Genetic diversity within cattle breeds was analyzed using three measures of genetic diversity namely allelic richness (AR), expected heterozygosity (He) and inbreeding coefficient (f). Genetic distances between breed pairs were evaluated using Nei's genetic distance. Population structure was assessed using model-based clustering (ADMIXTURE). Results of this study revealed that the allelic richness ranged from 1.88 (Afrikaner) to 1.73 (Nguni). Afrikaner cattle had the lowest level of genetic diversity (He = 0.24) and the Drakensberger cattle (He = 0.30) had the highest level of genetic variation among indigenous and locally-developed cattle breeds. The level of inbreeding was lower across the studied cattle breeds. As expected the average genetic distance was the greatest between indigenous cattle breeds and Bos taurus cattle breeds but the lowest among indigenous and locally-developed breeds. Model-based clustering revealed some level of admixture among indigenous and locally-developed breeds and supported the clustering of the breeds according to their history of origin. The results of this study provided useful insight regarding genetic structure of SA cattle breeds.Information about genetic diversity and population structure among cattle breeds is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of cattle breeds. This study investigated genetic diversity and the population structure among six cattle breeds in South African (SA) including Afrikaner ( Bos taurus) and Indicine cattle (Bos indicus). Bos indicus is subdivided into zebu proper and zebu crossbred-types and is phenotypically identifiable by the presence of a substantial cerciothoracic hump  generated from the Illumina Bovine SNP50BeadChip.Bos taurus (Angus = 31) and (Holstein = 29) cattle breeds were included in this study. Breeders and Research Stations which keep pure breeds of the populations included in this study were identified and requested to provide animals for blood sampling. All animal handling and sample collection were done according to the regulations of the Animal Ethics Committee of the University of Pretoria (E087-12). To maximize the genetic diversity within each sampled population, pedigree data were used to select against full and half sib animals. Figure A total of 249 animals including three indigenous breeds (Afrikaner = 44), (Nguni = 54), (Drakensberger = 47), one composite  (Bonsmara = 44), and two Angus = 3 and  according to the manufacturer's protocol. The protocol was adapted for the semen samples where Dithiothreitol (DTT) was added with proteinase K in the first step. Genomic DNA for all samples was quantified using a Qubit® 2.0 Fluorometer and the Nanodrop Spectrophotometer (Nanodrop ND-1000). In addition, gel electrophoresis was performed to quantify the DNA.Genotyping was conducted at the ARC-Biotechnology Platform with the Illumina BovineSNP50 BeadChip v2 which features 54,609 SNP probes distributed across the whole bovine genome with an average spacing of 49.9 kb  was determine within each population using ADZE v 1.07  and Inbreeding coefficient (f) was calculated using Plink v1.07 . Allelic richness  was estimated as one minus the average proportion of allele shared , respectively, and N is the number of loci tested.Pairwise genetic distance among cattle breeds was estimated based on Nei's unbiasedK. Most preferable K exhibit a low cross-validation error compared to other K-values. In the current study CV error estimates were plotted  across the six breeds.Parameter for SNP validation that included the level of polymorphism, minor allele frequency (MAF) and deviation from Hardy Weinberg equilibrium (HWE) for all six cattle breeds in this study were previously reported . In summary, examination across breeds revealed that about 56% of SNPs were polymorphic in all breeds and the distribution of MAF showed that nearly half of the SNPs (41%) showed a higher degree of polymorphism (MAF ≥ 0.05) across the breeds. With regard to deviation from HWE only between 5 and 6% of SNP were shown to deviate from HWE (R = 1.88) while the Nguni cattle had the lowest number of alleles per locus (AR = 1.73). However, the Afrikaner cattle was observed to have the lowest level of expected heterozygosity (He = 0.24) in this study. Among indigenous and locally-developed breeds the Drakensberg cattle (He = 0.30) had the highest level of genetic diversity. Looking across all six breeds Angus and Holstein cattle had the highest level of gene diversity (He = 0.31). The level of inbreeding was low across the breeds in this study ranging from 0.004 (Afrikaner) to −0.002 (Drakensberger).Table Bos taurus cattle 92% of genetic diversity occurred within breeds while only 8% occurred between the breeds  to 0.081 (Afrikaner-Drakensberger) among indigenous and locally-developed breeds and from 0.078 (Drakensberger-Angus) to 0.159 (Afrikaner-Holstein) across all six breeds  to 0.29 ± 0.004 (Angus and Holstein).Bos taurus breeds (Angus and Holstein) from indigenous and locally-developed cattle breeds  .Topological relationships between breeds, from Neighbor-Joining tree clearly separated ) Figure . Three mk = 6) demonstrated that among the SA indigenous and locally-developed breeds , the Afrikaner population had the least level of admixture while the Drakensberger had the most level of admixture. The Nguni cattle showed some signals of admixture with Afrikaner breed while the Drakensberger cattle revealed some signals of admixture with Nguni, Bonsmara, and Angus. Bonsmara cattle shared more genetic links with the Nguni cattle than with other indigenous breeds. When comparing all six breeds Afrikaner, Angus, and Holstein populations showed the lowest level of admixture in the current study.The proportions of individuals in each of the breeds in the six most likely clusters inferred by the ADMIXTURE are presented in Table e = 0.30) while the Afrikaner demonstrated the lowest level of genetic diversity. The lower level of genetic variability observed within the Afrikaner cattle could be due to the present of strong selection and use of elite sires which is common among stud and commercial herds and small effective population size. This lower level should be noted in Afrikaner and step toward increasing diversity should be prioritized. This could include exchange of bulls from the different genetic pools. The negative correlation observed between allelic richness and expected heterozygosity in the Afrikaner cattle could be attributed to the processes that differential affect these two measures of diversity, such as bottleneck, selection and increased gene flow between populations within the Afrikaner  demonstrated the highest level of genetic variability compare to all other breeds. The highest genetic diversity observed in Bos taurus breeds were in agreement with the results of Lin et al.  breed than in Ethiopia cattle breeds (between He = 0.37–0.38) based on SNP data. Heterozygosity values observed in this study were comparable to the previously reported heterozygosity among African (He = 0.25) and European (He = 0.30) cattle breeds using SNPs  observed among Ethiopia populations by Edea et al. . CombiniSTF) among the indigenous and locally-developed breeds was lower than African-Bos taurus pairs, ranging from 4 to 8%. This was lower than 12% observed among West African cattle breeds by Gautier et al.  to 0.25 (Angus and Holstein). Average genetic distance between pairs of animal (0.21) was previously reported within 19 cattle breeds  and Bos taurus cattle breeds was observed which suggested distinct genetic resource in South Africa cattle breeds that should be proper utilization and conservation in order to cope with unpredictable future environments. Information generated from this study forms the basis for future management of these cattle breeds.This study revealed low to moderate genetic diversity within six cattle breeds in South Africa and showed a closer relationship among indigenous and locally-developed cattle breeds. Clear genetic divergence between South African  and Sithembile O. Makina collected the genetic materials, carried out the laboratory analyses, statistical analyses, interpretation of the data and drafted the manuscript. Azwihangwisi Maiwashe and Farai C. Muchadeyi assisted with the acquisition of funding. All authors participated in the design and coordination of the study. Azwihangwisi Maiwashe, Farai C. Muchadeyi, Este van Marle-Köster and Michael D. MacNeil revised the manuscript critically for important intellectual content. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The roles and significance of STAT3 in cancer biology have been extensively studied for more than a decade. Mounting evidence has shown that constitutive activation of STAT3 is a frequent biochemical aberrancy in cancer cells, and this abnormality directly contributes to tumorigenesis and shapes many malignant phenotypes in cancer cells. Nevertheless, results from more recent experimental and clinicopathologic studies have suggested that STAT3 also can exert tumor suppressor effects under specific conditions. Importantly, some of these studies have demonstrated that STAT3 can function either as an oncoprotein or a tumor suppressor in the same cell type, depending on the specific genetic background or presence/absence of specific coexisting biochemical defects. Thus, in the context of cancer biology, STAT3 can be a friend or foe. In the first half of this review, we will highlight the “evil” features of STAT3 by summarizing its oncogenic functions and mechanisms. The differences between the canonical and non-canonical pathway will be highlighted. In the second half, we will summarize the evidence supporting that STAT3 can function as a tumor suppressor. To explain how STAT3 may mediate its tumor suppressor effects, we will discuss several possible mechanisms, one of which is linked to the role of STAT3β, one of the two STAT3 splicing isoforms. Taken together, it is clear that the roles of STAT3 in cancer are multi-faceted and far more complicated than one appreciated previously. The new knowledge has provided us with new approaches and strategies when we evaluate STAT3 as a prognostic biomarker or therapeutic target. Y705), a crucial event for the subsequent dimerization and activation of STAT3. Subsequently, the phosphorylated STAT3 (pSTAT3) molecules form homodimers, which migrate to the nuclei where they bind to the promoters of various target genes and regulate their transcriptions [Y705 or its nuclear translocation [STAT3 belongs to a family of transcription factors that transduces the cellular signals from a host of cytokines and soluble growth factors such as the IL-6 family cytokines, epidermal growth factor and platelet-derived growth factor ,2. In thriptions ,2. Many riptions ,3,4. In STAT3 gene ablation in mice results in embryonic lethality that occurs 6–7 days after inception [STAT3 knockout mice have provided evidence that STAT3 is required for the development and differentiation of various tissue types, such as the skin, immune system, liver, mammary gland, thymus and nervous system [STAT3 in keratinocytes was found to impair migration of keratinocytes and skin remodelling [STAT3 in the mammary glands was found to suppress apoptosis of the glandular epithelial cells and lead to delayed glandular involution [STAT3 has important and diverse biological functions in normal cells, and details can be found in a number of excellent reviews ,7,8. Brinception . Studiesodelling . In anotvolution . STAT3 ivolution . In anotvolution .STAT3C, which is a constitutive active STAT3 mutant construct [The oncogenic roles of STAT3 have been extensively published and reviewed in the literature ,2,3,4,5,onstruct ,15,16. Sonstruct . It has onstruct ,15,16, aonstruct . More reonstruct ,20,21,22et al. [To reinforce the concept that STAT3 is oncogenic when it is inappropriately or constitutively activated, many laboratories have shown that the dominant negative STAT3 mutant construct, often labeled STAT3-DN in the literature, can effectively mediate cell cycle arrest and/or induce apoptosis in cancer cells ,24,25. Set al. .et al. found that patients carrying tumors with phosphorylated STAT3Y705 (or pSTAT3) expression had significantly shorter overall survival compared with those carrying tumors without pSTAT3 [et al. also reported that a high expression of STAT3 mRNA or pSTAT3 protein significantly correlates with a short overall survival and event-free survival in a cohort of patients with diffuse large B-cell lymphoma [Constitutive activation of STAT3, which has been demonstrated in a broad spectrum of solid and hematological cancers, often correlates with an unfavorable prognosis in cancer patients ,4,27. A t pSTAT3 . In a stt pSTAT3 . Huang elymphoma . STAT3 alymphoma , esophaglymphoma ,33, headlymphoma ,35 and tlymphoma .In normal cells, physiologic activation of STAT3 in response to extracellular signals such as growth factors and cytokines is a transient event, largely due to the existence and operation of various negative feedback mechanisms ,38. The To avoid inappropriately sustained activation of STAT3, there are multiple negative regulators that can promptly silence STAT3 signalling. The suppressors of cytokine signaling (SOCS) and protein tyrosine phosphatases (PTPs) are two families of proteins that carry out this important function ,39,40. TSOCS3 has been found in various types of cancer, including lung cancer, head and neck squamous cell carcinoma, hepatocellular carcinoma and Barrett-associated esophageal adenocarcinoma [The homeostasis of STAT3 phosphorylation and activation is frequently disrupted in cancer cells due to the loss of SOCS expression . In thisarcinoma . Experimarcinoma ,44,45,46arcinoma ,48,49,50SHP1 in these cells can substantially decrease the level of STAT3 activation [SHP1 [PTPs belong to a large family of proteins with >100 members, which is responsible for counteracting the effects of protein tyrosine kinases and maintaining the overall homeostasis of protein tyrosine phosphorylation. PTPs are known to dephosphorylate and thus inactivate the JAK/STAT3 signaling . Similartivation ,52,53,54i.e., protein inhibitors of activated STAT3) is also known to inhibit STAT3 by reducing its DNA-binding and ability to regulate gene transcription. The expression of PIAS3 has been shown to be reduced in glioblastoma, and this finding correlates with an elevated level of STAT3 activation and increased cell proliferation [PIAS3 into lung cancer cell lines can suppress cell proliferation and enhance the sensitivity of cells to chemotherapeutic drugs [et al. found an inverse correlation between the expression levels of PIAS3 and pSTAT3 in lung squamous cell carcinomas [Other than the SOCS members and PTPs, PIAS3 (feration . Transfeic drugs . In pararcinomas . Cancer cells and some of their surrounding inflammatory cells have been shown to produce and release various soluble factors (notably cytokines) into the tumor microenvironment, such that STAT3 in the cancer cell population is activated excessively and continuously. Cytokines involved in these autocrine or paracrine stimulatory pathways include IL-6, IL-10, IL-11, IL-21, IL-23, leukemia inhibitory factor and oncostatin . Since SConstitutive activation of STAT3 in cancer cells also can be attributed to the expression of various oncogenic protein tyrosine kinases (PTKs). The oncogenic properties of these PTKs stem from the fact that they have escaped the normal cellular control due to a variety of reasons, such as gain-of-function mutations, gene amplifications or chromosomal translocations. One well-known oncogenic PTK is Src, which is known to be over-active in cancer cells. Normally, the activation status of Src is increased by de-phosphorylation of Y527 and phosphorylation of Y416. In cancer cells, de-phosphorylation of Y527 can be due to the activity of tyrosine phosphatases (such as PTP1B), Y527F mutation or deletion of Y527 ,62. Phosanaplastic large cell lymphoma kinase (ALK) gene on 2p23 to the nucleophosmin (NPM) gene on chromosome 5q35 [. In this type of lymphoma, NPM-ALK binds to, phosphorylates and activates STAT3, which has been shown to be central to the NPM-ALK—mediated tumorigenesis [STAT3 gene knockout dramatically decreased the malignant transformation by NPM-ALK [STAT3 is also known to be highly activated in ALK-positive anaplastic large cell lymphoma by the oncogenic fusion protein, NPM-ALK, a constitutively active tyrosine kinase resulted from the specific chromosomal translocation that fuses the ome 5q35 ,65. In tigenesis ,67,68,69 NPM-ALK .Gain-of-function mutations involving JAKs have been implicated in activating STAT3 in specific types of cancer. JAK2-V617F and other JAK mutants are known to activate STAT3 and contribute to the pathogenesis of chronic myeloproliferative neoplasms and leukemias ,71,72,73et al. has revealed a novel mechanism by which a transient inflammatory signal can initiate cellular transformation [v-src, the authors found that transient activation of v-src is sufficient to induce transformation in these cells. In this system, STAT3 activated by v-src enhances the transcription of miR-21 and miR-181b-1, both of which lead to activation of NF-κB by targeting and inhibiting the expression of two tumor suppressors PTEN and CYLD. Through multiple pathways, activated NF-κB increases the production of IL-6, which in turn sustains the activation of STAT3 [As mentioned above, constitutive STAT3 activation and loss of SHP-1 in ALK-positive anaplastic large cell lymphoma have provided an example of a positive feedback loop. In this section, we will summarize the findings of a number of more recent studies focusing on the autocrine and/or paracrine IL-6/STAT3 stimulatory pathway that forms a positive feedback loop ,77,78,79ormation . Specifiof STAT3 . Interesof STAT3 .et al. revealed another positive feedback loop that confers STAT3 with a persistent activation property in cancer cells as well as the immune cells present in the tumor microenvironment [Lee ironment . In thisironment . This poironment ,78.Helicobacter pylori-associated gastric cancer, the existence of a positive feedback loop between STAT3 and COX-2 was also demonstrated [A few recent studies demonstrated other positive feedback loops that connect STAT3, gene regulation, metabolism, survival and proliferation in cancer cells . In one nstrated .et al. identified seven STAT3 mutations in 6/114 hepatocellular adenomas examined. Notably, all six of these tumors were inflammatory hepatocellular adenomas [STAT3 gene in both T-cell LGL and CLPD-NKs, representing about 80% of all mutations detected [Recently, somatic mutations in STAT3 were discovered in hepatocellular adenomas and many types of hematopoietic malignancies, such as T-cell large granular lymphocytic leukemia (T-cell LGL), chronic lymphoproliferative disorders of natural killer cells (CLPD-NKs), diffuse large B-cell lymphoma, and CD30+ T-cell lymphomas ,93,94,95adenomas , suggestadenomas ,91,92,93detected ,91,93. Y705 and thus the activation of STAT3 [IFNGR2, BCL2L1 and JAK2 [Y705 phosphorylation and its transcriptional activity [Intriguingly, most of the STAT3 mutations discovered  reside in the SH2 domain that normally directs STAT3 dimerization, and many of these mutations were suggested to induce amino acid changes that confer higher hydrophobicity to the STAT3 SH2 dimerization surface, potentially facilitating phosphorylation of STAT3of STAT3 ,90. Corrand JAK2 ,90. Receactivity . Compareactivity . In the canonical pathway, the oncogenic function of STAT3 is dependent on its phosphorylation at Y705, and the subsequent dimerization and nuclear translocation . As a trCyclin D1, c-Myc, PLK-1 and Pim1/2 [et al. also showed that siRNA-mediated STAT3 knockdown in esophageal squamous cell carcinoma cells repressed cell proliferation and tumor growth in mice by suppressing PLK1 expression [Cell proliferation: It is well known that STAT3 increases the transcription and expression of multiple gene targets that are crucial in the regulation of cell cycle progression. These gene targets include d Pim1/2 ,87. Thusd Pim1/2 ,97. A spd Pim1/2 . Zhang eResistance to apoptosis: STAT3C has been shown to promote the survival of tumors cells in various models ,4. To acVEGF gene and promotes its transcription, thereby enhancing tumor growth and metastasis [et al. found that VEGF stimulates STAT3 phosphorylation and nuclear translocation in human dermal microvascular endothelial cells, and inhibition of STAT3 using a dominant negative construct significantly impaired VEGF-induced migration and tube formation of these cells [Induction of angiogenesis: STAT3 has been shown to augment tumor angiogenesis in multiple cancer types, such as melanoma, pancreatic cancer, cervical cancer and renal carcinoma ,107,108.tastasis ,104,105.tastasis ,104,105.tastasis ,110. Forse cells . STAT3-DN in pancreatic cancer cells suppressed tumor growth and liver metastasis in nude mice, whereas transfection of STAT3C enhanced tumor growth and liver metastasis [Promotion of invasion and metastasis: Previous studies have shown that STAT3 can promote invasiveness and the metastatic potential of cancer cells. In one study, transfection of tastasis . STAT3C tastasis ,113,114.tastasis ,119,120.tastasis ,121. Thetastasis ,123. Lastastasis . Evasion of anti-tumor immunity: The concept that STAT3 has a role in dampening the anti-tumor immune response came from the observation that tumor cell death induced by STAT3 blockade is associated with infiltration of various immune effector cells . SeveralSTAT3C in glioblastoma cells was found to increase the expression of several stem cell factors, such as Sox2, Oct4 and Nanog [et al. identified 15 genes that are required for the proliferation of the breast cancer stem cell population characterized by the CD44+CD24− immunophenotype, and five of these genes facilitate STAT3 activation [+CD24− breast cancer stem cells, and inhibition of JAK2 decreased the number of cancer stem cell number and blocked the growth of xenografts in mice [STAT3 and cancer stem cells: In recent years, accumulating evidence suggests that STAT3 carries a critical role in promoting the self-renewal of cancer stem cells ,20,21,22nd Nanog . Using a in mice . Y705. The non-canonical pathway comprises a number of biological functions of STAT3 that have been shown to be independent of its transcription activity or the phosphorylation of STAT3Gene silencing and regulation: Zhang et al. discovered that STAT3 interacts with DNA methyltransferase 1 (DNMT1) and histone deacetylase 1 (HDAC1), by which STAT3 facilitates gene methylation and silencing of SHP-1 in malignant T lymphocytes; furthermore, blocking the expression of either DNMT1 or STAT3 using siRNA was found to induce DNA demethylation and re-expression of SHP-1 in these cells [TP53, SHP-1, SOCS3 and CDKN2A, in melanomas; mutation of STAT3K685 or treatment with resveratrol (a histone deacetylase activator) was found to diminish the tumor-promoting function of STAT3 in melanoma [et al. demonstrated that histone acetyltransferase p300 is responsible for STAT3 acetylation at K685, and this process can be reversed by histone deacetylase 1 [se cells . In anotmelanoma . Yuan ettylase 1 . Recentltylase 1 .RANTES, IL-6, IL-8, MET and MRAS [. Third, in prostate cancer and chronic lymphocytic leukemia, phosphorylation of STAT3Ser727 rather than STAT3Y705 was found to be crucial for the nuclear translocation, DNA binding and the tumor-promoting function of STAT3. This finding correlates well with the observation that STAT3-DN does not interfere with the oncogenic function of STAT3 in these experimental models [et al. showed that un-phosphorylated STAT3 binds to the regulatory region of several pro-apoptotic genes  in tumor cells and prevents their expression by promoting a repressive chromatin structure [Previous studies have provided multiple lines of evidence that STAT3 can exert oncogenic functions that are independent of the phosphorylation of its Y705 residue. First, un-phosphorylated STAT3 has been found to migrate to the nucleus with the help of importin-α3 . Second,and MRAS ,132. Thil models ,134. Rectructure . Correlatructure . Mitochondrial STAT3: In 2009, two Science papers simultaneously reported the function of STAT3 present in the mitochondria [S727 rather than STAT3Y705 is required for the oncogenic role of mitochondrial STAT3 in the context of H-ras—induced transformation [S727 but not that of STAT3Y705 [chondria ,138. Speormation . More reTAT3Y705 . STAT3 modulates cytoskeleton and focal adhesions: Results from multiple studies support the concept that STAT3 can modulate the cytoskeletal structures of the cells via multiple mechanisms. As mentioned above, STAT3 has been shown to regulate the migration and invasiveness of cancer cells by transcriptionally up-regulating the expression of focal adhesion-associated proteins [STAT3 was found to promote the rearrangement of actin stress fibers and microtubules, and facilitates the formation of lamellipodia in prostate cancer cells [in vitro, and substantially enhance lung metastasis in vivo [proteins ,121. Intproteins ,141. In proteins ,143,144.er cells ,143; in  in vivo ,143. In  in vivo ,146. A rWhile the oncogenic effects of STAT3 have been well recognized, a relatively small number of studies published previously have shown that STAT3 carries tumor suppressor functions, a seemingly paradoxical notion. Importantly, some of these studies have proposed a novel concept that STAT3 can function as an oncoprotein or tumor suppressor in the same cells, and the decision is dependent on the genetic background and/or coexisting biochemical defects . This sein vitro and tumor formation in SCID mice, whereas siRNA knockdown of PTEN alone  resulted in significantly less tumorigenic effects in these cells. Furthermore, no significant tumor suppressor effect of STAT3 was observed in the presence of normal PTEN expression. In other words, in this experimental model, the tumor suppressor effects of STAT3 were revealed only in the absence of PTEN expression. Interestingly, in the same study using astrocytes harvested from the same conditional STAT3 knockout mice, the authors also found that transfection of EGFRvIII  in STAT3+/+ astrocytes resulted in tumor formation in SCID mice, whereas the same treatment did not result in any tumor formation in STAT3−/− astrocytes. Thus, the oncogenic effect of STAT3 was found to be dependent on the co-existence of EGFRvIII, which was shown to complex with STAT3 in the nuclei of these cells. Taken together, this study has demonstrated that STAT3 can function as a tumor suppressor as well as an oncoprotein, and the genetic background and/or coexisting biochemical defects of the cells play a key role in determining the functions of STAT3. The first experimental evidence to support that STAT3 carries tumor suppressor functions comes from a study published in 2008 . Using aARFp19 knockout mice, the authors found that transfection of STAT3 or STAT3C significantly suppressed tumorigenecity in a SCID mouse xenograft model. In comparison, transfection of a double STAT3 mutant in which both Y705 and S727 cannot be phosphorylated led to significant tumor growth in SCID mice. In the same paper, the authors also found that cells transfected with the double STAT3 mutant resulted in liver and lung metastasis after intravenous injection of the cells, while transfection with STAT3 or STAT3C significantly decreased the metastatic potential of these hepatocytes. While the mechanisms underlying these observations require further investigations, these results have suggested the relevance of ras and/or p19ARF in regulating the tumor suppressor function of STAT3. Another report describing the tumor suppressor effects of STAT3 was published in 2011 . Using rmultiple intestinal neoplasia (Min) gene, which is essentially the murine Apc (adenomatous polyposis coli) gene carrying a nonsense mutation at codon 850, leading to the production of a truncated and non-functional Apc protein. By crossing these animals with conditional STAT3 knockout mice, the authors were able to generate mice with which they assessed the impact of STAT3 knockout in the Apc(Min/+)-carrying intestinal epithelial cells. While deletion of STAT3 in the intestinal epithelial cells reduced the multiplicity of early adenoma formation , ablation of STAT3 in the later stage of tumor progression significantly increased the invasiveness of the tumors and decreased the survival of the animals . In another study, the tumor suppressor function of STAT3 was revealed in the Apc(Min/+) mouse model of colorectal cancer . In thisThe last example came from a recent study examining drug-induced liver carcinogenesis in conditional STAT3 knockout mice. It was found that the hepatocytes with STAT3 expression had a significantly less tumor formation induced by chronic carbon tetrachloride, as compared to hepatocytes with STAT3 knockout. In contrast, hepatocytes with STAT3 expression were found to have a significantly higher tumor formation induced by diethylnitrosamine, as compared to hepatocytes with no STAT3 expression . In otheet al. found that siRNA knockdown of STAT3 resulted in significantly increased tumor growth, and this observation correlated with increased glucose consumption, lactate production, and expression of HIF-1α target genes in the tumor cells [Several recent studies have shed insights into how STAT3 might mediate tumor suppressor effects. In one study using thyroid cancer cell lines and an SCID mouse xenograft model, Couto or cells . These f−/− mouse embryonic fibroblasts transformed by c-Myc, but exert no appreciable effects on the same type of fibroblasts that were transformed by ras [i.e., deficiency in both p53 and p16), it appears that c-Myc is the dominant oncogenic protein, and STAT3 is a tumor suppressor. Several proteins have also been implicated in regulating the function of STAT3 in cancer cells. In one study, STAT3C was found to suppress cell growth in p53d by ras . In anotd by ras . In contet al. found that that ablation of STAT3 significantly increase the invasiveness of colorectal cancer [In mice generated by crossing the Apc(Min/+) mice and conditional STAT3 knockout mice, Lee l cancer , a findil cancer . FurtherWhile the majority of studies evaluating the prognostic value of STAT3 and/or pSTAT3 have pointed to its oncogenic effects, a few studies have reported contradictory observations, with the expression of STAT3/pSTAT3 found to be “paradoxically” associated with a better prognosis in various types of cancer, including those of the head and neck, salivary gland, breast, nasopharynx and rectum. For instance, high expression of pSTAT3 or nuclear STAT3 in head and neck cancer was found to be associated with a favorable clinical outcome; the progression free survival for patients carrying tumors with high expression of nuclear STAT3 was significantly longer than that of patients carrying tumors with relatively low STAT3 expression . In anotARF and c-myc have been implicated in influencing the function of STAT3 in various experimental models. With this new knowledge, it is perceivable that the studies of different cohorts of tumors that are biased toward specific molecular profiles will lead to different conclusions regarding the prognostic significance of STAT3. It is important to point out that the contradiction regarding the prognostic significance of STAT3 does not appear to be due to cell-type specificity. To illustrate this point, STAT3 was reported to be a marker of worse clinical outcome in head and neck cancer as well as breast cancer ,162,163,STAT3 transcript generates two isoforms, STAT3α and STAT3β, which are generated by alternative splicing of exon 23. The relationship of these two isoforms and the initial characterization of STAT3β have been previously described [The escribed . BrieflyThe expression STAT3β is found ubiquitously, but its level is lower than that of STAT3α ,165. IntY705 (or pSTAT3β) has a much longer half-life and nuclear retention than phosphorylated STAT3αY705 (or pSTAT3α) [Previous studies have shown that STAT3α and STAT3β have significantly different biochemical and biological properties. Some of the early studies have showed that phosphorylated STAT3βpSTAT3α) ,171,172.pSTAT3α) . AnotherpSTAT3α) . MoreovepSTAT3α) . Cyclin D1, Bcl-xL and Mcl-1, leading to inhibition of tumor growth and promotion of apoptosis [Fas, it was shown that STAT3β could counteract the transcription suppressive function of STAT3α in this context [Without the STAT3 transactivation domain, STAT3β is expected to be unable to activate promoters carrying the interferon/IL-6—responsive element. With this in mind, it was postulated that STAT3β might function as a dominant negative factor that can somewhat neutralize and/or regulate the biological effects of STAT3α. Experimental data supports this concept, as STAT3β was found to inhibit the transcription activity of full-length STAT3 or STAT3α . In othepoptosis ,175,176. context . In the  context ,177,178.i.e., which carries the tyrosine 705 residue), the Src homology2 (SH2) domain that is responsible for STAT3 dimerization and its binding to the receptor complex , the coiled-coil domain that allows STAT3 to interact with other proteins, and the DNA binding domain. Accumulating evidence is in support of this concept. Schaefer et al. reported that STAT3β can cooperate with c-Jun to activate a promoter containing the IL-6 responsive element; interestingly, the nuclear extract from cells transfected with the STAT3β cDNA, but not that from cells transfected with the STAT3α cDNA, formed a complex with an oligonucleotide containing the STAT3 binding site [In addition to its dominant negative role, STAT3β is believed to carry other functions, considering the fact that it possesses most of the important STAT3 functional domains including the activation domain (ing site . In anoting site . Furthering site . Anothering site . LEDGF (lens epithelium-derived growth factor), PCAF (p300/CBP-associated factor), CCNC (cyclin C), PEX1  and STAT1β [et al. identified distinct gene sets modulated by STAT3α and STAT3β, respectively. Specifically, the authors identified 506 genes that are regulated by both STAT3α and STAT3β, with 651 STAT3α-specific target genes and 1331 STAT3β-specific target genes [More recently, it has become evident that STAT3β has its own set of target genes that is distinct from that of STAT3α, and these genes include d STAT1β . In the d STAT1β . In anotet genes . Taken tet genes . Y705 upon stimulation with oncostatin M in murine embryonic fibroblasts [Y705 stimulated by oncostatin M was transient, reaching a peak at 15 min after the exposure to oncostatin M exposure and decreasing to an undetectable level at 60 min. In contrast, in the presence of STAT3β, phosphorylation of STAT3αY705 was sustained at a high level for 120 min. Correlating with these findings, cells with STAT3β expression had a prolonged nuclear retention of STAT3. In the same study, it was also demonstrated that an intact SH2 domain is required for the interaction between the two STAT3 isoforms and the cross-regulation of STAT3β on STAT3α.It was recently found that STAT3β could directly regulate STAT3α. In one study, STAT3β was found to upregulate and prolong the phosphorylation of STAT3αroblasts . SpecifiSTAT3β significantly reduced the transcriptional activity of STAT3α, although the levels of pSTAT3α in the cytoplasm and nuclei were increased (data not shown).How are these findings related to the observation that STAT3 can function as a tumor suppressor? While the answer to this question needs further investigation, one may speculate that heterodimerization of pSTAT3α and pSTAT3β may sequester pSTAT3 away from its DNA target genes or interfere with the transcription activity of pSTAT3 due to the absence of the transactivation domain in STAT3β. Thus, in the presence of a high level of pSTAT3β, pSTAT3α becomes less available or effective in mediating gene transcription, despite the fact that its expression and nuclear retention are higher due to the stabilizing effect of pSTAT3β. Results from one of our recent studies support this concept. Using esophageal squamous cell carcinoma cell lines, we were able to demonstrate that the transfection of STAT3β transfection in murine B16 melanoma cells induced cell cycle arrest as well as apoptosis [STAT3β cDNA into established B16 melanoma xenografts in SCID mice induced apoptosis and suppressed tumor growth [STAT3β was found to result in marked shrinkage of xenografts in SCID mice, whereas siRNA knockdown of STAT3 resulted no significant effect on breast tumor growth [STAT3β-transfected cells as well as the bystander non-transfected cells in the same tissue culture, due to the production of TRAIL (TNF-related apoptosis inducing ligand) induced by STAT3β transfection [STAT3β cDNA in a human melanoma cell line increases FAS expression and enhances apoptosis induced by FAS-ligand and UV irradiation [Correlating with these findings, STAT3β has been shown to play a tumor suppressor role in various types of cancer, including melanoma, breast cancer and lung cancer ,181,182.poptosis . Furtherr growth . In anotsfection . Moreoveadiation . A few ySince the late 90s when the potent oncogenic effects of STAT3α was demonstrated in various experimental models, there have been numerous published clinicopathologic studies showing a significant correlation between a high expression level of STAT3 and/or pSTAT3  and a worse clinical outcome and/or adverse pathologic features. Nevertheless, as mentioned in ARF) as well as the simultaneous evaluation of STAT3α and STAT3β. From a technical perspective, the detection of the two STAT3 isoforms in tumor samples is currently feasible only by analyzing fresh tissues using western blots, with which STAT3α and STAT3β can be differentiated from each other based on the difference in their molecular weights . Immunohistochemistry, which has been used in the vast majority of clinicopathologic studies of STAT3 in the past, is only useful if antibodies specific for STAT3α or STAT3β (and their phosphorylated counterparts) are available. To our knowledge, most if not all commercially available anti-STAT3 or anti-pSTAT3 antibodies are not isoform-specific. We are aware of a small number of reports describing antibodies that recognize STAT3β but not STAT3α, although these antibodies are not widely available and/or extensively characterized. While we summarized all the existing data regarding STAT3β, we came to realize that virtually all published clinicopathologic studies pertaining the prognostic value of STAT3/pSTAT3 did not differentiate the two STAT3 isoforms. Considering the concept that STAT3 can be an oncoprotein or a tumor suppressor (as described in Since the initial description of the oncogenic role of STAT3 in the late 90s, much has been learned about the significance and the divergent roles of STAT3 in cancer biology. The experimental results summarized in this review have highlighted the concept that STAT3 can be friend or foe in cancer, and there are a relatively large number of factors that are known to dictate how STAT3 affects the malignant phenotypes and clinical behaviors of cancer. It is clear that the roles of STAT3 in cancer cells are substantially more complicated that it was appreciated previously. We believe that this new knowledge carries significant implications in how we should evaluate the prognostic value of STAT3 in tumors, and in how we design new anti-cancer drugs targeted against STAT3. More attention should be given to factors or processes that have not been discovered or appreciated until recently, including the non-canonical mechanism of STAT3, acetylation of STAT3, the dominant negative effects of STAT3β, as well as specific genetic background and/or coexisting biochemical defects that might functionally interact with STAT3. It is our hope that researchers in this field will take this new knowledge into consideration in their future studies.


Myriophyllum spicatum populations from the Qinghai-Tibetan Plateau (QTP) and adjacent highlands  by using microsatellite loci and environmental and geographical factors. Bioclim layers, hydrological properties and elevation were considered as environmental variables and reduced by principal component analysis. The genetic isolation by geographic distance (IBD) was tested by Mantel tests and the relative importance of environmental variables on population genetic differentiation was determined by a partial Mantel test and multiple matrix regression with randomization (MMRR). Two genetic clusters corresponding to the QTP and YGP were identified. Both tests and MMRR revealed a significant and strong correlation between genetic divergence and geographic isolation under the influence of environmental heterogeneity at the overall and finer spatial scales. Our findings suggested the dominant role of geography on the evolution of M. spicatum under a steep environmental gradient in the alpine landscape as a result of dispersal limitation and genetic drift.Understanding how natural processes affect population genetic structures is an important issue in evolutionary biology. One effective method is to assess the relative importance of environmental and geographical factors in the genetic structure of populations. In this study, we examined the spatial genetic variation of thirteen  Understanding how natural processes affect population genetic structures and gene flows is an important issue in evolutionary biology. Environmental variables can influence the movement of gametes, individuals, and groups of individuals among natural populations to affect gene flow patterns2455et al.For aquatic plants, given that they live in fragmented islands in terrestrial landscapes, severe genetic differentiation by geographic isolation may be common84Myriophyllum spicatum L.) is native to Europe, Asia and northern Africaet al. unpublished). In the waters of the Qinghai-Tibetan Plateau (QTP) and adjacent highlands, M. spicatum is a common submerged macrophyteM. spicatum and the underlying factors.Eurasian watermilfoil  for genetic analysis using microsatellite markers. The Bioclim layers (climate) and elevation were recruited as environmental variables along with the hydrological properties, which are important for aquatic plants19The number of alleles (NA), the number of effective alleles (NEA) and the genotype diversity (D) ranged from 30 to 75, 25.76 to 50.596 and 0.111 to 1, respectively . GloballM. spicatum populations were divided into two clusters, we found that the eight populations from the QTP were assigned to one cluster and the five populations of the adjacent highlands (YGP) were assigned to another cluster . For the QTP and YGP clusters, the first two axes explained 69.8% and 80.2% of environmental variables, respectively, which were used to calculate environmental dissimilarity. The relative contributions of environmental variables to the axes are presented in Table S2.r = 0.330, p = 0.012) was significant, whereas we could not detect significant correlations between genetic differentiation and environmental distance   and not significant genetic-environmental association   . With MM= 0.831)  and 4. I . With M= 0.831)  and 4.M. spicatum. The IBD pattern was further confirmed by landscape analyses at the overall and finer spatial scales when the influence of environmental factors was considered  help to transport the seeds of watermilfoils through digestive track242628M. spicatum populations in alpine landscapes, which could explain the greater prediction of IBD on genetic differentiation as a result of dispersal limitation and genetic drift.The IBD pattern was highly associated with limited dispersal among populations in alpine environments. Suren & OrmerodM. spicatum populations might be limited. Wang et al.M. spicatum in reacting to alpine environmental stress. In addition, phenological shifts  reduced pollen exchanges between the populations with different elevations and were thus considered to be an important mechanism for creating genetic divergence among populations along the elevational gradientM. spicatum populations with different elevations because the inter-population gene flow of this submerged macrophyte mainly relies on vegetative propagules for local propagation and on seeds for long-distance propagation rather than on pollen diffusionA series of environmental factors varied along the elevation gradient, and among them, climate was a fundamental factor that could cause genetic divergence in the population3030M. spicatum under a steep environmental gradient in an alpine landscape. However, physical barriers and climate rather than geographic distance significantly affected the population genetic differentiation of M. spicatum, as revealed at the country scale in China . Different results confirmed that the main factor driving genetic divergence might vary in different regions or at different scalesIn conclusion, we highlighted the influence of geographic isolation on the pattern of the gene flow and genetic differentiation of M. spicatum were collected at 13 sites of the QTP and its adjacent highlands in August 2012  following the manufacturer’s protocol.M. spicatumet al.We previously isolated 20 microsatellite loci from The indices of genetic diversity, the number of alleles (NA), the number of effective alleles (NEA), the number of genotypes (clone assignment), and Nei’sM. spicatum due to dosage problems. Additionally, the statistics of microsatellite markers were developed for diploid organisms and were not suitable for polyploid organisms43BBecause the allelic copies of microsatellites were ambiguous in polyploid species, we could not determine the exact genotypes of heterozygotes in Distinct genetic clusters of the 13 Eurasian watermilfoil populations were identified using an individually based assignment approach as implemented in STRUCTURE 2.3.4 48An alternative method, Discriminant Analysis of Principal Components DAPC, was alsMantel tests with 10,000 permutations were used to detect IBD pattern based on matrices of pairwise Bruvo distance and geographic distance among all populations, as implemented in GenAlEx 6.5 softwareThe climatic variables of the studied sites (to a resolution of 1 km) were extracted from the BioClim dataset with GIS information56We reduced the environmental variables by the components of principal component analysis (PCA) based on the 27 environmental variables  using the “prcomp” function in R. Prior to analysis, all Bioclim variables and elevation values were standardized.The correlations between genetic differentiation and geographic/environmental factors were determined by a combination of partial Mantel testsHow to cite this article: Wu, Z. et al. Great influence of geographic isolation on the genetic differentiation of Myriophyllum spicatum under a steep environmental gradient. Sci. Rep.5, 15618; doi: 10.1038/srep15618 (2015).


Chorthippus saulcyi moralesi), a narrow-endemic taxon restricted to the Central Pyrenees. More specifically, we analysed range-wide patterns of genetic structure and tested whether they were shaped by geography , topographic complexity and present and past habitat suitability models , and environmental dissimilarity .Understanding the underlying processes shaping spatial patterns of genetic structure in free-ranging organisms is a central topic in evolutionary biology. Here, we aim to disentangle the relative importance of neutral (i.e. genetic drift) and local adaptation  processes in the evolution of spatial genetic structure of the Morales grasshopper  contains supplementary material, which is available to authorized users. Understanding the factors structuring genetic variation in natural populations is a paramount topic in evolutionary biology , 2. The Beyond geography and the spatial configuration of connecting corridors and isolating barriers, environment can also play a major role in shaping spatial patterns of genetic differentiation , 15. ThiChorthippus saulcyi moralesi) (Orthoptera: Acrididae) is a narrow-endemic subspecies belonging to the Chorthippus binotatus group, exclusively distributed in central Aragón and Catalonia Pyrenean mountains , distanins (see ) and geoins .We extracted genomic DNA from a hind-leg of each individual using a salt extraction protocol . We amplARLEQUIN 3.5    and the K method . We usedPP 1.1.2  to alignRUCT 1.1  to produa priori delimitation of populations), several important differences exist in their analytical approaches. STRUCTURE suffers from the assumption of HWE and gametic disequilibrium .We applied circuit theory to model gene flow between populations and test the effects of different landscape resistance scenarios (IBR) on observed patterns of genetic differentiation , 74. We CAPE 4.0  to calcuUITSCAPE ,76.FST and FSTNA-values) using multiple matrix regressions with randomization  and climate dissimilarity  distance matrices in order to test a possible pattern of IBE. We used a backward procedure to select final models, removing non-significant variables from an initial full model including all explanatory predictors. We tested the significance of the remaining variables again until no additional term reached significance  as imple .capscale’ function in the package VEGAN [FST or FSTNA-values) was tested against the following variables: i) geographic distances (IBD), ii) elevation and iii) population’s PC scores of the first three axes from the PCA performed on the environmental data . Euclidean geographical distances between sampled populations were calculated using GEOGRAPHIC DISTANCE MATRIX GENERATOR 1.2.3 [pcnm’ function in the package VEGAN. Significance of the predictors was assessed using multivariate F-statistics with 9999 permutations using the ‘anova.cca’ function included in the package VEGAN. We first analyzed the relationship between the genetic distance matrix and each variable separately  and then we performed a partial dbRDA  for each variable while controlling for the influence of geography (fitted as covariate).Complementary to MMRR analyses, we tested the relationship between genetic differentiation, geography and environment using distance-based redundancy analyses . This age VEGAN  as impleOR 1.2.3 . GeograpSUNDER [SUNDER uses a Bayesian framework with a MCMC algorithm to estimate the magnitude of the effects of these variables and implements a model selection procedure by cross-validation to assess which sub-model (e.g. with or without the effect of environment) best fits to the data. Using the multinomial model, we ran SUNDER with 10 million of iterations for each data set, sampling every 1 000 iterations. We set to update in the MCMC iterations all parameters of the vector θ . We also set their initial state and upper bounds of their Dirichlet prior distributions following suggestions in [SUNDER analyses using as environmental matrices the elevation dissimilarity matrix (ELEVDIS) and the climate dissimilarity distance matrix (CLIMDIS), which we separately tested against IBR distance matrix based on a completely ‘flat’ landscape (IBD). Before analyses, we standardized all distance matrices by their respective standard deviations.We quantified the relative effects of geography and environment on genetic differentiation using SUNDER , a novelSUNDER , see alsSUNDER ). SUNDERtions in . We visuWe constructed past and present-day final climate niche models using six bioclimatic variables: annual mean temperature (Bio1), mean temperature of the coldest quarter (Bio11), annual precipitation (Bio12), precipitation of the driest month (Bio14), precipitation seasonality (Bio15), and precipitation of the warmest quarter (Bio18). This model had a very high value of area under the receiving operator characteristics curve (AUC = 0.919 ± 0.067 SD), indicating overall good performance. The predicted habitat suitability area in the present was consistent with the current distribution of the species, but some areas in the northern and west side of the Pyrenees where the Morales grasshopper has not been recorded were also predicted to be suitable for the species Fig. . ProjectFST-values ranged from 0.021 to 0.216 and 53 of 55 comparisons were significant after sequential Bonferroni correction .Pairwise STRUCTURE analyses on all populations showed that the best-supported number of clusters was K = 2 according to the ΔK method  was evaluated. Subsequent hierarchical analyses on different subsets of populations detected further genetic structuring , DAPC partitioned all individuals in western (TOR-NER-SAR populations), central-west (CHI-ASP), central-east (BOI-PER) and eastern (CAR-ERR-CRE and RAS) groups  was positively associated with geographic distance , topographic complexity (IBRTC) and current (IBRCURRENT), LGM (IBRLGM) and LIG (IBRLIG) habitat resistance distances when these variables were included alone into different models . Indeed, Mantel tests showed that all these variables were highly inter-correlated  was also correlated with all other variables, but with comparatively lower Mantel r values, whereas elevation dissimilarity (ELEVDIS) was only significantly correlated with CLIMDIS  . Analyses based on FSTNA yielded similar results, but models had generally lower values of coefficient of determination (r2)  and geography and one environmental predictor (climate PC2) that explained 46.80–53.66 % and 40.12–45.70 % of genetic variation, respectively .Marginal tests showed a significant association between genetic differentiation  were smaller than βE (representing the magnitude of the effect of environment), indicating a primordial effect of geographical distances and an absence of isolation by climate or elevation on genetic differentiation  and much higher than that shown for Ramburiella hispanica at a similar spatial scale , two species presenting a patchy distribution restricted to highly isolated and fragmented habitats [Despite its small distribution range (<200 km between the most distant populations), the Pyrenean Morales grasshopper exhibits a strong genetic structure congruent with the geographical location of its different populations. Our microsatellite-based clustering and phylogenetic analyses revealed a strong hierarchical structure and a relatively low degree of inter-group genetic admixture. In most cases, the distinct genetic clusters corresponded to single populations, which clustered in the hierarchically superior genetic group according to the main mountain chains of the region. In comparison with other Mediterranean orthoptera, the Pyrenean Morales grasshopper has a population genetic structure as remarkable as that found at a wider spatial scale in habitats , 81, 82.habitats . Despitehabitats . It coulhabitats . Thus, ohabitats , 85, 86.Our different landscape genetic approaches confirmed that neutral divergence resulted from the isolating effects of topography primarily drove the deep patterns of population genetic differentiation observed in the Morales grasshopper. The resistance model based on topographic complexity was the best fit to our data, indicating that physical features defining the abrupt landscape characterizing the Pyrenees shaped genetic differentiation. In particular, deep valleys with a north-south orientation and slopes that are generally steep and create canyons and ridges on the landscape crisscross the central and eastern portion of the Pyrenees inhabited by Morales grasshopper. Hence, these topographic features could become impassable barriers and restrict gene flow as has been previously documented for other species with limited dispersal capacities and inhabiting regions of remarkable topographical roughness , 87, 88.We found no support for ecological divergence and local adaptation processes have contributed to population genetic differentiation and the three different employed approaches  confirmed the consistency of this result. We did not find support either for altitude as an isolating mechanism despite elevation gradients have been previously found to be a significant driver of genetic and phenotypic variation in grasshoppers and many other organisms , 95. OurThis study emphasizes the importance of examining jointly different scenarios of population isolation to understand their contribution to the spatial distribution of genetic variation across a species range. Our analyses evidence the importance of topographic complexity in determining patterns of genetic differentiation, indicating that limited dispersal and drift, due to scarce population connectivity, is shaping the genetic structure found in our study system . FurtheThis study did not require ethical approval. Specimens were collected under license from ‘Gobierno de Aragón’, ‘Generalitat de Catalunya’ and ‘Ordesa y Monte Perdido National Park’. Our sampling procedures did not affect the survival of the studied populations.http://dx.doi.org/10.6070/H4QC01JB). All other data supporting the results of this article are included within the article and its additional files.Nuclear microsatellite data are available in the LabArchives repository (Not applicable.


A high level of genetic diversity was found in the A. E. Watkins bread wheat landrace collection. Genotypic information was used to determine the population structure and to develop germplasm resources.Triticum aestivum L.) from official channels of the board of Trade in London, many of which originated from local markets in 32 countries. The geographic distribution of the 826 landrace cultivars of the current collection, here called the Watkins collection, covers many Asian and European countries and some from Africa. The cultivars were genotyped with 41 microsatellite markers in order to investigate the genetic diversity and population structure of the collection. A high level of genetic diversity was found, higher than in a collection of modern European winter bread wheat varieties from 1945 to 2000. Furthermore, although weak, the population structure of the Watkins collection reveals nine ancestral geographical groupings. An exchange of genetic material between ancestral groups before commercial wheat-breeding started would be a possible explanation for this. The increased knowledge regarding the diversity of the Watkins collection was used to develop resources for wheat research and breeding, one of them a core set, which captures the majority of the genetic diversity detected. The understanding of genetic diversity and population structure together with the availability of breeding resources should help to accelerate the detection of new alleles in the Watkins collection. In the 1930s A. E. Watkins acquired landrace cultivars of bread wheat (The online version of this article (doi:10.1007/s00122-014-2344-5) contains supplementary material, which is available to authorized users. Triticum aestivum L.) is an important staple crop with over 600 million tonnes being harvested annually. Wheat was originally domesticated about 10,000 years ago in the fertile crescent (see Shewry Triticum dicoccoides), the donor of the A and B genome, with the wild diploid species Aegilops tauschii, the donor of the D genome  LCs from markets predominantly in Asia and Europe, but also from other parts of the world using connections with the London Board of Trade. In this way, he established a considerable wheat collection of over 7,000 accessions . Single seed descendents (SSDs) were developed over 6 generations for all 826 LCs. Initially, four seeds were sown for each LC. In 234 cases, phenotypes from the same accession showed striking differences in the first generation and, thus, two SSD streams were produced from those accessions, here called sister lines. All field trials were grown at Church Farm Bawburgh, Norwich, UK , under standard growing conditions, if not stated differently. Trials with replicates were planted in a randomised block design The majority of SSDs, developed from original accessions of the Watkins collection, were planted in 2006 in four replicates of 1 m × 1 m field plots without nitrogen fertiliser usage. The following traits were measured: mature plant height, ear emergence, grain yield, lodging, vernalisation requirement, presence of awns, thousand grain weight, grain length, grain width and grain surface area. The Watkins core set (119 LCs) and the Gediflux collection were grown in 2011 in field plots of 1.5 m × 4 m (single plots) and 1 m × 1m (three replicates), respectively, and assessed for mature plant height (Gediflux only), peduncle and internode lengths (Gediflux only), ear emergence, grain yield, thousand grain weight, grain length, grain width and grain surface area. A trait mean was calculated for the varieties of the core set for each trait and both years, 2006 and 2011, with exception of the Watkins 2011 data, where only single plots were measured. For comparison of phenotypic observations between those years, the 2006 data set values were adjusted to the 2011 level. Bi-parental SSD populations were developed for several LC accessions. For this the elite wheat cultivar ‘Paragon’ was crossed with selected Watkins LCs followed by four rounds of self pollination. The selection criterion for the LC parent was the display of a phenotype within the extreme borders of the phenotype range. Bi-parental populations were grown in 2011 for multiplication purposes in single 1 m × 1 m field plots and assessed for plant height and flowering times.Seeds for the Watkins collection were received from the John Innes Centre Germplasm Resource Unit (GRU http://www.wheat.pw.usda.gov/). Targeted markers were selected to fall on different chromosomes according to a published consensus map . The genotyping of the Watkins collection was conducted using 41 publicly available SSR markers. Public primer sets were used from JIC (psp), IPK Gatersleben (gwm/gdm), Wheat Microsatellite Consortium (wmc), Beltsville Agricultural Research Station (barc) and INRA (cfd/cfa) collections, and can be found on the GrainGenes website  according to Schuelke . PCR mixReactions that did not show an amplified product were repeated. A NULL allele was scored, if: (a) the repeat did not result in a PCR product; (b) the DNA quality was good, as could be seen from the scores of other markers; and (c) the number of missing amplifications for that marker was lower than 5 %. Otherwise, the data point was scored as missing.Of the 41 SSR markers, 39 markers had a good score with less than 5% missing values after one round of re-genotyping of missing calls. Some of the markers detected more than one locus. If a clear separation between loci could be made, the marker was scored multiple times, otherwise only scores for the most consistent locus were taken. This resulted in genotypic information for 45 loci (see Table dSWIp) was calculated when traits were measured in all three trials. For this, the overall phenotype range was divided into 12 phenotype classes of similar size. For each trial, the frequencies of scores in each class were determined. dSWIpwas calculated similar to dSWI(see Table S1) from these frequencies.The genetic diversity of a collection of cultivars was calculated using R software (vs. 3.02) . For the final selection of the core set of accessions the dSWI was used, as the highest value of the genetic diversity averaged over the four diversity indices was achieved for this index.A reduced set of LCs was initially selected from the diverse phenotypes. The diversity of the core was determined using the CoreHunter software  between 1.37 and 2.03). Watkins LCs with extreme phenotypes for any of the above traits were selected as parents for the development of bi-parental populations (Table S2). The collection was also scored for vernalisation requirement: 86% of the lines showed spring growth habit, and only 14% winter growth habit.The phenotypic diversity of the Watkins collection has been assessed in field trials for the majority of LCs (726 accessions). Scores for the traits adult plant height, heading date, and four grain characteristics were taken (Table S2). A wide range of phenotype scores were found for the traits scored, as indicated by high diversity values  was conducted. This showed that on average 15.7 (35.7 %) of the markers had different allele sets between sister lines . This gives some indication of the heterogeneity of the original LC accessions.dPIC value is just 0.57 (see Table S4), much lower than that found in the Watkins collection (0.75). Similar observations can be made for Nei’s gene diversity (dNei=0.62 vs. 0.78) and the Shannon–Weaver Diversity Index (dSWI=1.30 vs. 2.09). Detailed results can be found in Table S4.Using the genotype data generated by the Gediflux project .In order to understand the origin of the diversity better, the diversity of the three different wheat genomes were determined separately, by only using markers specific for single genomes. Average diversity values for A, B and D genome markers for the Watkins collection were found as δK statistics  did not carry a spring type alleles at any of the three dPIC) of 0.75 were found. These values are at the higher end of those found in other wheat LC collections (see Table S7 for a list of published studies). The genetic diversity of the IPK bread wheat collection of 998 LCs from 68 countries was reported as an average allele number of 18.1 per locus and a dPIC of 0.77  values are 0.78 versus 0.65 for the Watkins and the Gediflux collections, respectively. A more detailed comparison of the diversity levels for single markers was possible, due to 14 markers being shared between the studies. The Watkins diversity values were consistently higher for all 14 markers (see Table S9).The diversity levels found in the Watkins collection were also compared to those of the Gediflux collection, which stands for the type of Northern European germplasm breeding would be targeted for. It seems apparent from this comparison that the LCs assembled in the Watkins collection preserve a much higher level of genetic diversity than present in the MCs. The gene diversity .The Watkins collection is proven to host interesting alleles, particularly for disease resistance Dyck , which aFurthermore, a larger number of bi-parental mapping populations is under development, which will allow QTL mapping to be carried out. Transgressive segregants were discovered in several of these populations when scored for common traits. Furthermore, a number of QTLs have been identified. These observations and findings support the claim that the bi-parental populations will be valuable resources in the identification of new alleles in the wheat genome.World wheat yields have plateaued in the last 10 years. All possible efforts are necessary to achieve a sufficient increase in world wheat yields, to keep up with the world’s growing human population and the challenges climate change will pose for agriculture. The detailed genotypic description of the Watkins LCs collection and the development of the new breeding resources should ultimately improve and accelerate allele discovery for simple, as well as for complex, traits and hopefully help to achieve the successful improvement of bread wheat, in line with the growing demand.Supplementary material 1 (pdf 266 KB)Below is the link to the electronic supplementary material.


Nestor notabilis). Kea have undergone a long-lasting persecution between the late 1800s to 1970s where an estimated 150,000 kea were culled under a governmental bounty scheme. Kea now number 1,000–5,000 individuals in the wild and it is likely that the recent population decline may have reduced the genetic diversity of the species. Comparison of contemporary (n = 410), historical (n = 15) and fossil samples (n = 4) showed a loss of mitochondrial diversity since the end of the last glaciation  but no loss of overall genetic diversity associated with the cull. Microsatellite data indicated a recent bottleneck for only one population and a range-wide decline in eN dating back some 300 – 6,000 years ago, a period predating European arrival in NZ. These results suggest that despite a recent human persecution, kea might have experienced a large population decline before stabilizing in numbers prior to human settlement of New Zealand in response to Holocene changes in habitat distribution. Our study therefore highlights the need to understand the respective effects of climate change and human activities on endangered species dynamics when proposing conservation guidelines.The impact of population bottlenecks is an important factor to consider when assessing species survival. Population declines can considerably limit the evolutionary potential of species and make them more susceptible to stochastic events. New Zealand has a well documented history of decline of endemic avifauna related to human colonization. Here, we investigate the genetic effects of a recent population decline in the endangered kea ( Ovibos moschatus) were best explained by climate and associated habitat change [Mammuthus primigenius) appears to have been caused by the concomitant effect of over-hunting and climate change [Genetic diversity is an essential component of both the short and long-term persistence of species. It can be regarded as the basis for evolutionary potential  and favot change  while the change . It is tIn order to distinguish anthropogenic from natural drivers of population decline, one can refer to pre-human fossil records of the distribution of species. However, such data is often lacking or incomplete, leading to a potential bias in the estimation of population size and oversimplification of population dynamics, as critically addressed by Rawlence et al.  and AlleDNA-based approaches using contemporary samples only are routinely used to detect population declines –16 but cNestor notabilis), an endemic alpine parrot prehistorically present in both the North and South Islands of New Zealand [The avifauna of New Zealand evolved in the absence of mammalian predators , 21 and  Zealand –32 and n Zealand . Its inq Zealand , 35, opp Zealand –38 and m Zealand –41 promp Zealand  while a  Zealand  but it i Zealand . However Zealand . The estPrevious results have shown that despite its strong flying capabilities, the kea is subdivided into three distinct genetic clusters and that this population structure is most likely the result of postglacial recolonization processes but not a recent human-induced fragmentation . Kea popHere we test whether kea have experienced pre-human population declines or if the species started to decline only recently because of human persecution. We first assess changes in genetic diversity over time using microsatellite and mitochondrial data comparing contemporary, historical and fossil samples. Second, we look for a signature of ancient and recent bottlenecks using conventional methods for microsatellite data. We also look for a signature of population decline using coalescent-based Bayesian methods and estimate its timing and magnitude. Due to the magnitude of the reported recent population collapse , we expect to find extinct haplotypes and alleles among historical and fossil samples that are not present in contemporary samples and also a significant signature of a recent bottleneck.Fieldwork was conducted under the approval of the University of Otago Ethics Committee (No. 74/09) and DoC . No permits were required to sample fossil material from museums. Permission to sample museum specimens was obtained by NJR from individual institutions.We utilized a contemporary dataset from birds caught between 2010 and 2012 comprising 91 mitochondrial (mtDNA) control region (CR) sequences and 410 nuclear microsatellite genotypes (17 microsatellite loci) as described in Dussex et al. . AdditioAll ancient DNA (aDNA) extractions and PCR set up was carried out at the University of Otago in a purpose built aDNA laboratory physically isolated from other molecular laboratories . Strict Historical samples were genotyped for 17 microsatellite loci  using cyThe mtDNA CR from historical skins was amplified and sequenced using four primer pairs (producing ∼250 bp fragments including primers) , S1 Fig.2 (Life Technologies), 1 x Gold Buffer II (Life Technologies), 2.5 mM dNTPs (Bioline), 250 nM each primer, 1.25 U of AmpliTaq Gold DNA Polymerase (Life Technologies), and 2 µL DNA. Unsuccessful PCRs were repeated with 2 U AmpliTaq Gold DNA Polymerase and 4 µL DNA or 2 µL 1:10 DNA. PCR thermocycling conditions consisted of 94°C 9 min, 60 cycles 94°C 30 s, 50°C 45 s, 72°C 1 min, followed by a final extension of 72°C 10 min.Fossil samples were amplified and sequenced using seven primer pairs (∼150 bp fragments including primers) , S1 Fig.PCR products were run on a 2% 1 x TAE agarose gel. PCR products were purified  by incubation at 37°C for 30 min and 80°C for 15 min), and sequenced bi-directionally from independent PCR products using Big Dye Terminator technology and an ABI 3730xl. When an inconsistency between sequences from an individual was observed due to DNA damage (C-T and G-A transitions), additional PCRs and bi-directional sequencing were conducted, and a majority rule consensus applied to the four independent PCRs .Consensus sequences were obtained from the alignment of fragments made in Geneious 5.6.3 .P-value was determined using a Markov chain method following the permutation algorithm of Guo and Thompson [For microsatellite markers, we checked for scoring error due to stuttering, large allele drop-out, and null alleles using MICRO-CHECKER 2.2.3 . Tests fThompson . The sigThompson .Indices of genetic diversity were calculated for both contemporary and museum samples. To assess the level of genetic variation in microsatellites, FSTAT was used to calculate means of allelic richness corrected for sample size , observeh), nucleotide diversity  were computed in ARLEQUIN 3.5 [Mitochondrial diversity indices, such as number of haplotypes , numberQUIN 3.5  for contWe then constructed a temporal network to look at mitochondrial variation in kea through time using the R script TempNet v1.4. .K was estimated from 25 iterations of K values ranging from 2 to 4 (as the most likely K selected was 2 and 3 for contemporary data). The results obtained with STRUCTURE 2.2 were used as input in STRUCTURE HARVESTER [K-value the individuals‚ assignment coefficient (q) for each genetic cluster were averaged over the 10 runs with the lowest posterior probability using CLUMPP software [In order to compare historical and contemporary population structure, we examined the assignment of historical samples to contemporary populations. The rationale was that if the contemporary structure corresponded to the historical one, museum samples should cluster with samples of the same geographical origin. On the other hand, if the contemporary structure is the result of genetic drift caused by the recent population decline, historical samples should not cluster well with individuals of the same geographical origin or have more admixed membership. We first used STRUCTURE 2.2 , 66 to eARVESTER  to produsoftware . Finallysoftware .For all analyses described below, we first considered the three genetic clusters  identified in Dussex et al.  Fig. 1aFig. 1a aM-ratio test [M-P-Val and Critical_M [sp = 0.9 and sp = 0.78, 3.5 steps for multi-step mutations and pre-bottleneck eN values ranging from 500 and 5000. The mutation rate remained constant at μ of 5 × 10-4 [M-ratio was also calculated using only polymorphic loci to account for the upward bias in the M-ratio estimate, as implemented in ARLEQUIN 3.11 [eN through time, we used the coalescent-based Bayesian method of Storz and Beaumont [HN at T years in the past to the current CN, where the data are allele frequencies for unlinked microsatellites. These loci are assumed to have independent genealogies and to be evolving according to the strict single-step mutation model. Given the demographic parameters  and the mutation rate (μ), the genealogical history of the sampled data can be described using coalescent theory, and the posterior probability distribution of the genealogical and demographic parameters is estimated using Markov chain Monte Carlo (MCMC) simulations. We performed five simulations per dataset  with 2 × 109 iterations with parameter values recorded every 1 × 105 iterations resulting in 20,000 records. Prior distributions were set to the parameters of simulation 1  the heterozygosity excess method implemented in the software BOTTLENECK v. 1.2.02 . This aptio test  using M-itical_M , which i5 × 10-4 . The M-rUIN 3.11 . SecondlBeaumont  implemenlation 1 . After ceN (HN) was uniformly distributed between 10 and 2 × 105, the upper bound encompassing 150,000 kea reported being killed between the 1870s and 1970s [eN (CN) was uniformly distributed between 10 and 104. These values included the current and conservative population estimate of 1,000 to 5,000 birds [eN was uniformly distributed through time for 0 to 1,000 generations and bounded between 10 and 2 × 105 individuals. For the two models, a generalized stepwise mutation model [-5 and 1.00 × 10-3 substitutions/generations [Finally, two different demographic scenarios were compared within an ABC framework  using DInd 1970s . The pri00 birds  and alsoon model  was implerations –88. The erations , 90. TheeN through time and demographic scenarios are available as Further details on tests of bottleneck detection using moment-based methods, variations of For museum samples, two microsatellite loci (Nmed31 and Strhab43) failed to amplify. After Bonferonni correction, no significant departure from Hardy-Weinberg equilibrium was observed and no tests for linkage disequilibrium were significant. Signs of null alleles were detected in single loci in the Kahurangi (Strhab43), Arthurs Pass (Nnot8) and Mt Cook (Nnot38) populations.When comparing microsatellite genetic diversity among our historical and contemporary datasets, no extinct alleles were identified within the 15 skin samples . AllelicConsensus sequences for the mtDNA CR for historical and fossil samples varied between 245 and 616 bp . Three nq > 0.85) to their putative cluster of origin when considering two distinct genetic clusters . Individual NMW 12.211  showed again shared membership with q-values of 0.44, 0.28 and 0.28 for the north, central and south cluster respectively. Samples NMW 12.208 and NMW 49.760, putatively originating from the north cluster, also had admixed origins with q-values of 0.64 and 0.57 to the central cluster. However, those two samples had very high q-values of 0.94 and 0.88 to their cluster of origin when considering a scenario of two clusters. It is worth noting that because the distinction between the north and central cluster is not very strong and as gene flow is marked between these two clusters, shared membership between those two clusters is perhaps not surprising.When comparing historical and contemporary population structure using the Bayesian algorithm implemented in STRUCTURE, 14 individuals out of 15 clustered well with contemporary individuals from the same sampling locations. Of the 15 historical samples, 13 had high membership coefficients . Depending on the parameter set used, M-ratio values ranged from 0.76 to 0.86 (CM) that represents the threshold under which a bottleneck is supported. We only found evidence of a bottleneck when using a θ value of 1, corresponding to a pre-decline eN of 500 and only for the north and central clusters.When using the  to 0.86 . These vHN) than contemporary (CN) effective population sizes for both demographic models . Bayes factor values were >10 for all clusters and models indicating strong evidence for a population decline (CN/HN) clearly supported a population decline. Using an exponential model of population size change, the 90% highest posterior density (HPD) of the ratio r was of 0.0019–0.052, 0.053–0.068, 0.019–0.031 and of 0.0003–0.017 for Historical Canterbury, the north, central and south cluster samples respectively. These r values indicated that the contemporary eN is less than 10% of the historical HN.Historical Canterbury and all three contemporary genetic clusters examined showed larger historical ( decline . The ratHN) for the exponential model were historical-CanterburyN = 3,995, northN = 4,493, centralN = 3,823, and southN = 4,620 compared to modal values for contemporary effective population sizes (CN) of 131, 279, 106, and 28 respectively   , Fig. 5.HN and CN for the linear model were similar to the exponential model  in contemporary samples was probably due to the paucity of samples available to our study with robust locality data. However, aDNA data shows two haplotypes and three new substitution sites found among fossil samples suggesting that a loss of genetic diversity might have occurred prior to the cull of the late 1800s. A larger sample size representing a wider time period between the Otiran Glacial to European arrival in New Zealand would be required to estimate the timing of such a decline and the extent of genetic diversity loss with more accuracy.It is possible that our historical samples were collected after the peak of the government cull had already occurred. This is particularly true when considering the nature of the bottleneck. If for instance, the cull was rapid, of great intensity and resulted in populations being extirpated locally, it is quite likely that our historical sampling was not able to capture the effect of the bottleneck. If on the other hand, the cull lasted over a longer time span (> 10 years), we would be more likely to detect a loss of genetic diversity with our samples, as the commonly accepted date for the start of the government cull is 1860 . HoweverEven if we cannot exclude the possibility of populations being extirpated locally in areas of high conflict with farmers, it is likely that some other populations could have persisted in more remote areas without farming and remained unaffected by the cull, thus maintaining historical levels of genetic diversity. Although this has never been reported, because of the dispersive , 96 and Assignments of historical samples scored at 15 microsatellite loci did not suggest a different historical population structure, with most historical samples assigning well to their putative cluster of origin. This gives further support to the contemporary structure not resulting from genetic drift caused by the recent human-led population decline .The demographic histories of endangered and fluctuating populations is often complex. While conservationists often focus on the effect of human pressures on natural populations, the identification of a single decline event can be difficult, as the genetic variation of natural populations is influenced by multiple events varying in timing and nature. In this respect, kea is a good example as it has presumably been influenced by Pleistocene glaciations, climate change at the onset of the Holocene (11.6 kya), human colonization of New Zealand and by recent European persecution as well. Also, the usual rarity of kea fossils throughout New Zealand contrasting with a high abundance of remains in some sites  possiblyM-ratio method did not detect any bottleneck while a signature of a bottleneck was detected in the central cluster using the heterozygote excess method and the most appropriate TPM model with 90% stepwise mutations [Moment-based methods of bottleneck detection yielded contrasting results. The utations . As theoutations . Decreasutations , 98. Morutations . Often, utations . Thus, putations ), an aveThe detection of a bottleneck signal in the central cluster is consistent with the fact that the McKenzie area was an easily accessible area and one of the first regions where intensive farming occurred –102. TheThe modeling approaches implemented in MSVAR and DIYABC were the only methods that consistently identified population declines in the historical and contemporary samples examined. While mode estimates were slightly different, posterior distribution of parameters were overlapping among the two methods. However, the timing of this decline was not consistent with the reported cull of the late 1800s. Rather, our analysis identified a decline dating back a few hundreds of years ago (south cluster) to up to 6,000 years ago  and suggests that kea could have experienced a population decline prior to human arrival in the northern part of its range at least. The finding of kea fossils from the Pleistocene in some areas of the South Island where kea are now absent such as central and eastern Otago , coastalPachyornis australis) changed altitudinal, longitudinal and latitudinal ranges through the Late Quaternary in response to alterations in the distribution of suitable habitat [Climate warming at the end of the last glacial period  induced a change in environment in the South Island of New Zealand with the expansion of podocarp forest first , 109 and habitat . The fin habitat  and appa habitat , 113 may habitat  and newl habitat  suggest  habitat , 116. HoAnother important aspect to consider is the possible impact of pre-European Maori on kea population dynamics. Estimates of the timing of decline predate Maori arrival in New Zealand circa 1280 A.D., ) for the0 A.D.,  Arctocephalus gazella) during the 1800’s suggests that ABC approaches are powerful enough to detect recent demographic declines [Our results do not exclude the possibility of a recent 1800s to 1970s) human-induced population decline in kea and are consistent with the conclusion of Girod et al.  and the declines . The que70s humandeclines , 131–136First of all, the detection of a bottleneck is highly dependent on the amount of genetic diversity present before the decline. If kea populations declined before human arrival as supported by the results of MSVAR and DIYABC, it is very likely that the inherent paucity in genetic variation following this ancient decline  could have precluded further genetic loss , making eN (10- to 1000-fold) have been evaluated . If the estates , it is p estates , with ne estates , 141. Su estates , 143.Thirdly, it is plausible that the cull was not homogeneous over the whole range of the species and that population density could have been significantly reduced mainly close to sheep stations, while populations in remote areas might have survived the cull . Also, bFinally, as has been suggested for long-lived species such as white-tailed eagle , HawaiiaAlthough genetic signatures of historical contraction were detected, no recent bottleneck consistent with the report of an important cull was noted. This suggests that the cull was not strong enough to leave a genetic signature or that a combination of the factors discussed above reduced the probability of bottleneck detection. The discovery of additional mitochondrial diversity among fossil samples supports an ancient and possibly non-anthropogenic decline in kea, but further study of fossil samples is required to determine with more certainty the importance of habitat change during the Holocene on kea population dynamics. In a context of global climate change, our findings suggest that kea abundance could be further reduced by increasing temperatures either through associated habitat change, or through the effects of increased predator pressure in alpine habitats. While we cannot rule out the possibility that kea was once present in the lowlands and that it is now an alpine relict species because of habitat loss, persecution and predation, our study highlights the need to examine histories of endangered species and try and understand the respective anthropogenic and non-anthropogenic effects on population dynamics when proposing conservation guidelines.The lack of detection of a recent genetic bottleneck associated with a documented human-induced demographic decline further highlights the need to interpret results of tests for population bottleneck with great caution. As failure to detect a genetic bottleneck does not exclude the possibility of a recent population contraction, our results further highlight the need to consider historical and fossil genetic diversity and effective population size estimates to develop efficient conservation recommendations for species of conservation concern.While the species has been fully protected since 1986 , the intS1 Supporting Information(PDF)Click here for additional data file.S1 FigThe left hand side of the figure represents the 5‚ end of the sequence.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 TableeN (CN), historical eN (HN), mutation rate scaled in terms of contemporary population size (θ), and time (T). scale.Columns 3–6 show the starting values for the mean and variance of the prior distributions. Columns 7–10 show the means and variances (and their means and variances) of the hyperprior distributions. Parameters listed are generation interval (g), contemporary (PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 TableIndividuals with unusual assignments and referred to in the text are in bold.(PDF)Click here for additional data file.S1 Data(TXT)Click here for additional data file.S2 Data(TXT)Click here for additional data file.


Myotis lucifugus) and the northern long-eared bat . Using mitochondrial and microsatellite DNA markers, we examined population structuring within and among summering and swarming sites. Both species exhibited moderate degrees of mitochondrial DNA differentiation (little brown bat: ST(SWARMING)F= 0.093, ST(SWARMING)F= 0.052; northern long-eared bat: ST(SWARMING)F= 0.117, ST(SWARMING)F= 0.043) and little microsatellite DNA differentiation among summering and among swarming sites. Haplotype diversity was significantly higher at swarming sites than summering sites, supporting the idea that swarming sites are comprised of individuals from various summering sites. Further, pairwise analyses suggest that swarming sites are not necessarily comprised of only individuals from the most proximal summering colonies.During late summer and early autumn, temperate bats migrate from their summering sites to swarming sites, where mating likely occurs. However, the extent to which individuals of a single summering site migrate to the same swarming site, and vice versa, is not known. We examined the migratory connectivity between summering and swarming sites in two temperate, North American, bat species, the little brown bat ( Eschrichtius robustus) )M. nattereri) [Previous authors have suggested that weak nDNA structuring among summering colonies is a result of mating occurring away from summering colonies, where individuals from various summering sites congregate, facilitating gene flow , 78,79. ttereri)  and browttereri) . Althougttereri)  who idenWhile no patterns of IBD were identified in the northern long-eared bat for summering or swarming sites, we found patterns of IBD in the little brown bat. Mitochondrial DNA haplotypes revealed IBD for swarming sites, as did nDNA across summering sites. The little brown bat swarming sites had a maximum distance of 869 km apart, while the northern long-eared bat swarming sites were 309 km apart. The lack of isolation by distance observed in the northern long-eared bat may therefore be an artifact of sampling sites being too close together relative to the potential movement range of the species to detect patterns of IBD. In the Appalachian Mountains, Miller-Butterworth et al.  reportedFST values. Analysis of northern long-eared bat mtDNA only suggested significant differentiation between seasonal sites relative to summering site 29  and three swarming sites . In comparison to the other summering sites sampled, site 29 is located in south-west of Nova Scotia, and is the most distant of the northern long-eared bat swarming sites sampled.Within both species, haplotype diversity was significantly greater in swarming sites than summering sites, supporting the hypothesis that swarming sites are comprised of individuals from various summering sites. When we assessed genetic differentiation between swarming and summering sites, we identified high nDNA genetic similarity between the seasonal sites in both species. This weak structuring suggests high levels of gene flow across eastern mainland Canada. If swarming sites are the primary mating sites for both species, they are important for the maintenance of gene flow among summer colonies. Further examination of seasonal site connectivity using mtDNA data indicated complex structuring in both species. Analyses of the little brown bat mtDNA suggested that Quebec swarming sites show the greatest genetic differentiation from summering sites. Specifically, when compared to summering sites, two of the three Quebec sites  were part of 11 of the 14 significant pairwise As predicted, a greater degree of genetic structuring was found in the northern long-eared bat than the little brown bat. Clusters identified for the little brown bat using DAPC had high degrees of genetic similarity and these results complemented those from STRUCTURE and collectively suggest low genetic structuring. In comparison, the five clusters identified for the northern long-eared bat through DAPC appeared to have greater genetic differentiation; however, there was a high degree of similarity between three of the five clusters. Genetic similarity between the three clusters may explain the three genetically differentiated clusters revealed by STRUCTURE. However, in both species, STRUCTURE and DAPC analyses revealed that membership probabilities were not correlated with the geographic location of sites. The factors driving the identified genetic structuring remain unclear and should be examined further.Picoides arcticus) identified fine-scale genetic structuring but a lack of structuring over a large spatial scale [Chen caerulescens atlantica). Here it was suggested that structuring resulted from varying degrees of female philopatry between rearing and nesting seasons. As our study sampled females across the summering season, we were unable to identify if females exhibit varying degrees of philopatry between stages of pregnancy and rearing offspring. Additionally, fine-scale structuring may be a result of sex-biased dispersal. If this hypothesis is tested and sexes are examined separately, one sex should exhibit fine-scale structuring while the other lacks structuring [We suggest there are three potential explanations for the observed patterns of population structure in these species. First, structuring observed for both species may be a remnant of historical factors. As identified by Burns et al. , little al scale ,84. Withal scale . Lecomteal scale  identifiucturing .Despite the behavioural differences between the little brown bat and northern long-eared bat, our study found great similarity in the population genetic structuring across seasonal sites for both species. In both species, the genetic differentiation between summering sites is likely explained by philopatry. Swarming sites appear to be composed of individuals from various summering sites, and in both species weak structuring is likely driven by varying degrees of fidelity among individuals. As well, we determined that the overall population structuring, observed in both species, is not explained by geographic location or distance between sites.Pseudogymnoascus destructans, which inhabits bat hibernacula [P. destructans is crucial to understanding the spread of this disease. As well, fatalities of bats have been globally documented at wind turbine facilities [Migratory species, including bats, are at a heightened risk of decline due to their broad-scale movements and multiple habitat requirements. Management and conservation efforts require an adequate understanding of migratory processes, particularly in light of increased anthropogenic development. If any of the seasonal sites are disrupted or migratory barriers disconnect seasonal sites, local populations may experience local extinction. Within North America, many bat populations are declining as a result of White-nose Syndrome (WNS) resulting from the fungus ernacula . Bat popernacula ,88. As dcilities . As the cilities . AnalyseS1 TableM. lucifugus) and northern long-eared bat  from each sample site.Number of mtDNA and nDNA samples analyzed for the little brown bat ((XLSX)Click here for additional data file.S2 TableM. lucifugus) and northern long-eared bat  unless indicated. See Burns et al. [Primer concentrations were the same for both the little brown bat Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableN), number of haplotypes (H), haplotype diversity (h) with standard deviation (S.D.) and nucleotide diversity (π) with standard deviation (S.D.).This table includes the number of sequences obtained ((XLSX)Click here for additional data file.S5 TableFST values are located above diagonal and relative p-values are below the diagonal. Bolded values indicate statistically significant values after Bonferroni correction.Site numbers correspond with (XLSX)Click here for additional data file.S6 TableSite numbers correspond with (XLSX)Click here for additional data file.