Sci. Rep.6, 25695; doi: 10.1038/srep25695 (2016).


Myotis lucifugus) is one of the most widespread bat species in North America and is experiencing severe population declines because of an emerging fungal disease, white-nose syndrome (WNS). To manage and conserve this species effectively it is important to understand patterns of gene flow and population connectivity to identify possible barriers to disease transmission. However, little is known about the population genetic structure of little brown bats, and to date, no studies have investigated population structure across their entire range. We examined mitochondrial DNA and nuclear microsatellites in 637 little brown bats  from 29 locations across North America, to assess levels of genetic variation and population differentiation across the range of the species, including areas affected by WNS and those currently unaffected. We identified considerable spatial variation in patterns of female dispersal and significant genetic variation between populations in eastern versus western portions of the range. Overall levels of nuclear genetic differentiation were low, and there is no evidence for any major barriers to gene flow across their range. However, patterns of mtDNA differentiation are highly variable, with high ΦST values between most sample pairs , while low mitochondrial differentiation was observed within two groups of samples found in central and eastern regions of North America. Furthermore, the Alaskan population was highly differentiated from all others, and western populations were characterized by isolation by distance while eastern populations were not. These data raise the possibility that the current patterns of spread of WNS observed in eastern North America may not apply to the entire range and that there may be broad-scale spatial variation in the risk of WNS transmission and occurrence if the disease continues to spread west.The little brown bat ( Understanding how host movement patterns influence the transmission of pathogens is critical to the development of effective prevention and control strategies, and to the conservation and management of host populations during and after disease outbreaks. However, for many host species, data on individual movements and contact rates are difficult or impossible to collect because of cryptic behavior, the geographic scale of movements, or methodological considerations that limit our ability to follow individuals through time and space. Evidence from empirical studies employing population and landscape genetic approaches has demonstrated that landscape features, such as mountains and rivers that limit host gene flow, often represent barriers to disease transmission –6, althoPseudogymnoascus destructans (hereafter Pd), is a cold-loving fungus that affects bats during hibernation and subsequent arousal. Pd causes characteristic cup-like erosions of the epidermis of the wings and muzzle, and may invade sebaceous glands and hair follicles  and the natural log of geographic distance  identified K = 2 as the most likely number of genetic clusters, roughly corresponding to clusters east versus west of the Great Plains-Rocky Mountains transition, and not corresponding to subspecies affiliation  and concluded that clusters were valid. However, all clusters identified by the three algorithms failed to meet HWE expectations . Across the four sites  containing both carissima and lucifugus mtDNA haplotypes , cluster membership was independent of subspecies affiliation for the clusters defined by FLOCK  and GENODIVE , but was not for STRUCTURE .All clustering methods employed . There were clear signals of IBD for both mitochondrial  based on microsatellites according to cluster membership  regardless of coding scheme (see FST or G”ST) used.To assess the validity of clusters given the pattern of isolation by distance, we plotted geographic and genetic distance , but overall, it is clear that levels of nuclear genetic differentiation are low, and there is no evidence for any major barriers to nuclear gene flow across the range of little brown bats. However, some key spatial patterns emerge from our analyses, namely (1) patterns of mtDNA differentiation are highly variable, with high ΦST values between most sample pairs , while low mitochondrial differentiation was observed within two groups of samples found in central and eastern regions of North America ; and (3) western sites are characterized by significant isolation by distance based on microsatellites, while those in the east are not. These data raise the possibility that the current patterns of spread of Pd observed in eastern North America may not apply to the entire range of the little brown bat, and that there may be broad-scale spatial variation in the risk of WNS transmission and occurrence if the disease continues to spread west.The emergence and spread of WNS has decimated bat populations in affected areas and raised the specter of extinction for The presence of isolation by distance is a major confounding factor when examining levels of genetic structure among populations of widespread species such as little brown bats, because clinal variation may be interpreted as the presence of discrete clusters even in the absence of barriers to gene flow ,78,80. AWhat is clear from these data is that there is significant genetic variation among samples from east to west across the range of little brown bats , but levels of nucDNA genetic differentiation were relatively low, and there was no unambiguous evidence for any major barriers to gene flow that might severely restrict the spread of WNS. Gene flow in temperate bats is mediated through the permanent dispersal of individuals and the exchange of genes at mating congregations during swarming and hibernation. The lack of isolation by distance and low levels of nucDNA differentiation among sites in eastern North America is concordant with the continuous spread of WNS from its origin in New York, and indicates that gene flow via mating has occurred over wide geographic areas. Furthermore, the disease has passed, or is currently passing, through regions in which there are low levels of mtDNA differentiation among sites , or by Pd . Our observed discrepancy between mtDNA and nucDNA signals may be due in part to homoplasy, particularly at rapidly-evolving microsatellite loci. Alternatively, incomplete lineage sorting might be producing discordant patterns among loci, particularly for a species with a relatively large effective population size  and relatively recent divergence , with ts; as in ), and Wys; as in  provided Dewey’s  estimate–1.5 Mya ). A thir–1.5 Mya ), while Pd spores, and how contacts of varying durations and seasonal timings influence the risk of WNS transmission. Ultimately we need to learn whether brief contacts during mating can result in transfer of spores leading to infection or whether permanent dispersals are driving transmission. The usefulness of our genetic data on little brown bats also rests on the assumption that intraspecific transmission dynamics outweigh the impact of cross-species transmission, given that multiple sympatric bat species are affected by WNS. This assumption may be justified, as post-WNS population declines of affected bat species are not influenced by population sizes of other affected, cohabiting bat species [Our data show extensive spatial variation in levels of connectivity among little brown bat populations and provide valuable information for understanding past and future patterns of WNS spread. However, the use of genetic methods to infer patterns of transmission assumes that patterns of gene flow are indicative of the movement of infectious individuals, and we must recognize that the risk of disease transmission may be higher than genetic data may indicate because there may be more contacts among infected and susceptible individuals, including among members of multiple species, than just those that lead to gene flow. Urgent research is required to determine how and when individual bats may be exposed to  species , but resM. lucifugus populations are low, and we did not identify any major barriers to gene flow across their range. However, levels of genetic differentiation at both mtDNA and microsatellites are significantly higher among populations to the west of the Great Plains-Rocky Mountains transition, suggesting that the current pattern of spread of WNS and risk of transmission of Pd observed in eastern North America may not apply to the entire range of the little brown bat.In conclusion, this study identified high levels of genetic variation among populations of little brown bats across their range, and mitochondrial DNA sequences revealed considerable spatial variation in patterns of female dispersal. Overall levels of nuclear genetic differentiation among S1 Fig(DOCX)Click here for additional data file.S1 File(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.