Actinidia chinensis) market, which harms the interests of consumers. However, there is lack of rapid and accurate identification methods for commercial kiwifruit varieties. Here, twelve common commercial varieties of kiwifruit were morphologically discriminated. DNA barcodes of chloroplast regions psbA-trnH, rbcL, matK, rpoB, rpoC1, ycf1b, trnL and rpl32_trnL(UAG), the nuclear region At103 and intergenic region ITS2 were amplified. Divergences and phylogenetic trees were used to analyze the phylogenetic relationship of these twelve commercial kiwifruit varieties. The results showed that matK, ITS2 and rpl32_trnL(UAG) can be utilized as molecular markers to identify CuiYu, JinYan, HuangJinGuo, ChuanHuangJin, HuaYou, YaTe, XuXiang and HongYang. This provides experimental and practical basis to scientifically resolve kiwifruit-related judicial disputes and legal trials.The quality and safety of food are important guarantees for the health and legal rights of consumers. As an important special fruitcrop, there are frequently shoddy practices in the kiwifruit ( Actinidia chinensis), also called ‘the king of fruits’, is an important economical crop because of its exceedingly high content of ascorbic acid (vitamin C), dietary fiber, nutritional minerals compositions and other health beneficial metabolites [The quality and safety of food are important guarantees for the health and legal rights of consumers. Kiwifruit  developed by Li et al. [matK to be used to distinguish toxic hybrids form parental species [rpl32_trnL (UAG) had a greater degree of variation and could be used as the core barcode sequence of cherry plants. These genes have the potential to be as powerful as mitochondrial CO1 gene in identifying species [ITS2 to identify the varieties and provenances of Taiwan’s domestic and imported made teas [psbK-psbI spacers for identification of Emirati date palm (Phoenix dactylifera L.) varieties (cultivar-level) [Angelica anomala Ave’-Lall [The DNA barcoding technique is a quick and effective molecular marker technology for classification and identification of organisms by using a standard gene region . Since H in 2003 , it has  in 2003 ,7. DNA bfication . Noncodif Orchid ,10. ITS2l plants . Two chld plants . ycf1b wd plants . trnL wad plants . At103  . Jaakolave’-Lall . Single nucleotide polymorphism (SNP) is a single nucleotide variation with a specific and determined genetic location in at least 1% of the population . SNP is psbA-trnH, rbcL, matK, rpoB, rpoC1, ycf1b, trnL and rpl32_trnL(UAG), the nuclear region At103 and intergenic region ITS2), which were used for molecular identification. This study will not only lay a foundation for phylogenetic analysis but also provides experimental and practical basis for the rapid identification of kiwifruit.In this study, we attempted to use the morphological method combined with molecular biological methods for rapid cultivar identification of twelve kiwifruit commercial varieties in China. SNP typing method was evaluated by using ten candidate DNA barcoding markers  are 743 bp, 889 bp, 502 bp, 491 bp, 512 bp, 529 bp, 193bp and 1010 bp, respectively. Sequences of rbcL, psbA-trnH, rpoB, rpoC1 and trnL have no difference in these twelve kiwi commercial varieties  sequences is 100%. There are six haplotypes in rpl32_trnL(UAG) sequence. Bases at 285 bp and 385 bp of CuiYu are “A”, that of the other 11 commercial varieties are “T”. Bases at 662 bp of HongYang are “C”, that of the other 11 commercial varieties are “T”. Bases at 768 bp of YaTe are “G”, that of the other 11 commercial varieties are “A”. Bases at 837 bp and 841 bp of HuaYou are “C” and “T”, respectively, that of the other 10 commercial varieties are “A”. Bases at 852 bp of XuXiang are “T”, that of the other 11 commercial varieties are “A”.  and ITS2+matK+rpl32_trnL(UAG) are shown in ITS2, there is no difference among HuangJinGuo, ChuanHuangJin and HongYang. The genetic distance between them and nine other commercial varieties is 0.01. There is no difference among the nine commercial varieties , the genetic distance between HuaYou and HongYang, YaTe, XuXiang is 0.003. The genetic distance between HuaYou and JinYan, CuiXiang, FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo is 0.002. The genetic distance between HongYang and CuiYu is 0.003. The genetic distance between HongYang and YaTe, XuXiang is 0.002. The genetic distance between HongYang and JinYan, CuiXiang, FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo is 0.001. The genetic distance between CuiYu and YaTe, XuXiang is 0.003. The genetic distance between CuiYu and JinYan, CuiXiang, FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo is 0.001. The genetic distance between JinYan and YaTe, XuXiang is 0.001. There is no difference between JinYan and CuiXiang, FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo. The genetic distance between CuiXiang and YaTe, XuXiang is 0.001. There is no difference between CuiXiang and FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo. The genetic distance between YaTe and XuXiang is 0.002. There is no difference between YaTe and FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo. The genetic distance between FengXianLou and XuXiang is 0.001. There is no difference between FengXianLou and ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo. The genetic distance between ChuanHuangJin and XuXiang is 0.001. There is no difference between ChuanHuangJin and HaiWoDe, QinMei and HuangJinGuo. The genetic distance between HaiWoDe and XuXiang is 0.001. There is no difference between HaiWoDe and QinMei and HuangJinGuo. The genetic distance between QinMei and XuXiang is 0.001. There is no difference between QinMei and HuangJinGuo. The genetic distance between HuangJinGuo and XuXiang is 0.001 (ITS2+matK+rpl32_trnL(UAG), the genetic distance between YaTe and JinYan is 0.001. The genetic distance between XuXiang and YaTe, JinYan is 0.001. There is no difference between QinMei and YaTe, JinYan, XuXiang. There is no difference between HaiWoDe and YaTe, JinYan, XuXiang, QinMei. There is no difference between CuiXiang and YaTe, JinYan, XuXiang, QinMei, HaiWoDe. The genetic distance between HuaYou and YaTe, JinYan, XuXiang, QinMei, HaiWoDe, CuiXiang is 0.001. There is no difference between FengXianLou and YaTe, JinYan, XuXiang, QinMei, HaiWoDe and CuiXiang. The genetic distance between FengXianLou and HuaYou is 0.001. The genetic distance between CuiYu and YaTe, JinYan, XuXiang, HuaYou is 0.002. The genetic distance between CuiYu and QinMei, HaiWoDe, CuiXiang, FengXianLou is 0.001. The genetic distance between HuangJinGuo and YaTe, JinYan, XuXiang, HuaYou, CuiYu is 0.003. The genetic distance between HuangJinGuo and QinMei, HaiWoDe and CuiXiang, FengXianLou is 0.002. The genetic distance between HongYang and YaTe, JinYan, XuXiang, QinMei, HaiWoDe, CuiXiang, HuaYou, FengXianLou is 0.003. The genetic distance between HongYang and CuiYu is 0.004. There is no difference between HongYang and CuiYu. The genetic distance between ChuanHuangJin and YaTe, JinYan, XuXiang, HuaYou, CuiYu is 0.003. The genetic distance between ChuanHuangJin and QinMei, HaiWoDe and CuiXiang, FengXianLou is 0.002. There is no difference between ChuanHuangJin and HuangJinGuo, HongYang  sequences and ITS2+matK+rpl32_trnL(UAG) combination sequences were tested 1000 times by bootstrap method to build NJ phylogenetic tree , HuaYou, YaTe, XuXiang, HongYang, CuiYu could be distinguished from the other seven commercial varieties, respectively. tic tree . Phylogeequences  showed tcategory . Figure ITS2+matK+rpl32_trnL(UAG), CuiYu, JinYan, YaTe, XuXiang, HuaYou, HongYang, HuangJinGuo and ChuanHuangJin can be distinguished from the other four commercial varieties, respectively”, the morphology of 12 commercial kiwifruit was analyzed in In this study, 72 samples of 12 commercial varieties were collected in the market. We firstly conducted a morphological analysis of twelve kiwifruit commercial varieties that are easily confused in the market. Suggestions for morphological identification are proposed. By referring to the classification criteria of “Flora of China  and At103 were explored to quickly and accurately identify twelve kiwifruit commercial varieties in this study. The primer sequences used for amplification of these ten regions, the length of the products, and the amplification procedures are listed in psbA-trnH sequence of the intergenic region is one of the most mutated non-coding regions in the plant chloroplast genome and is often used as a barcode for material identification [psbA-trnH sequences of the 12 commercial kiwifruits are identical, which means that the psbA-trnH is not suitable for identifying the 12 commercial kiwifruits. Intergenic region ITS2  and the cp gene rbcL may have potential as universal plant barcodes [matK, rpoB and rpoC1 were outlined as viable markers for land plant barcoding [rbcL and matK as the core barcodes for plant species identification [rbcL, rpoB and rpoC1 sequences of the 12 commercial kiwifruits are unanimous, which indicates the three barcodes sequences are improper for the identification of the 12 commercial kiwifruits. ycf1b was reported as the most variable plastid genome region and can serve as a core barcode of land plants [At103 together with matK to be used to distinguish toxic hybrids form parental species [trnL was chosed as the barcoding gene for reference library constructing for wetland plants [trnL could not be used as the barcode to discriminate these 12 commercial varieties of kiwifruit.Ten DNA barcoding fication ,34. Howearcoding . CBOL fofication . The resd plants . At103 t species . Our resd plants , whose amatK sequences of 12 commercial kiwifruits. For matK, bases at 534 bp of Jin Yan are “G”, that of the other eleven commercial varieties are “A”, and bases at 777 bp of Cui Yu are “C”, that of the other eleven commercial varieties are “A”  had a greater degree of variation and could be used as the core barcode sequence of cherry plants [rpl32_trnL(UAG) sequence analysis of 12 commercial kiwifruit results showed that compared to rbcL, matK, psbA-trnH, rpoB, rpoC1, trnL and ITS2, the rpl32_trnL(UAG) sequence difference is the maximum. It was found that there are six SNPs in the 1010bp of rpl32_trnL(UAG) sequence. Bases at 285 bp and 385 bp of CuiYu are “A”, that of the other 11 commercial varieties are “T”. Bases at 662 bp of HongYang are “C”, that of the other 11 commercial varieties are “T”. Bases at 768 bp of YaTe are “G”, that of the other 11 commercial varieties are “A”. Bases at 837 bp and 841 bp of HuaYou are “C” and “T”, respectively, that of the other 10 commercial varieties are “A”. Bases at 852 bp of XuXiang are “T”, that of the other 11 commercial varieties are “A”. (rpl32_trnL(UAG), the genetic distance between HuaYou and JinYan, CuiXiang, FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo is 0.002. The genetic distance between HongYang and CuiYu is 0.003. The genetic distance between HongYang and YaTe, XuXiang is 0.002. The genetic distance between HongYang and JinYan, CuiXiang, FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo is 0.001. The genetic distance between CuiYu and YaTe, XuXiang is 0.003. The genetic distance between CuiYu and JinYan, CuiXiang, FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo is 0.001. The genetic distance between JinYan and YaTe, XuXiang is 0.001. There is no difference between JinYan and CuiXiang, FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo. The genetic distance between CuiXiang and YaTe, XuXiang is 0.001. There is no difference between CuiXiang and FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo. The genetic distance between YaTe and XuXiang is 0.002. There is no difference between YaTe and FengXianLou, ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo. The genetic distance between FengXianLou and XuXiang is 0.001. There is no difference between FengXianLou and ChuanHuangJin, HaiWoDe, QinMei and HuangJinGuo. The genetic distance between ChuanHuangJin and XuXiang is 0.001. There is no difference between ChuanHuangJin and HaiWoDe, QinMei and HuangJinGuo. The genetic distance between HaiWoDe and XuXiang is 0.001. There is no difference between HaiWoDe and QinMei and HuangJinGuo. The genetic distance between QinMei and XuXiang is 0.001. There was no difference between QinMei and HuangJinGuo. The genetic distance between HuangJinGuo and XuXiang is 0.001 (rpl32_trnL(UAG) showed more variable sites than the other plastid regions matK and rbcL. This result confirmed that rpl32_trnL(UAG) showed better discriminatory power than other plastid regions. Phylogenetic analysis further indicated that HuaYou, YaTe, XuXiang, HongYang, CuiYu can be distinguished from the other seven commercial varieties, respectively , and then found that, for ITS2+matK+rpl32_trnL(UAG), there is no difference between QinMei and YaTe, JinYan, XuXiang. There is no difference between HaiWoDe and YaTe, JinYan, XuXiang, QinMei. There is no difference between CuiXiang and YaTe, JinYan, XuXiang, QinMei, HaiWoDe. The genetic distance between YaTe and JinYan is 0.001. The genetic distance between XuXiang and YaTe, JinYan is 0.001. The genetic distance between HuaYou and YaTe, JinYan, XuXiang, QinMei, HaiWoDe, CuiXiang are 0.001. There is no difference between FengXianLou and YaTe, JinYan, XuXiang, QinMei, HaiWoDe and CuiXiang. The genetic distance between FengXianLou and HuaYou is 0.001. The genetic distance between CuiYu and YaTe, JinYan, XuXiang, HuaYou is 0.002. The genetic distance between CuiYu and QinMei, HaiWoDe, CuiXiang, FengXianLou is 0.001. The genetic distance between HuangJinGuo and YaTe, JinYan, XuXiang, HuaYou, CuiYu is 0.003. The genetic distance between HuangJinGuo and QinMei, HaiWoDe and CuiXiang, FengXianLou is 0.002. The genetic distance between HongYang and YaTe, JinYan, XuXiang, QinMei, HaiWoDe, CuiXiang, HuaYou, FengXianLou is 0.003. The genetic distance between HongYang and CuiYu is 0.004. There is no difference between HongYang and CuiYu. The genetic distance between ChuanHuangJin and YaTe, JinYan, XuXiang, HuaYou, CuiYu is 0.003. The genetic distance between ChuanHuangJin and QinMei, HaiWoDe and CuiXiang, FengXianLou is 0.002. There is no difference between ChuanHuangJin and HuangJinGuo, HongYang (ITS2+matK+rpl32_trnL(UAG) combination. We found that the genetic distance between samples of HaiWoDe, FengXianLou, QinMei and CuiXiang in this study is 0.000, which means that most of them have a unique ITS2, matK and rpl32_trnL(UAG) sequences. This feature is thus useful for identifying these twelve commercial varieties and related species, which is similar to previous study [Multi-site combination methods based on plastid (chloroplast) sequences are considered as effective strategies in the identification of plant species ,33,34. THongYang . These rus study .ITS2, psbA-trnH, rbcL, matK, rpoB, rpoC1 and rpl32_trnL(UAG) in 12 commercial kiwifruit, stable SNP loci of DNA barcode ITS2 are found between HuangJinGuo, ChuanHuangJin, HongYang and the other nine commercial varieties. Likewise, DNA barcode matK has stable SNP loci between JinYan, CuiYu and the other 10 commercial varieties. rpl32_trnL(UAG) has stable SNP loci between CuiYu, JinYan, YaTe, XuXiang, HuaYou, HongYang, HuangJinGuo and ChuanHuangJin and the other four commercial varieties. The presence of these SNP loci makes the identification of CuiYu, JinYan, YaTe, XuXiang, HuaYou, HongYang, HuangJinGuo and ChuanHuangJin kiwifruits possible. These results indicate that intergenic region ITS2 could distinguish HuangJinGuo and HongYang from the nine other commercial varieties, and matK could be utilized to distinguish JinYan and CuiYu from 10 the other commercial varieties. rpl32_trnL(UAG) could be utilized to distinguish HuaYou, YaTe, XuXiang, HongYang, CuiYu from the other seven commercial varieties. SNPs of ITS2, matK and rpl32_trnL(UAG) combining with the current plant DNA barcode systems could be used to accurately identify kiwifruit, which comprises samples of closely-related or subspecies from different cultivation areas. These results are good agreements with previous reports [The analysis of SNP loci is an important tool for analyzing small differences between populations ,38,39. IITS2 and cp gene matK and rpl32_trnL(UAG) combined together can distinguish CuiYu, JinYan, YaTe, XuXiang, HuaYou, HongYang, HuangJinGuo and ChuanHuangJin from the other four commercial varieties, respectively, that is consistent with the results of morphology and better than the results of morphology. These results will provide an experimental basis for the classification and identification of commercially available kiwifruits and will offer a theoretical support for scientific trials and forensic botany of such disputes.In conclusion, morphologically, JinYan, CuiYu, HuangJinGuo and HongYang can be discriminated. At the molecular level, SNPs based on DNA barcoding intergenic region Seventy two samples from twelve kiwifruit commercial varieties were collected in this study. Their names are HuangJinGuo, CuiXiang, QinMei, XuXiang, HuaYou, FengXianLou, YaTe, HaiWoDe, CuiYu, ChuanHuangJin, HongYang and JinYan. Six replicates were randomly selected for each commercial variety in the local market. According to our survey, the price of HongYang is 4 to 5 times than that of QinMei, HuaYou, YaTe and HaiWoDe in the market. Similarly, ChuanHuangJin, HuangJinGuo and JinYan, whose price are 3 to 4 times than that of QinMei, HuaYou, YaTe and HaiWoDe. HuangJinGuo, CuiXiang, QinMei, XuXiang, HuaYou, FengXianLou, YaTe and HaiWoDe were collected from Shaanxi province of China. CuiYu, ChuanHuangJin, HongYang and JinYan were collected from Si Chuan province of China  and At103) were designed based on DNA barcodes [E. coli. Positive clones were screened and sequenced in Beijing Genomics Institute.PCR primers of DNA barcodes  and ITS2+matK+rpl32_trnL(UAG) were carried out by ClustalX 1.8 [Multiple sequence alignment was performed by using the results of ltalin/. . Multi-stalX 1.8  software


While the pleasant scent of aromatic rice is making it more popular, with demand for aromatic rice expected to rise in future, varieties of this have low yield potential. Genetic diversity and population structure of aromatic germplasm provide valuable information for yield improvement which has potential market value and farm profit. Here, we show diversity and population structure of 113 rice germplasm based on phenotypic and genotypic traits. Phenotypic traits showed that considerable variation existed across the germplasm. Based on Shannon–Weaver index, the most variable phenotypic trait was lemma-palea color. Detecting 140 alleles, 11 were unique and suitable as a germplasm diagnostic tool. Phylogenetic cluster analysis using genotypic traits classified germplasm into three major groups. Moreover, model-based population structure analysis divided all germplasm into three groups, confirmed by principal component and neighbors joining tree analyses. An analysis of molecular variance (AMOVA) and pairwise FST test showed significant differentiation among all population pairs, ranging from 0.023 to 0.068, suggesting that all three groups differed. Significant correlation coefficient was detected between phenotypic and genotypic traits which could be valuable to select further improvement of germplasm. Findings from this study have the potential for future use in aromatic rice molecular breeding programs. At present, rice alone constitutes about 93% of the total food grains produced annually in Bangladesh3.Rice is the staple food source for over half the world’s population. In Bangladesh, rice production occurs over an area of 11.4 million hectares (ha), generating 51.6 million tons of rice annually4 and local landraces, including aromatic ones, which have often been cultivated in less than favorable ecosystems that cover 12.16% of the total rice growing areas1. Some of these local varieties have desirable characteristics around aroma, better taste, and higher cooking quality, all of which potentiate value-added parameters to the rice both socially and economically. These aromatic rice germplasm constitute a small but important group of rice genotypes familiar in many countries of the world for their aroma or super-fine grain quality or both5.Historically, thousands of local rice varieties have been cultivated across Bangladesh6, with more than 100 designated as aromatic varieties7. These aromatic germplasms are comprised of short and medium bold types with mild to strong aroma9.To date, more than 8,000 varieties, landraces, cultivars, and wild-types of rice from indigenous and exotic sources are preserved in the Bangladesh Rice Research Institute (BRRI) genebank11. To date, no attempt has been made improve aromatic rice germplasm either geographically or genetically, and to the authors’ knowledge, no information on the genetic diversity of local aromatic rice germplasm has been published to date. Research into genetic diversity and correlation among the aromatic germplasm available in Bangladesh would not only play a vital role for hybridization to increase production, quality traits, stresses and tolerances, and in general provide information to help breeders identify pre-breeding materials12, but also would preserve information about this rice. Presently, the valuable genetic wealth of aromatic and fine-rice genotypes is being eroded because of their poor yield and the introduction of high-yielding varieties. Beyond more immediate commercial applications, it is essential that these valuable rice germplasms are collected, properly conserved, phenotypically and genotypically characterized, where possible genetically enhanced, and effectively documented within the context of their importance in intellectual property rights (IPR) regimes as well.In general, aromatic rice germplasm are tall-statured, possess a fewer number of panicles, have high stem weight, lower yields, and are susceptible to lodging and pest damage. Aromatic germplasm emits aroma  due to the presence of a non-functional betaine aldehyde dehydrogenase 2 (BADH2), which is also responsible for low grain yieldD2 statistics offer a powerful tool for determining clustering patterns as a way to establish relationships between genetic and geographical divergence as well as investigating the roles of different quantitative characters toward maximum divergence13. However, assessments based only on plant phenotypes are not a reliable measure of genetic difference given the influence of environmental conditions. The advent of PCR-based molecular marker technology, however, provides highly effective and reliable tools both for measuring genetic diversity in crop germplasm and evaluating evolutionary relationships within and between plant populations, varieties, and species14.Genetic diversity in plants has long been assessed using morphological and physiological characters. Mahalanobis 17. They are abundant in eukaryotic organisms and often well-distributed throughout the genome19. Thus, SSRs are highly suitable for characterizing rice given their high reproducibility, simplicity, easy scoring ability, multi-allelic nature, hyper-variability, co-dominant inheritance, and genome-wide coverage20. SSRs are allele-specific and are co-dominant markers that have a high potential for identifying genetic diversity in an organism in a cost-effective manner22. To date, SSRs have been used for genetic diversity analysis, genetic characterization of genotypes, cultivar identification, marker-assisted selection breeding, and population structure assessment in multiple previously published rice genetic studies31.Across various molecular markers, simple sequence repeat (SSR) can serve as the marker for selection and affords some advantages over other markersWe present, in Fig. H-based evenness value across the traits indicates the effective representativeness of the diversity available in the germplasm. Evenness varies from 0 to 1. Table UPGMA-based dendrogram displayed major four groups for the 113 tested germplasm Fig.  based onGenerally, the Shannon–Weaver diversity index value represents the degree of diversity prevailing among the tested samples. Higher value indicates higher diversity and vice versa. In this study, the Shannon–Weaver index values ranged from 0.14 for leaf blade color to 1.71 for lemma-palea color, the latter showing considerable variation.Overall, 52 well-spread microsatellite (SSR) markers covering all 12 chromosomes were used to characterize and assess genetic diversity among 113 aromatic rice germplasm. Only 45 markers, however, showed clear and consistent polymorphic banding patterns and amplification of each genotype, indicating that these microsatellites were suitable for genetic diversity analysis. The remaining seven markers produced monomorphic bands revealing one allele at each locus in all the germplasm (not discussed here) and hence were not useful for this study.The gel picture for banding of the 113 aromatic rice germplasm with the RM447 marker is shown in Fig. Overall, 140 alleles were identified from the 113 tested germplasm with an average 3.11 alleles. The highest PIC value (0.67) was found for the marker RM114, with the lowest (0.051) observed for marker RM178, with a mean of 0.29. The most frequent major allele frequency (0.97) was found for the marker RM455 and the lowest (0.37) for marker RM283, with a mean of 0.77.Rare alleles are described as alleles with a frequency less than 5%. In general, markers detecting a greater number of alleles per locus detected more rare alleles. Fifty-two 52) rare alleles were identified at 45 SSR markers with an average of 1.15 alleles per locus  values (data not shown). At K = 3, all germplasm stratified into three populations , representing 56%, 14% and 30% of germplasm used in structure analysis respectively; Fig. FST statistics tested genetic variation among the three population with values of 0.39 0.11 and 0.28 for P1, P2, and P3, respectively, with an average 0.26, indicating a moderate population structure. Thus, the most structured population was P1, followed by P3, and then the P2 population. The specific FST values  for the three populations were calculated using STRUCTURE. The expected heterozygosity or averaged distances between individuals, in same cluster, were 0.23, 0.47, and 0.30, respectively. The largest genetic (net nucleotide) distance (0.12) was observed between P1 and P2, while, the genetic distances between P1 vs P3 and P2 vs P3 were 0.023 and 0.09, respectively. The neighbor-joining (NJ) tree and principal component analysis (PCA) based on population derived from the structure analysis were conducted. Both the NJ tree and PCA analyses further confirmed the STRUCTURE results. The tree model-based groups (P1–P3) were clearly separated in the NJ tree  to estimate the percentage of variation between populations and within populations. In the total genetic variance between populations, based on structure, we observed that 6% was attributed to the populations and the remaining 93% was explained by individual differences within populations Table . ApproxiIn general, the results from AMOVA and FST analysis were in agreement with the observations obtained through (i) the phylogenetic tree-based, (ii) similarity coefficient distribution, and (iii) structure analysis, confirming the presence of both a statistically moderate genetic diversity and a high level of population structure.17. While cultivated varieties of aromatic rice in Bangladesh developed as a result of farmers’ and scientists’ selection from within the existing and available genetic diversity in a diversity of environments, one can argue that modern breeding over the past two centuries, in some cases, has resulted in the release or maintenance of varieties that are considered uniform, less stable, and better adapted to controlled and limited environmental conditions32. This makes narrow genetic background varieties popular among farmers, even as these improved varieties may, in some cases, be more susceptible to biotic and abiotic stresses.Exploitation of genetic diversity, present in a crop species, can improve the target traits of that crop for the crop breeder, farmers, and ultimately consumersIn Bangladesh (as elsewhere), aromatic rice improvement requires (i) the identification of highly diverse germplasm, (ii) highly polymorphic molecular markers that, (iii) in turn, can be effectively utilized for the mapping of genes/QTLs for economically important traits and (iv) where a subset of these markers can be used in molecular breeding programs towards the development of improved varieties. Thus, steps towards understanding varietal characteristics of aromatic rice has the potential to play a vital role in future national and international breeding programs, especially where aromatic traits may be desirable for consumers. This research is a first step in a broader initiative to characterize the genetic base and improve the aromatic rice germplasm commonly grown in Bangladesh and conserved in the BRRI Genebank.34. Because qualitative and quantitative phenotypic traits can impact the genetic diversity of rice germplasm35, they can be used for evaluating that genetic diversity37. Specifically, the degree of diversity can be evaluated using Shannon-Weaver diversity index value (H). We observed the highest H in lemma palea color, but the phenotypic traits used for diversity analysis had considerable variation  and the highest number of alleles (5), indicating that it detected the highest level of polymorphism and the best marker for characterizing the aromatic rice germplasm. Markers RM342, RM 283, RM237, and RM 314 were also useful for molecular characterization of germplasm, though to a lesser extent.The PIC value of a marker is the probability of the marker allele that can be deduced in the progeny and is a good measure of a marker’s usefulness for linkage analysis48.Some of the SSR markers also generated germplasm-specific bands that can be used as molecular identity data for specific germplasm. For example, marker RM144 amplified all germplasm and showed specific fragments  types, have been characterized as a genetically distinct cluster58. Based on SSR marker analysis, Roy et al.15 reported three major groups from a set of 107 Indian aromatic rice accessions. In the present study, population structure analysis also revealed three populations (P1–P3) with a majority of germplasm in P1. This grouping agrees with genetic distance based clustering and PCA  denotes the highest optimal number of subsets (K)63 for an optimal number of divisions64. In this study, at K = 3, all 113 germplasm divided into three population, with 63 in P1, 17 in P2, and 33 in P3 , evenness, and diversity profile, as well as clustering and PCA analyses of 12 phenotypic traits, were determined using PAST  software68. Phenotypic frequency data of the 12 traits were analyzed using the Shannon–Weaver diversity index (H) given as:The seed of each tested germplasm was taken from BRRI gene bank and grown in the BRRI research field following a rice production technology previously described by Islam where k is the number of phenotypic classes for a trait and 69. Each PCR was carried out in a 20 μl reaction volume containing 1 μl of MgCl2 free 10 × PCR buffer with (NH4)2SO4, 1.2 μl of 25 mM of MgCl2, 0.2 μl of 10 mM of dNTPs, 0.2 μl of 5 U/μl Taq DNA polymerase, 0.5 μl of 10 μM forward and reverse primers, and 3 μl (10 ng) of DNA using a 96-well thermal cycler. An additional 10 μl of mineral oil was added in each well to prevent evaporation. Amplification was carried out using a G-storm PCR machine . Amplification conditions were one cycle at 94 °C for 5 minutes  followed by 35 cycles at 94 °C for 1 minute (denaturation), 55 °C for 1 minute , 72 °C for 2 minutes (extension) with a final extension for 7 minutes at 72 °C at the end of 35 cycles. After mixing with the loading dye, PCR products were run through polyacrylamide gels. A 50 bp DNA ladder was used to determine the amplicon size. Three 4 μl PCR products were resolved by running gel in 1 × TBE buffer for 1.5 to 2.5 h depending upon the allele size at approximately 90 volts and 500 mA electricity. Gels were then stained with 1 μg/mL of ethidium bromide and documented using a Molecular Imager gel documentation unit .DNA was extracted from young leaves of 20-day-old plants following the mini-scale method7. In this study, however, only the quantitative traits’ values were used for the Spearman’s coefficient calculation. Morphological and genetic distances among the genotypes were estimated, and ranking was done using Spearman’s coefficient with SSR analysis, 6328 = 70. To measure and compare the association between two criteria of rankings, Spearman devised the following formula for estimating rank correlation  subprogram using the unweighted pair-groups with the arithmetic averages (UPGMA) clustering method as implemented in NTSYS-pc. Population structure for 113 germplasm was constructed using STRUCTURE version 2.3.477. The number of clusters (K) we investigated ranged 1 to 15. The analysis used five replications for each K value. The model was used following admixture and correlated allele frequency with a 5000 burn period and a run length of 50000. The output of the analysis was collected using the STRUCTURE harvester and to identify three as the best K value based on the LnP(D) and Evano’s ΔK78. The principal components analysis (PCA) in a visualization technique that is commonly used in multivariate statistics, whereby the user can identify eigenvectors and amounts of variance and cumulatively explained variances per component80 was conducted using the SPSS (Version 16). In order to summarize the major variance patterns in the multi-locus dataset, an analysis of molecular variance (AMOVA) was performed using GenAlEx V6.581.The DNA fragment of each germplasm was amplified using SSR markers and analyzed using different software. Molecular weight for each amplified DNA fragment was measured using Alpha Ease FC 4.0 software. We determined, were using Power Marker version 3.25, the summary statistics, which included (i) the number of alleles per locus, (ii) the frequency of major alleles, and the polymorphism information content (PIC) valuesSupplementary Table


Breast cancer is the leading cause of cancer death in women worldwide which is closely related to metastasis. But the exact molecular mechanism of ERα-36 and STAT3 on metastasis is still not fully understood.MCF-7 and MDA-MB-231 human breast cancer cell lines and MCF-10A were overexpressioned or knockdown ERα-36 and STAT3 and tested for migration, invasion and proliferation assays. Direct interaction of STAT3 and ERα-36 were analyzed by coimmunoprecipitation assays. The effect of STAT3 and ERα-36 on MMP2/9 expression was analyzed by qPCR and western blotting. STAT3 phospholyation and acetylation by ERα-36 and p300 were observed and quantified by coimmunoprecipitation assays and western blotting.Cross-talk between ERα-36 and STAT3 was demonstrated to mediate through a direct physical association between the two proteins. Furthermore, the interaction between ERα-36 and STAT3 was demonstrated to give rise to functional changes in their signaling events. Both MMP2 and MMP9 expression require the binding of the newly identified protein complex, ERα-36-STAT3, to its promoter, the second phase, which is more robust, depends on ERα-mediated recruitment of p300 onto the complex and the subsequent acetylation of STAT3. In addition, STAT3 is tyrosine-phosphorylated in a biphasic manner, and the late phase requires ERα-36-mediated p300-dependent acetylation. Furthermore, interference with acetylation of STAT3 by overexpression of acetylation null STAT3 mutant led to the loss of MMP2 and MMP9 expression. ChIP analysis and reporter gene assays revealed that ERα-36-STAT3 complex binding to the MMP2 and MMP9 promoter led to an enhanceosome formation and facilitated MMP2 and MMP9 expression.Our studies demonstrate for the first time that the function of MMP2 and MMP9 in breast cancer cell migration, which is mediated by interactions between ERα-36 and STAT3. Estrogen exerts most of its biological effects through the estrogen-specific transcription factor, the estrogen receptor (ER) . The estERα-36 is generated from a promoter located in the first intron of the ERα-66 gene and differs from the ERα-66 by lacking both transcriptional activation domains (AF-1 and AF-2) but retaining the DNA-binding and dimerization domains, and partial ligand-binding domains , 6. ERα-STATs are a family of transcription factors that consist of seven members. They contain a dimerization domain, a coiled-coil domain, a DNA-binding domain, a src homology 2 domain, and a conserved single tyrosine residue whose phosphorylation is required for its activation in the N-terminal region, whereas a transcriptional activation domain is present in the C-terminal region . InteresOur previous and other studies have shown that STAT3 promoted breast cancer cell migration, and MRTF-A and STAT3 synergistically increased MDA-MB-231 cell migration by promoting the expression of migration markers Myl-9, Cyr-61, urokinase-type plasminogen activator (uPA) and osteopontin (OPN) and inhibiting the expression of breast cancer metastasis suppressor 1 BRMS1, , 12). An. An12]).It has been reported that MMP2 and MMP9 were closely related to cells migration in various cancers including bladder cancer, osteosarcoma, hepatocellular carcinoma, thyroid cancer, gastric adenocarcinoma –20, whicRecombinant human interleukin 6(IL-6), AG490, estradiol (E2) and tamoxifen (TAM) protein were obtained from R&D Systems and Sigma. Antibodies against MMP2, MMP9, p300, ERα-36, and STAT3 were purchased from Cell Signaling and Abcam; Myc, Flag and HA antibody were purchased from proteintech.2 incubator.MCF-7 and MDA-MB-231 human breast cancer cell lines and MCF-10A were grown in Dulbecco’s modified Eagle’s medium (DMEM)(GIBCO) supplemented with 10% fetal bovine serum at 37 °C in a 5% COThe pCDNA3.1-ERα-36-Myc contained a cDNA encoding amino acids 1–311 of human ERα-36, the pCDNA3.1-STAT3-Flag contained a cDNA amino acids 1–769 of human STAT3. The vector pCDNA3.1 was purchased from Promega. The promoter regions of MMP2 (− 904/+ 50) and MMP9 (− 4136/+ 18) were amplified by PCR and then were cloned into pGL3 luciferase reporter vector, respectively. The primers used to create MMP2-luc and MMP9-luc was as follow: MMP2: 5′- GTCAGCTAGCAGAGACGGTTGTCACAGGGA − 3′(sense) and 5′- AGACCTCGAGTGCTACTCCTGGCCTCTACG − 3′(antisense); MMP9: 5′- GTCAGCTAGCGCTTCCTTACTGACGGTGCT − 3′(sense), and 5′- AGACCTCGAGGGTGAGGGCAGAGGTGTC − 3′(antisense); the vector pGL3(Pomega) was used as a control. Additional luciferase reporter constructs of MMP2 and MMP9 promoter containing mutations to putative GAS were generated using the QuickChange site-directed mutagenesis kit . The MMP2-WT-luc GAS was mutated from -TTCGTGGAA - to -TAGACGTAA -(MMP2-M-GAS-luc) and these nucleotide mutations abolished STAT3-binding sites. The primers used were as follows: MMP2-M-GAS-luc: forward 5′: GGGGAATAGACGTAACTGAGGGCTCCTCCCCTTTTTAGACCATATA 3′; reverse 5′: CCTCAGTTACGTCTATTCCCCACTCACTCTTGAATCCTTTCCTGCG 3′. The MMP9-WT-luc GAS was mutated from - TTCCCCTGAA - to - TAGATCGTAA - (MMP9-M-GAS-luc) and this nucleotide mutations abolished STAT3-binding sites. The primers used were as follows: MMP9-M-GAS-luc: forward 5′: TTGCCATAGATCGTAAGCTAGGGATGGCAGGTGTGTTTGTGGGTTG 3′; reverse 5′: CCTAGCTTACGATCTATGGCAATTCCCACACTGACTCTTAGAGTCC 3′.2 incubator chamber. Pictures were acquired after 0 h and 24 h (in the case of MDA-MB-231) or 36 h (in the case of MCF-7) using a microscope.For wound healing assays, cells were seeded on 6-well dishes. Twenty-four hours after transfection, wounding was induced by scratching the monolayer with a micropipette tip and the dish was placed at 37 °C in a 5% COCell migration was tested in 8-μm-pore polycarbonate membrane Transwell chambers (Corning) essentially as described previously , 29, 30.RT-PCR and qRT-PCR analysis were carried out as described previously , 30. BriWestern Blotting was performed as described previously , 30. TheImmunocytochemistry assays were performed as described previously , 31. TheLuciferase assays were performed as described previously , 31. 24 ChIP analysis was performed using a commercially available kit (Enzymatic Chromatin IP (Magnetic Beads), Cell Signaling Technology). MDA-MB-231 cells were transfected with STAT3 and ERα-36/STAT3 for 24 h. Proteins bound to DNA were cross-linked using formaldehyde at a final concentration of 1% for 20 min at room temperature. Protein-DNA complexes were immunoprecipitated using primary antibodies for ERα-36 (Abcam). STAT3 and MMP2 promoter complexes and STAT3 and MMP9 promoter complexes were measured by PCR. The primers used for the amplification of the human MMP2 promoter was: MMP2-GAS-box: forward-5′ AGACGGTTGTCACAGGGAG 3′, and reverse-5′ ATGGCAATGTGGGGAGGT 3′. And the primers used for the amplification of the human MMP9 promoter was: MMP9-GAS-box: forward-5′ TGACTTGGTGTTAATAGGGACT 3′, and reverse-5′ GGCAGGATGTGGAGGAGA 3′. The samples were electrophoresed using a 2% agarose gel, and visualized by ethidium bromide staining.Plasmid expressing Myc-ERα-36 and Flag-STAT3 were cotransfected into MDA-MB-231 cells. Lysates were collected 48 h posttransfection, and Myc antibody (Protein tech) was used to precipitate Myc-ERα-36 or Flag antibody (Protein tech) was used to precipitate Flag-STAT3, respectively. The resulting mixture was washed, subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), transferred to a NC membrane , first revealed by Myc antibody (Protein tech) to visualize Myc epitoped ERα-36 or Flag antibody (Protein tech) to detect Flag epitoped STAT3, and then reprobed with Flag antibody (Protein tech) to detect Flag epitoped STAT3 or reprobed with Myc antibody (Protein tech) to detect Myc epitoped ERα-36, respectively.P value of 0.05 was considered statistically significant. Comparisons between 2 groups were analyzed by Student’s t-test. Data are presented as mean ± SD unless otherwise stated.Statistical analyses were conducted using Statistical Program for Social Science (SPSS) software 17.0 . Percentages and ratios were arcsine transformed prior to data analysis. A 2-sided Toward understanding the mechanisms of breast cancer progression, we have previously reported that STAT3 promotes breast cancer cell migration by regulating Cyr61 and Myl9 expression . InterleTo test the role of STAT3 in IL-6 induced MMP2 and MMP9 expression, we overexpress STAT3 transfecting the cells with plasmid pCDNA3.1-STAT3, over-expression STAT3 significantly induced IL-6-induced MMP2 and MMP9 expression  on multiple lysine (K) residues by the CBP/p300 histone acetyltransferase in response to cytokines and growth factor signaling. In contrast, recently study showed that insulin stimulated K87 acetylation promote STAT3 mitochondrial translocation and functions , 45. DesOur studies demonstrate for the first time that the function of MMP2 and MMP9 in breast cancer cell migration, which is mediated by interactions between ERα-36 and STAT3.


Nothophaeocryptopus (=Phaeocryptopus) gaeumannii, the causal agent of Swiss needle cast (SNC) of Douglas‐fir, is endemic to western North America where it exists as two sympatric, reproductively isolated lineages. The abundance of this fungus and the severity of SNC are strongly influenced by climate. We used statistical and population genetic analyses to examine relationships between environment, pathogen population structure, and SNC severity. Although N. gaeumannii Lineage 2 in western Oregon and Washington was most abundant where SNC symptoms were most severe, we did not detect a significant relationship between Lineage 2 and disease severity. Warmer winter temperatures were inversely correlated with foliage retention (AFR) and positively correlated with the relative abundance of Lineage 2 (PL2). However when distance inland, which was strongly correlated with both AFR and PL2, was included in the model, there was no significant relationship between Lineage 2 and AFR. Spring/early summer dew point temperatures also were positively associated with total N. gaeumannii abundance (colonization index (CI)) and inversely correlated with AFR. Warmer summer mean temperatures were associated with lower CI and higher AFR. Our results suggest that the two lineages have overlapping environmental optima, but slightly different tolerance ranges. Lineage 2 was absent from more inland sites where winters are colder and summers are warm and dry, while Lineage 1 occurred at most sites across an environmental gradient suggesting broader environmental tolerance. These relationships suggest that climate influences the abundance and distribution of this ecologically important plant pathogen and may have played a role in the evolutionary divergence of these two cryptic fungal lineages.The environment has a strong influence on the abundance and distribution of plant pathogenic organisms and plays a major role in plant disease. Climatological factors may also alter the dynamics of the interactions between plant pathogens and their hosts.  Nothophaeocryptopus gaeumannii population structure, and the severity of an important Douglas‐fir disease, Swiss needle cast. This ecological system was investigated by combining data from genetic markers with environmental data and disease severity data. The results of this study suggest that two reproductively isolated lineages of this fungal pathogen have differing environmental optima, but does not support previous studies suggesting that the lineages differ in their aggressiveness.This study was designed to test hypotheses about the relationships between the environment,  Rohde) Videira, C. Nakash., U. Braun & Crous. The inhibition of gas exchange and CO2 assimilation resulting from the cumulative occlusion of stomata by the pseudothecia of N. gaeumannii causes premature needle abscission , thereby altering the ecology and successional trajectory of Douglas‐fir in mixed natural stands  is a foliar disease specific to Douglas‐fir  have been documented, a causal relationship between Lineage 2 and the recent intensification and expansion of SNC in this region has been suggested  in SNC severity in western Oregon and Washington forests  and Washington (WA) Coast Ranges, (b) determine whether any relationship exists between SNC severity and the relative abundance of N. gaeumannii Lineage 2, and (c) examine the relationships between the genetic structure of N. gaeumannii populations, SNC severity, and key environmental variables with a multivariate statistical ordination. Given previous observations of the relationship between the spatial distribution of N. gaeumannii Lineage 2 and SNC severity, we aimed to test the hypothesis that the relative abundance of Lineage 2 within sites is positively correlated with disease severity. The analyses performed here were also designed to test the hypothesis that SNC severity and the spatial distributions of the two lineages  are correlated with environmental variables that have been previously identified as being critical for the development of N. gaeumannii.Our objectives for this study were to (a) assess the spatial distributions of the two 22.1Douglas‐fir foliage was collected from 23 sites in the western Oregon Coast Range in a plot network maintained by the Oregon State University Swiss Needle Cast Cooperative (SNCC)  (Difco Laboratories). Cultures were incubated at 18°C for a minimum of 2 months, and often up to 6 months.The protocol for isolating nd Stone . Briefly2.3The protocols for DNA extraction, PCR amplification, and genotyping of simple sequence repeats (SSRs) are described in Bennett and Stone . The DNeThe PCR amplicons were submitted to the Center for Genome Research and Biocomputing  for SSR genotyping via capillary electrophoresis with an ABI 3730 DNA Analyzer . Allele sizes and genotypes were assigned with ABI GeneMapper 4.1  and were also visually examined for each locus and isolate to confirm accuracy. A positive control isolate was included with each PCR and genotyping run to ensure that the results were consistent and reproducible. One of the loci, Pgdi5, did not amplify reliably for all isolates and therefore was omitted from the dataset prior to analysis. The actual number of trees per site represented by the isolates in the SSR dataset was often less than five due to a number of steps that may have resulted in sample loss, such as variation in the presence of pseudothecia, contamination during isolation and culturing, and inconsistent amplification during PCR. The genotypes used for this study included those analyzed in Bennett and Stone   and Nei's unbiased gene diversity   with a rarefaction sample size ≥ 10  Nei, , were capoppr 2.5.0   and the Washington Department of Natural Resources provided estimates of both average foliage retention (AFR) and colonization index (CI) for sites in OR and WA  and the percentage of stomata occluded by pseudothecia (N = 10 needles). The percentage of stomata occluded was calculated by averaging the numbers of pseudothecia from 100 stomata in each of three sections per needle  for each sample of ten 2‐year‐old needles from each of three canopy sections per tree  for ten trees from each site.Aerial disease survey data were obtained from the Oregon Department of Forestry , a linear mixed model was utilized to investigate the influence of the proportion of Lineage 2 on AFR with the distance inland from the coast held at its mean value (26 km). Distance inland is related to continentality and serves as a proxy for a complex combination of environmental variables  that are expected to influence Swiss needle cast severity and foliage retention :iY is the average foliage retention of the ith site. The intercept β0 is the mean average foliage retention when the relative proportion of Lineage 2 recovered at the site is 0 and the distance inland is 0 km, β1 is the coefficient for the effect of the relative proportion of Lineage 2 on mean average foliage retention, X1i is the relative proportion of Lineage 2 recovered from the ith site, β2 is the coefficient for the effect of the distance inland (km) on mean average foliage retention, X2i is the distance inland (km) of the ith site, and iε is the random effect of the ith site on mean average foliage retention, iε ~ N.Pearson correlation coefficients were calculated for all pairwise correlations between geographic and disease variables (for the 34 sites for which disease severity data were available) with the R package Harrell, . ScatterWickham, . To accoadegenet , the relative proportion of Lineage 2, latitude, longitude, elevation, and several environmental variables related to SNC severity and N. gaeumannii abundance .Nonmetric multidimensional scaling (NMDS) was used to visualize genetic differentiation between each of the sample sites. Roger's euclidean genetic distance Rogers,  was calcJombart, . The NMD33.1He) for the 1,061 isolates was 0.82 (Tables N. geaumannii lineages were strongly differentiated (G′ST = 0.941)  from 1,061 isolates collected from 35 sites in western Oregon and Washington. This includes 492 isolates collected in 2014 , and the highest CI was at site T5‐3  was calculated pairwise between the environmental, geographic, and disease variables in our dataset. PL2 was negatively correlated with distance inland   Figure , CI  recovered from the site, after accounting for distance inland   Figure . There wI Figure , though PL2 had the strongest relationship with the NMDS ordination   Figure , Table 5AFR was most strongly correlated with the ordination of genetic distance between the sites    was the environmental variable most strongly correlated with the genetic differentiation between sites , and with PL2   and average spring/early summer dew point temperature (MADT)  had much weaker, but nonetheless significant, correlations with the ordination, while average precipitation in the spring and early summer was not significantly correlated with the ordination , followed by elevation , and longitude   is still unclear and should be the focus of future studies.The separation of sites along axis 2 in the NMDS analysis Figure  reflectsN. gaeumannii Lineage 2 and severe SNC symptoms in western Oregon and Washington   Table , 2003. GN. gaeumannii Lineage 2. To date, aerial surveys have documented very low incidence of SNC symptoms along the Oregon coast south of Port Orford  is also strongly correlated with MADT, the variable most strongly associated with needle colonization by both lineages  but not CI (p > .05), and PPT was not significantly associated with either AFR (p > .05) or CI (p > .05) (Table MADT) was significantly correlated with CI suggesting that N. gaeumannii colonization is highest where moisture is abundant at warmer temperatures  and/or that AFR may be affected by environmental factors in combination with SNC severity. This variation in AFR in relation to N. gaeumannii abundance has been investigated for SNC in coastal Douglas‐fir  within ~20 km of the coast along the western slopes of the Oregon and Washington Coast Ranges where winter temperatures were the warmest (~6–10°C), summers were coolest (~14–16°C), and the dew point temperatures (the temperature at which the air was saturated with water vapor) were warmest (~9–11°C). These observations are in agreement with previous studies that demonstrated that warmer winter temperatures and abundant moisture in the spring/early summer, though not necessarily as precipitation, favor N. gaeumannii abundance and thus SNC severity  may have been affected by sampling bias. The sample of eight isolates from this site had MLGs that were very dissimilar from those collected from nearby sites. Because all isolates collected from CB5‐2 were Lineage 1, but Lineage 2 should have been relatively abundant given the site location, this site disproportionately influenced the fitting of the geographic variables in the NMDS, especially latitude. This suggests that the isolates at CB5‐2 were more similar to sites further north, even though this site is near the southern coast of Oregon.One site near Coos Bay, Oregon CB5‐2) did not fit with the overall trends observed for the geographic distributions of the two lineages in relation to coastal proximity. This site was within 25 km of the coast, but all of the isolates recovered from two separate trees were Lineage 1 (Table  did not N. gaeumannii, but environmental variables were also correlated with spatial genetic differentiation in the N. gaeumannii population. This suggests that climate may play a role in the evolutionary divergence of these cryptic fungal lineages. Climate change has the potential to be a driver of further genetic change in N. gaeumannii populations.The results of our analyses suggest that climate influences the population structure of this ecologically important Douglas‐fir pathogen. Not only does the environment influence SNC severity through direct effects on growth rate and reproduction of N. geaumannii lineages observed in this study is in agreement with the results of previous analyses that suggested that the lineages were reproductively isolated (Bennett & Stone, The strong genetic differentiation between the two N. gaeumannii was not possible with the available molecular tools. Recent advances in population genomics have enabled the identification of the molecular mechanisms involved in adaptation to local environmental conditions. This analytical framework could be applied to N. gaeumannii as a next step toward elucidating the environmental factors contributing to spatial genetic variability in its populations and identifying the mechanistic influences of these environmental factors on its biology.The scope of this study was limited due to the use of highly variable neutral markers, which are useful for identifying populations and estimating genetic differentiation but cannot be used for analyses of evolutionary processes such as natural selection and adaptation. Thus, a thorough demonstration of the role of environment in structuring populations of The authors have no competing interests to declare.PIB and JKS designed the study, PIB performed all laboratory work and data analyses, produced all figures, and drafted the manuscript. Editing of the manuscript was performed collaboratively by PIB and JKS.


Anguilla japonica), European eel (Anguilla anguilla), and American eel (Anguilla rostrata) are migratory, catadromous, temperate zone fish sharing several common life cycle features. The population genetics of panmixia in these eel species has already been investigated. Our extensive population genetics analysis was based on 1400 Gb of whole-genome sequence (WGS) data from 84 eels. It demonstrated that a Japanese eel group from the Kuma River differed from other populations of the same species. Even after removing the potential adapted/selected single nucleotide polymorphism (SNP) data, and with very small differences (fixation index [Fst] = 0.01), we obtained results consistently indicating that panmixia does not occur in Japanese eels. The life cycle of the Japanese eel is well-established and the Kuma River is in the center of its habitat. Nevertheless, simple reproductive isolation is not the probable cause of non-panmixia in this species. We propose that the combination of spawning area subdivision, philopatry, and habitat preference/avoidance accounts for the non-panmixia in the Japanese eel population. We named this hypothesis the “reproductive isolation like subset mapping” (RISM) model. This finding may be indicative of the initial stages of sympatric speciation in these eels.The Japanese eel ( Anguilla anguilla) and American eel (Anguilla rostrata) are considered open-ocean fish because their oceanic spawning areas are narrow and their migration routes are long [Marine environments contain few physical barriers. The levels of genetic divergence are low in pelagic fishes. Although they inhabit freshwater and brackish water during their long development, the European eel  inhabit the northwestern Pacific Ocean, including the waters of northern Philippines, Taiwan, China, Korea, and Japan [Japanese eels  of sequence data. Therefore, we sequenced data an average of 12-fold per eel. We then identified 30 million SNPs and used them in population genetics analysis. We identified sixty-eight entirely panmictic Japanese eels from Taiwan, Mariana, and four rivers in Japan. We also found a group of adult Japanese eels in the brackish waters of the Kuma River , which distinctly differed from the other populations.Recent population studies of European and American eels have revealed polygenic discrimination of habitat ecotype. There are numerous discriminated SNPs adapted to and/or selected for specific environments or habitats after birth ,20,21,22v/v ethanol, and stored at 4 °C until DNA extraction. Genomic DNA was extracted with a DNeasy blood and tissue kit  following the instructions of the manufacturer. The purity of the extracted genomic DNA was assessed by agarose gel electrophoresis. The DNA concentration was determined with a Qubit ds DNA HS assay kit and a Qubit 2.0 fluorometer . All animal experiments were approved by the Animal Experiment Committee of the Graduate School of Agricultural and Life Sciences, University of Tokyo (No. P14-952).Eighty-four samples were used in the analyses. Twenty glass eels were collected from the Sagami River, Kanagawa Prefecture. Another twenty-four were collected in Tainan and Pingtung City, South Taiwan. One yellow to silver eel was collected in the seawater estuary of the Tama River, Tokyo. Sixteen yellow to silver eels were collected in the seawater estuary wetlands of the Kuma River. Thirteen yellow to silver eels were collected in the freshwater part of the Tsuchi River, Kagoshima Prefecture. Three yellow to silver eels were collected in the freshwater part of the Takase River, Wakayama Prefecture. Seven silver eels were collected in the West Mariana Ridge, mid-Pacific Ocean) . Part ofA Nextera DNA sample preparation kit  was used to prepare the DNA libraries. Indices of the Nextera index kit  were used for identification purposes. The procedure was followed according to the manufacturer’s instructions. Agencourt Ampure XP beads  were used to purify tagmented DNA. Library quality, size, and concentration were determined with High Sensitivity D1000 Screen Tape, High Sensitivity D1000 Reagents, and an Agilent 2200 TapeStation , respectively. DNA library concentrations were determined by qRT-PCR with a KapaLibrary quantification kit . DNA libraries from the respective samples were mixed at equal concentrations and multiplex-sequenced with a HiSeq 2000 or HiSeq 2500 Sequencer  to acquire 2 × 100 bp paired-end read sequence data. All samples from each population were randomly mixed by lanes in multiplex sequencing to avoid sequencer or lane bias. Fourteen billion sequencing reads  were deposited in the DNA Data Bank of the Japan Sequence Read Archive (DDBJ SRA) for whole-genome re-sequencing of the Japanese eels (BioProject ID: PRJDB5707).A. japonica (accession number AB038556) previously reported [Tetraodon pufferfish [Anguilla species and subspecies were incorporated using the maximum likelihood approach [Conger myriaster), Kaup’s arrowtooth eel (Synaphobranchus kaupii), and the sawtooth eel (Serrivomer sector) were used as an outgroup. The general time reversible (GTR) + gamma distribution (G) + proportion of invariable sites (I) model was used as a molecular evolution model. Bootstrap was performed 1000 times and the data were expressed as percentages.Mitochondrial DNA (mtDNA) sequence data obtained with a next-generation sequencer were assembled de novo using MIRA4  with refreported  for eachreported , all sitfferfish ,27. Moleapproach . The comA. japonica draft genome (GenBank: AVPY00000000.1) [Two “paired-end read” output files were combined per individual for the raw data output. These were used as “single read” data. Low-quality reads were eliminated with Trimmomatic v. 0.33  and FAST00000.1)  with the00000.1) . Species00000.1)  as referFor each sample, the binary alignment map (BAM) file data were sorted and indexed with SAMtools version 1.2 . DuplicaThe total depth of all SNP sites was extracted and a histogram was created with a modal depth of 963. To eliminate SNP sites derived from repeats, SNPs with a total coverage depth in the range of 800–1100 were used in the population structure analysis. Certain SNPs were only observed in one sample and may have contained numerous sequence errors. Therefore, only SNP sites detected in >1 sample  were analyzed.Scores were assigned to biallelic SNP site genotypes as follows. Sites homozygously different from the reference sequence were designated a value of 2. Sites heterozygously different from the reference sequence were assigned a value of 1. Sites homozygously identical to the reference sequence rated 0. A matrix for 32 million SNP sites was generated for each sample. Cluster and principal component analyses (PCA) were run in the ‘hclust’ function, pvclust packages, and ‘prcomp’ function in R version 3.2.1. with ,40. GeneA. japonica genome assembly was also used. In that case, the scaffolds assigned to each chromosome covered ~50% of the genome length [Single nucleotide polymorphisms on the scaffolds anchored to each chromosome (linkage group) covered ~13% of the eel genome . These we length . The SNPFixation index (Fst) values were calculated for each pair of loci following the methods of Weir and Cockerham  and usinA. japonica samples analyzed in the present study originated from seven sites across Japan, Taiwan, and the West Mariana Ridge  generated similar results. It plotted all samples at the same point except for the KM individuals (including KM13-KM16), which were scattered across the horizontal PC1 axis. Therefore, they were distinct from all other locations b. We alsWe also performed a cluster analysis and PCA on several subsets of the ~32 million SNPs and obtained essentially the same results as above . We testSingle nucleotide polymorphisms with total depths of coverage ranging from 900 to 1000 were investigated for each chromosome to verify whether the KM individuals differed from the other samples for specific chromosomes or the whole genome. Since the reference genome scaffolds were not arranged on chromosomes, in 2014, Kai et al., assigned them to linkage groups (LG) . We extrK = 2, the KM individuals were distinct from those of most other localities, with a few exceptions. Therefore, there was a relatively high degree of genetic differentiation between the KM individuals and the other populations  values for the 32,312,607 SNP sites were calculated for each locality pair from the VCF file. The average Fst for all SNP sites was ~≤0.001 for eels from the Sagami River (Japan), Taiwan, Tsuchi River (Japan), Takase River (Japan), and West Mariana Ridge (mid-Pacific Ocean). The average Fst was 0.01 between the KM individuals and all other populations . TherefoAnguilla species, we examined their nuclear genomes by scoring the raw read mapping rates onto every published eel genome [To identify l genome ,33,34. TThe PCA, cluster, and genetic admixture analyses consistently indicated that, except for the KM individuals, the Japanese eels had high genetic uniformity. Previous studies showed that Japanese eel are panmictic. Those reports and the present study generally support the hypothesis that all Japanese eel spawn near the West Mariana Ridge and their larvae are then dispersed to various regions in East Asia ,16,17,18One previous study reported genetic differences between northern and southern eel groups . HoweverThese results resembled those reported for Atlantic, American, and European eels. Atlantic eels may be panmictic. Nevertheless, population genetic structures have been reported for them ,6,7,8. FHowever, our results also show that the KM individuals are genetically distinct from those sampled in other areas (hereafter referred to as the MGJE or the main group of the Japanese eel) . TherefoIf the KM population is, in fact, panmictic with the MGJE, then there are three possible explanations for the population genetics differences between them: chaotic genetic patchiness (CGP) ,50, sweeChaotic genetic patchiness is the result of mating among very few parents and incomplete mixing of their larvae with those from other parents en route to the habitat where they develop and mature. In the present study, however, the ages of the KM individuals varied by ≥4–8 years . TherefoAdaptation and/or selection within single generations have been reported for panmictic European and American eels ,21,22. DWe considered the possibility that KM eels are not panmictic with MGJE. Nevertheless, this scenario is improbable since the spawning area of the Japanese eel is the West Mariana Ridge, and the Kuma River lies within the habitat range of the Japanese eel. A recent study showed no genetic population heterogeneity between eels from Shimane and Kochi . The KumThe West Mariana Ridge is the only spawning area of the Japanese eel and this species may be non-panmictic. Therefore, we must consider the possibility of reproductive isolation among individuals within the same habitat and spawning area. This phenomenon can occur as a result of isolation by distance (IBD) or isolation by time (IBT). Isolation by distance and isolation by time were investigated to understand the genetic population structures of the European eel . HoweverSome reports suggest that IBD affects panmixia in European eels ,55 becauThe spawning areas of the American, European, and Japanese eels are not narrow points, but rather broad volumes extending several hundred kilometers horizontally ,2,3,4,10Multiple spawning areas and natal philopatry may not fully explain the genetic population heterogeneity of the Japanese eel. While certain reports showed genetic population structures in the European eel, they were taken to mean relatively less random mixing in their populations  ,50,51 orThe attributes of the KM individuals differ from those of European eels. The Kuma River (KM) individuals were adults differing in age by ≥4–8 years. It is, therefore, difficult to explain their genetic population heterogeneity with CGP and SRS . In geneAt first glance, the local selection in a single generation observed in the Japanese eel seems to be the most probable cause of the genetic distinction between the KM individuals and the MGJE. The habitat of the KM individuals is estuarine and clearly differs from the open ocean habitat. In addition, strong genetic differences were uniformly distributed on each chromosome. This uniformity resembles the flat Fst distribution around the selected loci of the selected individual panmictic European eels. However, the scores and proportions of outlier SNPs for the KM individuals were considerably higher than those for the European eel . A flat To establish this model, reproductive isolation is required. Decades of exploring the spawning area of the Japanese eel strongly suggests that this species has no other spawning ground besides the West Mariana Ridge. Consequently, IBD or subdivision within the existing West Mariana Ridge spawning area is probably the best model to explain the comparatively higher genetic difference of KM individuals relative to the MGJE. Moreover, natal philopatry is also an essential factor in the development of KM individuals.However, other factors in addition to IBD and philopatry may segregate the KM individuals. The estuarine wetland of the Kuma River is located within a larger area inhabited by the Japanese eel. It could, therefore, be traversed by the Japanese glass eel . NeverthIntegrating the aforementioned ideas, we present a hypothesis to explain the generation of KM subpopulations . We nameIn the aforementioned scenario, there should be a KM-type Japanese glass eel subpopulation. Therefore, identification of these KM individuals is one way to test this hypothesis. Moreover, all spawning area subdivisions must be defined and the habitat preferences of the eels in the Kuma River and elsewhere must be studied.The spawning areas of the American and European eels overlap in the Southern Sargasso Sea. Certain individuals from both species are known to mix. However, the two species are, and remain, different from each other. Several eel pairs have overlapping habitats and/or spawning areas, but continue to be independent as species. Our hypothesis may explain this sympatry and eel speciation.Although we performed WGS on 84 eels, in practice, fewer SNPs may have sufficed for us to arrive at the same conclusions stated above. RAD-tagging and other similar or related methods may be useful in further studies of the Japanese eel and other organisms. However, WGS generates the highest density of SNPs data, facilitates rapid and comprehensive identification of the genes associated with specific phenotypes, and detects ancient gene flows and structural polymorphisms. Aided by new genomic data , our SNPTemperate zone eels have several characteristic ecological features, including continent-size habitats and single narrow spawning areas. Despite their large biocycles, panmixia (random mating within a population) has been reported in these species. In the present study, however, we found evidence against panmixia in the Japanese eel. Initially, it was believed that this behavior could be explained by reproductive isolation. Nevertheless, the West Mariana Ridge is the only spawning area of the Japanese eel and reproductive isolation there is highly unlikely. We proposed the RISM model to understand this phenomenon. Finally, from the economic aspect, our results may indicate that various genetic populations can be “branded” independent of their aquaculture area and morphology, and would require the revision of resource management policies.


Microcebus ganzhorni. To this end, we supplement estimates of neutral genetic diversity with the assessment of adaptive genetic variability of the major histocompatibility complex (MHC). In addition, we address the challenges of long‐term genetic monitoring of functional diversity by comparing the genotyping performance and estimates of MHC variability generated by single‐stranded conformation polymorphism (SSCP)/Sanger sequencing with those obtained by high‐throughput sequencing , an issue that is particularly relevant when previous work serves as a baseline for planning management strategies that aim to ensure the viability of a population. We report that SSCP greatly underestimates individual diversity and that discrepancies in estimates of MHC diversity attributable to the comparisons of traditional and NGS genotyping techniques can influence the conclusions drawn from conservation management scenarios. Evidence of migration among fragments in Mandena suggests that mouse lemurs are robust to the process of fragmentation and that the effect of corridors is masked by ongoing gene flow. Nonetheless, results based on a larger number of shared private alleles at neutral loci between fragment pairs found after the establishment of corridors in Mandena suggest that gene flow is augmented as a result of enhanced connectivity. Our data point out that despite low effective population size, M. ganzhorni maintains high individual heterozygosity at neutral loci and at MHC II DRB gene and that selection plays a predominant role in maintaining MHC diversity. These findings highlight the importance of long‐term genetic monitoring in order to disentangle between the processes of drift and selection maintaining adaptive genetic diversity in small populations.Long‐term genetic monitoring of populations is essential for efforts aimed at preserving genetic diversity of endangered species. Here, we employ a framework of long‐term genetic monitoring to evaluate the effects of fragmentation and the effectiveness of the establishment of corridors in restoring population connectivity and genetic diversity of mouse lemurs  Indeed, the assessment of genetic variation throughout time provides the means to evaluate the effects of past and current events on the demographic and evolutionary history of wild populations  allows cost‐effective large‐scale estimates of genetic variation with the caveat that the direct comparison of the “new” with the “old” datasets can be challenging  are important markers providing insight into long‐term persistence of populations because they are considered to be associated with the ability of individuals to adapt to changing environments  and high‐throughput technologies . Prior to the establishment of corridors, work on the MHC class II DRB gene revealed a marked deficiency of heterozygotes and evidence of inbreeding  to connect forest fragments M13 and M15‐16. Introduced species have been chosen because they grow fast enough to produce vegetation structures that might be of use for forest species. Native trees grow too slowly. A corridor consisting of invasive Melaleuca quinquenervia had established itself without human intervention between M20 and M15‐16  located in the littoral rain forest of Mandena, south‐eastern Madagascar  during two sampling periods  Figure . Between2.2Extraction of genomic DNA from ear biopsies was carried out by using a Qiagen DNeasy Blood & Tissue Kit (Qiagen). During the first sampling period, the individual diversity of the MHC class II DRB exon 2 gene of mouse lemurs was investigated by SSCP and Sanger sequencing as detailed in Schad et al. , 2005. I2.3N1998–2003: 213, N2012–2016: 209), 262 replicates and eight negative controls  and the same MHC fragment was targeted. All samples from the first and second sampling periods were included in amplicon‐based NGS genotyping in two Illumina runs. The overall dataset corresponded to 684 samples genotyped from 422 individual mouse lemurs  > 1. The following models of codon evolution were computed: M7  and M8 (similar to M7 but assumes ω > 1). Model M7 served as a null model and was compared with model M8 by means of the likelihood‐ratio test. To identify the best fitting model, the twice log‐likelihood difference was compared with a chi‐squared distribution. Subsequently, if the model indicating selection (M8) resulted in a significantly better fit to the data, the Bayesian approach in CODEML was used to determine the identity of sites under positive selection.To check for the presence of codon sites exhibiting signals of historical positive selection, we used the codon‐based method (CODEML) included in the package PAML version 4.7 Yang, . Here, p2.6A), expected (HExp) and observed (HObs) heterozygosity and inbreeding coefficient (FIS) by using the software package Arlequin version 3.5.2.2  based on the recent recommendations of Wang  plus 2  the maximum value of the posterior probability distribution, ,  between fragments, MCMC runs were performed with 3 × 106 omitting the first 1 × 106 with a sampling frequency of 2000. Delta values were adjusted to optimize terminal proposed changes between chains . Convergence was verified by ensuring concordance between 10 replicate runs, each initialized with a different random seed.The patterns of contemporary gene flow were examined using the program BayesAss v.1.3  for each sampling period and 95% credible limits with the linkage disequilibrium method . This method delivers reliable estimates when using datasets with more than 50 individuals and when longitudinal comparisons encompass at least five generations between samples  and the Wilcoxon signed‐rank test with 100,000 iterations. In addition, we checked for mode shifts in the allele frequency distribution. Alleles at low frequency are likely to be lost in a population experiencing a recent bottleneck resulting in a shifted mode distribution compared with the L‐shaped distribution expected in stable populations  at each microsatellite locus, as the difference between observed genetic diversity (Hobs) and the equilibrium gene diversity (Heq) computed under the IAM, SMM and TPM models in the software BOTTLENECK (as described above). We used linear mixed models to test whether establishing connectivity between fragments results in significant reduction of gene diversity excess (reaching equilibrium). The model was fitted with sampling period as fixed categorical effect and locus ID as a random factor using the package lme4 in R  to expected values of homozygosity (Fexp) under the theoretical prediction that rare alleles under positive selection result in uniform frequency distributions that lie above neutral expectations. We calculated the normalized deviate of F (Fnd) to estimate the degree to which Fobs deviate from neutral expectations. Slatkin’s (Fnd < 0) indicate uniform frequency distributions  and suggest either balancing selection or population expansion while positive values (Fnd < 0) are interpreted as evidence of directional selection or population bottleneck . The direct assessment of patterns of variation across MHC and microsatellite loci enables us to account for the potential role of demography.To estimate the effect of contemporary selection at MHC, we used the Ewens–Watterson homozygosity (EW) test of neutrality , the mean number of private alleles  and the mean number of private alleles for pairs of populations. The rarefaction method was used as implemented in the software ADZE  while controlling for the effect at neutral FST estimates  and amplicon‐based NGS methods (NNGS = 213). MHC II DRB genotypes for 208 individuals were generated using both methods.The availability of samples from the first sampling period  showed one and two base pair differences to the previously known alleles Mimu‐DRB*5 (position 147) and Mimu‐DRB*7 (positions 37 and 119) .Eleven MHC II DRB exon 2 alleles were identified by using amplicon‐based NGS. Allele identities were based on 44 (25.7%) variable nucleotide positions and one insertion–deletion event at codon position 57  identified exclusively with SSCP/Sanger analysis were rare alleles and only found in a single individual each. Allele Mimu‐DRB*12, identified in 18 individuals with the SSCP/Sanger analysis, is unlikely to be an artefact but rather a contamination event. The individual carrying Mimu‐DRB*8 was no longer available for NGS analyses because of the lack of DNA.We found agreement in individual genotypes among 49.5% of the samples. Disagreement between genotyping approaches was largely attributed to allelic dropout using the SSCP/Sanger method. Among the samples that were identified as genotyping discrepancies, 87.6% were classified as homozygous with SSCP. The dropout event of a common allele in the population Miga‐DRB*1, Table , Table 1dN/dS ratios (ω) were almost twofold larger among the NGS allele variants  with more positively selected sites than those identified by the SSCP/Sanger sequencing method  was higher than expected . No evidence of inbreeding was found in the amplicon‐based NGS data (FIS = −0.07 to 0.00). Estimates of genetic diversity by using NGS data after the corridors were established , although slightly lower than those during the first sampling period (Table FIS = 0.01–0.08).Analyses of MHC DRB exon 2 genetic diversity conducted on samples from the first sampling period revealed contrasting patterns between the SSCP/Sanger sequencing and amplicon‐based NGS data. The SSCP dataset suggested marked deficiencies of heterozygotes  . Similar results were found when removing the forest fragment (M4‐5) as this fragment has been destroyed after the first sampling period. Inclusion would skew the comparisons between the first and second period  revealed similar results across sampling periods, with a minimum of K = 1 (using the maximum MedMeaK and MedMedK) and a maximum of K = 3  ancestral clusters. With K = 2, we found populations of the forest fragments in Mandena consisted of mixed ancestry with no clear grouping associated with the fragment of origin . Population structure inferred from the randomized subsampled balanced datasets was fairly consistent across the 20 replicated analyses (Supporting Information Table K = 1 (MedMeaK and MedMedK) or K = 2 (ln p(X|K) and ΔK)ancestral clusters  was found between some but not all fragments . All other pairwise comparisons indicate low levels of gene flow . Overall, these results suggest asymmetric gene flow between fragments.Evidence of contemporary gene flow (ts Table . EstimatNe 1998–2003: 186 (95% CI = 154–230); Ne 2012–2016: 247 (95% CI = 196–326). The Ne estimated for the overall dataset using the temporal method was 65 individuals (95% CI = 47–86).Effective population size, calculated using the linkage disequilibrium method (Waples and Do), was similar across sampling periods, H) before and after the establishment of corridors . Among the random effects, the variance explained by locus was large (Radj = 0.66–0.67), whereas the variance explained by fragment was small (Radj = 0.003–0.03).Evidence of a bottleneck was found under the IAM model for both sampling periods, as indicated by significance in the sign and Wilcoxon tests  and M20 (2012–2016) showed no significant deviations from neutrality . The overall estimate of population differentiation varied between sampling periods  than at microsatellite loci  whereas for the second sampling period no significant difference was found . Furthermore, no evidence of isolation by distance at MHC while controlling for neutral differentiation was found for either sampling period , suggesting that drift is not a major driver of MHC differentiation among forest fragments.We found no significant correlation between 4M. ganzhorni, a species of mouse lemurs that, to date, is considered to be restricted to the remnant littoral forests of south‐eastern Madagascar  with “new”  datasets delivered by the rapid development of sequencing technologies and their implications for the assessment of management strategies. We then discuss the evolutionary history of the population of M. ganzhorni in Mandena as suggested by genetic differentiation, the patterns of gene flow and effective population size. Finally, we evaluate the long‐term effects of fragmentation on genetic diversity at MHC DRB II gene and the influence of the establishment of corridors on neutral and adaptive genetic variability.In this study, we aimed at understanding the effects of habitat loss and fragmentation on the genetic diversity of 4.1He) was greatly underestimated (by about 50%) by using the traditional SSCP/Sanger sequencing method.One major purpose of our study was to evaluate to what degree genotyping results of the MHC II DRB gene assessed with traditional methods (SSCP/Sanger sequencing) were comparable with the data generated with high‐throughput technologies . This issue is relevant in the context of the long‐term monitoring of small populations for which previous studies involving traditional tools act as a baseline for future investigations that aim to inform management decisions. In our study, the individual genetic diversity  than by the amplicon‐based NGS . The latter is in contrast to previous work in which a lower number of alleles were detected by SSCP compared with NGS  identified by amplicon‐based Illumina sequencing was not resolved by SSCP. The latter largely explains why NSSCP = 14, NNGS = 11), estimates of dN/dS ratios (ω) were almost twofold larger among the NGS allele variants  with more positively selected sites being identified by the latter method   remained unchanged during the 18‐year time span covered in our study   between MHC II DRB gene and microsatellites to tease apart the role of selection and drift maintaining adaptive diversity in M. ganzhorni. The absence of a correlation between pairwise FST at MHC and microsatellite markers and the lack of evidence of isolation by distance when controlling for neutral differentiation confirm that selection, rather than drift, is a main driver of MHC variability. Intriguingly, the pattern of MHC differentiation varied between sampling periods. Before the establishment of corridors, MHC differentiation falls well below that found at microsatellite loci. This is an expected outcome for adaptive loci because balancing selection acts as a homogenizing force , whereas we recovered 11 alleles . Low MHC allelic diversity is probably associated with the much smaller population size of mouse lemurs in Mandena (Babik, Durka, & Radwan, M. ganzhorni in Mandena, we provide evidence of the role of historical and contemporary selection maintaining diversity at MHC II DRB gene. These findings are in agreement with the comprehensive assessment of the effects of population reductions on MHC variability in that while it appears that polymorphism is severely compromised in small populations (Eimes et al., Until a recent species reassessment (Hotaling et al., d et al.  found 615Outcomes of our study echo previous work that emphasize the relevance of long‐term genetic monitoring that capture both neutral and adaptive variability. In doing so, it is possible to disentangle the processes of demography and selection maintaining adaptive genetic diversity. We also point out that in light of the rapid turnover in sequencing technologies, results delivered by less sensitive methods can lead to erroneous conclusions when comparing results from traditional methods with results delivered by new generation sequencing.Long‐term monitoring enabled us to assess the value of corridors as a conservation strategy, a tool that has long been debated (Simberloff, Farr, Cox, & Mehlman, Microcebus found in evergreen rain forest west of the littoral forest belongs to a different species (M. tanosi; Hotaling et al., All together, we show that the interplay between immigration and gene flow among forest fragments has shaped the patterns of genetic variability of mouse lemurs in Mandena. The processes reconstructed in the present paper must have been confined mainly within the littoral forest fragments between Mandena and Lokaro, some 20 km northeast of Mandena. The fragments there are separated by heath‐type vegetation and spaced a few hundred to a few kilometres (Ganzhorn et al., M. ganzhorni maintains high levels of individual genetic diversity. Although unexpected, similar results have been reported in a variety of lemur species and interpreted as a signature of past population dynamics (e.g., Varecia variegata, Baden et al., P. perrieri, Salmona et al., Indri indri, Nunziata et al., Despite evidence of low effective population size, None declared. Click here for additional data file.