Elymus sibiricus L.) is an important native grass in the Qinghai-Tibet Plateau of China. It is difficult to grow for commercial seed production, since seed shattering causes yield losses during harvest. Assessing the genetic diversity and relationships among germplasm from its primary distribution area contributes to evaluating the potential for its utilization as a gene pool to improve the desired agronomic traits. In the study, 40 EST-SSR primers were used to assess the genetic diversity and population structure of 36 E. sibiricus accessions with variation of seed shattering. A total of 380 bands were generated, with an average of 9.5 bands per primer. The polymorphic information content (PIC) ranged from 0.23 to 0.50. The percentage of polymorphic bands (P) for the species was 87.11%, suggesting a high degree of genetic diversity. Based on population structure analysis, four groups were formed, similar to results of principal coordinate analysis (PCoA). The molecular variance analysis (AMOVA) revealed the majority of genetic variation occurred within geographical regions (83.40%). Two genotypes from Y1005 and ZhN06 were used to generate seven F1 hybrids. The molecular and morphological diversity analysis of F1 population revealed rich genetic variation and high level of seed shattering variation in F1 population, resulting in significant improvement of the genetic base and desired agronomic traits.Siberian wild rye ( Elymus sibiricus L., commonly known as siberian wildrye, is a perennial, cold-season, self-pollinating, and allotetraploid grass with the StStHH genome constitution (2n = 28) [E. sibiricus germplasm are especially rich and diverse in north China, where it is distributed primarily in Qinghai-Tibet Plateau, Inner Mongolia, Sichuan, Xinjiang, and Gansu Provinces [E. sibiricus has been widely grown for pasture and hay, owing to its excellent stress tolerance, good forage quality and adaptability to local environments, and it therefore plays an important role in animal husbandry and sustenance in North China [2n = 28) . Indigenth China .E. sibiricus is difficult to grow for commercial seed production since seed shattering can cause up to 80% yield losses if harvesting is delayed [E. sibiricus seed  is produced each year, accounts for over 90% of total seed yield. However, the average seed production of E. sibiricus is only 690 kg∙ha−1 (China Grass Internet). To reduce seed shattering and enhance seed production, one of the most important approaches is to explore genetic diversity.As an economically important species,  delayed . The proE. sibiricus germplasm, one important strategy is to develop novel breeding lines by using genetically and phenotypically diverse germplasm to cross with the adapted cultivars. Generally, these resynthesized breeding lines are genetically diverse from inbred line/cultivar [E. sibiricus germplasm from Qinghai-Tibet Plateau [E. sibiriucs germplasm with variation of seed shattering from its primary distribution area for improving our understanding of breeding materials and developing more efficient conservation and breeding strategies.Morphologically and genetically diverse germplasm is a potentially valuable source for the improvement of desired agronomic traits such as seed yield, quality and stress tolerance . To broacultivar , and hybcultivar . Recent  Plateau ,8 and su Plateau . It is, The development of neutral molecular markers has made it fast, reliable and accurate to reveal the genetic diversity of germplasm. Compared with other molecular markers like inter simple sequence repeat (ISSR), sequence-related amplified polymorphism (SRAP), and start codon targeted (SCoT) etc, EST-SSRs are highly polymorphic, abundant and are accessible to research in laboratories via published primers sequences. What is more, EST-SSRs have a higher level of transferability across related species than genomic-SSRs because EST-SSRs originate from the transcribed regions in genomes and possess conserved sequences among homologous genes . Along wE. sibiriucs accessions from North China; (ii) broaden the genetic diversity of E. sibiricus by crossing two genetically and morphologically diverse genotypes and assess genetic variation of the hybrid population.The pattern of genetic variability of the available germplasm substantially affects the choice of breeding materials and the success of plant breeding programs. The objectives of the present study were to (i) compare the genetic diversity and relationship among E. sibiricus accessions varied from 31.86 gf (PI655140) to 92.34 gf (ZhN06), with an average of 53.28 gf. Sixteen accessions had a relatively low seed shattering degree with BTS more than the average value of seven accessions including four wild accessions  and three cultivars  had a relatively high seed shattering degree with the BTS value of less than 40 gf. The other 13 accessions had a moderate seed shattering degree  values, the number of optimum groups was four . Among tThe principal coordinate analysis (PCoA) showed about 31.52% of the total variation was described by the first three PCo . The majp < 0.001) genetic difference among the five regions. A larger proportion variation (83.40%) was apportioned within geographic regions and 16.60% was apportioned between geographic regions  and ZhN06-1 (lowest seed shattering degree)were selected as parents to produce seven F1 lines (BEPF) (8.44%) is higher than bands exclusively present in parents (BEPP) (1.95%). 20.78% bands were shared by Y1005-1 and F1 lines, whereas 26.62% bands were shared by ZhN06-1 and F1 lines. These results showed that ZhN06-1 may have a higher heritability than Y1005-1.The high degree of genetic variation found in morphological traits is in accord with the genetic variability . The 40 1-1. Cluster II included the other six F1 lines, among them F1-2, F1-3 and F1-4 were clustered together in a major subgroup with F1-5, while F1-6 and F1-7 were in a separate subgroup. When compared with the phenotypic-based dendrogram, marker-based cluster revealed poor correlation with morphological characteristics  with genetic difference sand contrasting seed shattering habits were selected to produce F1 lines. Our results showed seed shattering degree in hybrid population ranged from 68.2 gf (F1-1) to 143.4 gf (F1-6), with an average of 97.2 gf. Three F1 individuals  had lower seed shattering degree than low seed shattering parent ZhN06-1 (97.2). Thus, these individuals could be used as breeding materials for developing low seed shattering cultivars in the future. Except for seed shattering, other traits such as flag leaf length and width also showed the positive heterosis. Our results confirmed that some morphological traits of E. sibiricus could be improved by means of hybridization. When compared with the phenotypic-based dendrogram, marker-based cluster revealed poor correlation with morphological characteristics. The phenotypic-based dendrogram using limited morphological data could be affected by environment factors. In comparison, a marker-based cluster is more efficient and allows genetic diversity analysis using any physiological stage or tissue, suggesting its potential in analyzing genetic diversity and relationship of E. sibiricus.Like most native grasses, interest . The cha1 lines and parents, respectively. These gained and missed bands were considered as polyploidization-induced rearrangements within coding regions. Hybridization of more genomes with different sizes and compositions in a single nucleus followed by chromosome doubling can induce several types of genomic modifications and rearrangement in the hybrids [Based on our results, the genetic diversity of hybrid population is 59.92%. Furthermore, 8.44% and 1.95% of polymorphic bands were exclusively present in F hybrids ,27. ThesE. sibiricus accessions were used in the study, comprising wild collections, breeding lines, cultivars, and cultivated types  method . DNA conElymus (Elw hereafter), Pseudoroegneria (Ps hereafter) and Leymus (Lt hereafter) EST database [E. sibiricus EST-SSR markers were developed by transcriptome sequencing [3 solution, and photographed by a digital camera .A total of 212 EST-SSR primers from different resources were used for genotyping, of which 100 EST-SSR markers were previously developed from database ,30,31 anquencing . The DNAquencing . Amplifi1 lines and their parents were germinated in plastic boxes with moistened blotter paper at room temperature. After germination seedlings were grown in a greenhouse under a 25/15 °C day/night temperature regimes until they were 8 weeks old. Then they were transplanted to field plots in the research farm, Yuzhong, Gansu, China . Plants were spaced 0.5 m within rows and 1 m between rows. A total of 12 phenotypic traits, including seed shattering (SS), plant height (PH), leaf length (LL), leaf width (LW), flag leaf length (FLL), flag leaf width (FLW), culm diameter (CD), culm number (CN), tiller number (TN), panicle length (PL), awn length (AL) and 1000-seed weight (1000-SW) were measured using the methods described by Zhao et al. [E. sibiricus accessions was determined by measuring pedicel breaking tensile strength (BTS), which is inversely proportional to shattering degree. Thirty randomly chosen spikelets of each plant were examined at 28 days after heading, and their average BTS values were calculated. The heterosis of hybrids were estimated on mid-parent values and high-parent value using the following formula: mid-parent heterosis (%) = (F1 − MP)/MP × 100%, higher-parent heterosis (%) = (F1 − HP)/HP × 100 %, where F1 is the mean of the hybrids, MP is the mean of parents, HP is the value of higher parent [The seeds of Fr parent .2 − q2, where p is frequency of present band and q is frequency of absent band [E. sibiricus accessions was analyzed using STRUCTURE v2.3.4 software with the ′admixture mode′, burn-in period of 10,000 iterations and a run of 100,000 replications of Markov Chain Monte Carlo (MCMC) after burn in [The amplified bands were scored as present (1) or absent (0), and only reproducible bands were considered. The resulting present/absent data matrix was analyzed using POPGENE 32 Version 1.31 . Number ent band . The Anaent band . The inpent band . Populat burn in . For eac burn in , maximum burn in . A dendrE. sibiricus accessions from its primary distribution area in China. The finding that larger variation existed within geographical regions will provide a guideline for the collection and conservation of E. sibiricus germplasm. More genetic variation of the species can be captured when sampling a larger number of plants from special eco-geographical regions. Meanwhile, cross breeding is an effective way to obtain more genetic and phenotypic variation. F1 lines of E. sibiricus exhibited a higher genetic variation in the major agronomic traits. In addition, some F1 lines showed obvious heterosis over parents, especially in seed shattering performance. These hybrids could be used as important genetic resources for genetic improvement of E. sibiricus in future breeding improvement programs.This study showed a high level of genetic diversity and a clear population structure of 36


Historically, craniofacial genetic research has understandably focused on identifying the causes of craniofacial anomalies and it has only been within the last 10 years, that there has been a drive to detail the biological basis of normal-range facial variation. This initiative has been facilitated by the availability of low-cost hi-resolution three-dimensional systems which have the ability to capture the facial details of thousands of individuals quickly and accurately. Simultaneous advances in genotyping technology have enabled the exploration of genetic influences on facial phenotypes, both in the present day and across human history.There are several important reasons for exploring the genetics of normal-range variation in facial morphology.    - Disentangling the environmental factors and relative parental biological contributions to heritable traits can help to answer the age-old question “why we look the way that we do?”    - Understanding the etiology of craniofacial anomalies; e.g., unaffected family members of individuals with non-syndromic cleft lip/palate (nsCL/P) have been shown to differ in terms of normal-range facial variation to the general population suggesting an etiological link between facial morphology and nsCL/P.    - Many factors such as ancestry, sex, eye/hair color as well as distinctive facial features  can be identified or estimated using an individual’s genetic data, with potential applications in healthcare and forensics.    - Improved understanding of historical selection and adaptation relating to facial phenotypes, for example, skin pigmentation and geographical latitude.    - Highlighting what is known about shared facial traits, medical conditions and genes. The facial surface is readily visible and identifiable with a close relationship to the underlying cartilaginous and skeletal structures . DiffereTable 1) and the human embryonic sequence of events can be visualized which aids understanding of the movement of the facial processes followed by their fusion  genes, and local retinoic acid gradients , computerized tomography (CT), and cone beam computerized tomography (CBCT). Many of these techniques have been evaluated in terms of facial coverage, speed of capture, processing time, accuracy, validity and cost . For an Assessment and judgment of the face and body can be traced back to the ancient Greeks and Egyptians when mathematical methods such as Fibonacci series and the golden proportion (1:1.618) were applied to art and architecture as a method of defining attractiveness and beauty . Facial PAX3 gene associated with the shape of the nasal root area (r = 0.7) and poor association for naso-labial angle (r = 0.16)  have been used in different studies and all have identified the oot area . The use = 0.16) .Standardized clinical facial charts/tables/measures are routinely used for newborns  and other specialties such as, ophthalmology and orthodontics. There are many published norms for different racial/population groups used to identify individuals who fall within the normal range and identify any facial dysmorphologies.The soft tissue facial variation has been explored in a large Caucasian population of 15-year-old children  recruited from the Avon Longitudinal Study of Parents and Children (ALSPAC). Face height (28.8%), width of the eyes (10.4%) and prominence of the nose (6.7%) explained 46% of total facial variance . There wFacial morphology refers to a series of many different complex traits, each influenced by genetic and environmental factors. In particular, the strong effects that genetic variation can have on facial appearance are highlighted by historical portraits of the European royal family, the Habsburgs (1438–1740). Presumably because of frequent consanguineous marriages, later Habsburg rulers often had extreme facial phenotypes such as the characteristic “Habsburg” jaw (mandibular prognathism). Indeed, the last Habsburg King of Spain, Charles II, was reported to have had difficulties eating and speaking because of facial deformities. The influence of genetic variation is also evident in non-consanguineous families, where dental and facial characteristics are common among siblings and passed on from parents to their offspring . Twin stContrastingly, previous estimates suggest that antero-posterior face height, mandibular body length, ramus height, upper vermillion height, nasal width and maxillary protrusion are more strongly influenced by environmental factors . HoweverGRID2 and ELAVL2 genes resulting in cleft lip and palate . Many of these substances can cross the placenta  and Figure 1 . For detailed information on the biological basis of individual genes, the reader should refer to the original articles. Different facial measures have been applied to facial images obtained from a variety of acquisition systems (2D and 3D). Genes are likely to influence more than one facial trait. For instance, the PAX3 gene is associated with eye to nasion distance, prominence of the nasion and eye width, side walls of the nose, and prominence of nose tip. Similarly, the naso-labial angle will be associated with nose prominence and DCHS2 is linked to both traits.Genome-wide association studies (GWAS) have investigated the association between normal facial variation and millions of single nucleotide polymorphisms (SNPs). GWAS studies coupled with high-resolution three-dimensional imaging of the face have enabled the study of the spatial relationship of facial landmarks in great detail. Over the last 6 years there has been significant progress with 9 published GWAS which have identified over 50 loci associated with facial traits . The genPRDM16 is linked to the length and the prominence of the nose as well as the width of the alae, SOX9 is thought to be related to the shape of the ala and nose tip, variation in SUPT3H is thought to influence naso-labial angle and shape of the bridge of the nose, while centroid size  and allometry (relationship of size to shape) have been linked to PDE8A and SCHIP17 genes, respectively, .Some reported genes appear to influence different parts of the face. ctively, . Eye wid regions . A signiIdentifying genetic variants influencing facial phenotypes can lead to improved etiological understanding of craniofacial anomalies, advances in forensic prediction using DNA and testing of evolutionary hypotheses.MAFB which is associated with face width in normal variation  is a birth defect with a complex etiology, primarily affecting the upper lip and palate . Previouariation . Furtherariation .SNAI1), obtuse nasolabial angles, more protrusive chins , increased lower facial heights (SNAI1), thinner and more retrusive lips and more protrusive foreheads  in the nsCL/P relatives compared to controls. There was also greater asymmetry in the nsCL/P group   of children with nsCL/P and unaffected relatives. There was reduced facial convexity (d SNAI1) .There is evidence that nsCL/P genetic risk variants have an additive effect on philtrum width across the general population. This association suggests that developmental processes relating to normal-variation in philtrum development are also etiologically relevant to nsCL/P, highlighting the shared genetic influences on normal-range facial variation and a cranio-facial anomaly .TBX15 (Cousin syndrome)  (PAX3 (Waardenburg syndrome)  , PAX1 (Oyndrome)  and PAX3yndrome) . It has yndrome) .Shared genetic pathways may influence both normal-range variation in facial morphology and craniofacial anomalies. Disentangling these shared pathways can improve understanding of the biological processes that are important during embryonic development.Over time, facial morphology across populations has been influenced by various factors, such as migration, mate-choice, survival and climate, which have contributed to variation in facial phenotypes. Genetic and facial phenotype data can be used to improve understanding of human history.Ancestry and physical appearance are highly related; it is often possible to infer an individual’s recent ancestry based on physically observable features such as facial structure and skin color. Indeed, previous studies have demonstrated that self-perceived and genetically inferred ancestry are associated with facial morphology, particularly with regards to the shape of the nose . Facial Historical migrations, such as the European colonization of Latin America, led to genetic admixture  , which gFacial phenotypes can influence mate choice and be under selection pressures. These factors can then affect reproductive behavior and lead to population-level changes in facial variation as certain facial phenotypes are favored. Previous studies have suggested that facial features such as attractiveness , hair coThe possible evolutionary advantages of facial phenotypes have been discussed extensively but anthropological hypotheses can be tested using genetic and facial phenotype data. For example, a masculine face has been hypothesized to be a predictor of immunocompetence . A previPAX3 gene is associated with the distance between the mid-endocanthion point and surface nasion with a mean distance of 17.5 mm with differing axis values up to 6.7 mm (x), 17.7 mm (y), and 18.9 mm (z). Although, it is known that the PAX3 influences the prominence of the bridge of the nose it is more challenging to know to what extent this influences adjacent facial regions in each individual. In addition, genetic and environmental factors will have subtle influences on the face. Although the molecular understanding of genetic variation influencing facial morphology is improving, the use of DNA as a prediction tool is still a long way off. However, recent studies suggest that DNA has the potential to identify an individual from a small group of possible candidates . The growing number of GWAS datasets has allowed exploration of the shared genetic influences on different phenotypes  heritable changes in gene expression which are not explained by changes to the DNA base-pair sequence. Epigenetic processes include DNA methylation, histone modification and chromatin remodeling, which can affect gene expression by regulating transcription . EpigeneCraniofacial epigenetic studies to date have largely focused on orofacial clefts. Previous epigenome-wide association studies (EWAS) have found evidence of differential DNA methylation between cleft cases and controls , as wellSimilarly, epigenetic processes may mediate the effects of germline genetic variation. Many of the previously discussed genetic variants associated with facial traits in GWAS reside in non-protein coding regions of the genome with unclear functional relevance. One possibility is that these variants may influence facial phenotypes through gene regulation pathways involving epigenetic processes. Indeed, a previous study demonstrated that a major risk locus for non-syndromic cleft lip/palate (nsCL/P), in a non-coding interval, is involved in the regulation of gene expression in the developing murine face  while anDespite the promise of early craniofacial epigenetic studies, there are important caveats worth noting. First, a major issue is that epigenetic modifications can vary across different tissues. Previous studies have used DNA methylation in blood as a proxy for methylation in lip and palate tissues. Despite some evidence for positive correlation between blood and lip tissue DNA methylation , the extThe gene regulatory systems are complex and numerous and detailing these regulatory mechanisms has been the goal of the NIH Roadmap Epigenomics Project whereby next generation sequencing technologies  are employed to map DNA methylation, histone modifications, chromatin accessibility in a variety of research media such as, animal models  and stem cells and regulated human fetal tissues . EnhanceThe craniofacial region is made up of a series of complex structures which contribute to overall facial shape. Twin studies have indicated that facial shape is mainly due to genetic influences (≈75%) although the percentage variance explained in GWAS studies is extremely low generally explaining less than 2% of the total variance. GWAS may be underestimating and twin and family studies overestimating the levels of heritability. Facial shape and features are the result of mutations, genetic drift, recombination and natural selection. Rare Mendelian mutations, low frequency segregating variants, copy number variants and common variants contribute to complex phenotypes. Genetic interactions or epistasis may also explain the low levels of variance recorded. In addition, there is evidence of pleiotropy, quantitative phenotypes and Mendelian traits all influencing multiple phenotypes suggesting a large number of loci contribute additively to facial variation. Epigenetics focuses on the functional components of the genes and gene activities. The genome is comprised of 3.2 billion nucleotides wrapped in octomeric units of histones (chromatin). Modifications to chromatin through methylation, acetylation, phosphorylation or other processes are known to influence gene expression. Most epigenetic changes are transient and not generally heritable. However, a small proportion of epigenetic changes are transgenerational . HoweverIncreasing the sample sizes of genetic studies of facial morphology through international collaborations, such as the type II diabetes consortia DIAGRAM , will heFocusing on specific phenotypes and genetic variants in families will identify additional rare variants should be followed-up with a combination of genotyping and deep re-sequencing of the variants or genes of interest in large numbers of cases and controls. The analytical techniques (particularly the bootstrapped response-based imputation modeling (BRIM) and hierarchical spectral clustering analysis) employed by There have been nine GWAS studies and it is appropriate to try and integrate their findings through a meta-analysis. Different genetic models, genotyping and imputation techniques have been employed and the between-study heterogeneity should be considered.Defining facial shape can be undertaken in different ways but it is important to appreciate that there will be associations with not only with other facial features but also body phenotypes and medical conditions. Visualizing and automatic detection of facial phenotypes and determining their prevalence in population groups will facilitate case-control evaluations to determine genetic variants. So far, all GWAS studies have studied the static face but capturing the face during simple facial actions in a population (dynamic movement with or without speech) will enable the exploration of combined neurological and morphological features by assessing both speed and range of movement.Population cohort studies enables researchers to study the environmental, disease and metabolic risk factors and genetic interactions from pre-birth throughout the lifecourse. Ideally facial images should be captured at birth, 5, 9, 12, 15, and 18 years of age and repeated every 10 years of age to capture facial features. During the pubertal growth period (9–18 years) facial images should be captured more frequently and if studying pubertal influences facial images should be captured at least every 6 months. Acquiring as much information as possible in relation known genetic additive effects, environmental factors and previous medical histories of family members  will provide further insights into facial relatedness.in vivo or alternatively in vitro creating similar morphological tissues with intact innervation, blood and lymphatic systems that could be transplanted later may become reality in the future. Future, environmental epigenetic studies will show whether particular chemicals map to corresponding sensitive genomic regions. It is important to identify early life exposures (particularly conception to birth) that may influence later life health outcomes.With improving knowledge of the controlling mechanisms for normal and abnormal facial development, it is logical to pursue healthcare strategies in the first instance to prevent craniofacial anomalies arising, with discussion of risks with genetic counseling, possibly future gene therapies and the follow up with minimally invasive or non-surgical, scarless procedures to correct craniofacial anomalies such as cleft lip and palate and control vertical and horizontal growth particularly of the upper and lower jaws and nose. Surgical procedures are not always simple as often in CL/P patients there is often insufficient tissues available . Prevention may be challenging  as facial development occurs very early in gestation during a period whereby the mother is often unaware she is pregnant. Controlling the mechanisms of normal growth The face develops very early in gestation and facial development is closely related to the cranial neural crest cells. Disruption in early embryological development can lead to wide-ranging effects from subtle neurologic and facial features, which includes asymmetry, to significant impact on facial shape as characterized by a CL/P or in anomalies observed in craniofacial syndromes.Heritability studies have provided insight into the possible genetic and environmental contributions to face shape. However, the sample sizes and inconsistencies in research design and particularly statistical management have yielded mixed results. Further detail is required on the heritability of facial features with particular attention to inherited pathways of specific facial features in homogenous populations and populations with significant admixture.From birth to adulthood there are significant body and facial changes. Further work is required to explore the importance of the various biomedical markers and medical conditions  on the growth of the face, for example, remodeling of the facial skeleton, spatial changes of the constituent parts of the facial skeleton through sutures, condylar and nasal cartilages as well as the soft tissues, neural and vascular networks. The GWAS studies have provided insights into the genetic influences on facial shape. However, large-scale population studies are needed to identify more genetic variants not only in the context of facial shape but general body development with particularly attention to puberty. With any change in face shape the complex processes and communications at the biological and genome levels need to be identified and explained. The sheer volume of data collected in imaging genetics from images (hundreds of thousands of points), omics datasets  as well as biomarkers for medical conditions generates massive and complex data sets.The size and heterogeneity of these data sets pose new challenges to efficiently and effectively, store, simplify and analyze the relative interactions and influences for a large number of face shape variables. The aim will be to continually develop and advance existing computerized tools and algorithms to solve these complex problems and this will require a multidisciplinary and internationally based team.Impressions of an individual’s health are integral to social interactions and judgments are made on the visual appearance of skin, degree of roundness of the face and facial expression . There hSR and LH outlined the overall manuscript. SR and SL wrote the section “Heritability”. LH and AZ wrote the section “Environmental Influences”. SR, ES, SL, and LH wrote the section “Craniofacial Shape Gene Discovery”. LH and SR wrote the section “Estimating Identity”. SR, ES, LH, and SL highlighted the shared facial traits. All authors actively participated in editing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor is currently collaborating with author SR and confirms the absence of any other collaboration.


Isolation by distance (IBD) is a natural pattern not readily incorporated into theoretical models nor traditional metrics for differentiating populations, although clinal genetic differentiation can be characteristic of many wildlife species. Landscape features can also drive population structure additive to baseline IBD resulting in differentiation through isolation‐by‐resistance (IBR). We assessed the population genetic structure of boreal caribou across western Canada using nonspatial (STRUCTURE) and spatial (MEMGENE) clustering methods and investigated the relative contribution of IBD and IBR on genetic variation of 1,221 boreal caribou multilocus genotypes across western Canada. We further introduced a novel approach to compare the partitioning of individuals into management units (MU) and assessed levels of genetic connectivity under different MU scenarios. STRUCTURE delineated five genetic clusters while MEMGENE identified finer‐scale differentiation across the study area. IBD was significant and did not differ for males and females both across and among detected genetic clusters. MEMGENE landscape analysis further quantified the proportion of genetic variation contributed by IBD and IBR patterns, allowing for the relative importance of spatial drivers, including roads, water bodies, and wildfires, to be assessed and incorporated into the characterization of population structure for the delineation of MUs. Local population units, as currently delineated in the boreal caribou recovery strategy, do not capture the genetic variation and connectivity of the ecotype across the study area. Here, we provide the tools to assess fine‐scale spatial patterns of genetic variation, partition drivers of genetic variation, and evaluate the best management options for maintaining genetic connectivity. Our approach is highly relevant to vagile wildlife species that are of management and conservation concern and demonstrate varying degrees of IBD and IBR with clinal spatial genetic structure that challenges the delineation of discrete population boundaries. IBD was first described by Wright  and indiRangifer tarandus caribou) is   What model of population genetic structure best describes boreal caribou across a continuous distribution in western Canada? (2) Do IBR hypotheses associated with anthropogenic features , waterbodies, and wildfires contribute to the observed patterns of genetic variation? and (3) What boundaries best capture the patterns of spatial genetic variation and could be used to delineate MUs for conservation purposes? We apply both aspatial and spatial methods as part of a framework for delineating management units for vagile wildlife species that present a natural clinal pattern of population genetic structure.22.12. Local population boundaries outlined in the federal recovery strategy  to identify tracks and foraging sites made by caribou in snow. A field crew subsequently visited each foraging site by helicopter to collect fecal pellets, and coordinates for each collection site were recorded. Our samples from the Boreal Shield ecozone in Saskatchewan were obtained from blood blots or vials collected from individual boreal caribou handled during radio‐collaring in the winters 2013–2014  across the study area was determined using the individual‐based Bayesian clustering program STRUCTURE version 2.3.4 ) and delta K (ΔK) plots  between genetic distance and the spatial distance among individuals. These variables are extracted using “mgQuick” function , observed heterozygosity (Ho), number of alleles across loci (A), allelic richness (Ar), and individual inbreeding coefficient (FIS) along with statistical significance were further calculated using SPAGeDi. Each cluster was further tested for allele frequency deviation from Hardy–Weinberg equilibrium (HWE) and linkage equilibrium (LE) using the probability test in GENEPOP version 4.2 . Due to different levels of anthropogenic disturbance and general landscape heterogeneity across the study area, we tested landscape effects on genetic variation separately for each first‐order cluster identified by STRUCTURE and MEMGENE. Landscape variables hypothesized to cause landscape resistance to gene flow for boreal caribou across the study area included main roads, water bodies, and recent wildfires. It is known that various anthropogenic habitat disturbance features affect boreal caribou movements and distribution  were a matrix of MEM spatial variables (mgQuick results) describing spatial components of genetic variation and a matrix of dummy explanatory variables coding the polygon membership of samples. The latter partitioned the sampling locations into hypothesized MUs across the study area. Four hypothesized population boundary scenarios were tested delineating the study area into five units. Only scenarios with the same number of units are directly comparable in this analysis, as there is no method available to penalize the addition of dummy variables representing unit membership that will inflate the proportion of variance explained. MU delineations were based on second‐order STRUCTURE (Scenario 1) and MEMGENE (Scenario 2) results, as well as local populations outlined in the federal recovery strategy while limiting partitioning of Manitoba into North and South ranges  from western Manitoba  and East (Cluster 2)  Figure a. Furthe3.3K = 2  and ranged from 0.01 to 0.08  and Scenario 2 (reflecting second‐order MEMGENE results), followed closely by Scenario 4  Table , Scenari44.1FST and number of migrants per generation, which revealed higher genetic differentiation and lower levels of dispersal between clusters found further apart compared with neighboring central clusters. Additionally, the two central clusters Cluster 2A and Cluster 2B had low levels of genetic differentiation and were the only second‐order clusters found with loci deviating from HWE, providing further evidence of immigration  and non‐random mating, and suggesting that individuals in these clusters do not form demographically independent groups. The high level of dispersal and low genetic differentiation identified between Cluster 2A in southeastern Saskatchewan and Cluster 2B in the Manitoba North range supported findings by Ball et al. (FST =0.03) between these areas. Additionally supported by our results was the genetic connectivity previously found in the Interlake and Bog regions in Manitoba and extending into southeastern Saskatchewan , but IBD was still moderate in the eastern part of the study area and within clusters with relatively high levels of landscape fragmentation . The strong level of IBD present within and among clusters suggests that dispersal is based heavily on geographic distance rather than social behavior that would support demographically and structurally independent groups. Because the boreal caribou ecotype is genetically different from neighboring barren‐ground and eastern migratory ecotypes (see Klütsch et al., The significance of IBD across the study area and within each genetic cluster supported the hypothesis that boreal caribou maintain a natural clinal pattern of genetic structure. Spatial autocorrelation persisted across a large spatial scale supporting previous telemetry‐based studies that reported relatively large home range sizes for the ecotype (McLoughlin et al., l et al.  that ideMEMGENE, a visualization approach based on the predicted values of a spatial regression rather than on cluster assignment, was able to confirm areas of genetic discontinuity in our study while further identifying finer‐scale genetic variation not detected by STRUCTURE. MEMGENE avoids the requirement that spatial genetic clusters are in HWE, which is central to the algorithm in the program STRUCTURE (Pritchard et al., 4.2Further evaluating IBD within a landscape genetics framework aided in explaining many of the spatial genetic discontinuities detected by STRUCTURE and MEMGENE while disentangling the confounding effects of IBR caused by landscape heterogeneity. For example, roads can help explain some of the genetic discontinuity detected between clusters, such as the highway running north between Clusters 1A and 1B in Saskatchewan and the high density of high‐use roads and highways in south‐central Saskatchewan surrounding Prince Albert National Park. Furthermore, the strong genetic differentiation in the Manitoba South range, known in this study as Cluster 2C, supports previous studies that found roads and waterbodies in this region to restrict boreal caribou dispersal (Ball et al., The contributions of both IBD and natural and anthropogenic landscape features in restricting gene flow were significant, suggesting that the relative importance of these processes in driving boreal caribou population structure can be difficult to partition. Disentangling the effects of IBD and IBR is difficult in regions presenting limiting resistance to gene flow (e.g., Ruiz‐Gonzalez, Cushman, Madeira, Randi, & Gómez‐Moliner, 4.3Analyzing multiple MU scenarios provided a comprehensive analysis of population‐level delineation based on patterns of genetic variation and revealed that current boreal caribou population units identified in the federal recovery strategy across our study area do not capture population genetic structure of the ecotype. Our analyses of genetic connectivity across the study area further confirmed that discrete population boundaries do not exist. Most evidently, the localized populations in the Manitoba North range have been found to be too genetically connected to be considered as independent herds. Also, range division of north and south Saskatchewan, based on ecozone boundaries, does not reflect the genetic connectivity results captured in our analyses. We therefore recommend that, together with other ecological factors affecting boreal caribou conservation and recovery (e.g., Environment Canada, 5We showed that IBD played a dominant role in shaping spatial patterns of genetic variation across the landscape. Landscape genetics was useful in explaining the genetic discontinuity detected and the contribution of natural and anthropogenic landscape variables in restricting gene flow. As a result, estimating the relative contribution of IBD and IBR on genetic differentiation helped characterize connectivity at and below the population level, revealing a baseline clinal pattern of genetic variation across the study area.Strong IBD results provided challenges for population‐level delineation under assumptions of spatial and demographic independence as defined for a local population in the federal recovery strategy. The combined resistance effect of the landscape variables evaluated here, and IBD, was not large enough to cause strong breaks in gene flow and higher level divergence that are needed to classify discrete populations or MUs (Palsbøll et al., None declared.MM and PJW conceived and designed the project. Data collection was organized by DH, MM, TT, and PDM. PP analyzed the data. Extension of MEMGENE methods and support for data analysis were provided by PG. Reagents, materials, and other tools for analysis were contributed by PJW. All authors contributed critically to drafts and gave final approval for publication.https://doi.org/10.5061/dryad.7k2g187Data available from the Dryad Digital Repository:  Click here for additional data file.


Oryza rufipogon Griff. is a perennial species of wild rice widely distributed along the channels and rivers of the Mekong Delta, Vietnam. This study attempted to find centers of diversity among wild rice populations in this area and their inter‐relationships. The highest genetic diversity was found in the Dong Thap population and the lowest in the Can Tho population. Maternal diversity evaluated using chloroplast INDELs detected ten plastid types, five of which were novel relative to other Asian countries. The mitochondrial genome suggested two unique deletions. One 699‐bp deletion via short tandem repeats was accompanied by another deletion including orf153. All accessions carrying the mitochondrial type were found in a particular plastid type. This unique maternal lineage was confined to specific channels where it showed vigorous vegetative growth in comparison to upstream areas where various maternal lineages and maximum genetic diversity occurred. This area along the Mekong Delta is a center of not only nuclear but also maternal diversity. The area is also rich in wild rice species such as O. rufipogon, O. nivara, and O. officinalis. The delta is also the biggest rice granary in the country, playing a pivotal role in food security and accounting for more than 50% of total production, making Vietnam the second largest rice exporter in the world  their distribution along the delta based on maternal lineage. It was anticipated that the results would provide insight into the natural wild rice resources in this area that could be useful for biological conservation as well as exploitation in rice breeding programs.The aims of the present study were to clarify (a) the genetic diversity of 22.1Wild rice accessions were collected from the upstream to downstream reaches of the Mekong River in Vietnam during the period 2010–2015. The samples were classed into four distinct populations according to the geography and ecology of the Mekong river, as well as local expert opinions. These were named the Dong Thap population of 25 accessions, the Intermediate population of 59 accessions, the My Tho population of 27 accessions, and the Can Tho population of 55 accessions Figure .A core collection derived from the National Bio‐Resource (NBR) Project in Japan  using the primers orf153f:GTCTAGGGCTTCATCTTATGCC (forward) and CTAAGAAATCAGTAGAAATCGGG (reverse) which makes a 460 bp amplified product in Nipponbare mitochondrial genome (NC_011033).The PCR conditions were preheating at 94ºC for 3 min, 30 rounds at 94ºC for 10 s, 55ºC for 30 s and 72ºC for 30 s, and 72ºC for 5 min. The PCR products were then subjected to 0.8% agarose gel electrophoresis in 0.5 × TAE buffer because of the expected size of amplicons.A wild rice accession from the Can Tho population was subjected to next‐generation sequencing to obtain resequencing data against the mitochondrial genome. Details of the NGS protocol have been reported previously . The amplified DNA fragments of both chloroplast and nuclear were mixed with a loading dye for electrophoresis on 6% denaturing polyacrylamide gel at 1,500 V for 2 hr in 0.5 × TBE buffer. The gels were then visualized by silver staining .Eight chloroplast INDELs (cpINDELs) developed in our previous study . Genetic distances among accessions, the numbers of alleles (Na), observed heterozygosity (Ho), and expected heterozygosity (He) were calculated.The data were subjected to principal component analysis using GenAlEx software  INDELs were genotyped. Only cpINDEL5 was monomorphic among all of the accessions collected in Vietnam. Remaining cpINDELs represented alternative genotypes except for cpINDEL3 carrying multiple alleles. These allelic combinations were used to identify different chloroplast types as plastid types , and the lowest in RM6947 (Na = 2) in the overall population. The observed heterozygosity (Ho) ranged from 0.054 to 1.000 among the 20 loci and from 0.60 to 0.644 among the populations. The Dong Thap population showed the highest diversity, He = 0.659, whereas the Can Tho population showed the lowest at He = 0.300. The He scores for the My Tho and Intermediate populations were He = 0.613 and He = 0.557, respectively. All accessions in the seven subpopulations from Can Tho shared the single genotypes over 20 loci. Twelve of the 20 examined loci were heterozygous, and the remaining eight were monomorphic. Vegetative propagation was inferred from the genotypes. The same genotypes were not found among other materials examined.Genetic diversity was estimated using 20 SSR markers Table . The higA phylogenetic tree was constructed based on a distance matrix generated with the 20 SSR markers Figure . As the 4O. rufipogon specimen collection along the Mekong River and attempted to know how they distribute along the river system by using maternally inherited markers. The unique genetic resource in O. rufipogon in the Mekong Delta yielded five novel plastid types, among which Type 15 was accompanied by a unique mitochondrial lineage showing marked rearrangement of the mitochondrial genome. The maternal lineage might have arisen upstream of the delta and become dispersed into the downstream area. The maternal lineage at Dong Thap, however, did not share the same nuclear genotype as those at Can Tho. One descendant belonging to the maternal lineage may have had the ability to form clones and occupy particular channels. Highly vegetative propagation and migration by drifting across canals may also have affected the structure of the Can Tho populations. This is the unique nature of wild rice inhabited along the Mekong Delta.Previous studies have attempted to characterize and exploit wild rice species in the Mekong Delta without investigating their origin, or clarifying genetic variations among them Buu, . The preOryza species are suspended in Vietnam, although accessions have been exploited for breeding programs to some extent. Meanwhile, wild populations have been seriously threatened and faced with extinction due to infrastructure development. Many subpopulations investigated in this study have been severely degraded by human disturbance; at least one wild subpopulation at site P78 has completely disappeared because of road construction. Although several wild species have been preserved at the CLRRI gene bank for ex‐situ conservation, the entire range of genetic variation has not been covered. Therefore, effective conservation management for O. rufipogon in upstream areas such as Dong Thap is becoming even more of an urgent priority. Our assessment of the genetic diversity would be available to collect valuable resources efficiently before they would be extinct.In contrast to the unique population in Can Tho, high genetic variation was found in the upstream area, at Dong Thap. This higher variation allowed a breeding program that successfully created AS996, carrying higher acid sulfate tolerance (Can & Lang, None declared.DTL and BCB managed core collection of perennial wild rice. DTL, BCB, NTL, IN, KT, and RI surveyed natural populations. DTL contributed to analyze with molecular markers. RI set up markers and genome analysis. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Molecular Biology and Evolution and in this issue of Genome Biology and Evolution suggests that we may have only begun to scratch the surface on the number of variations present in the genetic codes of all living organisms.Crack open a biology textbook and you will find a standard icon of molecular biology: the table summarizing the standard genetic code. This refers to the set of rules by which the cell “decodes” the information contained in DNA and “translates” it into the amino acids that make up proteins. For example, in virtually all organisms, the codon (3-letter DNA sequence) “AGA” tells the translation machinery to add the amino acid asparagine. Although a few deviations in the genetic code used by various organisms are known, research recently published in Molecular Biology and Evolution, a group of researchers including Emmanuel Noutahi, Virginie Calderon, Mathieu Blanchette, Nadia El-Mabrouk, and Bernd Franz Lang from the Universite de Montreal, Institut de Recherches Cliniques de Montreal, and McGill University published an analysis of the mitochondrial genomes of 51 green algae and land plants . Because of this, “Comparative/evolutionary bioinformatics procedures such as CoreTracker are now in a position to not only predict deviations of the genetic code but also provide clues with regard to the underlying mechanism.” Indeed, in light of their results, the researchers proposed that the genetic code deviations in the Sphaeropleales mitochondria actually contributed to their unusual genome organization. Based on this theory, after the migration of some mitochondrial genes to the nuclear genome during the genome reduction process, “UCA”  was reassigned to a termination codon. This would have made it impossible for additional mitochondrial genes to be transferred to the nucleus, resulting in a mitochondrial genome that was intermediate in terms of size.analysis, published in the current issue of Genome Biology and Evolution , and although changes to the tRNA repertoire, structure, and specificity may be inferred to some degree, biochemical confirmation is critically required.” This limitation was also pointed out by Eliáš, who noted that his group plans to employ proteomics methods to verify some of their bioinformatic predictions concerning the various presumably reassigned codons. “Unfortunately,” continues Eliáš, “nonstandard genetic codes are generally found in organisms that are difficult to study by direct biochemical or genetic approaches, so it remains a challenge to get a deeper understanding of the molecular mechanisms behind the observed changes in codon meaning.”Future investigations will almost certainly uncover additional, as-yet-unidentified changes in the genetic code in various organisms. Indeed, notes Eliáš, “We are also analyzing some exciting new cases of genetic code modification in nuclear genomes of certain obscure protists that were uncovered by our survey of publicly available sequence data.” Furthermore, the authors of the Noutahi et al. study point out that, given “how rapidly this field is evolving, due to an ever increasing number of reports on deviations from the standard genetic code—particularly, in eukaryotes and their organelles… the journey to understanding codon evolution with all of its mechanistic implications has just begun.”


As an example of infectious disease detection, HIV-1 p24 could be measured at 0.0065 IU/assay , and as a marker for a lifestyle-related disease, adiponectin could be detected at 2.3 × 10−19 moles/assay. In particular, despite the long-held belief that the trace amounts of adiponectin in urine can only be detected using a radioisotope, our ultrasensitive ELISA was able to detect urinary adiponectin. This method is highly versatile because simply changing the antibody enables the detection of various proteins. This assay system requires only the measurement of absorbance, thus it requires equipment that is easily obtained by medical facilities, which facilitates diagnosis in hospitals and clinics. Moreover, we describe an expansion of our ultrasensitive ELISA to a non-amplification nucleic acid detection method for nucleic acids using hybridization. These de novo methods will enable simple, rapid, and accurate diagnosis.For the diagnosis of disease, the ability to quantitatively detect trace amounts of the causal proteins from bacteria/viruses as biomarkers in patient specimens is highly desirable. Here we introduce a simple, rapid, and colorimetric assay as a de novo, ultrasensitive detection method. This ultrasensitive assay consists of a sandwich enzyme-linked immunosorbent assay (ELISA) and thionicotinamide-adenine dinucleotide (thio-NAD) cycling, forming an ultrasensitive ELISA, in which the signal substrate  accumulates in a triangular manner, and the accumulated thio-NADH is measured at its maximum absorption wavelength of 405 nm. We have successfully achieved a limit of detection of ca. 10 Examining patient specimens for the presence of proteins from pathogenic bacteria and viruses or as biomarkers enables rapid and accurate diagnosis, resulting in early treatment. Immunochromatography and enzyme-linked immunosorbent assay (ELISA) are commonly used methods for this purpose ,2. The sThe major challenge for protein quantification is that the proteins cannot be amplified like nucleic acids using polymerase chain reaction (PCR). We hypothesized that we could, however, amplify the detection signal of specific proteins. For the production of a user-friendly diagnosis system, we designed a method for amplifying the signals produced by the antigen-antibody reaction in ELISA. How can ELISA signals be amplified? One method is enzyme cycling . One of The present review describes the detailed mechanisms of an ultrasensitive ELISA for diagnosis. This ultrasensitive ELISA consists of sandwich ELISA and thionicotinamide-adenine dinucleotide (thio-NAD) cycling. In the thio-NAD cycling, thio-NAD is reduced to thio-NADH, and the accumulated thio-NADH is measured at an absorbance of 405 nm using a microplate reader ,7. ExampThe ultrasensitive ELISA that we developed consists of two parts: a sandwich ELISA and thio-NAD cycling  6,7]. I,7. I6,7]a is the turnover ratio of ALP per min; b is the cycling ratio of 3α-HSD per min; and n = minutes of measurement time. The amount of the target protein is determined by measuring the absorbance using a microplate reader at 405 nm, which corresponds to the maximum absorption wavelength of thio-NADH (exactly 400 nm). Therefore, the signals to be used for quantifying the proteins can be obtained in a short period of time.The first substrate applied to ALP in the sandwich ELISA was 17β-methoxy-5β-androstan-3α-ol 3-phosphate. ALP hydrolyzes this substrate to 17β-methoxy-5β-androstan-3α-ol, which was not measured in our system. The 17β-methoxy-5β-androstan-3α-ol was used as the substrate for thio-NAD cycling. 3α-HSD with the cofactor thio-NAD oxidizes 17β-methoxy-5β-androstan-3α-ol to 17β-methoxy-5β-androstan-3-one, resulting in thio-NADH. Then, 3α-HSD with the cofactor NADH reduces 17β-methoxy-5β-androstan-3-one to 17β-methoxy-5β-androstan-3α-ol by the opposite reaction, resulting in NAD. Thio-NADH accumulates in a triangular fashion expressed as follows:We show the typical protocol to detect a reference ‘adiponectin’ antigen using ultrasensitive ELISA as follows. This protocol was modified from Watabe et al. .1. Coat a primary antibody.2CO3 (pH 9.6) to a concentration of 2 μg/mL. Add 100 μL of the antibody into each well of 96-well microplates. Incubate for 1 h at room temperature.Dilute the first antibody with 50 mM Na2. Wash microplates.Wash the microplates 3 times with TBS including 0.05% Tween 20.3. Block nonspecific binding sites.Dilute the TBS including 10% BSA 10 times with TBS. Block nonspecific binding sites by filling wells with this solution at 300 μL/well. Incubate for 1 h at room temperature.4. Wash microplates.Wash the microplates 3 times with TBS including 0.05% Tween 20.5. Add an antigen.Dilute the antigen with TBS including 0.1% BSA to 5–40 pg/mL. Add 100 µL of this antigen solution to each well. Incubate overnight at 4 °C.6. Wash microplates.Wash the microplates 9 times with TBS including 0.05% Tween 20.7. Add an enzyme-linked secondary antibody.Dilute an enzyme-linked secondary antibody with TBS including 0.1% BSA and 0.02% Tween 20 to 10 pmol/mL. Add 100 µL of this antibody solution to each well. Shake the microplates for 1 h at room temperature.8. Wash microplates.Wash the microplates 9 times with TBS including 0.05% Tween 20.9. Add a thio-NAD cycling solution.Dissolve 1 mM NADH, 3 mM thio-NAD, 0.1 mM 17β-methoxy-5β-androstan-3α-ol 3-phosphate, and 30 U/mL 3α-HSD into 0.1 M Tris-HCl (pH 9.5). This is referred to as a thio-NAD cycling solution. Add 100 µL of this thio-NAD cycling solution to each well.10. Measure absorbance.Measure the absorbance at 405 nm and 660 nm with a microplate reader every 5 min for 1 h at 37 °C. The absorbance at 660 nm is used as a reference for background correction.For detection of the specimens obtained from patients , we always checked a spike-and-recovery test. The spike-and-recovery test is a technique for analyzing and accessing the accuracy of ELISA for particular specimen types. It is used to determine whether analyte detection can be affected by the difference between the diluent used for preparation and the experimental specimen matrix. To perform a spike-and-recovery test, a known amount of analyte  is added to a matrix . This ‘addition’ is called a ‘spike’. The concentration of the added analyte in the matrix is determined from standard curves prepared. The concentrations denote the spike recovered in the matrix. All the experimental procedures for this test were the same as for the above ultrasensitive ELISA experiments without the antigen solution.We determined the limit of detection (LOD), the minimum limit of quantification (LOQ), and coefficient of variation (CV) for the following sections. The experimental data were obtained by subtracting the mean value of the blank signals from each of the corresponding measured data points. The LOD was estimated from the mean of the blank, the standard deviation of the blank, and a confidence factor of 3. The minimum LOQ was estimated by the same method that was used for the limit of detection, but with a confidence factor of 10. The CV calculated from 3 data points was obtained for a reference antigen at a given concentration, and the value should be under 10%.As an example of a diagnosis of an infectious disease, we applied our ultrasensitive ELISA to detect a specific protein for diagnosing HIV infection . The fou−18 and 10−17 moles/assay), respectively [The LOD and minimum LOQ for HIV-1 p24 in our ultrasensitive ELISA was 0.0065 and 0.0242 IU/assay  can detect 101- or 102-order copies of nucleic acids [In our previous study, we compared the LOD between our ultrasensitive ELISA and a nucleic acid test (NAT) . The numic acids . That isAdiponectin is an adipocyte-derived vasoactive peptide . Serum a−19 moles/assay) and 2.7 pg/mL , respectively, for our ultrasensitive ELISA, when the molecular mass was assumed to be 300 kDa and the volume of the assay was 100 μL. Our ultrasensitive ELISA, therefore, succeeded in detecting urinary adiponectin at the subattomole level. We then attempted to determine the urinary adiponectin concentrations in healthy subjects and DM patients. The urinary adiponectin concentrations were corrected on the basis of the creatinine concentrations. The mean urinary adiponectin levels of healthy subjects were 3.06 ± 0.33 , and those of the DM patients were 14.88 ± 3.16 (ng/mg creatinine). That is, the urinary adiponectin levels were significantly higher in DM patients (p < 0.05) than in healthy subjects. Further, a threshold of urinary adiponectin levels could be set at 4.0 ng/mg creatinine to distinguish between healthy subjects and DM patients [The LOD of adiponectin and minimum LOQ were 0.81 pg/mL  international standard insulin reference or its equivalent products, is poor. As a practical manner, the use of recombinant human insulin is strongly advised from the standpoint of calibration traceability . When thes/assay . These v−19 moles/assay) [When we used a recombinant insulin reference  in our ultrasensitive ELISA, the LOD was 0.0047 pg/assay  . BecauseUltrasensitive detection of proteins involved in lifestyle-related diseases is important. Patients with these diseases, such as DM, must continuously provide blood samples for repeated testing. If the LOD of a specific protein can be improved for early diagnosis, it may obviate the need for blood sampling. Urine, saliva, and tears may include the same proteins, but in very small amounts when compared to blood. Thus, our ultrasensitive ELISA can be used for the noninvasive detection of proteins in urine, saliva, and tears, thereby alleviating patient pain and discomfort.We are now applying this ultrasensitive ELISA to detect specific proteins from active bacilli  and path−18 moles in the protein experiments is equivalent to approximately 106 molecules. We hypothesized that if we replaced the antigen-antibody reaction for proteins with the hybridization of nucleic acids, nucleic acid detection could be performed at the level of 106 copies in another new assay. In the planned system, we will prepare two different nucleic acid probes, similar to the two different antibodies in ELISA, one of which is used to immobilize the target nucleic acid sequence and the other for ALP labeling. Here, we should consider the size of ALP compared with that of the nucleic acid probes. The molecular mass of ALP is 80,000–150,000 Da. As expected, our preliminary experiments indicated that locating the ALP near the probes inhibits hybridization, resulting in poor sensitivity. We thus consider that a small spacer is needed between the probes and the ALP. We are now thinking that fluorescein isothiocyanate ((FITC), molecular mass 389 Da) could function as this spacer. Thus, the secondary probe is linked to FITC, and the FITC binds to an anti-FITC antibody that is labeled with ALP. Our preliminary data suggested that we could measure nucleic acids at 106 copies/assay. Further, by increasing the number of secondary probes, we expect to improve the sensitivity.The value of 10Why are we working to develop a new nucleic acid detection system with a sufficiently high sensitivity to replace PCR? We think that PCR has many critical drawbacks, including, e.g., (1) non-specific or false-positive amplifications, (2) target sample volume limits, (3) deactivation of amplification enzymes, (4) the complicated techniques, (5) the difficulty in designing probe sequences, and (6) expense. Non-specific or false-positive amplifications occur due to excess DNA input, long targets, or contamination. The sample amount used is ca. 1 μL, which means that at least 1000 copies must be included in a 1-mL volume. This low concentration may contribute to the production of false negative results. The deactivation of enzymes further deteriorates the amplification efficiency. Further, the PCR techniques are complicated. A new method that can handle a large volume and avoid amplifying the nucleic acids is therefore needed. We refer to our new nucleic acid detection system as a ‘non-amplification nucleic acid detection method’. This assay can be used not only in laboratories but also on-site in place of PCR.We developed an ultrasensitive ELISA comprising of a sandwich ELISA and thio-NAD cycling to detect trace amounts of proteins for the diagnosis of disease. This system is also suitable for single-cell analysis . Expandi


Triturus marmoratus) at 11 different locations in Western France. Samples were collected in different regions with contrasting agricultural landscapes . We found a strong genetic structure between the northern and southern sampling sites. Isolation by distance was recorded after 62 km, but within the northern region, little or no genetic structure was detected over large distances (up to 114 km). Genetic structure at shorter distance (43 km) was found between sites situated in landscapes with larger amounts of arable lands. A significant positive relationship was found between the pairwise genetic distance (Fst) between sites and the amount of arable land together with the distance between sites. Our results suggest that the Loire River might act as a corridor for the marbled newt, while arable land might act as a barrier. Finally, although a large city is located between sampling sites, no effect was detected on population structure.Amphibians are particularly sensitive to landscape fragmentation. Potential barriers between breeding sites can negatively influence the dispersal of individuals and increase genetic structure between populations. In this study, we genotyped 10 microsatellites for 334 marbled newts ( Among the principal causes of decline, habitat loss and fragmentation play an important role2. The latter can be defined as the reduction of overall available habitat, leading to the division of a favourable habitat into smaller patches surrounded by a matrix of non-favourable habitat4. Habitat fragmentation can be caused by various factors such as urbanisation, transport infrastructures, deforestation and agricultural activities5. It is well recognised that habitat fragmentation negatively affects populations, both at a demographic and a genetic level. For instance, decreasing habitat size has a direct, adverse effect on population size6. Fragmentation also results in lower migration rates between habitat patches7. In an isolated population, the low immigration rate of new individuals from other populations can lead to insufficient gene flow to maintain genetic diversity. This happens when isolation between populations is so strong that inbreeding and genetic drift are no longer counterbalanced by gene flow8. The resulting erosion of genetic diversity will negatively affect fitness and increase extinction risk9. It is therefore important to detect the most isolated populations so as to increase gene flow between populations and to preserve corridors.Amphibians are one of the most threatened taxa in the world, with 32.5% of the species ranked as vulnerable10. Migrations are the movements within the life cycle of an individual (e.g.: movement from wintering sites to reproduction sites) that happen annually, whereas dispersal involves one-way movements from one habitat patch to another. Because dispersal plays an important part in reducing genetic structure between populations, changes in landscape characteristics that greatly influence the dispersal of individuals can have strong negative impacts on populations2.Many amphibian species are particularly sensitive to habitat fragmentation because during their life cycle individuals must move between different habitat types. Amphibians can undergo two types of movements: migration and dispersal11. Related agricultural practices such as tillage, drainage of wetland areas, intense grazing and use of large quantities of pesticides have negatively impacted biodiversity by transforming the landscape and making it unsuitable for certain species12. Moreover, the need for high productivity resulted in the transformation of small fields into large open fields at the expense of hedgerows11. Because some animals use hedgerows as habitat and corridors14, their destruction induced significant habitat loss and fragmentation for many species13.In France, the agricultural landscape has changed significantly since the 1950’s. After the Second World War, the growing demand for resources induced a shift from small-scale to large-scale agricultural exploitations that has led to land use modification11 could negatively impact our study species, the marbled newt (Triturus marmoratus), which is mostly found in agricultural landscapes composed of conserved meadows, hedgerows15, and forest areas17. Its distribution area includes western France and the northern Iberian peninsula18. Despite being categorised as Least Concern by the IUCN, it is protected by Annex IV of the EU Habitats Directive and Annex III of the Bern Convention. The species is also locally endangered in central France19. The major threats for the marbled newt are degradation of its habitat , disease, non-native predators, and illegal capture for the pet trade. Like many amphibian species, marbled newt populations are declining19 and little is known about their population structure. Understanding the influence of landscape on the genetic structure of the marbled newt is important for taking conservation measures to restore gene flow and protect sensitive populations8.The destruction of hedgerows in favour of large fieldset al.20 found genetic structure for the marbled newt and the crested newt (Triturus cristatus) at a local scale (less than 10 km) whereas Prunier et al.21 found no genetic structure at larger scale (more than 10 km) for another large newt, the alpine newt . An important dispersal ability was also found for a small-bodied species, Lissotriton helveticus, that could rapidly colonise restored ponds in a forested landscape, resulting in a weak genetic structure22.Previous studies on newts reported different results at different scales regarding population structure and dispersal capacities of the marbled newt, or closely related species. Jehle 2. Depending on the studied species, the time needed to observe a change in population structure between different sites may vary. In our case, the landscape changed 70 years ago. Based on the results of Prunier et al.21 on a closely related species, the alpine newt, we estimate that this is enough time to detect the influence of agricultural landscape on population structure.Polymorphic genetic markers, such as microsatellites, are particularly useful in observing the influence of landscape characteristics on population structure. They allow us to study the relationships between different sites and estimate the connectivity between populations at different geographical scales23. We also expected that the agricultural landscape would negatively influence the dispersal of the marbled newt, resulting in a higher population structure.In the light of the previously cited results, we defined two main goals for this study: (1) to investigate the population structure of the marbled newt in the west of France at different scales , and (2) to test the influence of the agricultural landscape on population structure. Specifically, we expected the spatial genetic structure would be mostly driven by isolation by distance, but also to some extent by landscape structureet al.24). No potential null alleles were detected, and in total the data set had 4.75% missing data.Among the 10 selected loci, no significant linkage disequilibrium was found after Bonferonni correction , a significant deviation from the Hardy-Weinberg equilibrium was found after Bonferroni correction for four loci . No positive correlation was found between FAmong all 334 individuals distributed on 11 sites . While most of the sampling sites were assigned to a group with an average membership coefficient >0.9, three sampling sites  presented more admixture than the other sites with an average membership coefficient of 0.80, 0.61 and 0.74 for SW1, SW2 and SE2, respectively. Apart from these three sites a clear separation was revealed by STRUCTURE between the northern sites  and most of the southeastern sites   Fig. . Within E5) Fig. . No subsE5) Fig. . No cleaThe results from the DAPC scatter plot presented some similarities with the results from STRUCTURE. There was a clear genetic structure between three clusters. The first cluster gathered four sites , the second gathered the sampling sites SW1 and SW2 while the last gathered sites SE1, SE2, SE3, SE4 and SE5 , SE2 and SE4 , and SE4 and SE5   and with the northern sites   . With the DAPC analysis, NW2 has an intermediate genetic identity between the other northwestern sites (NW1 and NW3) and site NE1 (northeastern site).The STRUCTURE analysis revealed that the first cluster associated the northern group more with the southwestern group (SW1 and SW2). However, pairwise FRegarding the Southern cluster, we detected a population structure at a smaller spatial scale than for the northern cluster. STRUCTURE and DAPC found some genetic structure between SE3 and SE4 + SE5 even though those sites are 43 km apart.Groups for the analysis of molecular variance (AMOVA) calculation were defined according to the STRUCTURE results by creating six different groups: NW2 and NW3; SW1; NE1; SE2; SE3; SE4 and SE5. The results indicate that 84.01% of the genetic variance can be found within sampling sites, 2.47% among sampling sites but within groups and the remaining 13.52% is attributable to among-groups variability.The Mantel correlogram showed a significant positive correlation between genetic and geographic distance for the two first classes, 0 to 23 km and 23 to 62 km , indicating that populations separated by less than 62 km tend to be genetically similar. No significant correlation was detected for class 62 to 139 km. A significant negative correlation was detected for the two last classes, 139 to 177 km and 177 to 216 km  Fig. .Figure 4st was best explained by the model including the interaction: “amount of arable land” with “pairwise distance”. The 95% confidence interval did not overlap 0 for the interaction , and was therefore considered informative27. The effect of the interaction . Moreover, isolation by distance was detected for distance greater than 62 km.et al.23, also in Western France, for the marbled newt. For the expected heterozygosity, Jehle et al.23 found a minimum and maximum of 0.20 and 0.55, respectively, while we found a minimum and maximum of 0.36 and 0.54, respectively. For the observed heterozygosity, the minimum and maximum were respectively 0.15 and 0.47 for Jehle et al.23 and 0.27, 0.53 for our study. These results indicate a similar genetic diversity between the two studies.Observed and expected heterozygosity are similar to those found by Jehle At inter-regional scale, over all sampling sites, the STRUCTURE results showed that “two clusters” ΔK2 = 131.82) was the most likely scenario Fig. . However1.82 was st between northern and southeastern sampling sites ranged from 0.130 (between NW2 and SE2) to 0.319 (between NW3 and SE4) . This result supports the hypothesis that agricultural landscape could be a barrier to marbled newt dispersal and could increase isolation between sites.The presence of genetic structure between SE3 and the cluster including SE4 + SE5, situated 43 km away from each other, is in contradiction with the Mantel correlogram that showed a significant positive relationship between genetic and geographic distance up to 62 km . However, the results of STRUCTURE and the DAPC show that NW1 belongs to the same cluster as NW2 and NW3. Beyond the relatively important distance separating NW1 from the other two sites, the probable connectivity between those localities is interesting due to the presence of potential barriers to dispersal between those sites. A large urbanised area (the city of Nantes) and a large river (the Loire River)  in this study contrasts with the low migration and dispersal capacity of most amphibians51, hedgerows and forests exist. Such connection might facilitate movements and dispersal between reproduction sites, while forests are also used as a shelter against predation and desiccation17. Because of the low dispersal capacity of the marbled newt, a dense pond network could be needed to maintain genetic exchange at distances >10 km in areas with a large amount of arable land. Moreover, large rivers and cities did not seem to represent barriers to dispersal in our study. In fact, the Loire River seems to act as a dispersal corridor for the marbled newt and possibly also for other amphibians. A test to identify the direction of gene flow between our localities could provide an improved understanding of the influence of the river on newt dispersal. On the other hand, genetic structure was found in areas with large amounts of arable land. Therefore, presence of arable land seems to have a greater impact on the marbled newt’s gene flow than do large anthropic constructions, such as cities. In light of our results and of current habitat losses in the west of France, we recommend the following guidelines for the conservation of marbled newt populations: (1) to maintain a dense network of reproduction sites (suitable ponds in proximity of forested areas); (2) to protect hedgerow, meadow and forest landscapes and focus on the restoration of natural areas where intensive agricultural landscapes predominate; (3) finally, it could be beneficial to protect ponds close to rivers in order to promote long distance gene flow.Our results indicate that the survival and dispersal of marbled newt populations could be largely dependent on landscape type and the associated dispersal corridors available in the surrounding areas. Our study supports the idea that populations far apart might be genetically well connected, provided that good dispersal paths such as rivers2 and unseparated by fragmenting elements were considered as one location in the analysis in order to have a large enough sample size. This threshold was chosen to be more conservative than Smith and Green50, who estimated that population differentiation for amphibians mostly occurs at distances above 10 km.Samples were collected on 11 different locations in 34 different ponds distributed at a regional scale in Western France   were along the Loire Valley. The Loire Valley is dominated by a mix of large and small pastures delimited by hedgerows called “bocage”. This landscape is also characterised by a low proportion of large field crops (arable land): cereals, field corn and oil seed (as defined in Laurent52) Fig. . An exce52) Fig.  with a l53. Moreover, chytridiomycosis protocols were followed as advised by the French herpetological society (Société Herpétologique de France). No individuals were injured during capture and handling and all were successfully released after DNA sampling.All trapping and handling procedures were in accordance with the relevant guidelines and regulations and were approved by the appropriate authority, the Directions Régionales de l’Environnement, de l′Aménagement et du Logement (DREAL). Only non-invasive methods were used to collect DNA and individuals were released immediately after samplinget al.54. For each pond all captured individuals were sampled at once during the same session in order to avoid replication. Individuals were caught either with landing nets or Ortmann’s funnel traps55.DNA collection of epithelial cells was realised by buccal swab following Pidancier 56. To compare population structures, 10 microsatellite markers were used in this study. Nine were developed specifically for the marbled newt24 and one (Tcri27) was developed for the crested newt57. PCR and genotyping were performed by the Gentyane INRA platform . We analysed the genotyped data with Genemapper v4.0 (AppliedBiosystems™).DNA extractions were performed using the salting out protocol of Sunnucks and Hales58 and significance levels were adjusted with Bonferroni correction. A possible Wahlund effect for populations presenting a deficit in heterozygotes was tested for the correlation between Fis and Fst per locus. Fis and Fst per locus were obtained with the R package hierfstat v0.04-2259 implemented in R v3.3.260, then a linear regression was calculated in order to observe the relationship between both variables25. Expected heterozygosity (Hepx.), observed heterozygosity (Hobs.), and average number of alleles per locus were calculated with the software Genetix v4.05.261. The allelic richness (Ar.) was plotted using allele rarefaction curves, as calculated by the package PopGenKit v1.0 , implemented in R v3.2.262. After visual verification, the minimum sample size was set to 14 in order to have a good estimation of the number of alleles. Pairwise Fst and significance probabilities were obtained with the software Arlequin (Ver 3.5)63. Only sample sites (n = 8) with more than 20 individuals were kept for this analysis. This minimum was set according to Kalinowski64, as it give good results for Fst larger than 0.01. Fst significance was corrected for multiple testing with the Bonferroni method.Deviation from Hardy-Weinberg equilibrium was calculated using GENEPOP v4.226 using a burn-in period of 150,000 followed by 106 Markov chain Monte Carlo (MCMC) iterations. Default settings were used (Admixture model) for all parameters but for the LOCPRIOR option (the sample location was used as a prior), which was enabled as it tends to perform better than models without LOCPRIOR enabled65. For each analysed dataset, 10 replicate runs were performed for each value of the number of clusters (K) from 1 to 10. The most likely number of clusters (K) was determined using the calculation of delta K (ΔK) described in Evanno et al.66, as implemented by the web interface of STRUCTURE HARVESTER67, but also visually directly from STRUCTURE’s bar plots .3.426 us 68 Fig. . When th69, as a complementary analysis for population structure. This method applies a discriminant analysis on data previously transformed using a principal component analysis (PCA). DAPC optimises the genetic variance between groups while minimising the variance within groups, in order to show better separation of the different groups. For the PCA calculation, after visual interpretation, we decided to retain 35 PCs as it conserved 96% of the variance. Regarding the discriminant analysis, the first two eigenvalues were kept, after visual interpretation, as they were carrying most of the information. The results of the DAPC were gathered in a scatterplot, with individuals represented as dots and the different groups as inertia ellipses.Discriminant analysis of principal components (DAPC) was run using the dapc function in the R package adegenet63.The smallest clusters detected by STRUCTURE were used as predefined groups for the AMOVA in order to detail our population structure. The AMOVA was calculated with the software Arlequin (Ver 3.5)st/1 − Fst). The calculation was performed with the R package vegan , function mantel.correlog, with 9999 permutations and significance level <0.05 (adjusted for multiple testing with the Holm methods).Isolation by distance among populations was tested with a Mantel correlogram using the matrix of pairwise Euclidian distance (in kilometres) between sample sites and the matrix of pairwise standardised genetic distances between sampling sites (Fhttp://www.esri.com/arcgis/about-arcgis), a 10 km wide corridor between every sampling site. We then used the CORINE (Coordination of Information on the Environment) land cover data from Copernicus land cover monitoring services70 to record the different habitat types within each corridor. The amount (in percent) of different habitat types within each corridor was then calculated with the software FRAGSTATS (v4.2)71. In order to detect the influence of large field crops on genetic distance we used the proportion of arable land (code 21) within each corridor and accounted for the distance between sites. We chose arable land as it is the CORINE land cover habitat that best represents the large field crops and intensive agricultural activities. In our sampling area we only had one type of arable land, non-irrigated arable land (code 211), and so for the rest of the article we will refer to it simply as arable land. We then created a linear model with pairwise Fst as a response variable and proportion (in percent) of arable land, and pairwise distance as explanatory variables. We also included the interaction between the two explanatory variables in order to account for the combined effect of both pairwise distance and amount of arable land. The three candidate models were then compared using Akaike’s Information Criterion corrected for sample size (AICc)72 with the function model.sel from the R package MuMIn73. The model with the lowest AICc score was selected, and if ΔAICc was <4, the most parsimonious model was chosen. Finally, parameters that had zero in their 95% confidence interval were considered non-informative27. A graph presenting the results of the best model was then created using the R package ggplot274 (Fig. We tested the influence of the agricultural landscape on genetic distance by analysing habitat types between sampling sites. To do so we created, on Arcgis 10.4 (Esri, Redlands, CA, USA,