Neurergus kaiseri, within its entire range in southwestern Iran. We identified two geographic regions based on phylogenetic relationships using Bayesian inference and maximum likelihood of 779 bp mtDNA (D-loop) in 111 individuals from ten of twelve known breeding populations. This analysis revealed a clear divergence between northern populations, located in more humid habitats at higher elevation, and southern populations, from drier habitats at lower elevations regions. From seven haplotypes found in these populations none was shared between the two regions. Analysis of molecular variance (AMOVA) of N. kaiseri indicates that 94.03% of sequence variation is distributed among newt populations and 5.97% within them. Moreover, a high degree of genetic subdivision, mainly attributable to the existence of significant variance among the two regions is shown . The positive and significant correlation between geographic and genetic distances  following controlling for environmental distance suggests an important influence of geographic divergence of the sites in shaping the genetic variation and may provide tools for a possible conservation based prioritization policy for the endangered species.Species often exhibit different levels of genetic structuring correlated to their environment. However, understanding how environmental heterogeneity influences genetic variation is difficult because the effects of gene flow, drift and selection are confounded. We investigated the genetic variation and its ecological correlates in an endemic and critically endangered stream breeding mountain newt,  Distribution of genetic variation across animal and plant populations is under influence of the environmental heterogeneity which has long been recognized as an important factor in the evolution of fitness-related traits in the wild . Such spPopulation divergence generated by geographical or environmental isolation as a consequence of natural or anthropological variation are normally well documented in the form of demonstrating association between genetic variation and geographic or environmental distances ,2. HowevN. kaiseri is a poorly known amphibian that is restricted to the highlands of the Zagros Mountains in southwestern Iran. This mountain newt is endemic to the first order streams at elevations ranging between 800 and 1500 m a.s.l. [N. kaiseri has been evaluated as a critically endangered species by International Union for Conservation of Nature (IUCN) criteria [In recent years, combining genetic, geographic and ecological data have been used for various conservation purposes including delimiting or defining population distinctiveness of various types , mappingm a.s.l. . N. kaiscriteria . This spN. kaiseri populations exhibited genetic differentiation using traditional population genetics techniques , (2) examine on the ecological differences found between the regions associated with populations that show genetic differentiations and (3) associate the pattern of genetic differences with spatial differences to provide tools for a possible conservation-based prioritization policy for the Kaiser’s mountain newt.The main aims of this study were to (1) determine whether The permit for collecting toe clips from live Kaiser’s mountain newts in 10 of 12 known breeding streams was issued by Kermanshah Department of Environment. None of the breeding streams are located in any of the four sanctuaries  under legal control of the Iranian Department of Environment. Meanwhile, these streams which are located at high elevation in remote parts of Zagros Mountains are not owned by private sectors therefore access to these areas doesn't require any permit. No approval was obtained for ethical conduct of the present study because laboratory and field studies in Iran are not directed by law.This study was conducted across the entire species’ natural distribution range in southern Zagros Mountains in Iran encompassing a minimum convex polygon of 789 km2 . The locN. Kaiseri) from 10 out of 12 known breeding streams throughout the range of N. kaiseri in Iran . We used ArcGIS 9.3  and Google Earth  to calculate the area of the convex polygon encompassing the localities and to determine linear distances between localities and the breeding streams where newts were observed.™), following the manufacturer’s instructions . For genetic analysis we used Polymerase Chain Reaction (PCR) to amplify a 779 base-pair fragment of the mitochondrial (mtDNA) D-loop region using primers L-Pro-ML 5′-GGCACCCAARGCCAAAATTCT-3′ and H-12S1-ML 5′- CAAGGCCAGGACCAAACCTTTA-3′ [We extracted total genomic DNA from each sample using Tissue Kits (GenNetBioCTTTA-3′ . In the http://www.ncbi.nlm.nih.gov/BLAST/) and 779 bp were included for the final alignment. The D-loop region sequences from N. microspilotus, N. strauchii and Triturus karelinii as closely related taxa were used as out-groups. N. microspilotus has recently been considered synonymous with N. derjugini [The resulting sequences of the D-loop fragment were edited manually and automatically aligned using the BioEdit v. 5.0.9 program . The ideerjugini . Individerjugini  and Maxierjugini . The apperjugini  using AkIn ML analysis a starting tree was obtained by BIONJ and nodal support was estimated from 1500 bootstrap replicates. Bayesian Inference was performed for 5,000,000 generations. After discarding 5000 sampled trees, a consensus tree with posterior probabilities was generated and visualized using the FigTree v1.3.1 . In thisN. kaiseri in different populations, genetic distances, net average distances and standard error estimates were computed using Kimura 2-parameter [Additionally, genetic diversity was estimated for all populations based on haplotype diversity (h) and nucleotide diversity (π). Values for the numbers of polymorphic sites, parsimony informative site, and the mean numbers of pairwise differences among sequences and number of transitions and transversions were also calculated using the software DnaSP version 4.0  and Arlearameter  with 1,0arameter . UncorreT-Test (2- tailed). Molecular variance was assessed using separate analyses of molecular variance (AMOVA) with 10,000 permutations [N. kaiseri in Arlequin version 3.1 [CT), among populations within regions (θSC) and within populations (θST).To assess differences between northern and southern regions in nucleotide and haplotype diversities we used Independent-Sample utations  at seversion 3.1 . First twww.worldclim.org; [2 at the equator). Initially, the correlations among all 21 WorldClim bioclimatic variables and topographic variables for all locations were calculated to exclude the highly correlated ones (r>75), whilst keeping variables such as climatic averages and extremes. Second, we chose 12 of the bioclimatic variables to describe the ecological characteristics of the sampled stands. The 12 bioclimatic variables included averages, extremes and seasonal variation in precipitation and temperature, and the topographic variable altitude. The following 12 climatic variables were included in the final subset: annual mean temperature, isothermality, maximum temperature of warmest month, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, precipitation of wettest month, precipitation seasonality, precipitation of wettest quarter, precipitation of coldest quarter and elevation . The multivariate procedures provide an intuitive interpretation. We extracted environmental data at each occurrence point of the 10 sampling populations and two remaining localities resulting in a total of seven occurrence points for the northern region and five points for the southern region, in ArcGIS. The relative contribution of each environmental parameter to the formation of clusters was then represented in a PCA distance biplot, and the magnitude and statistical significance of each variable among the occurrence clouds in the PCA graph were assessed. We evaluated environmental variable importance by correlating each variable with axis scores from the PCA ordination.We performed principal components analysis (PCA) to further investigate ecological differentiation within ST) against geographic distance (in km) between pairs of populations [ST) and the matrix of environmental distances variables. In addition, a three-way Mantel test was applied between the matrix of pairwise genetic differentiation index (θST) and the matrix of environmental distances while accounting for geographical distances among populations. The significance level was assessed after 10,000 permutations as implemented in Arlequin version 3.1 [We tested the isolation by distance among the studied samples by plotting the matrix of pairwise genetic differentiation index  of N. kaiseri was different up to 8 nucleotide substitutions with the rest of haplotypes. Haplotype diversity values (h) ranged from 0 in DMA, DAR and CH, to 0.71 in TZ population (t-test (2-tailed) show that there wasn’t a significant difference in nucleotide  and haplotype  diversities between northern and southern regions. Pairwise uncorrected Kimura 2-parameter genetic distances between all populations (N. kaiseri is 1.00±0.4%.We identified 7 unique haplotypes  from a tpulation . Nucleotpulation . The aveulations  in diffeN. kaiseri (N. kaiseri forms a monophyletic lineage with respect to the congeneric species (N. microspilotus and N. strauchii) and Triturus karelinii. In the Bayesian tree  explained 86.66% and 5.31%, respectively, of the total variation and clearly separated the northern and southern localities along temperature and precipitation gradients and revealed a northern and a southern group , Fig 4. The analyzed populations revealed significant levels of isolation by distance  due to limited mobility over land in restricted ranges reported for other species of stream breeding amphibians [N. kaiseri with a very small distribution an important genetic variation exist which may have been shaped by isolation by distance rather than by ‘‘isolation by environmental distance”. There are reports of several species of salamanders with low mobility and strong fidelity to their breeding habitats with a genetic structure shaped under the influence of the Pleistocene glaciation [N. kaiseri could have been caused by isolation of populations in two refugia or changes in dispersal pattern of N. kaiseri under the influence of the adverse climatic conditions during the Pleistocene.The genetic analysis results in present study agree with the expectations of low genetic diversity within populations and high genetic differentiation between populations , precipitation of seasonality (B), precipitation of Wettest Quarter (C), and precipitation of Coldest Quarter (D). As is shown in As the climatic factors used in the principal components analysis  are highcompared . Fig 6 dN. kaiseri [Neurergus microspilotus a closely related species to N. kaiseri occurring in western Zagross Range [The detected IBD pattern in this study explained the genetic divergence better than the IBED pattern . Mantel tests across 10 populations detected low but significant correlations between genetic distance and geographic distance . However, similar correlation between genetic distance and environment distance was not significant . Such geographic isolation may partly be enforced by rough topography in a very steep and dry terrain causing limitation in gene flow among northern and southern population of  kaiseri . Limited kaiseri  includinss Range .N. kaiseri, our study found no clear and significant correlation between genetic and environmental variable. The absence of a clear genetic differentiation might suggest that high gene flow among newts in breeding streams is still maintained despite habitat differentiation. There are recent evidences that support such gene flow in amphibians because of their ability to migrate over longer distances than previously presumed (e.g. [N. kaiseri with high longevity of up to 14 years [2) with a small nearest neighbor distance 11.84 km [The Mantel tests showed positive, but statistically not significant, correlation between genetic and environmental distances for all populations. Contrary to our expectations of observing an effect of two disjunctive environments of more humid habitats at higher elevation (northern region), and drier habitats at lower elevations (southern region) on the spatial genetic structure of ed e.g. ,54,55). ,55. N. k14 years  living i11.84 km  avoid orN. kaiseri. Effect of habitat heterogeneity on the genetic structure of N. kaiseri cannot be ignored, but it is not strong enough to lead to a clear independent conclusion. Further studies based on more appropriate genetic markers and ecological studies are needed to better understand the underlying causes and mechanisms leading to the observed genetic structure. These studies should be based on larger sample size and use of more, already developed microsatellite loci [According to the results obtained from present study an association exists between geographic distance and population genetic structure in the Endangered Kaiser’s Mountain Newt, ite loci  to test This study demonstrates the clear reciprocal monophyly obtained from the D-loop region with the lack of shared haplotypes between southern and northern populations. Genetic structure determined in the present study shows a clear historical phylogenetic pattern supported by recent genetic exchange as evidenced by the distribution of haplotypes among populations within and between the regions. Positive and significant correlation between geographic and genetic distances following controlling for environmental distance suggests a possible impact of geographic divergence shaping the genetic variation. Combination of genetic analysis and environmental data demonstrates a clear ‘‘isolation by distance” pattern and has provided broad conservation benefits as a combined approach that can provide valuable information regarding the likelihood studies for re-introduction programs or designating a protected area for an endangered species.


Dallia (blackfish: Esociformes: Esocidae). The genus Dallia groups between one and three nominal species of small, cold- and hypoxia-tolerant freshwater fishes restricted entirely in distribution to Beringia from the Yukon River basin near Fairbanks, Alaska westward including the Kuskokwim River basin and low-lying areas of Western Alaska to the Amguema River on the north side of the Chukotka Peninsula and Mechigmen Bay on the south side of the Chukotka Peninsula. The genus has a non-continuous distribution divided by the Bering Strait and the Brooks Range. We examined the distribution of genetic variation across this range to determine the number and location of potential sub-refugia within the greater Beringian refugium as well as the roles of the Bering land bridge, Brooks Range, and large rivers within Beringia in shaping the current distribution of populations of Dallia. Our analyses were based on DNA sequence data from two nuclear gene introns (S7 and RAG1) and two mitochondrial genome fragments from nineteen sampling locations. These data were examined under genetic clustering and coalescent frameworks to identify sub-refugia within the greater Beringia refugium and to infer the demographic history of different populations of Dallia.Pleistocene climatic instability had profound and diverse effects on the distribution and abundance of Arctic organisms revealed by variation in phylogeographic patterns documented in extant Arctic populations. To better understand the effects of geography and paleoclimate on Beringian freshwater fishes, we examined genetic variability in the genus Dallia. Four of these genetic clusters are present in Alaska: (1) Arctic Coastal Plain genetic cluster found north of the Brooks Range, (2) interior Alaska genetic cluster placed in upstream locations in the Kuskokwim and Yukon river basins, (3) a genetic cluster found on the Seward Peninsula, and (4) a coastal Alaska genetic cluster encompassing downstream Kuskokwim River and Yukon River basin sample locations and samples from Southwest Alaska not in either of these drainages. The Chukotka samples are assigned to their own genetic cluster (5) similar to the coastal Alaska genetic cluster. The clustering and ordination analyses implemented in Discriminant Analysis of Principal Components (DAPC) and STRUCTURE showed mostly concordant groupings and a high degree of differentiation among groups. The groups of sampling locations identified as genetic clusters correspond to geographic areas divided by likely biogeographic barriers including the Brooks Range and the Bering Strait. Estimates of sequence diversity (θ) are highest in the Yukon River and Kuskokwim River drainages near the Bering Sea. We also infer asymmetric migration rates between genetic clusters. The isolation of Dallia on the Arctic Coastal Plain of Alaska is associated with very low estimated migration rates between the coastal Alaska genetic cluster and the Arctic Coastal Plain genetic cluster.We identified up to five distinct genetic clusters of Dallia. An inferred historical presence of Dallia across the Bering land bridge explains the similarities in the genetic composition of Dallia in West Beringia and western coastal Alaska. In contrast, historic and contemporary isolation across the Brooks Range shaped the distinctiveness of present day Arctic Coastal Plain Dallia. Overall this study uncovered a high degree of genetic structuring among populations of Dallia supporting the idea of multiple Beringian sub-refugia during the Pleistocene and which appears to be maintained to the present due to the strictly freshwater nature and low dispersal ability of this genus.Our results support a scenario with multiple aquatic sub-refugia in Beringia during the Pleistocene and the preservation of that structure in extant populations of The online version of this article (doi:10.1186/s12862-015-0413-2) contains supplementary material, which is available to authorized users. These fSequences were aligned using CodonCode Aligner version 3.0.3  and haplWe used two different methods for comparative purposes. For both of these genetic clustering algorithms we utilized individuals from which at least one nuclear intron sequence was available.adegenet R package [adegenet package [find.clusters function within adegenet. For each k of population clusters, find.clusters calculates the Bayesian information criterion (BIC) for the corresponding K-means for each k. The resulting plot of BIC scores for K-means of each k, aids identification of the most appropriate number of k groups by identifying the appropriate amount of parameterization for the data. The population clusters identified by find.clusters were used as prior assignments in DAPC [dudi.pca) and Discriminant Analysis  utilizing the packages ade4 [mass [The first method to evaluate population structure was Discriminant Analysis of Principal Components (DAPC) from the  package , 66. Unl package . The con package . To perf in DAPC , 67. DAPges ade4 –70 and me4 [mass .K) within Dallia were calculated for K = {1,…,8} in two independent runs. In STRUCTURE, we specified the admixture and correlated allele frequency ancestry and frequency models respectively. For each value of K we allowed the alpha parameter to be inferred. The two independent structure runs for each K were compared to each other to assess convergence and averaged for comparison to other values of K. For K = {1,…,8} the negative natural log likelihood of the probability of data given a particular K (-lnP (X | K)) was compared across K values to identify the optimum K. The value of -lnP (X | K) increased towards K = 4 then declined, indicating an optimum. Visualization of the output from STRUCTURE was done with Distruct [Our second approach to identify genetic clusters, was STRUCTURE version 2.3.3 , 73. UniDistruct .Dallia. Secondarily, we investigated nuclear signal to examine congruency between the two data types. We applied DAPC to the nuclear intron dataset alone to determine if and what genetic clusters are supported by the nuclear component of our dataset.As we expected much more variation in mitochondrial data, it was important to establish that the nuclear data provided some information regarding genetic relationships among sampled locations of Dallia were performed in Isolation with Migration (IM) [Dallia, we used a series of pairwise IM comparisons.Pairwise comparisons between genetic clusters of ion (IM) , 76 to et) at some time in the past for two populations. Migration rates between the two descendent populations were estimated (m1 and m2). The population genetics parameter θ, which combines effects of effective population size (NE) and mutation rate (μ),was estimated individually for three populations in each IM analysis: the ancestral population, and both descendent populations . We did not choose to estimate the change in population size (s parameter) in these IM analyses. Importantly, each parameter in IM is combined with mutation rate (μ) such that t = t (generations) * μ m1 = m /μ, θ1 = 4*N1 (effective population size 1) * μ.IM uses a MCMC algorithm to simulate genealogies based on a two-population and six-parameter model. We used IM to estimate the divergence time , but omitted those data from the clustering and demographic analyses presented in this study.The final dataset includes sequences of RAG1-I2 from 70 individuals, of S7-I1from 77 individuals, and mtDNA data  from 124The haplotypic phase of heterozygous individuals was resolved with high posterior probabilities (>0.90) for all but three individuals in the RAG1-I2 alignment and another set of three individuals in the S7-I1 intron alignment. We have used the highest probability phase for the following analyses and the robustness of assignment is confirmed by separate iterations of PHASE with different seeds. Ambiguous sites within an individual not having a phase posterior probability > 0.90 are coded as missing data for use in analyses requiring phase information such as IM and recombination test. For calculating summary statistics based on site-frequency spectra  with some individuals placed in this group from other geographic regions .DAPC applied to a nuclear intron dataset of eighty-three individuals total with nineteen individuals represented by only one locus supports three genetic clusters  to be peripheral. Under this model we conducted three pairwise comparisons between the Coastal Alaska population and each of the other three populations.Data from 19 sampling locations were grouped into five distinct genetic clusters as indicated by DAPC. We included the following four genetic clusters as populations in IM analyses : coastal Alaska, West Beringia, interior Alaska and Arctic Coastal Plain with sample sizes shown in Table ΘA. For the coastal Alaska and West Beringia and coastal Alaska and Arctic Coastal Plain comparisons a flat posterior probability distribution for ΘA is produced. In the coastal Alaska and interior Alaska population comparison, the posterior probability distribution of ΘA has a clear peak but the tail of the distribution extends past 200. While the t parameter appeared to converge, effective sample size (ESS) values for this parameter remained below 50 between all runs. Output from IM analyses is available as posterior distributions in Additional file Parameter convergence is possible for all parameters except θ x mean migration rate m)/2)  is consistently much larger than that for the populations with which it was compared which range from 0.22 to 0.62  populations.The estimated  4.10 to .68 is coΘ among genetically structured populations are available. The results of the study are important for better understanding the role of Beringia as an aquatic refugium during the climatic instability of the Pleistocene glaciations, the influence of glaciations on freshwater fish genetic diversity, and the role of the Bering land bridge in aquatic migration.The study was undertaken to evaluate patterns of survival and dispersal within Beringia during the Pleistocene and any role Pleistocene climatic instability may have had in diversification of this genus. Our results have demonstrated unique findings at a higher resolution within Beringia than other studies of freshwater fishes. We provide evidence for sub-structuring within the greater Beringia refugium, and with combined nuclear and mitochondrial data, estimates of migration rates and Dallia consistent with biogeographic barriers such as the Bering Strait and mountain ranges. Subdivision of Dallia within Beringia into up to five different genetic clusters was supported by DAPC. The results of DAPC and STRUCTURE generally identified divisions among sampling locations in a congruent manner, the exception of the Nome sample location (sample location 14), which the DAPC analysis resolved as a distinct genetic cluster. The lack of support in STRUCTURE for more than four populations is likely a result of haplotype coding instead of SNP coding which was used in DAPC, perhaps resulting in an inability to further resolve population subdivisions. Nonetheless, both STRUCTURE and DAPC consistently supported a North-South Beringia divide in population structure. The Arctic Coastal Plain Dallia are genetically distinct from those found south of the Brooks Range, which has also been observed in other Beringian fishes and had been hypothesized for Dallia [Dallia, corroborating further subdivision of the greater Beringian refugium beyond a North-South Beringia divide [Thymallus arcticus), mitochondrial and microsatellite DNA identified two refugia within Beringia- North and South Beringia though fewer sampling locations were used [Dallia in this study. The south Beringian stock of Stamford and Taylor [Dallia. Likewise, studies of whitefish (Coregonus spp.) largely appear to identify Beringia/Alaska as a refugium, [e.g. C. nasus) appear to form a discrete group with the exception of those from the Arctic Coastal Plain, which are more similar to MacKenzie River populations in Northern Canada. This North Beringia and South Beringia divide is apparent in phylogeographic studies of freshwater fishes, but the further refinement of sub-refugia in Beringia in freshwater fishes this paper has not been demonstrated before.The two genetic clustering algorithms used as population boundary identification methods (DAPC and STRUCTURE) provided evidence of strong population structure across the current geographic range of r Dallia , 54, 77.a divide  presenteere used . The Nord Taylor  was assoum, e.g. , 13. AlaDallia does not represent survival of lineages in separate sub-refugia but rather indicates isolation of lineages into different areas after dispersal from the same refugium. Indirectly, the restricted distribution of Dallia, low dispersal ability of the genus and occupation of areas that were nearly entirely unglaciated during the last glacial maximum argue for little post-glacial range expansion. One possible direct approach to address this issue is to estimate the divergence times between interior Alaska and coastal Alaska genetic clusters as populations with the divergence time (t) parameter in IM analysis. If the time of divergence between populations were greater than the last glacial maximum, it would indicate that populations predated the last glacial maximum and did not originate from the same refugium. However, the ESS of t remained low even the run lengths were increased substantially and IM operating and run parameters were adjusted, indicating poor ability of our dataset to support robust estimates of time of origin. Estimates of intrapopulation coalescent times conducted previously with mitochondrial data for Dallia phylogeographic units, which are highly congruent with genetic clusters identified in this study, argue for Dallia persisting through glacial cycling in more than one region south of the Brooks Range [An important possibility is to consider if the apparent genetic structuring of ks Range .The presence of “refugia within refugia” in the Iberian Peninsula are known to confound phylogeographic analyses, leading to invalid conclusions regarding inferred refugia . Consequa priori expectations based on large gaps in the distribution of suitable habitat between Arctic coastal plain and Bering Sea drainages. The Yukon and Kuskokwim rivers promote asymmetrical migration in a downstream fashion between the interior Alaska and coastal Alaska populations. The higher rate of downstream migration is compatible with the low dispersal ability expected from Dallia.Estimates of migration rates  between genetically-delimited populations appear to be mostly congruent with features of the landscape, with variation among migration rates associated to migratory barriers or conduits. Mean migration rate estimates between Coastal Alaska and Arctic Coastal Plain populations of 0.12 to 0.52 individuals a generation indicate restricted movement of individuals around the Brooks Range. This inference is consistent with Dallia are currently moving across the Brooks Range or the Bering Sea, however the migration rates from IM are integrated across time since the populations’ divergence. The estimated migration rates between populations that appear to cross impassable barriers are most likely evidence of historically higher migration around or across the current barriers of the Brooks Range and Bering Sea. Glaciation cycle-mediated changes in sea level and precipitation may be expected to produce changing levels of connectivity between adjacent drainages for populations of Dallia, which depend on highly connected coastal plain or wetland hydrological networks to move between adjacent river drainages. Even considering the fact these measures are averages, the very low migration rates of less than one individual per generation present in the Arctic Coastal Plain to coastal Alaska comparison are low enough to allow genetic differentiation under the island model of migration [In the Arctic Coastal Plain to coastal Alaska and the West Beringia to coastal Alaska population comparisons, the IM model produces estimates of migration across what should be impassable barriers for this species. We should consider the results of a general model like IM from a realistic viewpoint. It is very unlikely that igration .Dallia across Beringia. The fossil record for Dallia suggests that prior to the Pleistocene glaciations this genus had a much larger range. Two fossils have been recovered extending the historical range of Dallia in both East and West Beringia. In East Beringia, a fossil assigned to Dallia was recovered from Homer, Alaska on the Kenai Peninsula from the Late Miocene [Dallia population and 200 kilometers from the nearest Dallia population on the Alaska Peninsula across an ocean strait [Dallia were extirpated from parts of Siberia during the heavy glaciations of the Illinoan [Paleoclimatic instability affected the distribution and sizes of populations of  Miocene . The fosn strait . In Westn strait . It is hIllinoan .Dallia was constricted by paleoclimatic instability, from which Dallia did not re-expand at the end of the Wisconsinan glaciations. The same pattern of range reduction during the Pleistocene occurred in the Pacific Northwest in Novumbra, an ecologically similar relative of Dallia [Novumbra has one extant species, and while fossil evidence shows the genus was once more widespread in the Pacific Northwest, Novumbra currently occurs only in Western Washington State, USA as a result of climatic changes during the Pleistocene [While post-Pleistocene range expansion is typical for many fish species via pro-glacial lake mediated dispersal , it appef Dallia . Novumbristocene , 84.Dallia may have survived in higher numbers and were able to migrate more effectively due to the generally much wetter conditions and presumably increased aquatic connectivity [Dallia in many parts of its range leading to isolation and reduction in population sizes and genetic variability. Sea level retreat on the other hand opened up more habitat along the coasts of Asia and North America. In particular, the Bering land bridge contained large rivers and associated deltas with low gradient and more rainfall than other parts of Beringia [Dallia habitat of the Bering land bridge and is central in the current distribution of the genus. Therefore, the large estimated θ for the coastal Alaska population relative to the other populations suggests that the Bering land bridge and immediately adjacent parts of Alaska in the Yukon-Kuskokwim Delta region historically were and continue to be a center of Dallia genetic diversity.During times of moderate conditions during interglacial periods ectivity . ChangesBeringia , 85. Theθ than that estimated for contemporary populations, but we were unable to generate reliable estimates of ancestral θ in this study for all comparisons. A lack of shared variation between the coastal Alaska and Arctic Coastal Plain populations may prevent convergence of the ancestral θ parameter. However, in the case of the coastal Alaska and West Beringia comparison and coastal Alaska and interior Alaska comparison, insufficient data is present to estimate this particular parameter completely.Our analysis supports a much larger ancestral population Dallia are intriguing. Examined populations of D. pectoralis have been found to display spatially segregated karyotypic variation and to have markedly different growth and maturation rates. Karyological data show that sample location 12  and sample location 20  originating from D. pectoralis sampled within Alaska differ in chromosome number and variability of chromosome number among cells in an individual [Dallia in Russia applied to individuals of D. pectoralis from sample locations 12 and 20 did not show any significant differences [Two aspects of biological variation among populations of dividual . The diadividual  used to ferences .D. pectoralis were found to be comprised of distinct mitochondrial lineages with only one shared haplotype in one individual identified [Dallia to adapt to different conditions [Dallia to be quite high with nuclear and combined nuclear and mitochondrial data. Our data indicate private alleles in Arctic Coastal Plain Dallia and very low migration rates between the Arctic Coastal Plain and coastal Alaska populations.Further, interior Alaska and coastal Alaska entified  and evidentified , 50 whicnditions . The mitnditions  did indinditions . In thisDallia. First, morphological variability of Dallia across its range is poorly described. Therefore, reported differences may represent within species variability. Second, morphological variation in Arctic fishes can be attributed to rapid adaptation following glacial retreat. Parallel evolution in different phylogenetic lineages can produce convergence on similar ecotypes (i.e. benthic vs limnetic) in short order. Any observed morphological variability could have arisen on a very short  time scale and does not necessarily correspond to persistence in glacial refugia over hundreds of thousands or more years. The severity of the Illinoan glaciations also supports that Dallia survived only in East Beringia, which would not support separate refugia in Chukotka for the genus at deep time scales [The lack of morphological support for the observed karyological differences and the small sample sizes examined by Balushkin and Chereshnev  in descre scales . Third, Dallia with genetic data. However, our inability to determine nuclear loci sequences from D. admirabilis limits our ability to test the hypothesis of a species boundary between D. admirabilis and D. pectoralis. Regardless, genetic divergence among genetic clusters of Dallia evaluated through Fst is high in this study, either in STRUCTURE or DAPC defined values. Preliminary microsatellite data indicates that interior Alaska and coastal Alaska samples show significant genetic differentiaion [Dallia are the most divergent from ancestral allele frequencies, with DAPC Arctic Coastal Plain Dallia are not the most distinct from other clusters. It appears that Arctic Coastal Plain Dallia represent at the very least a distinct and isolated population of D. pectoralis.An original motivation for this study was to better delineate species of entiaion . Under tDallia examined in this study are similar genetically across the present Bering Sea . Unfortunately our sampling does not fully encompass the putative species level diversity in the genus. Eastern Chukotka hosts all three nominal species of Dallia [Evidence from migration rates indicates that the Bering land bridge served to connect eastern Chukotka with western Alaska. Paleoclimatic instability served to facilitate intercontinental exchange instead of generating species divisions between continents within this fish genus. The populations of f Dallia , 54 but Dallia persisted in Beringia in up to five distinct areas through the most recent glacial oscillations. The pattern of genetic structuring of Dallia indicates that (1) the Bering land bridge provided connectivity between Asia and North America for Dallia and (2) divergences within Alaska as opposed to across the Bering Sea are greater in this putatively primary freshwater fish. The genetic subdivisions of Dallia also indicate that Beringia contains several potential sub-refugia. This demonstration of several sub-refugia within Beringia for a freshwater fish indicates that further phylogeographic studies including Beringia should take steps to avoid being misled by potential diversity within Beringia.Overall, our results support the hypothesis that populations of All nucleotide sequences determined for this study have been submitted to GenBank under accessions KP411389 - KP411458 and KP411459 - KP411534. Previously determined sequences from mitochondrial DNA are available on GenBank as accessions JX961713–JX962051.


Plasmodium falciparum infections using seasonally-driven effective reproduction rates for clonal propagation, external importation, and the outcrossing of strains both within and between infections. We explore quantitatively the relationship between transmission intensity and genetic observations, for example the repeated observation of identical strains and their persistence across successive transmission seasons. Extending to a spatially connected network of human and parasite populations, we model the sensitivity of genetic sequencing to identify the relative contributions of local hotspots versus re-importation in sustaining transmission in pre-elimination settings. Finally, we model the effects of local transmission intensity and anti-malarial drug pressure on the population-level genetic signatures of emerging drug resistance.A model of malaria transmission dynamics is presented that tracks the genetic barcodes of individual


Ziziphus is a genus of spiny shrubs and small trees in the Rhamnaceae family. This group has a controversial taxonomy, with more than 200 species described, including Chinese jujube (Ziziphus jujuba Mill. var. jujuba) and Indian jujube (Z. mauritiana), as well as several other important cultivated fruit crops. Using 24 SSR markers distributed across the Chinese jujube genome, 962 jujube accessions from the two largest germplasm repositories were genotyped with the aim of analyzing the genetic diversity and structure and constructing a core collection that retain high genetic diversity. A molecular profile comparison revealed 622 unique genotypes, among which 123 genotypes were genetically identical to at least one other accessions. STRUCTURE analysis and multivariate analyses (Cluster and PCoA) roughly divided the accessions into three major groups, with some admixture among groups. A simulated annealing algorithm and a heuristic algorithm were chosen to construct the core collection. A final core of 150 accessions was selected, comprising 15.6% of the analyzed accessions and retaining more than 99.5% of the total alleles detected. We found no significant differences in allele frequency distributions or in genetic diversity parameters between the chosen core accessions and the 622 genetically unique accessions. This work contributes to the understanding of Chinese jujube diversification and the protection of important germplasm resources. Ziziphus jujuba Mill. var. jujuba), which belongs to the buckthorn family (Rhamnaceae), is an important deciduous fruit tree that is typically grown in temperate and subtropical areas. It is indigenous to the middle and lower reaches of the Yellow River of China and was first domesticated 7,700 years agoChinese jujube (Z. jujuba Mill. var. spinosa (Bunge) Hu ex H. F. Chow)Chinese jujube originates from its wildtype, sour jujube , which have been used for linkage map construction (unpublished data) and genetic diversity estimations4Traditionally, plant cultivar differentiation was based on morphological characteristics and pedigree information. However, morphological descriptions can possess limitations, as morphology can be influenced by environmental factors and requires skilled assessmentManagement of large germplasm collections is often costly, time-consuming, and labor-intensive, limiting the breeding capacity and in-depth explorations of the germplasms. Because these collections often contain redundant accessions, so it is urgent to build a core collection, which, as the representative germplasm resources of the entire germplasm collection, preserves the maximum genetic diversity and minimum repetition of a crop speciesHere we aimed to (1) develop molecular fingerprints and determine the genetic redundancy in the germplasm accession collections, (2) identify the genetic relationships among these accessions, (3) evaluate the level of genetic diversity in the collections, and (4) construct a suitable core collection to be used as a germplasm resource.Twenty-four SSR loci were employed to identify unique genotypes among the 947 diploid accessions. In total, 622 distinct SSR genotypes were detected in the collections . In totaAll 24 SSR loci successfully amplified polymorphic and reproducible alleles in 622 genotypes. The 24 SSRs yielded high discriminating capacity, as deduced from the low cumulative identity probability (PI) of 8.6E-19 . PrivateA total of 215 alleles were detected in the 622 unique diploid genotypes, ranging from 3 alleles at BFU0584 and BFU0733 to 21 alleles at BFU0308, with a mean of 8.96 alleles per locus. The Ne value ranged from 1.35 alleles at locus BFU0521 to 8.97 alleles at locus BFU0308, with an average of 3.15 alleles per locus . The allOf the 947 diploid accessions, 479 were from the Cangzhou repository and 468 were from the Taigu repository. As for the 622 distinct genotypes, Cangzhou and Taigu repositories possessed 362 and 315 genotypes, respectively . Thus, CThe Cangzhou and Taigu repositories exhibited 196 and 176 alleles, with averages of 8.17 and 7.33 alleles, respectively . The numThe mean expected heterozygosity (He) and observed heterozygosity (Ho) were 0.60 and 0.64 for Taigu, whereas the average values were 0.58 and 0.62 for Cangzhou. The mean PIC in Taigu (0.56) was similar to that of Cangzhou (0.53) .In the absence of clear-cut origins of the accessions, a non-stratified strategy was adopted for the genetic structure analysis. Our results showed a clear peak for ΔK at K = 3 , where aStructure suggest moderate genetic differentiation, as indicated by the global Fst value of 0.11 (P < 0.01).Statistical analysis indicated that the percentage of genotypes with a membership coefficient ≥90% was 63.83%. A total of 83.28% of genotypes exhibited a membership coefficient ≥80%, and only 3.38% of the accessions exhibited a membership coefficient of 5% or less. Based on standard permutation tests of the full data set, the groups defined by A principal coordinate analysis (PCoA) roughly divided the 622 unique accessions into three clusters . PrincipStructure results, with the exception ofclade 2, in which a few accessions were assigned to group 3.The dendrogram divided the 622 diploid accessions into three major clades . OverallTo determine the optimal core size, 21 sampling percentages from the whole collection were designed, combined with two sampling strategies. As illustrated in In the present study, we constructed an integrated applied core collection for Chinese jujube that includes 20 retained accessions with applications to genetic research and breeding programs, all of which were chosen based on fruit cracking, fruit size, fruit shape, and commercial importance in the jujube industry . Then, aThe mean values of Na, Ne, Ho, He, and PIC from the core collection were greater than or equal to the 622 diploid accessions . HeterozZiziphus jujuba Mill. var jujuba accessions. Now, we interpret our results with regard to genetic diversity and the causes of the genetic redundancy. The present status of genetic structure is briefly discussed. In addition, we further explain the efficiency of the strategy used to construct the core collection. Genetic redundancy is an important issue in plant genetic resource management. The identification of duplicates is important in germplasm repositories, particularly when considering the construction of core collections.The aim of this work was to identify the genetic diversity, genetic structure, and a core collection of et al.Different rates of duplication have been extensively reported in soybean18The study evaluated the genetic diversity of a large Chinese jujube collection (622 unique diploid genotypes), representing the largest and most extensive study of this species to date. The number of alleles per locus (mean = 8.96) was much higher than that detected in 76 major Chinese jujube cultivars (mean = 5.70)CitrusDiospyros kaki ThumbCastanea crenataIn accordance with our previous studies, which revealed the mean values of Ho (0.678) and He 0.621) using 31 SSRs using 31Structure analysis showed that the grouping was largely consistent with the UPGMA clustering  and the National Foundation for Improved Cultivar of Chinese Jujube, Cang County, Hebei Province (Cangzhou)  . The samr jujube . All colA set of 24 SSR loci scattered throughout the genome were selected on the basis of their polymorphism and reproducibilityPCR products were analyzed via capillary electrophoresis using an ABI 3730XL DNA Sequencer . Alleles were identified using the GeneMarker v 1.75 software package .Microsatellite alleles were corrected using FlexiBin v 2Structure 2.3.349Structure, including the genotype numbers of each cluster, major allele frequency, number of alleles, genetic diversity, and polymorphic information content. An unweighted pair group method with an arithmetic mean (UPGMA) dendrogram was constructed using PowerMarker v 3.25A Bayesian clustering analysis was implemented in Given the absence of detailed genetic information about the accessions, a non-group-based strategy was adopted. Four steps were executed: (1) Twenty-one core collections corresponding to different scales were constructed to identify the optimal core collection size . Five independent runs were then repeatedly performed for each core selection by using two algorithms, including simulated annealing algorithm and random algorithm, implemented in PowerMarker to determine the optimal core collection size. (2) Several accessions that are agronomically  and commercially important  were selected as the retained accessions  was used to assess the final core collection by performing Levene’s test and T-test for Na, Ne, Ho, He, and PIC values between the core and the entire unique collection. The comparison of allele frequency were carried out with Microsoft Excel .How to cite this article: Xu, C. et al. Identifying the genetic diversity, genetic structure and a core collection of Ziziphus jujuba Mill. var. jujuba accessions using microsatellite markers. Sci. Rep.6, 31503; doi: 10.1038/srep31503 (2016).


Brucellosis is the most common bacterial zoonosis, and serological tests are routinely used in brucellosis control and eradication programs. In order to improve the accuracy of serological diagnostic method used in bovine brucellosis detection, this study developed an improved competitive ELISA with higher specificity and good sensitivity.Brucella lipopolysaccharide. One monoclonal antibody 3 F9, presented C epitope specificity, was used to develop a competitive ELISA for the serological detection of bovine brucellosis. The competitive ELISA, a commercial competitive ELISA kit, the rose-bengal plate agglutination test, and a microplate agglutination test were all used in the detection of 6 hyperimmune antisera against other commonly cross-reacted bacterial pathogens and 110 clinical bovine serum samples. The results of the test comparisons indicated that the competitive ELISA had higher specificity than the commercial competitive ELISA kit and RBT, and comparable sensitivity with the commercial ELISA kit.This study prepared 12 monoclonal antibodies against smooth This study provided a valuable detection tool with high specificity and good sensitivity, which prevent the wrong-culling of bovines in the eradication campaigns of bovine brucellosis.The online version of this article (doi:10.1186/s12917-015-0436-3) contains supplementary material, which is available to authorized users. Brucella genus, which infect a wide range of mammals, including dogs, ruminants, humans, and marine mammals. Within the last few years, brucellosis has re-emerged, presenting severe public health challenges and major economic burdens globally  / OD [buffer control] × 100).The microplates were coated with heat-killed whole-cell Brucella infection, which were previously confirmed negative through the RBT and a microplate agglutination test (MAT), were used to determine the PI cut-off value that was designed as the mean PI of negative sera + 2 standard deviations (SD), in order to ensure that 95 % of PI values for the negative sera fell within this range.Sixty-three bovine serum samples from cattle without a history of E. coli O157, S. gallinarum, P. multocida, Y. enterocolitica O:9, F. tularensis LVS, and O. anthropic ATCC49188. PI for these antisera were also calculated.To evaluate its specificity, the cELISA was used to detect polyclonal antisera against Brucella-Ab C-ELISA, Sweden), RBT, and MAT. The ELISA kit was used in the manner as recommended by the manufacturer. The RBT was operated as previously described [The 6 polyclonal antisera mentioned above and 110 bovine serum samples were diagnosed using the cELISA, a commercial cELISA kit (SVANOVIR® escribed . The MATescribed , but witSPSS v20.0 software was used to analyze the result of a normality test of the negative sera PI values using a Shapiro-Wilk test. The software was also used to analyze the degree of agreement between the cELISA, a commercial kit, RBT, and MAT by kappa statistics.


Populus species P. alba (White poplar) and P. tremula (European aspen) and their hybrids as a model, we examined levels of inter- and intraspecific variation, heritabilities, phenotypic correlations, and the genetic architecture of 38 compounds of the phenylpropanoid pathway measured by liquid chromatography and mass spectrometry (UHPLC-MS). We detected 41 quantitative trait loci (QTL) for chlorogenic acids, salicinoids and flavonoids by genetic mapping in natural hybrid crosses. We show that these three branches of the phenylpropanoid pathway exhibit different geographic patterns of variation, heritabilities, and genetic architectures, and that they are affected differently by hybridization and evolutionary constraints. Flavonoid abundances present high species specificity, clear geographic structure, and strong genetic determination, contrary to salicinoids and chlorogenic acids. Salicinoids, which represent important defence compounds in Salicaceae, exhibited pronounced genetic correlations on the QTL map. Our results suggest that interspecific phytochemical differentiation is concentrated in downstream sections of the phenylpropanoid pathway. In particular, our data point to glycosyltransferase enzymes as likely targets of rapid evolution and interspecific differentiation in the ‘model forest tree’ Populus.The mechanisms responsible for the origin, maintenance and evolution of plant secondary metabolite diversity remain largely unknown. Decades of phenotypic studies suggest hybridization as a key player in generating chemical diversity in plants. Knowledge of the genetic architecture and selective constraints of phytochemical traits is key to understanding the effects of hybridization on plant chemical diversity and ecological interactions. Using the European  Plant secondary metabolites—chemical compounds not directly involved in plant growth, development, and reproduction—have a variety of roles, ranging from pigmentation to protection from ultraviolet (UV) radiation to defence against insect herbivores –4. TheseThe evolution of plant secondary metabolite diversity represents a topic of considerable interest in evolutionary biology and plant science –8. In thPopulus species, these proportions of traits shared between parental species and natural hybrids  arerations; . While tPopulus.Plant secondary metabolism is postulated to have emerged from primary metabolism in response to environmental pressures. It is thought that gene duplication, neofunctionalization, metabolic diversification, and convergent evolution have all contributed to its emergence and expansion during the evolution of plant lineages , 7, 25. Populus alba (White poplar) and P. tremula (European aspen), two ecologically divergent Eurasian species that form part of a species complex in section Populus of genus Populus  calculated with Restricted Maximum Likelihood (REML) in SPSS Statistics 20. This approach accounts for the unbalanced design resulting from variable numbers of individuals available for each family [The genetic component of phenotypic variation in the abundances of targeted phenylpropanoid metabolites was estimated by two different methods: (1) Mantel correlations between pairwise genomic similarities  and phenotypic similarities in natural populations in the absence of pedigree information , based oh family . As micrh family , the oriin situ genetic mapping (‘admixture mapping’). The mapping panel was identified based on the genetic admixture proportions Q (below) among >800 trees genotyped in our laboratory. The genotypic composition of the mapping panel was estimated with 77 DNA microsatellite markers from all 19 chromosomes of Populus [Natural hybrid zones are considered as useful tools to study the genetic basis of trait differences between diverging populations and species , 47. The Populus . Althoug Populus , we chos Populus , 50.The admixture mapping approach applied in this study is based on linear regression analysis and model selection . In shorAs an indication for potential long-term selective pressures acting on these genome regions, departures from neutrality of marker loci associated with phytochemical QTL were examined in the form of interspecific heterozygote excess  in natural hybrids relative to the remainder of the genome , followiPopulus showed clear patterns of phenotypic correlations in full-sib hybrid families [Previous work focusing on flavonoids in families . In the families , 57. PheP. alba and P. tremula  were found for several traits of all three functional groups and were most frequent in salicinoids with low levels of substitution  and isorhamnetin-derivatives in the flavonoid group. The h2 of one phytochemical trait exceeded 1, reflecting the experimental trade-off of measuring phenotypes at early life stages that matter most to tree establishment but are also most strongly affected by maternal effects [Great variation in heritability across phytochemical traits was detected by both, Mantel correlations (MC) in natural populations and half-sib analysis in the common garden trial , p<0.05 stimates ; Table 1 effects .P. alba-dominance for three, P. tremula-dominance for four, and additivity for the remaining QTL . These differences in geographic structure between different subgroups of phenylpropanoids can plausibly be explained by (1) differences in levels of available genetic variation among phytochemical traits representing these subgroups of the pathway [Interspecific variation in phenylpropanoid abundances in European chniques . The firchniques , 24 or schniques . Still, chniques , 59, 60. tremula . However tremula . Here, w pathway , 60 and/ pathway .Our genetic mapping results show that flavonoids exhibit a stronger genetic component  variation alone may explain geographic structure as observed for flavonoids in the present study , local aPopulus . ReciproPopulus .Coarse-scale genetic mapping based on genetic markers from all 19 poplar chromosomes provided a glimpse of the genetic architecture of 38 phenolics identified and quantified by UHPLC-QTOF-MS and condensed tannins . In partPopulus species , which further validates our results obtained by admixture mapping. In the following paragraphs, we detail our findings for each branch of the phenylpropanoid pathway .P. alba and P. tremula retain unexpectedly high levels of interspecific heterozygosity  at many loci in the genome [A growing body of literature reports the ability of herbivore and pathogen communities to drive rapid evolution in short-lived host plant species , 10. Here genome , despitee genome . Selectie genome  were caue genome . When the genome ) and thePopulus alba and P.tremula are hybridizing, ecologically divergent species with porous species boundaries [undaries , 40, 50 undaries , 27 and undaries –31 to exChanges in evolutionary rates with pathway position and flux control have been described in plant secondary metabolites, with selective constraints decreasing along pathways , 30, 73.As described in  and plotPopulus represent a large family of 236 identified genes , a gene family with greatly increased diversity and complexity in Populus. Our results demonstrate how different groups of secondary metabolites are differentially affected by evolutionary constraints and hybridization in plant taxa with porous genomes. Future work should combine the metabolomic approach taken here with proteomics, transcriptomics, and high-density genotyping of appropriate association mapping panels phenotyped in controlled environments. This combination of approaches should help towards closing current knowledge gaps regarding the origin and maintenance of functionally important phenotypic differences during plant population divergence and speciation.Phytochemical traits from different branches of the phenylpropanoid pathway differ greatly in proportions of intra- and interspecific variation, heritabilities, genetic architectures, and phenotypic correlations in hybridizing Eurasian S1 FigPopulus alba (in red), P. tremula (in green) and their hybrids, P. x canescens (in blue). Ellipses represent the 95% confidence intervals and colored dots represent averages for each group. For (A) and (B), symbols specific to each species and hybrid zone  represent individual trees. The first two principal components (PC’s) are plotted for (A) PCA of seven chlorogenic acids explaining 71.6% of the phenotypic variation; (B) PCA of the 13 salicinoids explaining 85.1% of the phenotypic variance within this subgroup; (C) PCA of all 38 phenylpropanoid in 133 individuals from the common garden explaining 58.9% of the phenotypic variance. For PCA for flavonoids see main paper.Principal Component Analysis (PCA) of secondary metabolites identified by UHPLC-QTOF-MS in (PDF)Click here for additional data file.S2 FigP. alba, P. tremula and their hybrids from three natural hybrid zones. For compound diversity see Normalized peak areas used as measures of relative abundances of compounds were summed within groups of aglycones and moieties for (PDF)Click here for additional data file.S1 MaterialsB) Molecular genetic analysis of common garden seedlings.(PDF)Click here for additional data file.S1 TableP. alba and P. tremula) were characterized with 77 microsatellite DNA markers in a previous study [Hybrid individuals and plants from each parental species (us study . The com(PDF)Click here for additional data file.S2 TableA) and gene diversity (He) in the common garden trial are indicated.Information for the 16 microsatellite marker loci used to identify parental species and hybrids and to estimate the correlation of paternity (Cp) in the common garden. These 16 microsatellites are a subset of the genome-wide marker panel used for admixture mapping in natural hybrid zones and are fully described . Localiz(PDF)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 TableShown are means ± standard deviations of traits quantified for three natural hybrid zones  and a common garden trial established from seeds from the Italian hybrid zone. Numbers of individuals (n) for each species in each hybrid zone are indicated.(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 TableP. alba and P. tremula, for markers representing putative QTL for salicinoids and flavonoids, and for all 34 markers representing putative phytochemical QTL in the present study.Contingency tables presenting counts of the presence (yes) or absence (no) of an excess of interspecific heterozygote , for all(PDF)Click here for additional data file.


Synthetic genetic transistors are vital for signal amplification and switching in genetic circuits. However, it is still problematic to efficiently select the adequate promoters, Ribosome Binding Sides (RBSs) and inducer concentrations to construct a genetic transistor with the desired linear amplification or switching in the Input/Output (I/O) characteristics for practical applications.i.e., a constitutive promoter-RBS library, a repressor-regulated promoter-RBS library and an activator-regulated promoter-RBS library, are constructed for systematic genetic circuit design using the identified kinetic strengths of their promoter-RBS components.Three kinds of promoter-RBS libraries, According to the dynamic model of genetic transistors, a design methodology for genetic transistors via a Genetic Algorithm (GA)-based searching algorithm is developed to search for a set of promoter-RBS components and adequate concentrations of inducers to achieve the prescribed I/O characteristics of a genetic transistor. Furthermore, according to design specifications for different types of genetic transistors, a look-up table is built for genetic transistor design, from which we could easily select an adequate set of promoter-RBS components and adequate concentrations of external inducers for a specific genetic transistor.This systematic design method will reduce the time spent using trial-and-error methods in the experimental procedure for a genetic transistor with a desired I/O characteristic. We demonstrate the applicability of our design methodology to genetic transistors that have desirable linear amplification or switching by employing promoter-RBS library searching. Synthetic biology aims to perform various specific functions in organisms by inserting a designed gene network. In the past, synthetic biology could be classified as having two broad purposes. The first was to create artificial life from natural biology using the synthetic methods. The other was to assemble some functional components using interchangeable natural components which are nonexistent in natural biology . A lot ohttp://www.partsregistry.org), formed by MIT, shows many standard BioBricks, and these standard biological components provide synthetic biologists with a quick and standardized way of constructing gene circuits. Also, BioFAB provides some sorts of biobricks (see http://biofab.org/data), enabling the rapid design and prototyping of genetic constructs. However, in the past, the strength of a promoter and an RBS, which are the main components of transcription and translation, were defined according to their relative strength with other promoters and RBSs. Now, however, the promoter-RBS strength can be quantified by measuring the fluorescence of proteins whose coding gene is constructed at the downstream of the promoter-RBS component.In many industries, such as the electronics and manufacturing industries, the characterization and standardization of components and the institution of specifications are already key elements in the production line. In the synthetic biology, the expression of a specific protein needs a promoter, a ribosome binding side (RBS), a protein coding sequence and a terminator, which are DNA fragments. The Registry of Biological Standard Parts  is constructed for genetic transistor design requiring prescribed I/O characteristics, which is used by searching the most appropriate sets of promoter-RBS components and concentrations of inducers via the genetic algorithm (GA).In this section, we introduce the characterization and standardization of promoter-RBS libraries and employ a dynamic mathematical model to construct the promoter-RBS libraries according to the identified kinetic strengths of promoter-RBS components, populated via experimental data.In a systematic design procedure, the characterization and standardization of components are important preparatory tasks before practical design process. These can save designers a significant amount of time and avoid unnecessary trial-and-error attempts. In the field of synthetic biology, a particular technique was developed to create standard interchangeable biological components called BioBricks ,31. ThesBioBricks are DNA fragments with specific functions, and include promoters, ribosome binding sites (RBS), repressors, activators, reporters and terminators. In the database, there are only a few BioBrick components that are well-characterized. The well-characterized BioBrick components are conducive to the systematic design of synthetic genetic circuits. In order to facilitate the design of synthetic gene circuits, wider libraries of well-characterized BioBricks need to be constructed.In our promoter-RBS libraries, the library indexes are the kinetic strengths of promoter and RBS, which are considered together as a promoter-RBS component because the gene expression is regulated by a promoter-RBS component. The kinetic strength of a promoter-RBS component can be systematically identified by a stochastic model which simulates the dynamic behavior of promoter-RBS components under some external molecular or environmental noises. In order to identify the kinetic strength of a promoter-RBS component, the green fluorescence gene is embedded into the downstream of the promoter-RBS component. By measuring the fluorescence dynamic time profile and using the nonlinear least squares method , we ideni.e., constitutive, repressor-regulated and activator-regulated promoter-RBS libraries. The promoter-RBS components in promoter-RBS libraries and all BioBrick components used in this study are listed in Additional file The construction procedure of the promoter-RBS libraries can be generally divided into four steps : (i) choAfter the introduction of regulatory functions of promoter-RBS components and the construction of the promoter-RBS libraries, we design a synthetic genetic circuit, similar to a transistor, with prescribed I/O characteristics of amplification or switching through the external inducer. Before the construction of the synthetic genetic transistor, we introduce the simply operation of an electronic transistor in Additional file xprotein of the transistor for amplification or switching behavior. The genetic transistor consists of the repressor-regulated promoter-RBS component c3 and a repressor coding gene. The input repressor xrepressor2 to the genetic transistor is controlled by the repressor-regulated promoter-RBS component c2, which is regulated by the corresponding repressor. The input repressor xrepressor2 will form the complex and restrict the production of output protein xprotein by binding the corresponding repressor-regulated promoter-RBS component c3 to decrease its kinetic strength. However, when the inducer is added, this inducer will bind input repressor xrepressor2 and prevent it from binding to the repressor-regulated promoter-RBS component c3. Then, both the kinetic strength of repressor-regulated promoter-RBS component c3 and the production of output protein xprotein will increase. The dynamic model of a genetic transistor is described as follows:A genetic transistor is shown in Figure xrepressor2 and xprotein denote the concentrations of input repressor2 and the output protein of the genetic transistor, respectively, and γprotein denotes the degradation rate of the protein.where c2 so that the input reporter protein x1 can be measured by input fluorescence g1 and the output reporter protein x2 can be measured by output fluorescence g2. Note that RFP is used to measure input while GFP is used to measure output. Additionally, for the convenience of the input regulation, we construct an input signal generation device with the concentration of inducer I1 to control the input g1 of the genetic transistor circuit. Then, the dynamic model of a synthetic genetic transistor circuit with I/O measure devices under environmental disturbances is described by the following set of equations:However, the protein concentration is difficult to directly measure and quantify. To determine characteristics of the synthetic genetic transistor, a genetic transistor with measurement circuit is constructed as shown in Figure m1 and m2 denote the maturation rates of reporter1 x1 and reporter2 x2, respectively, and vi(t), i = 1, 2,   ⋯, 6 denote the noises.where To explore the I/O characteristics of a synthetic genetic transistor with the function of amplification or switching, the steady state model of (2) is given byi = 1, 2,   ⋯, 6 denote the noises at the steady state.where m1 ≈ m2, xrepressor2/xprotein ≈ x1/x2 ≈ g1/g2, i.e., we could use the x1/x2 or g1/g2 ratio to replace the I/O characteristic of the synthetic genetic transistor. Further, the I/O characteristic can be controlled and regulated by the selection of promoter-RBS components c3 and inducer concentration I2. Therefore, we need to define the I/O characteristic of synthetic genetic transistor circuits to design a genetic transistor with the desired I/O characteristic. This is done as follows:From (3), if yss denotes the I/O response of the synthetic genetic transistor circuit between input signal g1 and output signal g2, and g1e and gn1 denote the lower bound and upper bound of g1. a.u. stands for arbitrary unit.where c1 and c2 can be selected to control input signals xrepressor1(c1) and xrepressor2 in (3). In general, genetic components are inherently uncertain in the biological system as a result of gene expression noises in transcription or translation processes, thermal fluctuations, DNA mutations, evolutions, context-dependence between promoters, 5′UTRs, and coding sequences, as well as parameter estimation errors   can be set by transforming the inducer concentration I1 into the input fluorescence according to (2) or (3) as followsIn Figure Input operation range:I1 and g1 denote the inducer concentration and input fluorescence, respectively, Il1, and Iu1, denote the lower and upper bound of inducer concentrations, respectively, and gl1, and gu1, denote the lower and upper bound of input fluorescences respectively.where c1 and c2 can be designed and selected to match the input operation range. However, due to the limited size of our promoter-RBS libraries and for the convenience of design, we will select the repressor-regulated promoter-RBS component c2 from the corresponding promoter-RBS library.Note that, in the future, when the promoter-RBS libraries are large enough, the promoter-RBS components c3 from the repressor-regulated promoter-RBS libraries and concentration of inducer I2, i.e., {c3, I2}, so that the I/O characteristics of the synthetic genetic transistor in (4) in a specific input range g1 ∊  can match the desired I/O response similar to (B2), i.e.,From the above analysis, the design purpose of an amplifier will lead to the selection of a suitable repressor-regulated promoter-RBS component gl1, and gu2, denote the lower bound of input fluorescence g1 and upper bound of output fluorescence g2, respectively, and gain denotes the amplification gain of the genetic transistor.where i.e., a high level signal can be switched into a low level signal and vice versa. In the switching behavior of synthetic genetic transistor, each promoter-RBS component has its own basal level. Thus, when the input signal increases, the output signal will rapidly decrease to the basal level. Therefore, the desired I/O response of a switch is described as follows:On the other hand, the switching behavior will occur when the input signal has a small variation .where c3 in the repressor-regulated promoter-RBS libraries and the concentration of inducer I2 to minimize the following cost function , while the cost function is calculated in each iteration of the selection process. Then, GA would select the most adequate promoter-RBS component c3 from the corresponding repressor-regulated promoter-RBS library and inducer concentration I2 ∊  to minimize the cost function.Based on the design procedure of a genetic transistor using the promoter-RBS library searching method with GA, the promoter-RBS component We have presented the construction and design procedure of a synthetic genetic transistor. In this section, the synthetic genetic transistor is designed and simulated to verify the I/O characteristics of amplification and switching. Subsequently, based on our promoter-RBS libraries, the amplification gain in a specific input operation range and switching level are designed by employing GA to select the most adequate promoter-RBS components and inducer concentrations. Finally, to support future application of this method, a look-up table for genetic transistors is built for different genetic transistor design specifications.c1, c2} = {J6, L3} are selected to obtain the maximum input operation range. The dynamic model and the steady state model have been described in (2) and (3). The input operation range and desired I/O response of genetic transistor are specified as follows:Consider the amplifier design of the synthetic genetic transistor. Firstly, to obtain the I/O characteristics of amplifier, promoter-RBS components {Input operation range:andin vivo are given bywhere -2 is the desired amplification gain as shown in Figure vi(t) are independent Gaussian noises with zero mean and unit variance. Finally, GA is employed to search a set {c3, IaTc} from corresponding libraries to minimize the following cost function:and the environmental disturbances c3, IaTc} = {T3, 0 ng/ml}. The estimation of I/O response of genetic transistor based on experimental results is shown in Figure g1 ∊  under the intrinsic fluctuations and environmental disturbances.Then, the most adequate promoter-RBS component from the corresponding library and aTc concentration are found to be {c1, c2} = {J6, L3} are selected to obtain the maximum input operation range. The dynamic model and the steady state model have been described in (2) and (3), respectively. The input operation range and desired I/O switch response are specified as follows:Consider the switch design of the synthetic genetic transistor. The switch design procedure is similar to the amplifier design procedure of a synthetic genetic transistor. Firstly, to obtain the complete I/O characteristics of switching, promoter-RBS components {Input operation range:andLs denotes the low level of switching or basal level of promoter-RBS component c3. Note that the standard deviations of parameter fluctuations that are supposed to be tolerated in vivo and from environmental disturbances are the same as in (12). Finally, GA is employed to search a set {c3, IaTc} from corresponding libraries to minimize the following cost function:where c3, IaTc} = {T3, 0 ng/ml}. The estimation of I/O response of genetic transistor based on experimental results is shown in Figure Then, the most adequate promoter-RBS component from the corresponding library and aTc concentrations are found to be {c3 and inducer concentration using the proposed library-based searching method. However, not just the promoter-RBS component c3 ∊ LibTet can be selected to achieve the amplification or switching design specification of the synthetic genetic transistor circuit, but also other promoter-RBS components, i.e., LibLac, can be selected to achieve the desired I/O response. However, for various design specifications, more promoter-RBS libraries are needed to achieve these design specifications.According to the above examples, the amplification or switching I/O characteristics of a synthetic genetic transistor with different design specifications can be achieved by selecting the most adequate promoter-RBS component For the convenience of synthetic genetic transistor design for synthetic biologists, one look-up table has been built for the various design specifications as shown in Table in vivo behavior of a gene circuit [One major aim of synthetic biology is to construct a gene circuit with the desired functionality of an organism. Recently, promoter libraries and promoter-RBS libraries have been built to simulate the  circuit ,38,39. Bin vivo. The design specification of amplification or switching in the genetic transistor can be achieved by the library-searching method using GA. By optimally matching the desired I/O response of amplification or switching, the most adequate set of promoter-RBS component and inducer concentration {c3, I2} can be selected to construct a genetic transistor with the desired design specifications. The library-searching method using GA is introduced to reduce the number of trial-and-error attempts, as well as the searching time in libraries when the libraries have a large number of components. Furthermore, for the convenience of synthetic genetic transistor design for synthetic biologists, one look-up table has been built for the various design specifications as shown in Table In this study, by the promoter-RBS libraries we established, a genetic transistor has been constructed and implemented. Additionally, the synthetic genetic transistor can perform amplification and switching like an electronic transistor according to its I/O characteristics. The I/O characteristics of the synthetic genetic transistor circuit are simulated by a mathematic model with random parameter fluctuation to guarantee the robustness of the design For applications of the genetic transistor, the various biological components need to be characterized and standardized. By using characterized and standardized genetic components, the design specification of a genetic transistor can be set and the look-up tables can be used to support the genetic circuit design. The genetic transistor described here has a number of potential applications. The amplifier can be used to amplify the oscillation signal reversely and linearly. Based on the designed oscillatory genetic circuits -13,40-42i.e., a constitutive promoter-RBS library, repressor-regulated promoter-RBS library and activator-regulated promoter-RBS library, were established for constructing synthetic gene circuits with the desired transistor amplification or switching function. The amplification gain and switching level of a genetic transistor could be calibrated by selecting adequate promoter-RBS components and inducer concentrations from the corresponding libraries. For the measurement of I/O response, we could embed an additional repressor-regulated promoter-RBS component with reporter protein at the input terminal to measure the input signal while replacing the output protein with a reporter protein to measure the output signal. Further, for the convenience of input regulation, an external circuit was constructed to control the input signal using the concentration of inducer. Based on the desired I/O response in relation to amplification or switching of a genetic transistor, the GA-based searching algorithm was introduced to search for the most appropriate set of promoter-RBS components and inducer concentration from the corresponding promoter-RBS libraries to achieve prescribed I/O characteristics in the genetic transistor. In the simulation results for this study, we demonstrated that the genetic transistor designed here has the prescribed function of amplification or switching. By the library-searching method using GA, different design specifications of amplifier or switch could be achieved the most appropriate set of promoter-RBS components from the corresponding promoter-RBS libraries and inducer concentration within a feasible region. Finally, a look-up table was built for genetic transistor design with different genetic transistor design specifications. Using this table, we could easily select an adequate set of promoter-RBS components and inducer concentration to construct the desired genetic transistor. This innovation saves much time in trial and error attempts in the iterative experimental procedure.In this study, three kinds of libraries, The authors declare that they have no competing interests.BSC formulated the research topic and gave the guideline, YYL and CYH performed the computation and simulation. LJL, CCC, HCC, THY, CL, ZX and RHH performed the experiments. All authors have written and revised the manuscript together. All authors read and approved the final manuscript.Supplementary Appendix.Click here for file