We conducted a nationwide study comparing self-identification to genetic ancestry classifications in a large cohort (n = 1752) from the National Marrow Donor Program. We sought to determine how various measures of self-identification intersect with genetic ancestry, with the aim of improving matching algorithms for unrelated bone marrow transplant. Multiple dimensions of self-identification, including race/ethnicity and geographic ancestry were compared to classifications based on ancestry informative markers (AIMs), and the human leukocyte antigen (HLA) genes, which are required for transplant matching. Nearly 20% of responses were inconsistent between reporting race/ethnicity versus geographic ancestry. Despite strong concordance between AIMs and HLA, no measure of self-identification shows complete correspondence with genetic ancestry. In certain cases geographic ancestry reporting matches genetic ancestry not reflected in race/ethnicity identification, but in other cases geographic ancestries show little correspondence to genetic measures, with important differences by gender. However, when respondents assign ancestry to grandparents, we observe sub-groups of individuals with well- defined genetic ancestries, including important differences in HLA frequencies, with implications for transplant matching. While we advocate for tailored questioning to improve accuracy of ancestry ascertainment, collection of donor grandparents’ information will improve the chances of finding matches for many patients, particularly for mixed-ancestry individuals. The National Marrow Donor Program (NMDP) registry is the ra priori, it is important to not privilege any one measure of race/ethnicity or ancestry over any other without formal, rigorous testing. Although many studies have examined the relationship between self-identified race/ethnicity and genetic ancestry[However, racial and ethnic self-identification among Americans is a complex process that draws on information about known ancestry, appearance, how people were raised, where they grew up, whether they experienced discrimination, and by whom; and an individual’s responses can also change over time–9. Some  ancestry–20, prevHere, we present the first simultaneous assessment of multiple approaches to measuring self-reported ancestry with direct comparison of survey responses with genetic markers, and examine implications for unrelated donor HLA matching in HSCT. We conducted a nationwide study comparing several forms of self-identification to genetic classifications, in a large cohort from the NMDP. We captured multiple facets of self-identification, including race/ethnicity and geographic ancestry. For many Americans these dimensions will yield consistent responses, but for others each measure produces a different response. Our aimOur cohort includes 1752 individuals who responded positively to a letter of request for participation in the study by mailing back both a cheek swab and a completed paper-and-pencil survey questionnaire. Recipients of the invitation letter were selected randomly with respect to original self-identification from the NMDP donor pool. In order to assess concordance of self-identification with high-resolution HLA genotype, only donors for whom sequence-level data were available for five HLA loci  were recruited. Because we are interested in improving classification for donors with rare HLA types, we oversampled (2:1) individuals whose HLA genotype is underrepresented in the registry (fewer than 11 copies among 12 million). The National Marrow Donor Program institutional review board approved the study (Study #0292); all subjects gave written informed consent. Response rates by original registry race/ethnicity classification and gender are given in Supplemental materials . The bas“Mark one or more boxes to show the racial or ethnic group(s) you use to describe yourself.” The list of possible responses included the five major racial categories  recommended by the Office of Management and Budget (OMB) in its 1997 directive[How do other people in this country typically classify you?”). This question was based on an instrument developed by the Center for Disease Control’s Measures of Racism Working Group for the annual Behavioral Risk Factor Surveillance System survey (http://www.cdc.gov/brfss/).In one part of the questionnaire , we askedirective. We alsodirective. In the From what countries or parts of the world did your ancestors come?” These instruments were modeled on items used by the General Social Survey since 1972[If possible, in the boxes below, enter the best origin code for each of your grandparents”) using numerical codes adjacent to each of the geographic ancestry options. In order to compare this self-identified geographic ancestry to self-identified race/ethnicity and measures of genetic ancestry, we aggregated specific country or region responses into broader categories , and each of these measures is similarly correlated with Asian HLA origin and genetic ancestry (−15), but the latter tracks somewhat more closely with African HLA origin and genetic ancestry (−15), with race/ethnicity being slightly more predictive of European HLA origin and genetic ancestry (−15) and have Amerindian HLA origin and genetic ancestry (−15), have much lower proportions of Amerindian genetic ancestry, and are less likely to have Amerindian HLA origin (−15), but not any specific genetic ancestry.Across all classifications and question formats, we find very high correlation between the mean proportion of African, European, Asian and Amerindian genetic ancestry in each subpopulation, and the frequency of the respective HLA haplotypes origins in those individuals . Of those, nearly two-thirds self-identified their race/ethnicity as Hispanic (n = 28) or African American (n = 4); however, mean European genetic ancestry for self-identified Hispanics and African Americans who reported European geographic ancestry  is not statistically significantly higher than that for individuals with the same self-identified race/ethnicity who do not report European geographic ancestry (0.63 and 0.27).In some cases when geographic ancestry yields less consistent classifications relative to genetic ancestry than those based on race/ethnicity, we find the differences also vary by gender. More than 10 percent of our cohort reported North American Indian geographic ancestry but did not self-identify as American Indian with respect to race/ethnicity (n = 189). The mean Amerindian genetic ancestry proportion (0.018) for these individuals is similar to those not reporting North American Indian ancestry (0.019). Strikingly, significantly more women  than men  report North American Indian ancestry in addition to another geographic ancestry without also identifying their race/ethnicity as American Indian (p< 0.001). Further, and of most importance in transplant matching, selection of more than one race/ethnicity or geographic ancestry by men is much more predictive of multi-origin HLA than multi-origin reporting among women, even though men are less likely to identify with multiple race/ethnicities or geographic ancestries. For example, 28% of men who self-identify with two or more race/ethnicity groups have multi-origin HLA haplotypes; in contrast, just 9% of women selecting more than one race/ethnicity group have multi-origin HLA haplotypes (p<0.05).In contrast to reporting generic family origins, when respondents select origins for specific grandparents the resulting classification using the number of grandparents with a given geographic ancestry identifies sub-groups of respondents with more uniform genetic backgrounds. Sub-classification by the number of grandparents with Sub-Saharan African or African American ancestry, for example, shows a clear pattern of increasing African genetic ancestry proportions with increasing number of reported African-ancestry grandparents . The subIn medical practice and biomedical research, subjects’ self-identified race/ethnicity is frequently collected–35, ofteWith the aim of improving matching algorithms for bone marrow transplant, this study sought to determine how various measures of self-identification intersect with genetic ancestry. Our finding that nearly one in five respondents provided answers inconsistent between reported race/ethnicity and geographic ancestry highlights the subjective nature of self-identification. In comparing measures of self-identification, we do not mean to imply that we think responses should always be consistent across measures or that one type of response is more “correct” than another. Rather, we want to underscore how different measures of self-identification reveal different information. Moreover, it is apparent that there is no clear ‘winner’ in the complex process of self-identification in the U.S. with respect to genetic ancestry; best practices for collecting this information for some populations may be ineffective for others. Unraveling the myriad historical, political and social factors contributing to these differences among measures of self-identification, and between self-reported and genetic ancestry measures, is beyond the scope of this work. Instead, our results point to the need for more research to identify the specific nature of this variation and refine how we collect and interpret measures of race/ethnicity and geographic ancestry in medical contexts.As expected, in certain cases, geographic ancestry reporting more closely corresponds to genetic ancestry compared to self-identification using standard race/ethnicity categories. While often classed together as “Hispanic” by registries and in much biomedical research, individuals reporting Latin American origins display markedly different proportions of African and Amerindian genetic ancestry than those who report Caribbean origins,52; our Starting in 2000, the U.S. Census questionnaire allowed respondents to select more than one self-identified race. In 2010, over 9 million Americans identified with two or more races, a 32% increase from the previous decade, and a substantial fraction of our cohort reports multiple race/ethnicities or geographic ancestries. Likewise, individuals who self-identify as Hispanic now comprise the largest racial/ethnic group within the United States after non-Hispanic Whites,54. In oa priori racial or ethnic classification scheme.A major limitation here is over-representation of individuals who self-identify as White compared to the current U.S. population and an over-representation of individuals with European-origin HLA types compared to the overall makeup of the registry; additional work in this area will profit from study of larger samples of subpopulations with non-European origins and ancestries. We focused on low-frequency HLA haplotypes in this study under a working premise that individuals with complex ancestries, which might not be fully captured by the current registry questionnaire, were more likely to have rare HLA types. We hypothesized that alternate forms of self-identification might provide improved matching for these individuals. Further, we sought a cohort with diverse ancestry, but did not wish to base our sampling strategy on current registry race/ethnicity classifications. However, the survey response rates varied by gender and original registry race/ethnicity classifications yielding a less diverse sample, and results must be interpreted within this context. Importantly, 62% of respondents were women whose original registry classification was White, compared to only 35% of those individuals who received the initial letter inviting participation in the study. This group had the highest participation rate overall (10%), while the lowest participation rates were among African American men (<1%). Additionally, genetic ancestry proportions estimated in this study are sensitive to the markers examined and population samples used as background, and thus resolution of these proportions will be somewhat limited for some populations. Nevertheless, the strong correlation between AIMs and HLA in our study supports the notion that AIMs can track with medically important variation. Finally, our analysis did not find an association between the frequency of individuals’ HLA types in the registry with any specific self-identification measure or inconsistency in the survey responses. However, given our finding that classification is variable depending on the format for self-identification, this pilot study benefited by not sampling according to any Despite cultural and scientific controversy, collection of data regarding race, ethnicity and ancestry will likely continue to play a role in medicine and biomedical research, compelling the need to understand better how patterns of self-identification interact with genetic classifications and health outcomes for all Americans. Although personalized medicine and whole-genome sequencing may eventually circumvent the need for ancestry self-identification in some settings, at present this approach is cost-prohibitive for donor registries, and impossible in other cases where biological samples are not collected. When relying on self-reporting, it is important to consider both the political origins of our now-standard classification schemes –57 and tS1 FigThe registry collapsed all responses into the broad categories listed at the top of each answer section. Individuals selecting “Hispanic” were assigned to a separate category.(TIFF)Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 FigFor this run, k = 4, popflag = 1, burnin = 10,000 and reps = 10,000. Individuals are represented in the following order from left to right: HGDP African populations, HGDP Middle Eastern populations, HGDP European populations, HGDP Central/South Asian populations, HGDP East Asian populations, HGDP Amerindian populations, HGDP Oceanic populations, study respondents. Broad continental ancestries are color-coded as follows: African = yellow; European = green; Asian = red; Amerindian = blue.(ZIP)Click here for additional data file.S1 TableThe number of individuals in each category  and the percentage of the total in that category is given in parentheses. Response rates are calculated as Respondents/Initial contact in each category, as well as combined (“All”) for both genders. The ratio of female to male respondents in each registry classification group is given to the right.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Pearl millet is a staple food for people in arid and semi-arid regions of Africa and South Asia due to its high drought tolerance and nutritional qualities. A better understanding of the genomic diversity and population structure of pearl millet germplasm is needed to support germplasm conservation and genetic improvement of this crop. Here we characterized two pearl millet diversity panels, (i) a set of global accessions from Africa, Asia, and the America, and (ii) a collection of landraces from multiple agro-ecological zones in Senegal.PstI-MspI reduced representation libraries. We used these SNPs to characterize genomic diversity and population structure among the accessions. The Senegalese landraces had the highest levels of genetic diversity (π), while accessions from southern Africa and Asia showed lower diversity levels. Principal component analyses and ancestry estimation indicated clear population structure between the Senegalese landraces and the global accessions, and among countries in the global accessions. In contrast, little population structure was observed across in the Senegalese landraces collections. We ordered SNPs on the pearl millet genetic map and observed much faster linkage disequilibrium (LD) decay in Senegalese landraces compared to global accessions. A comparison of pearl millet GBS linkage map with the foxtail millet  and sorghum (Sorghum bicolor) genomes indicated extensive regions of synteny, as well as some large-scale rearrangements in the pearl millet lineage.We identified 83,875 single nucleotide polymorphisms (SNPs) in 500 pearl millet accessions, comprised of 252 global accessions and 248 Senegalese landraces, using genotyping by sequencing (GBS) of We identified 83,875 SNPs as a genomic resource for pearl millet improvement. The high genetic diversity in Senegal relative to other regions of Africa and Asia supports a West African origin of this crop, followed by wide diffusion. The rapid LD decay and lack of confounding population structure along agro-ecological zones in Senegalese pearl millet will facilitate future association mapping studies. Comparative population genomics will provide insights into panicoid crop evolution and support improvement of these climate-resilient crops.The online version of this article (doi:10.1186/s12864-015-2255-0) contains supplementary material, which is available to authorized users. Ensuring food security in the world’s arid and semi-arid regions is a great challenge due to rapid population growth and strong effects of climate change in these regions . Food sePoaceae family) and was domesticated over 4500 years ago from Pennisetum glaucum ssp. monodii  of  of 47] oer) Fig. . The glo8 reads , corresponding to an average of 0.64 million reads per individual. For Lib_384, there were 184,113,400 reads with the barcode and cut site overhang (88.3 %), and for Lib_288 there were 84,493,320 reads (39.0 %) with a good barcode and cut site overhang, for an overall average of 63.6 % reads with a good barcode. After merging, 18,126,075 tags were generated, and 1,460,464 tags were retained after filtering, which were covered by 246,284,116 matching reads. Totally, 83,875 SNPs were identified across all 511 pearl millet accessions with average depth of 1264 tags per site, across all samples  and one 288X multiplexed library (“Lib_288”), and generate a total of ~110 GB of raw data Fig. . Two libAnalysis of the SNPs showed 63.4 %  of the nucleotide changes were transitions, while 36.5 %  of the SNPs were transversions Table . The obsP-value = 0.004). However, nucleotide diversity in other individual countries was lower than for the Senegalese landraces  among countries, the values were higher between Senegalese landraces and any other countries  of the whole panel was 0.224. The nucleotide diversity for global accessions (0.190) was slightly, but significantly, higher than that of Senegalese landraces (0.187) . After filtering for missing data, linkage disequilibrium, and minor allele frequency, we obtained 8377 SNPs to conduct PCA on all accessions, and 7269 SNPs to conduct PCA on the Senegalese landraces. The total amount of genetic variation explained by first ten principal components (PCs) was 11.52 %. The first principal component (PC1), which explained 4.93 % of variation, separated the Senegalese landraces from the global accessions Fig. . The secK = 2) , one mainly from Yemen and Kenya, and another mainly from southern Africa and the rest of the world. At K = 5, the inbred lines were divided into three groups, corresponding to Yemen, Kenya, and southern Africa and India.Next, we investigated the population structure with model-based estimation of ancestry using ADMIXTURE. The lowest cross-validation error was observed when two genetic groups were assumed  and sorghum (Sorghum bicolor), we performed a BLAST against the foxtail millet and sorghum genomes using the GBS tags that had been used to construct the genetic linkage maps [To characterize the synteny of pearl millet with foxtail millet (age maps . More taPstI-MspI) GBS approach is a powerful method for non-model species to identify high density markers [In the present study, we characterized 500 pearl millet accessions at 83,875 SNPs discovered and genotyped using a non-reference GBS approach . For non markers . The hig markers . Future  markers , 50.Arabidopsis (2.4) [Due to biased mutational processes in plant genomes (e.g. cytosine deamination), the ratio of transitions to transversions for bona fide polymorphisms is expected be much greater than one. Consistent with this expectation, the transition/transversion ratio for the pearl millet SNPs was 1.74 (Table is (2.4) . The difis (2.4) . Compareis (2.4) , 29, extis (2.4) , 29, futDuring crop diffusion genetic bottlenecks can occur because of reduced effective population size, if only part of the genetic diversity introduced into the new regions and gene flow with other populations is restricted . Here, win situ diversity from farmer’s fields  with ex situ collections from crop genebanks . Indeed, the inbreeding coefficients of global accessions were substantially higher than the Senegalese landraces , were used as plant materials. The 249 pearl millet accessions were collected by ISRA in 1992 to 1993  and 262 pearl millet accessions , and were grounded with a ball mill (Retsch). Genomic DNA was extracted with the Biosprint kit (QIAGEN). Two hundred forty nine Senegalese landraces were grown in experimental field in Senegal, and leaves were sampled after geminated. The DNA for 249 Senegalese accessions was extracted with MATAB protocol  at ISRA PstI and MspI) protocol developed by Poland et al. [PstI (CTGCAG) and MspI (CCGC) restriction enzymes , then barcoded adapters were ligated to each sample. Instead of NEB4 buffer CutSmart Buffer (New England Biolabs) was used in digestion and ligation. Samples were pooled by plate into a single library and purified with a QIAquick PCR purification kit  before PCR amplification. Products in the size range from 250 to 400 bp (including adapter and primer sequence) were selected with the Pippin Prep system (Sage Science). All libraries were adjusted to 4 nM concentration. Four 96-plex libraries  were pooled to form a 384-plex library (Lib_384) and three 96-plex libraries  were pooled to form a 288-plex library (Lib_288)  Fig. . The 384TASSEL 4  for follFST between populations were calculated using vcftools v0.1.13 [t-test in R 3.1.2 [The minor allele frequency was analyzed with TASSEL 4 . The neu v0.1.13 . The gen R 3.1.2 .snpgdsLDpruning function was used to filter the SNP data with following options: method = “corr”, ld.threshold = 0.5, maf = 0.05, missing = 0.8. The snpgdsPCA function was used to conduct PCA with default setting. The SNP data used for PCA was extracted with custom R script and was used for the second population structure analysis method, a model based estimate of ancestry, which was carried out using the ADMIXTURE 1.23 [K = 1 to K = 10. The optimal number of subpopulations was determined with lowest Cross-validation error. The VCF format data was converted to data format required by ADMIXTURE 1.23 using PLINK 1.07 [Two approaches were used to infer the population structure. First, Principal Component Analysis (PCA) was carried out using SNPRelate_1.0.1 R package . The snpURE 1.23 , with thINK 1.07 .The genetic distance matrix between individuals was generated and neighbor joining tree was constructed using TASSEL 4 , and washttp://foxtailmillet.genomics.org.cn/) [http://phytozome.jgi.doe.gov) [The reference genomes for foxtail millet and sorghum (v1.4) were downloaded from BGI-Shenzhen (org.cn/)  and Phytdoe.gov) , 78, resdoe.gov)  were mapdoe.gov)  with a Edoe.gov) .No research involving human subjects, human data, or regulated vertebrates or invertebrates was included in this study.


STAT3 has been traditionally considered as nontargetable or undruggable, and the lag in developing effective STAT3 inhibitors contributes to the current lack of FDA-approved STAT3 inhibitors. Recent advances in cancer biology and drug discovery efforts have shed light on targeting STAT3 globally and/or specifically for cancer therapy. In this review, we summarize current literature and discuss the potential importance of STAT3 as a novel target for cancer prevention and of STAT3 inhibitors as effective chemopreventive agents.Signal Transducers and Activators of Transcription (STATs) are a family of transcription factors that regulate cell proliferation, differentiation, apoptosis, immune and inflammatory responses, and angiogenesis. Cumulative evidence has established that STAT3 has a critical role in the development of multiple cancer types. Because it is constitutively activated during disease progression and metastasis in a variety of cancers, STAT3 has promise as a drug target for cancer therapeutics. Recently, STAT3 was found to have an important role in maintaining cancer stem cells  STATs, a family of transcription factors first identified in 1994 . All STAAmong all STAT proteins, STAT3 plays a central role in development and carcinogenesis, since it critically regulates the transcription of multiple key genes involved in cell proliferation, differentiation, apoptosis, angiogenesis, immune responses and metastasis B. The STIn normal cells, STAT3 activation is tightly regulated and transient.in vivo growth hormone treatment’s rapid induction of STAT3 activation via tyrosine phosphorylation [c-myc and pim, are essential for cell survival and cell cycle transition [Cell proliferation is the increase in cell number resulting from cell growth and division. Proliferation is induced by growth factors and cytokines, for which STAT3 is an important signaling mediator, as seen with rylation ,24. Actirylation . In hearrylation . Apoptosrylation , and invrylation ,29. IL-6ansition .Differentiation is the process through which cells become more specialized. During tissue repair and normal cell turnover, stem cells divide and differentiate into daughter cells. There is direct evidence that STAT3 induces myogenic differentiation through interaction with MyoD, which is the essential transcription factor in myogenic differentiation . IntriguSTAT proteins were first discovered in studies of interferons in 1990s and were originally characterized as critical mediators in the immune system . The sigSTAT3 is also involved in maintenance and differentiation of stem cells, which because of their capability of self-renewal and differentiation are an important source for tissue repair. There is evidence that STAT3 is one of the signaling components for pluripotent embryonic stem cell-mediated cardiomyogenesis: inhibition of STAT3 drastically suppresses leukemia inhibitory factor-mediated differentiation . STAT3 iAccumulating evidence shows that persistent STAT3 activation is required for aberrant cell proliferation in carcinogenesis. In gastric cancer, IL-26 activated STAT3 signaling induces up-regulated expression of Bcl-2, Bcl-XL and c-Myc, which leads to persistent cell proliferation . The actWithout new blood vessel formation, the tumor cannot grow beyond the size of 2–3 mm in diameter ,61. STATImmunosurveillance plays a pivotal role preventing carcinogenesis by detecting and eliminating abnormally transformed cells. However, abnormal cells can evade immune monitoring and eventually form malignant cancers. The many mechanisms by which cancer cell evade detection include reduced expression of cancer antigens and major histocompatibility complex (MHC)-I and MHC-II molecules for T cells and increased immunosuppressive cytokines. STAT3 activation inhibits expression of inflammation cytokines and interrupts maturation of DCs, both processes lead to reduced expression of MHC-II and consequently decreased antigen presentation and T-cell activation . AnotherCancer stem cells (CSCs), also called tumor initiating cells (TICs), a group of special cancer cells present in tumors, possess characteristics associated with normal stem cells, such as self-renewal and specifically, the ability to generate diverse tumor cells, contributing to tumor heterogeneity. CSCs are responsible for the relapse and metastasis and the resistance to treatment. The function of activated STAT3 in CSCs is suggested by the co-expression of STAT3 with the markers of pluripotent stem cells, Oct 3/4 and Nanog . STAT3 mMalignant transformation—the transition from a normal cell to a cancerous cell—may occur as a primary process in normal tissue or secondarily as malignant degeneration of a benign tumor. Transformed cells have abnormal characteristics compared with the characteristics of their untransformed counterparts. First, after transformation, cells no longer require contact with the surface of a culture dish to grow and a reduction in contact inhibition. Second, transformed cells can grow in multiple layers, whereas normal cells usually grow in a single layer. Third, transformed cells have a decreased requirement for nutrients in media, even in the absence of growth factors. Finally, transformed cells may replicate limitlessly and become immortal. STAT3 is an essential factor in oncogenic cellular transformation, since it is frequently and persistently activated in various types of cancers  and consG-protein coupled receptors (GPCRs) can play a role in malignant transformation by inducing STAT3 phosphorylation. After a ligand binds to a GPCR, the GPCR undergoes conformational change and is activated. It then activates an associated G-protein by exchanging the G-protein’s bound GDP for a GTP. The G-protein’s α subunit with the bound GTP then dissociates from the β and γ subunits to affect intracellular signaling proteins or target functional proteins directly. GPCRs may signal through G-protein-independent mechanisms; for example, they may signal independently through Src (see below). Activated by hormones, GPCRs induce phosphorylation of STAT3  and resuMany growth factor receptors, such as EGFR and PDGFR, have an intrinsic kinase domain. Upon binding a ligand, the growth factor receptors become activated and self-phosphorylate tyrosine residues in its cytoplasmic tail ,95. SuchIL-6/JAK/STAT3 is the canonical STAT3 activation signaling pathway. Since the receptor of IL-6 does not contain a kinase catalytic domain, it induces STAT3 phosphorylation by activating members of the JAK family ,102. Cytin vivo studies also indicate that STAT3 contributes to tumorigenic transformation. One study used a K5.Cre × STAT3flx/flx mouse model of skin-specific STAT3 deficiency to show that loss of STAT3 led to significant reduction of epidermal hyperproliferation due to the down-regulated expression of c-myc [T2, with a tamoxifen inducible Cre gene under the control of K5 promoter, was used to study STAT3 effect on tumor transformation in a temporally controlled and inducible pattern. The study demonstrated that temporal disruption of STAT3 at initiation stage of skin carcinogenesis could promote apoptosis and prevent transformation by inhibiting Bcl-XL [2Pb.STAT3C mice, expressing constructively activated STAT3 in prostate, exhibit significant hyperplasia and intraepithelial neoplasia in the prostate. Sixty percent of STAT3+/Pten+/− mice developed prostate tumors within 12 months of age [in vivo evidence that STAT3 could serve as a target for preventive intervention.The findings of several of c-myc . Moreoveof c-myc . Anotherg Bcl-XL . Similarg Bcl-XL . Finallys of age . These sAlthough STAT3 is constitutively active in numerous types of cancers including breast cancer, skin cancer, ovarian cancer, prostate cancer, multiple myeloma, lymphomas and leukemia, brain tumor, Ewing sarcoma, gastric cancer, esophageal cancer, colon cancer and pancreatic cancer, one report showing STAT3 mutation in the SH2 domain in large granular lymphocytic leukemia, resulting in an increased activity of the protein . No otheet al. found that gene expression pattern differs between CD44+/CD24− and CD44−/CD24+ populations. The IL-6/JAK2/STAT3 pathway was found to be active in CD44+/CD24− breast cancer cells, and inhibitors blocking this pathway suppressed growth of xenograft tumors. In addition, it has been reported that a non-CSC population can be converted into a CSC-like population via the IL-6/JAK1/STAT3 signaling pathway through upregulating of OCT-4 [Accumulating evidence indicates that STAT3 plays a key role in maintenance of stem cell-like breast cancer cells, which have been shown to be related to tumor recurrence, metastasis and chemo-resistance ,113. Difof OCT-4 . STAT3 iSeveral studies suggest that STAT3 represents a promising molecular target for the prevention of ER-negative and triple-negative breast cancers (TNBC). STAT3 plays a critical role in the development of estrogen receptor (ER)-negative breast cancer. ER, a member of the nuclear receptor superfamily, is a ligand-activated transcriptional factor  and playOn the other hand, acetylation at the STAT3 K685 site enables STAT3 interaction with DNA methyltransferase 1 (DNMT1) and directs DNMT1 to the ERα gene promoter; then DNMT1 methylates the CpG islands in the promoter of ERα gene and suppresses its transcription . These cSelective estrogen receptor modulators (SERMs) are ER ligands that have selective functions in different tissues. For example, in bone and cardiovascular system, they act like estrogen, whereas in others tissues, they block estrogen action. The classic SERMs tamoxifen and raloxifene have both been shown to help prevent ER-positive breast cancer . BecauseIn addition to breast cancer, STAT3 stays constitutively activated in other types of cancers. STAT3 has been shown to be constitutively activated or overexpressed in colon cancers . STAT3 i1) Direct inhibition of STAT3 protein with inhibitors targeting one of three structural domains of STAT3, namely (a) SH2 domain, (b) DNA binding domain, and (c) N-terminal domain, which suppresses processes related to STAT3 signaling and functional role by blocking phosphorylation, dimerization, nuclear translocation and DNA binding [2) Indirect blocking the upstream regulators of STAT3 pathway.As mentioned earlier, STAT3 regulates the expression of various genes involved in proliferation, apoptosis, angiogenesis, invasion, and metastasis . Skin ca binding ,137; or The SH2 domain of STAT3 plays an essential role in STAT3 activation by mediating the interaction of STAT3 with phosphorylated tyrosine residues on the cytoplasmic portion of receptors. The SH2 domain also plays a critical role in the formation of STAT3 dimer, where the SH2 domain of one STAT3 monomer binds to the phosphorylated tyrosine (pTyr) motif of the other. Thus, inhibition of SH2 domain suppresses the phosphorylation and activation of STAT3 protein. Peptidomimetics, a major category of inhibitors that binds to the SH2 domain of STAT3, mimic pTyr-Xaa-Yaa-Gln motif and inhibit STAT3 dimerization by competitive binding to the SH2 domain . The phoSTAT3-dependent transcription in both human and mouse cells . A novelAnother group of SH2 domain blockers is derived from the natural compound curcumin. Modification of curcumin with proline produces the most potent conjugate inhibitor of STAT3 dimerization . CryptotBecause phosphopeptides have low cell penetration efficiency, investigators have explored the potential of a new series of small molecules: STA-21, stattic, S3I-201 and BP-1-102, as STAT3 SH2 domain inhibitors. STA-21 is a natural deoxytetrangomycin, an angucycline antibiotic, that specifically binds to SH2 domain and abrogates STAT3 dimerization and nuclear translocation, thereby significantly inhibits breast cancer cell growth and survival . StatticInterestingly, celecoxib, a cyclooxygenase-2 inhibitor, also binds to the SH2 domain of STAT3 and competitively inhibit native peptide binding, leading to suppressed tyrosine phosphorylation and reduced cell viability and migration in human rhabdomyosarcoma cells . AnotherPhosphorylation of Tyr-705 in the SH2 domain is not only required for dimerization, but also important in STAT3-DNA binding. Therefore, STAT3 inhibitors that target SH2 domain also inhibit STAT3-DNA interaction. For example, in targeting the SH2 domain, S3I-1757 interferes with STAT3-DNA binding, and this interference suppresses the expression of STAT3 target genes, including Bcl-XL, survivin, cyclin D1, and MMP-9, thereby promoting apoptosis and inhibiting proliferation . This efTo regulate gene expression, it is essential for STAT3 to bind to the gene’s promoter. Thus, STAT3 activity can potentially be inhibited by targeting the STAT3 DNA binding domain to prevent interaction with the target gene’s promoter. While this strategy is plausible in theory, it has not been adequately explored, possibly because targeting the SH2 domain can induce multiple inhibitory effects on STAT3 function. Endogenously, the HIC1 (Hypermethylated in cancer 1) gene, a tumor suppressor, forms complex with STAT3 protein through direct interaction between the C-terminal domain of HIC1 and the DNA binding domain of STAT3. The net result of this interaction is prevention of STAT3 binding to the promoter of the target genes, such as the genes encoding VEGF and c-Myc .One new platinum (IV) compound, IS3-295, inhibits the DNA-binding capability of STAT3 in a non-competitive manner; the mechanism it uses remains unclear. Other platinum (IV) compounds, such as CPA-1, CPA-7 and platinum (IV) tetrachloride, also inhibit STAT3 DNA-binding, thereby suppressing cell growth and increasing apoptosis in several types of human cancer cells . Furtherin vitro. Although in vivo findings showed only weak interaction between DBD-1 and STAT3 DNA binding domain, DBD-1 can induce significant apoptosis in murine melanoma B16 cells [DBD-1, a small peptide aptamer, can specifically recognize the DNA binding domain in STAT3 16 cells .The N-terminal domain of STAT3 has eight helices, which have multiple biological activities, including dimer formation, binding to promoter and assembly of transcriptional machinery ,191. ComAll the inhibitors discussed above may interact with STAT3 protein directly to execute their inhibitory functions. However, the STAT3 signaling pathway is a multiple-component cascade, other inhibitors may suppress STAT3 protein by indirect mechanisms: decoy oligodeoxynucleotide can block STAT3 binding to its target gene promoters, siRNAs can inhibit STAT3 mRNA translation, and inhibitors of upstream kinases such as JAK and Src can suppress STAT3 phosphorylation and activation.cis-element can bind to transcription factor with high affinity. The decoy ODN competes with the endogenous cis-elements for the binding site of the targeted transcription factor, thereby down-regulating target gene transcription by STAT3. This strategy has been proven useful in in vitro and in vivo studies, with ODNs blocking STAT3-dependent transcription of cyclin D1, c-Myc, Bcl-XL, and survivin [A STAT3-decoy oligonucleotide (ODN) can trap an activated STAT3 dimer in the cytoplasm by inhibiting interaction between active STAT3 and importin, resulting in increased apoptosis in colorectal cancer cells . Anothersurvivin . The redsurvivin ,195 and survivin ,197. Thesurvivin , uses ansurvivin , therebysurvivin ,157,193.survivin ,159,160 survivin , via recsurvivin . Challenin vivo and prevents oral squamous cell carcinoma growth and proliferation [Peptide aptamer KDI1 forms complex with EGFR through interaction with its intracellular domain. KDI1 does not block EGFR’s RTK activity directly but interferes with EGF-induced phosphorylation of Tyr-705 in STAT3 protein . Anotherferation . FGFR isferation .Since phosphorylation is essential for STAT3 biological activity, blocking the upstream kinases responsible for STAT3 phosphorylation represents a rational approach to prevent carcinogenesis. STAT3 is phosphorylated by several protein kinases located in the cytoplasm. Here, we focus on two classical kinases, JAK and Src.JAK inhibitors AG490 ,198, WP1The Src inhibitor dasatinib also decreases STAT3 phosphorylation ,174, cauA few newly developed compounds exert their antitumor effects through synergic inhibition of multiple cytoplasmic kinases. The indirubin derivative E738 , 6-bromoSince tyrosine phosphorylation is essential for STAT3 activity, removal of the phosphate moiety from STAT3 by phosphatase can inhibit STAT3 signaling. SHP1  and SHP2in vitro and/or in vivo inhibitory effect against STAT3. Curcumin as a non-specific STAT3 inhibitor reduces cell proliferation and colony formation in cell culture and preclinical model in lung cancer via reducing the phosphorylation of STAT3 and inhibit the activation of STAT3 [Litsea glutinosa, showed significant inhibition of STAT3 activity by inhibiting JAK via blocking tyrosine kinase domain JH1 in AKs [There are plenty of natural products that can inhibit STAT3 activity, in addition to inhibit other target molecules such as AP-1 and NF-κB. Several natural or dietary compounds have been shown to possess of STAT3 . Curcumiof STAT3 . Xanthohof STAT3 . Brevili1 in AKs . Benzyl 1 in AKs . Naturalin vitro and xenograft breast tumors derived from TNBC MDA-MB-231 cells, via oral administration [In the past decade, fragment-based drug design (FBDD) has been an efficient strategy for generating novel lead molecules against therapeutic targets . New chestration . In our 1) Bcl2, Bcl-XL and c-Myc, which promote proliferation and cancer cell survival and inhibit tumor cell apoptosis; (2) VEGF, MMP, and bFGF, which promote angiogenesis; (3) CXCL10, NF-κB and IL6/JAK, which are involved in immunesurveillance; (4) Oct3/4, Nanog and IL6/JAK/STAT3, which maintain CSC functions; and (5) GPCR, RTK and cytokines, which are responsible for malignant transformation (Various human cancers, including breast cancer, may be susceptible to the preventive or therapeutic effects of STAT3 inhibitors. Aberrant STAT3 signaling promotes carcinogenesis and tumor progression through deregulation of the expression of downstream genes that control the proliferation, apoptosis, angiogenesis, metastasis, immunesurveillance, malignant transformation and maintenance of CSCs. These genes include the ones encoding (ormation . TargetiThe current status of cancer chemoprevention is well summarized in recent literature ,203,204.


In this study, we have explored the population genetics of Plasmodium vivax isolates from Iquitos, the main city in the Peruvian Amazon, and 25 neighbouring peri-urban as well as rural villages along the Iquitos-Nauta Road.Characterizing the parasite dynamics and population structure provides useful information to understand the dynamic of transmission and to better target control interventions. Despite considerable efforts for its control, P. vivax isolates were collected and successfully genotyped using 14 neutral microsatellites. Analysis of the molecular data revealed a similar proportion of monoclonal and polyclonal infections in urban areas, while in rural areas monoclonal infections were predominant (p = 0.002). Multiplicity of infection was higher in urban (MOI = 1.5–2) compared to rural areas (MOI = 1) (p = 0.003). The level of genetic diversity was similar in all areas  though genetic differentiation between areas was substantial . Principal coordinate analysis showed a marked differentiation between parasites from urban and rural areas. Linkage disequilibrium was detected in all the areas (p<0.0001). Gene flow among the areas was stablished through Bayesian analysis of migration models. Recent bottleneck events were detected in 4 areas and a recent parasite expansion in one of the isolated areas. In total, 87 unique haplotypes grouped in 2 or 3 genetic clusters described a sub-structured parasite population.From April to December 2008, 292 Our study shows a sub-structured parasite population with clonal propagation, with most of its components recently affected by bottleneck events. Iquitos city is the main source of parasite spreading for all the peripheral study areas. The routes of transmission and gene flow and the reduction of the parasite population described are important from the public health perspective as well for the formulation of future control policies. We present the population genetics of malaria vivax parasites in a large area of the Peruvian Amazon. Our results showed that the parasite population had a predominant clonal propagation, reproducing themselves with identically or closely related parasites; therefore, the same genetic characteristics are maintained in the offspring. The clonal propagation may favour the higher levels of genetic differentiation among the parasites from isolated areas compared to areas where human migration is common. The patterns of gene flow have been established, finding Iquitos city as a reservoir of parasite genetic variability. Moreover, a recent reduction of the parasite population was observed in areas where recent control activities were performed. This research provides a picture of the nature and dynamics of the parasite population which have a significant impact in the malaria epidemiology; therefore, this knowledge is crucial for the development of efficient control policies. Plasmodium vivax caused about 14.2 million malaria cases outside sub-Saharan Africa in 2013 ; and systematic monthly follow-up was carried out thereafter. For the purpose of the current study, all D0 (before-) and D1 (after treatment) isolates were analysed, including a blood sample for microscopy (thick and thick film) and a blood spot on filter paper (BSFP) .Patients were examined daily during the treatment and followed up weekly with blood sampling according to WHO guidelines for The study was approved by the Ethical Boards of Universidad Peruana Cayetano Heredia, Peru , the institutional review board of the Institute of Tropical Medicine Antwerp and the University Hospital of Antwerp, Belgium. Adult participants provided informed written consent, and a parent or guardian of any child participant provided informed written consent on their behalf.P. vivax infection and estimate the parasite density  assuming 8000 WBC/μl) [P. vivax mono-infections were confirmed by species-specific PCR (ssPCR) [All slides were read by microscopy (thin and thick smear) to confirm  WBC/μl) , 21. Par WBC/μl) . P. viva (ssPCR)  and geno (ssPCR) , 25. Bri (ssPCR) . MS PCR  (ssPCR) , 14.The allele fragment sizes recovered from the capillary electrophoresis were determined using Genemapper . Only fragments with ≥100 relative fluorescence units (RFU) were considered as ‘real’ alleles. In case of the presence of two or more alleles, only alleles with RFU ≥30% of the dominant allele RFU were considered for further analysis. The genetic and statistical analysis were performed mainly at study areas level but whenever feasible also at the village level to assess the influence of individual villages on the areas. SPSS for Windows v.20  was used to perform non-genetic statistical analysis.2 test and the average MOI compared using the Kruskal-Wallis test.Infections were classified as monoclonal  or polyclonal . The haplotypes  were determined using GenAlEx v.6.5 , 27. TheHe) was determined per locus and per study area using GenAlEx. The He represents the probability of finding a different allele for a given locus in any pair of samples randomly drawn from the same population. He varies from no diversity at all (0) to highly diverse (1). Overall monoclonal/polyclonal infections with complete/incomplete MS allele data were included for the calculations of these metrics. The He and the allelic richness were compared among study areas using Kruskal-Wallis Test.The mean number of alleles and the number of private alleles  were determined for each area using GenAlEx. The allelic richness, a non-biased-by-sample size measure of the number of alleles, was determined using FSTAT v.2.9.3 . The expPHIPT statistic, an estimate of the proportion of the parasites’ genetic variance between areas relative to the total variance, was calculated. When a third hierarchical level (villages) was included the proportion of variance between the areas (PHIRT), among the villages (PHIPR) and within the villages (PHIPT) were calculated. Probabilities for the AMOVA indexes were calculated based on individual randomizations (9999 permutations) [PHIPT values. PHIPT, analogue of the fixation index Fst, suppresses the within-population variance and ranges from 0 (no differentiation) to 1 (full differentiation). PHIPT was determined only for isolates with full haplotype data [The 2-level hierarchical genetic variation of the parasites between and within study areas was analysed by the Analysis of Molecular Variance (AMOVA) using GenAlEx , 27, andtations) , 27. Paiype data , 27.PHIPT matrix obtained via AMOVA and the individual-by-individual binary genetic distance matrix  [Rxy>0) between the genetic PHIPT matrix and geographic distances, so called the Isolation-by-distance (IBD) hypothesis. Geographic distances were computed using geospatial data for each village and converting UTM to km with GenAlEx [Principal Coordinate Analysis (PCoA), which plots the major relationships within a multivariate dataset, was generated with GenAlEx by intercomparison of geographic areas and villages using the nalysis) , 27. Onl GenAlEx , 27.The presence of linkage disequilibrium (LD), the non-random association of alleles from different loci, was evaluated by determining the standardized Index of Association  of parasites that shared the same alleles in at least 7–13 loci [K)[K from 1 to 15 (10 iterations each), consisting of a burn-in period of 50,000 iterations followed by 150,000 Markov Chain Monte Carlo (MCMC) iterations, assuming a mixture model and correlated allele frequencies. The most likely K was defined according to Evanno et al.[K, ΔK, using STRUCTURE HARVESTER v0.6.94 [K, burn-in 50,000 iteration followed by 500,000 MCMC iterations [K-means clustering method implemented in the software GENODIVE v.2.0b23 (OS X 10.6 operating system) was used [K-means clustering which divides a number of individuals into an a priori assigned number of groups (K) in such a way that the within-group diversity is minimized and the between-group is maximized [pseudo-F statistic  was calculated using the simulated annealing with 150,000 steps as convergence method and the number of algorithm repeats set to 50. GENODIVE retrieves the Rho index  as a measure of genetic differentiation between the genetic clusters [He, pairwise PHIPT and multilocus and pairwise-loci LD were assessed as described above.Genetic structure. The genetic structure was explored using three different approaches using eBURST, STRUCTURE and GENODIVE. The software eBURST v.3 assigned the haplotypes to genetic clusters (‘–13 loci . Haploty loci [K). STRUCTUno et al. by calcu v0.6.94 . The haperations . Additioerations  and DISTerations . CLUMMP was used . This meaximized . In ordeclusters . Only isP. vivax haplotypes [PHYLOViZ v1.0 was used to display the phylogenetic relationship through minimum spanning trees (MST) among plotypes . MST’s wM) and population sizes (Θ); and calculates marginal log-likelihoods (log mL) for each model using a thermodynamic integration. The log mL’s were used to calculate the log Bayes factors  and probabilities [Fst-like calculations and through a long chain of 1x106 recorded genealogies at a sampling increment of 100 iterations after discarding the first 10,000 as burn-in for each locus. To improve the MCMC searches, static heating scheme with 4 chains with temperatures 1, 1.5, 3 and 1’000,000 was set. Uniform-distributed priors for the migration parameters were set to range 0–100. Brownian motion approximation to the ladder model was selected as microsatellite data model for faster calculations [Nm) was calculated as the product of M by Θ(recipient). The effective population size (Ne) was also calculated: Ne = Θ/2μ; where μ = mutation rate per generation per locus, μ only described for P. falciparum = 1.59 x 10−4 (CI 95% 3.7x10-4–6.98x10-5) [The gene flow was inferred through the assessment of 13 migration models using a Bayesian approach  based on the coalescence theory and implemented in MIGRATE-N v3.6 . MIGRATEbilities . The besulations . The num98x10-5) .He). In contrast, the deficiency of He may indicate a recent population expansion. Using the Wilcoxon rank test, BOTTLENECK determines if a significant number of microsatellites exhibits excess or deficiency of He under the most strict mutational models: Infinite Allele Model (IAM) and the Stepwise Mutational Model (SMM) and also the Two-phase model (TPM) which allows to fractionate the proportion of mutational models [He<0.5). Since the microsatellites we used may not follow the same mutational model we also performed the analysis separately for perfect 3-4-bp repeats microsatellites  and imperfect microsatellites  which may tend to follow the SSM and IAM, respectively [We tested the deviation from the mutation-drift equilibrium due to recent bottleneck or rapid expansion events in the parasite population using the software BOTTLENECK v1.2.02 , 48. Aftl models , 49. Theectively , 50. TheP. vivax isolates (out of 302) with genotyping success on 56.2% (out of 292 isolates) with all 14 loci, 62.3% with 11–13 loci and 81.5% with 10 or less loci (efficiency of PCR amplification for each locus tabulated in p = 0.002), i.e. A1 and A4 presented higher frequency of monoclonal infections while in A2 and A3 the proportions of mono/polyclonal infections were similar (p = 0.003).Overall we have analysed 292  similar . The medHe = 0.74 (range 0.66–0.76), median allelic richness = 4.6 alleles (range 3.6–4.9), and did not differ significantly between areas (He: p = 0.32); allelic richness: p = 0.17), He) of each locus is presented in PHIPT = 0.17, p = 0.0001).The overall genetic diversity estimates among the areas described a median PHIPT 0.02 within A1), presence of related parasites in A2, A3 and A5 and genetic differentiation of parasites from San Carlos village (A4)  .PHIPT = 0.27, p = 0.0001), while little differentiation was found between the villages  (PHIPT matrix Rxy = -0.41 p = 0.17)  with a high differentiation among the parasites within the villages (= 0.004) . The PCo= 0.004) . The genp<0.0001) (p<0.0001). The number of isolates decreased drastically when only monoclonal infections were considered . Further LD analysis was performed at village-level only for those villages with more than four isolates. LD was found in villages within A1 ; A2 ; A3  and A4 . In contrast to the multilocus LD analysis, the pairwise LD analysis was performed by including isolates with missing alleles and showed presence of LD in all areas. The pairwise LD was found mainly between loci located in different contigs therefore the LD found among loci within the same contigs did not alter the outcome  was found when all isolates (MOI≥1) or only monoclonal (MOI = 1) isolates were analysed (<0.0001) . LD rema outcome .PHIPT among clusters = 0.25, p = 0.001) and 6 singletons, where one cluster accounted for 91% (116/128) of the isolates. The K-means clustering divided the population into 2 and 3 genetic clusters and significant genetic differentiation between clusters was found . STRUCTURE analysis was performed to infer cluster assignment including only isolates with up to 3 missing alleles and with known haplotypes. Using STRUCTURE results HARVESTER predicted the most likely number of clusters being K = 2, followed by K = 3, and K = 7   while 18 isolates from San Carlos village belonged to cluster 1  , A1 contained one cluster and hybrid samples while the other cluster was present in all the other areas . The extent of genetic diversity and LD within the clusters decreased when K = 7  and linkage disequilibrium was found for all the clusters (p<0.01).87 unique haplotypes were found (52.9% of them in A1) in 128 isolates without allelic missing data . The twond K = 7 . Using aarasites . The AMO26–0.59)  which we26–0.59) . When thplotype) . Hierarcer areas . When K The minimum spanning tree in nd and 3rd best models which consider 3 and 5 populations with asymmetric migration were also explored in detail (Nm<1). The 5-population model III adds unidirectional gene flow from A1 to A5 which is in line with the phylogenetic results  probably due to related parasite among A3 and A4.The gene flow was assessed evaluating parasite migration models which relied on the combined knowledge of the genetic structuring of the parasite population and the known human mobility patterns Figs  and 7A. n detail . The 3-plts Figs  and 7B. lts Figs  and 7B. lts Figs . HoweverHe in areas A1, A2 and A3 under IAM (p<0.002) and TPM , indicating a recent bottleneck event and a deficiency of He for A4 under SMM (p = 0.002), possibly indicating a rapid expansion (He deficiency under SMM (p = 0.002). A5 was not included in the analysis (n = 4). Similar results were obtained when the analysis was performed separately for perfect and imperfect microsatellites as shown in He in A1 was further investigated by grouping the isolates by village (whenever the sample size was sufficiently large): excess of He in all villages was found but in addition deficit was also found in Manacamiri village .BOTTLENECK analysis using 10 polymorphic markers showed a significant number of microsatellites had an excess of xpansion . HoweverP. vivax parasites circulating in the Peruvian Amazon. We determined and compared the genetic diversity and multiplicity of infection in five areas in and around Iquitos. We further unravelled the population structure by assessing the LD, genetic differentiation and determined the most likely number of genetic clusters and the genetic relationships among parasites. Multiple gene flow models were assessed determining the parasite migration patterns that may affect the genetic structuring. Moreover the occurrence of recent bottleneck events as result of recent malaria control programs were also explored. The findings support a sub-structured parasite population with a predominant clonal propagation and revealed that Iquitos city (A2) is the source of parasite spreading for all the other areas due to socio-economic patterns. Recent bottleneck events were found only in areas where intervention control programs were carried before we started the sample collection.The present study provides a very comprehensive dataset on population genetics and gene flow analysis of P. vivax infections were the most frequent, confirming previous studies from the Peruvian Amazon [MOI, the average number of distinct parasites infecting an individual in a specific area, has been used as a proxy of malaria transmission , and it n Amazon , 14, 51.n Amazon . MoreoveP. vivax reservoir but also by the high mutability of the microsatellites [Previously, varying levels of genetic diversity have been reported in endemic settings of the Peruvian Amazon revealing different transmission patterns , 15, 51.tellites  or overetellites .He. In the present study we reported coexistence of LD and extensive diversity also reported in other studies [P. vivax infections in the Peruvian Amazon the rate of allele mutations occurring within the host needs to be further explored. On the other hand, the extent of LD could be overestimated due to some of the recruited vivax patients were living in the same household [In our study the levels of genetic diversity remained at intermediate levels with no substantial differences between areas. The high rates of gene flow found may have increased the levels of  studies –57. In t studies , 57. Conousehold .P. vivax populations from South America [P. falciparum despite the possibility of recombination with sympatric unrelated clones P. falciparum would prefer self-fertilization in order to gain biological and evolutionary adaptations to its environment [P. vivax may have also in-built, active clonal behaviour in order to take advantage during its adaptation to the environment/hosts. Further study is needed to verify the in-built clonal behaviour and its impact on the epidemiology.Similarly to other  America , 58–62, ironment . The higK = 2 to: (1) look for hierarchical structure and; (2) to avoid misleading cluster assignment [Genetic clustering approaches revealed the presence of at least two or three independent parasite clusters in our study population. We rerun STRUCTURE within one cluster of the cluster when signment . Sub-strsignment .He detected in A4 confirmed the rapid expansion of these clones with clustered transmission previously described after 2-year follow up [Little to moderate differentiation among the parasites was found in the urban areas A2, A3 and A5 (“urban cluster”) where continuous human mobilization and parasite gene flow on the Iquitos-Nauta Road occur. Conversely, different clusters of parasites were found circulating in the area A1 and A4 increasing the levels of genetic differentiation. In the San Carlos village (A4) was detected a group of parasites circulating only within this village . Major dollow up .Determining the transmission patterns is a priority for the implementation of control and elimination programs . Our iniHe reduction because rare alleles will be lost with little influence on the He [He and significant intra-area genetic variation were found. Only areas with n>15 isolates  and the effect of control policies on the parasite population is a priority from the public health perspective as well for the formulation of future control policies and assessment of current control/elimination strategies.In conclusion, we have elucidated the population genetics of S1 Table. The number of isolates is described for each village.(PDF)Click here for additional data file.S2 TableHe) calculated for each marker using pooled isolates from all areas. The efficiency of amplification was calculated as the percentage of isolates with positive PCR amplification for a specific locus over the total number of analysed isolates (n = 292).The allelic size range is described in base pairs (bp). In this table, the level of polymorphism of each locus was described as the expected heterozygosity ((PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 TableThe models were constructed considering 5, 3 and 1 populations evaluating from 1 to 14 combined parameters (Θ and M) using MIGRATE-N.(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S1 FigRxy>0) between the genetic PHIPT matrix and geographic distances, so called the Isolation-by-distance (IBD) hypothesis. The genetic and geographic distance were not correlated (Rxy = -0.41 p = 0.17).Mantel test for Matrix Correspondence was performed using GenAlEx (1) to test the occurrence of a positive correlation ((PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.


Narrowing the genetic base available for future genetic progress is a major concern to plant breeders. In order to avoid this, strategies to characterize and protect genetic diversity in regional breeding pools are required. In this study, 89 winter wheat cultivars released in Croatia between 1936 and 2006 were genotyped using 1,229 DArT (diversity array technology) markers to assess the diversity and population structure. In order to place Croatian breeding pool (CBP) in a European context, Croatian wheat cultivars were compared to 523 European cultivars from seven countries using a total of 166 common DArT markers. The results show higher genetic diversity in the wheat breeding pool from Central Europe (CE) as compared to that from Northern and Western European (NWE) countries. The most of the genetic diversity was attributable to the differences among cultivars within countries. When the geographical criterion (CE vs. NWE) was applied, highly significant difference between regions was obtained that accounted for 16.19% of the total variance, revealing that the CBP represents genetic variation not currently captured in elite European wheat. The current study emphasizes the important contribution made by plant breeders to maintaining wheat genetic diversity and suggests that regional breeding is essential to the maintenance of this diversity. The usefulness of open-access wheat datasets is also highlighted. Triticum aestivum) is an allohexaploid, combining the genomes of three ancestral diploid grass species, the A-genome of Triticum urartu, the B-genome from a species related to Aegilops speltoides and the D-genome of Aegilops tauschii  and biochemical (isozymes and storage proteins) markers . With thIn the past two decades there was an explosion of different types of DNA markers  such as:de novo for each species (or a group of closely related species), because they are mostly located in non-coding regions where the nucleotide substitution rate is higher than in coding regions  of a specific DNA fragment from the total genomic DNA, and simultaneously types several thousand loci in a single assay . AlthougThe main objectives of this study were to use DArTs for (1) assessing genetic diversity available in the Croatian winter wheat breeding pool; (2) describing genetic population structure; (3) providing new information about the level of genetic diversity and structure from a single eco-geographic region; and (4) placing the genetic relationship in the Croatian breeding pool (CBP) in the wider context of European-wide winter wheat diversity as facilitated by combined, open-access DArT genotypes.Supplementary Table S1). The cultivars were bred in two regions: eastern Croatia (PIO) and western Croatia .A set of 89 winter wheat cultivars belonging to three different breeding programs  released in Croatia in the period 1936 to 2006 was selected to represent changes in breeding activities in the country over 70 years . The second was a European diversity panel (ED) consisting of 94 mostly European wheat varieties recently described by Supplementary Table S2.In order to place the CBP in a European context, accessible DArT genotypes from two published winter wheat panels were included in this study. The first was the TriticeaeGenome (TG) panel: a panel of 376 elite wheat varieties from France, Germany and the UK (http://www.triticarte.com.au/content/DNA-preparation.html) and sent to Triticarte for DArT analysis using common wheat PstI(TaqI) version 2.5 array. A total of 1531 DArT markers were scored, out of which 1,229 markers were retained, based on Q value  above 80, for further analysis.For DArT analysis of the CBP, DNA was extracted from young wheat leaf tissue from a single plant of each genotype using the protocol recommended by Triticarte Pty. Ltd. (Supplementary Table S2). The 166 DArT markers were assigned map positions based on published mapping information , Shannon’s diversity index  as assessed using DArT markers was analyzed using the following parameters: percentage of polymorphic loci  for each program was calculated as average over all marker loci:In order to quantify rare markers in wheat cultivars from each program we calculated the modified ‘frequency down-weighted marker values (DW)’ based on the measure proposed by I is the number of markers, ijp is the frequency of ith marker in a group of cultivars j and iP is the frequency of ith marker in the total dataset. Thus, we even out the unequal sample sizes. The value of RI is expected to be higher for a program in which overall rare markers were frequent among cultivars.where The estimates of diversity parameters between cultivars were compared using repeated measures analysis of variance carried out using PROC GLM in SAS v. 9.2 .DiceD;Genetic distances between pairs of cultivars were calculated by Dice’s distance coefficient DDice;. ClusterThe analysis of molecular variance AMOVA;  using ARK) ranging from 1 to 11 were carried out on the Isabella computer cluster at the University of Zagreb, University Computing Centre (SRCE). Each run consisted of a burn-in period of 200,000 steps followed by 106 Monte Carlo Markov Chain (MCMC) replicates assuming an admixture model and correlated allele frequencies. The choice of the most likely number of clusters (K) was determined according to ad hoc statistic ΔK, as described by A model-based clustering method was applied to infer genetic structure of Croatian winter wheat breeding pool using the software STRUCTURE ver. 2.3.3 . Ten runTotalSh) of a breeding program could be calculated from the mean proportions of membership and related to the mean within-cultivar diversity (MeanSh) calculated by averaging the Shannon’s diversity indices of the cultivars belonging to a breeding program. Thus, the admixture level of a breeding program can be separated into the proportion of admixture attributable to within-cultivar  and among-cultivar [/TotalSh] admixture.The admixture was quantified using Shannon’s diversity index based on proportions of membership in each cluster inferred by STRUCTURE. In this sense, Shannon’s diversity index will be equal to zero for a cultivar having 100% of its genome estimated to belong to a cluster while it will reach its maximum value when the proportions of membership of a given cultivar are equal in all clusters (the most admixed cultivar). Furthermore, the total diversity . The estimates of diversity parameters among countries were compared as above. Post hoc Bonferroni’s adjustments were used to compare the means of diversity estimates among countries at significance level P < 0.05.Diversity of DArT markers among wheat cultivars from seven European countries  has been assessed using the same parameters as above  was carried out using Genetix 4.05 .ST values that are equivalent to the proportion of total variance that is partitioned between groups of cultivars originating from two different countries. To obtain a distance matrix, ϕST values were interpreted as the inter-country distance average  within and among seven European countries and (B) among regions , among countries within regions and within countries. The Central European region was comprised of Croatia and Hungary while the Northern and Western European region included the remaining countries. Pairwise comparisons examined with AMOVA resulted in ϕ average . A clustA model-based clustering method was applied to infer genetic structure of Croatian winter wheat breeding pool using the software STRUCTURE ver. 2.3.3 . The genSh) used to quantify the genetic diversity was 0.78 for PIO and 0.76 for Bc/FAZ. The average effective number of alleles per locus (EN) was 1.63 and 1.61, the expected heterozygosity (EH) 0.36 and 0.35, with an average of 0.38, while the polymorphism information content (PIC) was 0.29 and 0.28, with an average of 0.30, were found for PIO and Bc/FAZ, respectively. The rarity index (RI) was 0.985 for PIO and 1.036 for Bc/FAZ. A slightly higher genetic diversity was found in the winter wheat breeding pool from eastern Croatia (PIO), while overall rare markers were found more frequently among cultivars from western Croatia .A total of 1,229 polymorphic DArT markers were included in the diversity analysis of 89 Croatian wheat cultivars. The Shannon’s diversity index (DiceD) estimated for DArTs was 0.27. Four indistinguishable pairs of cultivars were found , all originating from the breeding program carried out at the Agricultural Institute Osijek (PIO). The highest genetic distance (DiceD = 0.49) was calculated between cultivars C31 Lana and C38 Marija bred by the Bc Institute for Plant Breeding and Production of Field Crops Production, Zagreb (Bc).The average genetic distance . The larger group included 72 cultivars of different origin (49 from PIO and 23 from Bc/AFZ) suggesting that breeders used the same or similar genetic material as parental lines. As a result, some cultivars from different breeding programs could show greater similarity than those from the same breeding program. The smaller group was comprised of 17 cultivars (14 from PIO and 3 from Bc/AFZ), that were mostly newly registered cultivars, except the cultivar Marija (Bc), which was one of the mostly widespread cultivars in the region during the 1980s and frequently used in crossings as a one of the parents.The Neighbor joining tree shows differentiation of the CBP into two clusters  suggested the existence of moderate level of genetic differentiation between programs.Hierarchical analysis of genetic diversity using AMOVA was performed to analyze the partitioning of the genetic variation between and within breeding programs in CBP. Although most of the genetic diversity was attributable to differences among cultivars within a breeding program (94.36%), the significant ϕK followed the same pattern . The highest ΔK values were observed for K = 2 (5.47) followed by that at K = 5 (4.20). The proportion of membership of each Croatian wheat cultivar in each cluster at K = 2 and 5 is shown in Figure 1. At K = 2 the great majority of the cultivars were assigned to cluster A while the cluster B was comprised of 17 cultivars in complete concordance with the results of distance-based clustering analysis . All the cultivars had membership probabilities higher than 90% in a particular cluster. At K = 5 the majority of cultivars assigned to cluster B at K = 2 were included into cluster E.The population structure of Croatian wheat cultivars was assessed using the Bayesian model-based clustering method. In general, average estimates of the likelihood of the data, conditional on a given number of clusters, ln[Pr(X| K)], kept increasing with higher K (number of clusters), but the standard deviations among different runs for each K = 5 is shown in Supplementary Figure S1.The mean proportion of membership of Croatian wheat cultivars bred by the Agricultural Institute Osijek (PIO) and the Bc Institute for Plant Breeding and Production of field crops or the University of Zagreb, Faculty of Agriculture (Bc/FAZ) in each cluster at TotalSh = 0.674) was slightly higher than those of Bc/FAZ  cultivars while the mean within-cultivar diversity of PIO cultivars (MeanSh = 0.285) was lower than those of Bc/FAZ (MeanSh = 0.301). Thus, the admixture attributable to within-cultivar component was lower than that attributable to among-cultivar component in case of PIO breeding program  while considering Bc/FAZ breeding program the opposite was true (52.28 vs. 47.72%).The mean proportions of membership of PIO cultivars were in each of the five clusters ranged from 0.099 to 0.293. Bc/FAZ cultivars showed substantially higher proportions of membership in two clusters  while the proportions in the rest of the clusters was lower than 0.100. Shannon’s diversity index based on proportions of membership in each of the five clusters was used to assess the admixture levels of the Croatian breeding programs. Total Shannon’s diversity index of PIO cultivars (K = 5 the number of cultivars classified as representative of a cluster (having more than 90% of their genome estimated to belong to a cluster) was 23, nine belonged to a cluster with membership probabilities between 75 and 90% while 57 could be considered as mixed . Out of 26 cultivars bred by Bc/FAZ, six were classified as representative, all of them belonging to cluster D. On the other hand, seventeen out of 63 PIO cultivars classified as representative belong to all the detected clusters (A–E). The majority of cultivars from both programs were classified as mixed .At Supplementary Table S2). The 166 DArT markers in the thinned, combined CBP/EBP dataset were relatively evenly distributed across the bread wheat genome, although there were only 15 markers on the D genome, with no markers on either 4D or 6D (Supplementary Table S4). Using Shannon’s diversity index (Sh) it was found that genetic diversity varied from the highest levels found in Central European countries , toward the lowest, found in Nordic countries . The same pattern was observed for the expected heterozygosity (EH) and PIC. The highest number of effective alleles (EN) was found in Croatian and the lowest in the UK wheat pool (1.68 vs. 1.45). The rarity index (RI) value was 1.32 for Hungarian and 1.28 for Croatian wheat pool (Table 2).To contextualize the genetic diversity of the CBP, a total of 166 polymorphic DArT markers were included in the diversity analysis of 523 European wheat cultivars from seven countries . ϕST value among countries was moderate, but highly significant . When the geographical criterion (CE vs. NWE) was applied in the two-way AMOVA, highly significant difference between regions was obtained  that accounted for 16.19% of the total variance while the differences among countries within regions accounted for only 3.50% (Table 3). Pairwise ϕST values among countries ranged from 0.021 between France and UK to 0.277 between UK and Hungary with the average value of 0.122 (Table 4). All the ϕST values were highly significant. Genetic differentiation between CBP and that of other countries was higher than average in all cases except in case of Hungary (ϕST = 0.074). As expected, the UPGMA tree based on inter-country ϕST values showed clear separation of CE cultivars from those belonging to NWE countries. In accordance with FCA results, the further subdivision between German and Swedish breeding pool from French, UK, and Danish cultivars was observed .One-way AMOVA showed the most of the genetic diversity was attributable to the differences among cultivars within countries . The highest ΔK value were observed for K = 2 (13075.54) followed by that of K = 3 (429.51), while all the subsequent Ks had substantially lower ΔK values (<5). The clusters identified at K = 2 and K = 3 correspond well with those identified by FCA and UPGMA analysis based on pairwise ϕST values. At K = 2, the cluster A contained the majority of cultivars from NWE countries, while the majority of cultivars from CE countries belonged to cluster B . At K = 3, the cluster A was split into two subclusters (A1 and A2).By analyzing the population structure of 523 cultivars from seven European countries using a Bayesian approach the similar pattern was observed, namely, that the average estimates of ln[Pr(X| K)] as well as the standard deviations among runs kept increasing with higher K (K = 2) and three (K = 3) clusters is presented in Figure 4. As in FCA and UPGMA analysis, the divergence between subclusters A1 and A2 was moderate in comparison to the split between the two subclusters and the subcluster B as shown by much larger net nucleotide distance (provided by Structure) between A1 and B as well as A2 and B  than between A1 and A2 (0.06).The most of the UK cultivars were assigned to the subcluster A1, the subcluster A2 comprised most of the cultivars from Germany and Sweden, while Danish and French cultivars were almost equally distributed among the two subclusters. The average proportion of membership of wheat cultivars in each of the seven European countries in each of the two  were used as progenitors and sources of earliness, high yield capacity, and shorter straw. From the mid 1950s onward, domestic material was crossed mostly with wheat varieties originating from Italy, countries of former Soviet Union, the USA, and France . The latK = 5 the proportions of membership of PIO cultivars are more evenly distributed among clusters in comparison to those bred by Bc/FAZ leading to a higher total admixture of PIO cultivars based on Shannon’s diversity index. It follows that Bc/FAZ breeding program relies on narrower genetic base that could be efficiently broaden by identifying suitable cultivars belonging to underrepresented subclusters  and incorporating them into crossing schemes. However, the admixture attributable to among-cultivar component in case of PIO breeding program is higher than that attributable to within-cultivar component due to the fact that the cultivars classified as representative of different cluster make 26%. It would suggest that the inter-cluster cultivar crossings is likely to result in novel combinations of promising traits.At Combining the genotypic information from the CBP with two additional EBP open-access datasets  in the cThe results show higher genetic diversity and rare marker frequency in the winter wheat breeding pool from Central Europe  as compared to the Northern and Western European (NWE) countries . In agreement with this, AMOVA showed the most of the genetic diversity was attributable to the differences among cultivars within countries (>80%), which is with the consensus of other reports . RousselK value was found at K = 2 , but at K = 3 there was further separation as well . Similar results were reported by K = 2 for wheat varietities originating from UK, Germany and France, but further separation within French cluster was not due to geographic origin or linked to the breeding companies, but rather reinforcing the idea that unique elite variety extensively used as progenitor caused this subdivision.When genetic structure of our CBP/EBP wheat panel was analyed the highest ΔFurther selective changes between NWE and CE wheat breeding pool is due to difference causes by latitude effects, where there is presence of photoperiod sensitive and insensitive alleles causing different adaptation patterns to variable environments.This provides context for the genetic diversity found and indicates that although the CBP has historically derived parental germplasm from various European and International programs (as above) the diversity retained within it is regionally distinct.The use of a combined dataset in the current study shows that open-access datasets and common genotyping platforms represent an opportunity to use data-mining to unlock genetic potential. Although useful here for placing the CBP within the context of the wider EBP this approach was limited in this case due to the lack of overlapping DArT markers between the three discrete datasets.In terms of usefulness in future studies  it is also holds limited potential due to the lack of genome-wide coverage, particularly on the D-genome. DArT markers have been superseded by SNP markers which can be applied either singly , or in array form. The availability of affordable, high-density markers will add value to future diversity studies, although significant insight into genetic diversity has been possible using DArTs in the current study.The current study emphasizes the important contribution made by plant breeders to European wheat genetic diversity as each program tends to represent a pool of regional divergence. This suggests that maintenance of crop diversity as a whole would benefit from an increase in the number of regional breeding programs rather than the consolidation that is often seen, particularly in commercial breeding. As demonstrated here, DArTs showed their efficacy in describing genetic diversity and population structure of the CBP. Likewise, they provided an insight into the distribution of genetic variance within a European context which is mostly held within, rather than between, geographic regions.Conceived and designed the manuscript: DN, AB, ZŠ, MS, and NG. Contributed reagents/materials/analysis tools: GD, DN, ZŠ, RŠ, and KD. Analyzed the data: ZŠ, AB, and RH. Wrote the manuscript: DN, AB, ZŠ, MS, NG, KD, GD, RH, and RŠ.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.