Borrelia burgdorferi species complex, which are transmitted by ticks among various wildlife reservoirs, such as small mammals and birds. These features make the Borrelia burgdorferi species complex an attractive biological model that can be used to study the diversification and the epidemiology of endemic bacterial pathogens. We investigated the potential of population genomic approaches to study these processes. Sixty-three strains belonging to three species within the Borrelia burgdorferi complex were isolated from questing ticks in Alsace (France), a region where Lyme disease is highly endemic. We first aimed to characterize the degree of genetic isolation among the species sampled. Phylogenetic and coalescent-based analyses revealed clear delineations: there was a ∼50 fold difference between intra-specific and inter-specific recombination rates. We then investigated whether the population genomic data contained information of epidemiological relevance. In phylogenies inferred using most of the genome, conspecific strains did not cluster in clades. These results raise questions about the relevance of different strategies when investigating pathogen epidemiology. For instance, here, both classical analytic approaches and phylodynamic simulations suggested that population sizes and migration rates were higher in B. garinii populations, which are normally associated with birds, than in B. burgdorferi s.s. populations. The phylogenetic analyses of the infection-related ospC gene and its flanking region provided additional support for this finding. Traces of recombination among the B. burgdorferi s.s. lineages and lineages associated with small mammals were found, suggesting that they shared the same hosts. Altogether, these results provide baseline evidence that can be used to formulate hypotheses regarding the host range of B. burgdorferi lineages based on population genomic data.Lyme borreliosis, one of the most frequently contracted zoonotic diseases in the Northern Hemisphere, is caused by bacteria belonging to different genetic groups within the  Zoonotic diseases caused by pathogens that are transmitted among different host species represent an emergent threat for human health In particular, the selective constraints on pathogen genomes can be highly heterogeneous. For example, infection-related genes are expected to be affected by disruptive and/or negative frequency-dependent selection due to the molecular interactions occurring among pathogens, hosts, and the hosts' immune systems. Therefore, within bacterial lineages associated with similar hosts these genes would be predicted to share similar features due to host-driven selective sweeps, DNA exchange by recombination, or convergent evolution. Patterns of diversity in these genes could thus potentially reveal which lineages infect overlapping host communities. Alternatively, housekeeping genes, which are involved in basic cell cycle and metabolic functions, are mostly subject to purifying selective pressures et al.Using appropriate genome regions, analyses of demographic and epidemiological processes from population genomic data rely on models that describe both the way pathogens spread within and among susceptible individuals, as well as processes that govern genome evolution. These two aspects have historically been investigated independently in the literature. On the one hand, the spread of pathogens has often been studied by epidemiologists using population dynamic models that take into account complex infection and transmission processes et al. revealed the impact of recombination on the evolution of bacterial pathogens, which can be hidden by differences in the reproductive success of genotypes a prioria priori, and a random sample of strains is obtained for each selected factor a posteriori with regard to the observed patterns of polymorphism before fitting more complex evolutionary and epidemic models to the genomic data.In core regions of bacterial genomes, sequence diversity emerges through both mutation and homologous recombination. Twenty years ago, Maynard Smith Borrelia burgdorferi species complex as our model system. This species complex includes the bacteria that cause Lyme borreliosis, one of the most common vector-borne diseases in the Northern Hemisphere. In Europe, most human cases are caused by Borrelia afzelii, Borrelia burgdorferi sensu stricto (s.s.), and Borrelia gariniiIxodes ricinus among different reservoir species. The ticks can become infected during their first blood meal, which occurs before they molt from larvae into nymphs. They can also acquire and/or transmit bacteria during their other blood meal(s), which occur before the molt from nymph to adult  and before laying eggs .To assess the potential of population genomic approaches in the study of multi-host zoonotic disease, we chose the B. burgdorferi complex do not share a common, fixed set of host species (referred to hereafter as host range) Borrelia garinii and Borrelia valaisiana, which are only distantly related to each other, both infect birds B. afzelii, Borrelia spielmanii, and Borrelia bavariensis, which do not cluster together in phylogenies, infect small mammals B. burgdorferi s.s. appears to be broader than that of most Borrelia, as this species has been identified in both small mammals and birds Bacteria within the B. burgdorferi species complex. From a population genetic point of view, differences in the population size and the migration potential of reservoir species have been shown to influence MLST diversity patterns of these bacterial species at a continental scale B. burgdorferi s.s. in the USA B. burgdorferi species complex obtained from different populations and host species at the infection-related gene ospCDifferent approaches have been used to investigate the influence of host communities on the diversity of the B. burgdorferi species complex B. burgdorferi species complex to hosts B. burgdorferi s.s., was also used to investigate evolutionary processes within this species B. burgdorferi s.s., the selected isolates were chosen in order to capture a maximum of genetic diversity The development of genetic and genomic tools has allowed the in-depth investigation of genes involved in infection phenotypes within the B. burgdorferi s.s., B. garinii, and B. afzelii isolated from questing ticks in France, in a region where Lyme borreliosis is highly endemic. Using a hierarchical sampling scheme at a regional scale, we aimed to investigate the genetic diversity within and isolation among species of the complex. We also evaluated multiple models, involving both evolutionary and epidemiological constraints, and examined the information obtained from them to understand the forces that have shaped diversity within this species complex. This lead to different hypotheses regarding the evolution and the epidemiology of these bacteria. The data gathered here are then discussed in the context of what is currently known about the ecology and evolution of these vector-borne pathogens.Here, we investigated the genomic diversity of strains of B. burgdorferi species complex were recovered from questing nymphs and adults of I. ricinus that were sampled at two sites largely covered by dense and continuous forests; these sites were located near the towns of Munster and Guebwiller, in Alsace (France) Isolates of the Borrelia species Ticks were collected at each site in two consecutive years (2003 and 2004) by dragging a large piece of cotton fabric across the vegetation and leaf litter B. burgdorferi complex, ii) to examine the distribution of diversity within sympatric species of the complex at a regional scale, and iii) to compare the genetic structures of different bacterial species, we selected a set of strains that included 25 genotypes of B. burgdorferi s.s., 34 genotypes of B. garinii, and 4 genotypes of B. afzelii. Within each species, genotypes were randomly selected. The size of the three samples was determined by the rate of success of the isolation procedure rather than by the observed frequency of the different species within ticks. Moreover, we selected isolates with the goal of having a similar number of strains for each sampling site within each species. We also tried to select strains that were sampled during the same year in order to limit confounding factors: all B. garinii and B. afzelii strains were isolated in 2004, whereas an equal number of B. burgdorferi s.s. strains were isolated in 2003 and in 2004 to study species delineations within the  in 2004 .To obtain enough material for sequencing, bacteria were iteratively cultured using 50 ml of BSK-H medium (Sigma-Aldrich) until high bacterial densities were reached. All strains underwent fewer than 15 passages from tick incubation to DNA extraction in order to limit the possibility of plasmid loss. Independent DNA extractions were performed using the DNeasy Blood & Tissue Kit . The quantity of DNA after extraction was measured by spectrophotometry at 260 and 280 nm , and DNA extractions were sent to Genoscreen  to be tagged with standard multiplex identifiers . Samples were then mixed to prepare libraries, which were distributed among three-quarters of a GS FLX Titanium (Roche) sequencing plate.In order to analyze sequence data, raw reads were first mapped on reference sequences. Contigs were aligned with reference genomes. The genetic structure of the sample was studied using single nucleotide polymorphisms (SNPs)-based analyses at intra- and inter-specific levels. Additionally, phylogenetic analyses were performed at the same scales. Loci showing atypical polymorphism patterns were identified using allelic spectrum-based approaches and the functions they encode were analyzed. Finally, the results of SNP-based analyses on the chromosome, which revealed homogeneous polymorphism patterns, were used to fit a long term coalescent based model and a basic, shorter term, phylodynamic model. These different steps are described in details below.B. burgdorferi s.s., B. bavariensis/B. garinii, and B. afzelii. For each of the three mappings, reference sequences of the chromosome, the circular plasmid cp26, and the linear plasmid lp54 were chosen from public databases (B. burgdorferi species complex gsMapper software (Roche) parameterized as follows: the length of seeds used to anchor alignments was fixed to 10 base pairs (bp) and the identification of three seeds per sequence was required for alignment analysis. Identity thresholds were fixed at 60 bp and 60% identity. The results of mapping onto a given replicon of a given species were stored if more than 90% of the reference sequence was covered. Then, a strict consensus sequence was generated for each replication unit of each bacterial isolate from the alignment of the stored mapping results. This was performed using a program we developed in Pascal; unless otherwise indicated, programs developed in Pascal were used for all analyses described below.To obtain robust data, each sequence read was mapped independently onto reference sequences of atabases . We focurogressiveMauve software uscle software Finally, to compare our genomic data to published sequences , we consB. burgdorferi s.s. and B. garinii, a set of SNPs was for each species that included all polymorphic sites in the chromosome that were identified in at least 90% of strains. The two sets of SNPs were analyzed separately using the program Structurev2.3.4 to identify potential populations and explore their degree of admixture SNPs were identified using multiple alignments. To explore the population structure of HST values HST is a multiallelic numerical analog of Wright's FST, and is obtained using the formula HST = 1-HS/HT, where HS and HT represent Nei diversity indices within and among populations, respectively. We calculated HST for selected SNPs at different levels of genetic resolution: i) among species of the complex, to assess their genetic isolation from each other and ultimately identify evidence of inter-specific homologous recombination; and ii) within studied species and between sampling sites, in order to investigate geographic isolation and identify genomic regions evolving under particular selective pressures B. burgdorferi s.s. sampled in 2003 and those sampled in 2004 (data not shown), we did not consider this potential structuring factor in the analyses described above. The significance of the HST values was tested with a Monte-Carlo approach (p = 0.05): for each SNP, 1000 simulated datasets that distributed individual genotypes at random among populations were created. Additionally, we compared the distribution of HST values in bacterial populations of B. burgdorferi s.s. and B. garinii that were sampled in different locations using a Wilcoxon rank-sum test with continuity correction, performed in R In order to limit the impact of base-calling errors  and to obtain sufficient statistical power for subsequent analyses, we selected SNPs that: i) were present in all individual strains and ii) had at least two alleles with frequencies higher than 10%. Differentiation measures reflect the genetic variability among groups of individuals relative to the variability of a whole sample HST values depends on the genetic linkage between SNPs, we investigated the impact of homologous recombination on the genetic diversity of the studied strains. Standardized linkage disequilibrium measures between pairs of SNPs were obtained using the D' statistic As the distribution of hyML software To conduct detailed analyses of the phylogenetic relationships among genotypes using the alignments of the three replication units, contiguous 1-kb-long windows were created. We decided not to work at the gene level in order to standardize the amount of phylogenetic information contained in each unit. A phylogenetic search that applied a maximum-likelihood approach using PplitsTree 4 software aup* 4.0 b10 To illustrate global genetic relationships within and among species, phylogenetic networks, based on chromosomal sequences of sampled genotypes and reference genomes, were constructed with SNext, we assessed whether the percentage of windows in which a given species was not monophyletic could be explained not by recombination but simply by a lack of resolution. With this aim, we simulated 300 alignments of 63 sequences of 1000 kb according to i) the GTR+I+G model described above and ii) a neighbor-joining tree S which is based on the number of segregating sites along the alignment π, which is based on the average divergence among samples B. burgdorferi species complex, values of Tajima's D B. burgdorferi species complex vary widely in length, with some too short to provide an adequate amount of mutation for this type of analysis. Gapped sites were not considered, as in Tajima's original publication. Tajima's D statistic measures the difference between the two estimators of θ previously described, standardized by the variance of this difference. Under the neutral evolutionary model, which assumes that polymorphisms segregate at mutation–drift equilibrium, Tajima's D is expected to be null. Positive Tajima's D value can be induced by balancing selection, due to the maintenance of highly divergent variants. Conversely, negative Tajima's D values can be the result of purifying selection or a selective sweep, which both result in an excess of weakly divergent alleles. However, these two patterns (positive or negative) may alternatively be due to demographical/epidemiological processes that affect the whole genome. Trying to distinguish between potential whole-genome versus gene-region-specific process, we looked for windows within each species' sequence that were characterized by a highly structured pattern of diversity. These genome regions might be evolving under host-driven selective pressures. Towards this end, we first obtained the distribution containing Tajima's D values for every window of chromosomal data for each studied species. As Tajima's D values were, on average, negative  Watterson's θtive see , standarGenes that overlapped the selected windows were identified and compared among the different species, and we created a non-redundant list containing the selected genes. The function and the cellular localization of the proteins encoded by the selected genes were studied using the online pipeline SLEP ospC gene on the cp26 plasmid was characterized by a peculiar polymorphism pattern. To illustrate the amount of incongruent phylogenetic information in ospC sequences and flanking regions, Neighbor-Net networks, based on uncorrected p-distances, were obtained from alignments of the ospC gene, the 2,000 bp before and after the gene, and regions located further upstream and downstream.Differentiation measures, Tajima's D values, and linkage disequilibrium patterns revealed that a 4-kb region around the B. burgdorferi species complex, we developed a coalescent model to investigate the divergence between B. burgdorferi s.s. and B. garinii. This model used species-specific properties to simulate sequence datasets and employed Approximate Bayesian Computations (ABC) As described in the The original coalescent model can be defined as a Markov process describing how, assuming a Wright-Fisher or Moran model of genetic variation, sampled individuals would share a common ancestor  as one goes back in time In its simplest form, the coalescent model assumes a lack of recombination and a lack of population structure; however the model has been extended to allow for such evolutionary events as homologous recombination B. burgdorferi and B. garinii, are simulated, characterized by constant effective population sizes of 1*Nc and 2*Nc, respectively. Coalescence events are only possible within populations, and unidirectional homologous recombination is allowed both within and between populations. In each generation, for each individual, any sequence site in a given population is susceptible to intra-population homologous recombination at respective rates of intra1r and intra2r. Similarly, recombination events can occur between individuals belonging to different populations at rates inter1r and inter2r. After M * (1*Nc+2*Nc) generations during which the two populations evolve independently, a new step is initiated. The two populations merge into a single ancestral population, with effective population size 3*Nc and intra-population recombination rate intra3r. As it is impossible for the populations to coalesce into a single common ancestor if the probability of recombination is higher than the probability of coalescence, if the common ancestor as not been reached after (M+S) *(1*Nc+2*Nc) generations, recombination is stopped. Once a complete genealogy is obtained, mutations are added along branches at a rate μ per site per generation according to the Jukes–Cantor model The model we developed for this study assumes two steps. During the first step, two populations, representing B. burgdorferi s.s.) and 32 samples for the second (B. garinii). In our initial model (M0), all the variables described above were assumed to be independent. In model M1 and all subsequent models, we assumed inter1r = inter2r. In models M2 and M3, we assumed, respectively, 1c = 2 = c3c and intra1r = intra2r = intra3r, while the M4 model incorporated both of these assumptions. We finally investigated an M5 model that assumed inter1r = inter2r = intra1r = intra2r = intra3r and 1c = 2 = c3c. After exploring model behavior, we performed 50,000 simulations for each model, fixing the parameters as follows to shorten computation time: N = 1e+09, μ = 1e-07 and S = 5. Based on the relationship we observed between linkage disequilibrium and physical distance on the genome  was monitored over time in hosts and vectors. Our aim was to identify epidemiological settings that would allow for the maintenance of the observed level of diversity within each species.The main genotypes that we observed in hostsN, were simulated to represent the reservoir of infection-causing bacteria at the Munster and Guebwiller sites. Each host population came into contact with a population of nymphs, with constant and equal populations sizes ticksN. Due to the complete turnover in nymphs that happens each year, larvae were not explicitly included in the model but implicitly appeared at each generation as a source of new susceptible ticks that are coming in contact with hosts. Likewise, adults were not included, as they occur at lower densities and their preferred host are large mammals, which are not known to be a reservoir of the B. burgdorferi species complex. At each iteration of the model, each host was exposed to a variable number of nymphs, this number followed a Poisson distribution with parameter ticksN/hostsN. The bacterial genotype (or lack thereof) within each tick that fed on each host was recorded to obtain the number of contact between each individual host and each bacterial genotype. A host became infected by the ith bacterial genotype according to a binomial distribution which had as parameters the number of contacts this host had with the ith genotype and the probability THiI of infection occurring during each contact. Infections were persistent, so that an infected host retained this status for its whole life. Then, the entire tick population was completely replaced with susceptible individuals. These new ticks represented uninfected larvae who, in the current iteration would, feed, become infected, molt into nymphs and then infect hosts during the next iteration. Next, each tick thus fed on a host chosen at random in the population. For each of the bgN different bacterial genotypes, ticks became infected according to a Bernoulli distribution which took as parameters the probability HTiI of infection per contact if the host was infected, and 0 if the host was uninfected. Later, migN host individuals were exchanged between the two host populations following a Poisson distribution with parameter migF/hostsN, where migF is a scaling parameter. Finally, a proportion hostsR of each host population was renewed with susceptible individuals.In this model, one iteration represented one year. Two host populations of equal and constant sizes, B. burgdorferi s.s. and B. garinii samples. Therefore, for each simulation, the model was run for 500 iterations in order to allow enough time to reach this equilibrium. We began each simulation with the assumption that each host, was infected by a single bacterial genotype, and that bacterial genotypes occurred at equal frequencies within and among populations. In such an epidemiological model, one of the two main outcomes is expected: either more pathogens are lost during population replacement than are created by new infections, which leads to the extinction of pathogens; or the number of new infections per iteration is much higher than the number of individuals lost during replacement, which leads to high pathogen prevalence and the maintenance of most genotypes in co-infected individuals. Here, we focused on sets of parameters that would enable the creation of intermediate situations. In order to identify these situations, we started simulations with a high number of genotypes, i.e. bgN = 20; assumed strong rates of transmission during contact between hosts and ticks, THiI = HTiI = 0.8; and, as high Nticks/Nhosts ratios lead to explosive pathogen dynamics, we assumed that ticksN/hostsN = 1. To explore the remaining parameter space, we performed simulations using combinations of the following values: hostsN set at either 100, 300, 500, or 900 individuals; migF set at either 0, 0.2, 0.4, 0.6, 0.8, or 1; and hostsR set at values from 0.32 to 0.42 with a 0.005 step from one to the next. Fifty simulations were performed for each combination of parameters.An important criterion in the parameterization of our model was that the number of genotypes present in the model at equilibrium (taking into account genetic drift and migration) was equal the number of observed genotypes in our B. burgdorferi s.s., 14 pathogens were sampled from 14 infected ticks from the first population (Munster) and 9 pathogens were sampled from the second population (Guebwiller). For B. garinii, 13 pathogens were sampled in the Munster population and 19 in the Guebwiller population. We computed the Euclidean multivariate distance between the observed and simulated values using the following normalized summary statistics: i) genotype richness among pathogens sampled in each population  and in the two populations combined (TR); ii) Nei diversity indices within each population  and differentiation between populations measured with HST. Sets of parameters were accepted and stored if the distance was lower than a tolerance threshold, defined so that less than 1% of simulations were accepted. For B. burgdorferi s.s., the observed statistics were 1R = 3, 2R = 2, TR = 3, 1H = 0.62, 2H = 0.44, and HST = 0.09. For B. garinii, observed statistics were 1R = 6, 2R = 6, TR = 8, 1H = 0.80, 2H = 0.78, and HST = 0.05.To evaluate the fit of the outcome of each simulation to our observed data, we compared different summary statistics using techniques similar to an ABC approach. At the end of each simulation, a certain number of ticks were randomly selected, with the goal of mimicking our real-world sampling of chromosomal sequences; the number of ticks selected corresponded to our actual sampling effort in each site. For We obtained 2,351,440 sequence reads in total, which corresponded to chromosomal coverage ranging from 2.2X to 27.2X, with an average of 8.5X. Raw reads were deposited to the Sequence Read Archive (SRA) database under accession SRP031890. Whole Genome Shotgun projects have been deposited in GenBank under the accessions listed in B. burgdorferi s.s. and IPT95 and IPT107 of B. garinii, which were subsequently excluded from further chromosomal analyses. For the plasmids cp26 and lp54, we obtained 90% reference sequence coverage for 61 strains, excluding IPT95 . Strains IPT107 and IPT136 of B. garinii did not reach the coverage threshold for cp26 and lp54, respectively, and were therefore excluded from further analyses of those respective plasmids.The raw sequences obtained for each genome were mapped onto reference sequences of the chromosome and plasmids cp26 and lp54. For the chromosomal sequences, we obtained at least 90% coverage of at least one of the reference sequence for 59 out of the 63 genotypes. The four strains for which we did not find 90% coverage were IPT27 and IPT70 of B. burgdorferi s.s.; ii) 10119, 451, and 873 in B. garinii; and iii) 8498, 568, and 762 in B. afzelii .To perform SNPs-based analyses, we defined unique sets of markers based on sequence alignments. For the entire species complex, 39757, 1569, and 3658 SNPs were identified along the chromosomal, cp26, and lp54 sequences, respectively. Within species we identified i) 5342, 480, and 403 SNPs in B. burgdorferi s.s. strains included in the network form a clade that is separate from that of strain SV1, for which a unique species name, B. finlandensis, has been proposed B. garinii strains included in the network were more closely related to each other than to the B. bavariensis PBi reference strain. However, as previously described B. burgdorferi s.s. and B. finlandensis, and between B. garinii and B. bavariensis, species pairs for which we measured respective average divergences around 2.2% and 2.8%.To illustrate global phylogenetic relationships among the sampled and reference strains at the species level, we constructed a Neighbor-Net network based on chromosomal data . Within Borrelia species and/or broader genetic groups using different sets of genetic data  and B. garinii (12%) could simply be explained by a lack of phylogenetic information, and not the presence of recombination. However, when we generated 1-kb windows from simulated data that did not allow for recombination, the percentages of windows that did not support the monophyly of the two bacterial species were significantly lower than those observed from the actual molecular data . Likewise, more than 80% of SNP sites were significantly differentiated between sequences of B. garinii and B. afzelii . species . In geneB. burgdorferi species complex by calculating D′ linkage disequilibrium values for all pairs of SNPs, both in i) all genomes grouped together and ii) within groups of conspecific genomes, assigned to B. burgdorferi s.s., B. garinii, or B. afzelii  but lower than that of B. afzelii . Nevertheless, this last value should be treated with caution due to the low sample size used to generate it. When we examined the individual replication units in the whole sample, we found a negative relationship between linkage disequilibrium and physical distance, but the decrease of D' values leveled off after 500 bp  grouped with the reference strain B. garinii PBr, which had been isolated in Germany in the 1980's.Strains assigned to enotypes . These gB. afzelii strains isolated in this study were closely related to reference strains PKo and ACA-1. However, it also showed that this group of sampled strains is not monophyletic.Finally, the network showed that the tructure analyses to describe the population structure of B. burgdorferi s.s. and B. garinii in more detail. The population structure of strains of B. burgdorferi s.s. suggested an optimal value of K = 7 populations : θ afzelii . Among oB. burgdorferi s.s. strains or the B. garinii strains from both of our sampling sites. Interestingly, these values were higher and, in the case of B. burgdorferi s.s., considerably higher than those that we calculated from all available genomes (B. burgdorferi s.s. and 0.81 for B. garinii (respective standard errors of 4.3e-05 and 4.2e-05). As for inter-specific data, we found a negative relationship between linkage disequilibrium and physical distance within species, and the decrease of D' values especially leveled off after 500 bp in B. garinii  than the distribution of values obtained from B. garinii, indicating that the former species was significantly more differentiated between our two study sites , the genes within these highlighted windows contained a significantly lower proportion of cytoplasmic protein-encoding genes and, instead, were significantly enriched in surface lipoprotein-encoding genes . More precisely, genes that encoded cytoplasmic proteins  only represented 55% of genes with high Tajima's D values even though they made up 67% of the genes within the total proteome encoded by the chromosome, cp26, and lp54. Genes annotated as encoding for membrane-associated proteins made up 23% of genes with high Tajima's D values and 22% of genes in the studied proteome, while genes described as encoding exported proteins represented 4% and 5% of those respective groups. Finally, 18% of the genes with high Tajima's D values were annotated as lipoprotein-encoding genes, which make up only 6% of genes in the proteome as a whole. Another observation adds weight to the unique position of lipoprotein-encoding genes in this sample is that the only gene found in high-D-value windows in all three ospC gene produced peculiar results in our analyses of linkage disequilibrium, genetic differentiation, and Tajima's D values, we constructed phylogenetic networks of the ospC gene and flanking regions . To obtain quantitative support for our inference, we used Bayes factors to compare different models that had different constraints applied to the parameters of interest  were approximately 8200 and 14500 respectively. The inter-specific recombination rates, inter1r = inter2r, was estimated to be 3.1e-09, which was 50 times lower than the intra-specific recombination rates, intra1r = intra2r = intra3r = 1.7e-07. The ratio of the recombination rate to the mutation rate was approximately 1.7. The model estimated that the two species shared a common ancestor around 490,000 generations ago. The ancestral population size estimate should be treated with caution as the posterior distribution from which it came indicates a large degree of uncertainty regarding the outcomes of the model . We then plotted the values of the parameters for the selected simulations in order to identify patterns of epidemiological relevance  is lower than the 5% divergence usually observed among bona fide species B. burgdorferi s.s. and B. garinii.Some exceptions to the monophyly of the sampled species were found: c in 12% . The majB. burgdorferi species complex, shown by both the observed genomic data and the results of our coalescent-based model, could be explained by various hypotheses. From an ecological point of view, a lack of physical contact between genetic groups in the species complex as a result of association with different vertebrate hosts may result in divergence if mutations accumulate via genetic drift and/or selection B. garinii from B. bavariensis could be explained by this hypothesis, as it is associated with a shift in host range B. finlandensis from B. burgdorferi. We also did not find obvious evidence for homologous recombination between B. valaisiana and the studied B. garinii genomes, even though both species co-infect the same host species in our sampling location This sharp decrease between inter-specific and the intra-specific recombination rates in the et al. ospC. The high genetic diversity of the ospC gene is probably influenced by a combination of selective pressures. The first type of selective pressure may be a consequence of the function of the lipoprotein encoded by ospC, which binds the tick salivary protein Salp15 Borrelia dissemination within hosts. Disruptive selective pressures on the various alleles of ospC could give these bacteria the ability to interact with different host species. Indeed, the multiple niche model has been suggested to be a critical driver of the diversity of these bacteria ospC has antigenic properties ospC diversity was obtained from a recent study ospC genotypes and the rodent hosts they infect, suggesting their limited spread in the host community; and ii) different ospC alleles associated with a common genetic background, suggesting the influence of diversifying selective pressure.We used Tajima's D statistic to determine which genes might be affected by selective pressures, and we found that genes involved in lipoprotein production had a greater representation among genes with high Tajima's D values than would have been predicted from their prevalence in the overall proteome of each of the species studied. In this, our results concur with the genetic evidence reviewed by Brisson ospC alleles and potential host species. However, our phylogenetic analysis of the flanking regions of ospC revealed ambiguous relationships between B. burgdorferi s.s. and both B. afzelii and B. spielmanii, an observation that probably indicates some degree of recombination among these taxa despite high levels of divergence. As recombination requires physical contact between genotypes, the recombinant genotypes we observed provide evidence that co-infections with B. burgdorferi s.s., B. afzelii, and B. spielmanii strains occurred in the past. B. burgdorferi s.s. can be found in both birds and small mammals, while the two other species are usually associated with small mammals. Therefore, one explanation might be that the B. burgdorferi s.s. lineages we sampled are infected the same hosts as B. afzelii and B. spielmanii. The reticulations in the ospC phylogeny suggest that inter-specific recombination may thus be involved in the emergence of new genotypes within the B. burgdorferi species complex  within B. burgdorferi s.s. B. burgdorferi s.s. and B. garinii from our analysis of population structure, in which we estimated effective population sizes and analyzed genetic differentiation between Munster and Guebwiller. As expected from our measurements of diversity, our coalescent models suggested that the effective population size of B. burgdorferi s.s. was lower than the effective population size of B. garinii. Moreover, differentiation measures, which are inversely proportional to migration rate, were higher for B. burgdorferi s.s. than for B. garinii.Linkage disequilibrium values within ocations . Neverthn IPT126 . FurtherB. burgdorferi s.s. and B. garinii greatly preceded the establishment of the studied transmission networks. In order to more directly investigate current patterns of diversity, then, we implemented a simple phylodynamic model, which did not examine the diversification phase of the different genotypes but focused only on the maintenance of diversity. This model confirmed that the maintenance of the observed level of diversity requires a higher host population size and higher host migration rates in B. garinii than in B. burgdorferii s.s. These results are similar to those obtained from MLST data in a comparison of the genetic structures of rodent-associated B. afzelii and bird-associated B. garinii at a broader geographic scale B. burgdorferi s.s. and B. garinii was coherent with the phylogenetic signal identified in the ospC gene region and both sets of results support the hypothesis that small mammals might be the reservoir of the B. burgdorferi s.s. strains studied here. From a purely academic point of view, a simple phylodynamic model that does not assume hypotheses about selective pressures contrary to multiple-niche model and negative frequency-dependent selection, allows researchers to explain the maintenance of numerous bacterial lineages with realistic host population sizes. We thus advocate that further efforts are required to incorporate explicit epidemiological constraints into evolutionary models in order to study the genome of both tick-borne and host associated strains and obtain further insight into the evolution of the B. burgdorferi species complex.However, these measures, by themselves, may not directly help in understanding the observed diversity patterns. The phylogenetic relationships between our sequences and reference sequences, which were isolated years ago in different areas, suggested that the diversification of the observed lineages of Figure S1tructure runs.Mean likelihood values of S Structure analyses were conducted using K values ranging from 1 to 15; for each value, the analysis was repeated five times and the mean likelihood value is plotted here. Analyses were based on all the polymorphic sites identified in at least 90% of B. burgdorferi and B. garinii strains using multiple chromosomal alignments. For each species, analyses were performed assuming correlations among linked loci and allowing admixture among potential populations. Green points correspond to results for B. burgdorferi s.s. and red points to results for B. garinii.(TIFF)Click here for additional data file.Figure S2Genetic differentiation between isolates from Munster and Guebwiller for B. burgdorferi s.s. and B. garinii. The distributions of HST values were obtained from within-species comparisons of SNPs between isolates from Munster and Guebwiller for B. burgdorferi s.s. (left column) and B. garinii (right column). The sequence alignments used in the comparisons were obtained from (A and B) the chromosome, (C and D) the cp26 plasmid, and (E and F) the lp54 plasmid. Red circles correspond to values that showed a significant level of differentiation (p<0.05).(TIFF)Click here for additional data file.Figure S3Distribution of Tajima's D values obtained from contiguous 1-kb windows for the three species of Borrelia included in this study. Values were calculated for A) chromosomal, B) cp26, and C) lp54 alignments. Green lines indicate the mean value of Tajima's D. Blue lines represent the mean of Tajima's D values calculated from chromosomal data and are replicated in each plot. Red lines indicate the 5th and 95th percentiles of chromosomal Tajima's D values for each species.(TIFF)Click here for additional data file.Figure S4Posterior distributions of parameters of the coalescent model M3. Distributions were obtained from the simulations, of the coalescent model M3, we selected (based on their similarity to our observations); (A) 1N = 1*Nc, the population size of B. burgdorferi s.s., (B) 2N = 2*Nc, the population size of B. garinii, (C) 3N = 3*Nc the ancestral population size, (D) the recombination rate within the B. burgdorferi s.s. species intra1r, (E) the recombination rate within the B. garinii species intra2r, (F) the recombination rate within the ancestral population intra3r, (G) the inter-specific recombination rate inter1r, (H) the inter-specific recombination rate inter2r, (I) M*(1*Nc+2*Nc) the time backward until the two populations merged.(TIFF)Click here for additional data file.Table S1Description of the 63 strains of the B. burgdorferi species complex that were isolated and sequenced in this study.A: M: Adult male; F: Adult female; N: Nymph. All strains were isolated from Ixodes ricinus ticks.(DOC)Click here for additional data file.Table S2Percentage of the length of reference sequences onto which raw sequences were mapped for each strain of the B. burgdorferi species complex examined in this study. Reference sequences came from i) the chromosome (Chr), the circular plasmid cp26, and the linear plasmid lp54 of B. burgdorferi s.s. strain B31; ii) the chromosome of B. bavariensis strain PBi and the cp26 and lp54 plasmids of B. garinii strain Far04; iii) the chromosome, cp26 plasmid, and lp54 plasmid of B. afzelii strain PKo.(DOC)Click here for additional data file.Table S3Description of the 23 sequenced strains isolated and sequenced in previous studies and used as references in this study.(DOC)Click here for additional data file.Table S4Bayes factors for each pair of coalescent models. Each cell gives the ratio of the number of simulations selected for the column model to the number selected for the row model .(DOC)Click here for additional data file.


Patella ulyssiponensis and Patella rustica as models for identifying factors affecting gene flow in marine organisms across the North-East Atlantic and the Mediterranean Sea. A set of allozyme loci and a fragment of the mitochondrial gene cytochrome C oxidase subunit I were screened for genetic variation through starch gel electrophoresis and DNA sequencing, respectively. An approach combining clustering algorithms with clinal analyses was used to test for the existence of barriers to gene flow and estimate their geographic location and abruptness. Sharp breaks in the genetic composition of individuals were observed in the transitions between the Atlantic and the Mediterranean and across southern Italian shores. An additional break within the Atlantic cluster separates samples from the Alboran Sea and Atlantic African shores from those of the Iberian Atlantic shores. The geographic congruence of the genetic breaks detected in these two limpet species strongly supports the existence of transpecific barriers to gene flow in the Mediterranean Sea and Northeastern Atlantic. This leads to testable hypotheses regarding factors restricting gene flow across the study area.Knowledge of the scale of dispersal and the mechanisms governing gene flow in marine environments remains fragmentary despite being essential for understanding evolution of marine biota and to design management plans. We use the limpets  Understanding the mechanisms that govern gene flow between locations is an essential issue for both scientists interested in evolution and speciation processes and ecological managers responsible for the implementation of sustainable management practices for exploited or endangered taxa. In the marine environment, high genetic homogeneity is expected across vast areas, as gene flow is assumed to occur over large geographic scales due to the lack of obvious barriers to dispersal and to the existence of pelagic larvae in many species Here we studied patterns of gene flow in marine organisms across the North-East Atlantic (NEA) and Mediterranean Sea (MS). Regions of restricted gene flow have been reported in this area for many marine taxa Patella ulyssiponensis Gmelin, 1791 and Patella rustica Linnaeus, 1758 were chosen as model organisms for the present study as these species i) have very limited mobility as adults, allowing patterns of genetic substructure to be more directly related to factors affecting larval dispersal; ii) they are very abundant and conspicuous on Mediterranean and NEA rocky shores. These two species have high census density and pelagic larvae which may spend up to 31.5 days in the water column P. ulyssiponensis suggests no differentiation across Iberian Atlantic shores P. rustica, previous studies revealed high genetic homogeneity along the Iberian coast The limpets i) test if gene flow across this area is better explained by a large panmictic population, a pattern of IBD or if there is evidence for barriers to gene flow and ii) evaluate whether species show congruent geographic patterns. Our results show that gene flow across the study area is not homogenous suggesting the existence of barriers affecting multiple species.In the present work we combined the use of clustering algorithms with geographic cline analyses to describe patterns of genetic variation over the range of these species and infer possible factors influencing gene flow. More specifically we aim to P. rustica and P. ulyssiponensis, identified according to this taxonomy were collected from 19 localities , Malic enzyme , Isocitrate dehydrogenase , Glutamate-oxaloacetate transaminase , Phosphoglucomutase , Peptidase D , Glucosephosphateisomerase  and 6- Phosphogluconate dehydrogenase . For P. ulyssiponensis PGD was not scored and only one locus was detectable for PGM. Electrophoretic conditions, buffer systems and staining procedures are described elsewhere For et al.Total genomic DNA was extracted from a portion of foot muscle following standard procedures Bayesian clustering algorithms which identify clusters of individual genotypes by maximising Hardy-Weinberg and linkage (HWL) equilibria . In the i) a spatial analysis at the individual level, ii) a spatial analysis at the level of sampling locations, iii) a non spatial analysis at the level of sampling locations. Runs were carried out for maximum K set to 5, 10 and 15. To check for consistency of admixture results, nine runs were performed for each species, with different numbers of iterations  and different numbers of reference individuals . All analyses were performed with 20 instantiations of the selection of reference individuals.The clustering algorithm implemented in BAPS v4.14 P. ulyssiponensis and proportion of individual's genome assigned to the Western Mediterranean cluster in P. rustica) were assumed to be binomially distributed around the cline expectations for each locality , BAPsP x Ne], where Ne is the effective sample size; Pe is the cline expectation; BAPsP is the estimated source frequency, cf. Likelihood clinal analyses AnalyseMathematica v7.0 A model of clinal change was constructed that allows for two distinct sigmoid clines across the studied transect. The centres and widths of these two clines were independently estimated, with the proportion of genome assigned to a given cluster at the beginning of the second cline equal to that at the end of the first. Maximum likelihood estimates were calculated as in 0H - the data are explained by finite sampling from a panmictic system; 1H - the data are explained by a single clinal change across the study area; 2H - the data are explained by two distinct clinal changes across the studied area. Likelihood comparison is simplified by considering nested hypotheses: 0H is nested within 1H when the width of clinal change in 1H becomes very wide with respect to the scale of the study region. Likewise 1H is nested within 2H when the second clinal change in 2H becomes very wide with respect to the scale of the study region. To test if clines differed between species (regarding their centre location and width) we introduced another hypothesis: 3H - the data are explained by two clines whose shape is shared (same centres and widths) between species. The likelihood of this hypothesis was then compared to the likelihood of the two species having two clines that differ in both their centre location and width. Likelihood ratio tests were used to compare alternative hypotheses.With the exception of Agadir (AGA), our sampling localities form a coastal-linear transect . Our cliFst-based comparisons of locality samples. ARLEQUIN 3.1 Fst values between samples and to test their statistical significance (1000 permutations). The Fst values obtained between sampling locations from different groups were compared to those obtained between sampling locations from the same group using a randomisation test. For each test instance the Euclidean distance between the average Fst values obtained between and within given groups of sampling locations was calculated. Each sampling location was then randomly assigned to a group to obtain a randomised distribution of Fst distances between groups (1000 permutations). Significance was determined by comparing the observed Fst distance between groups with the randomised Fst distribution (cf www.cse.csiro.au/poptools/).The results obtained from the combined use of the clustering algorithm and clinal analyses were contrasted with ution cf . This prFst/(1−Fst) and the log of the shortest distance by sea between each location, with 1000 replicates to test significance.For both species, IBD was tested within each HWL cluster. IBD was not tested over the entire dataset, as it may confound the effect of cluster boundaries with the effect of distance P. ulyssiponensis and P. rustica is deposited in the Dryad repository: http://dx.doi.org/10.5061/dryad.8c26c.Genotypic data from allozyme loci for both P. rustica and P. ulyssiponensis with TCS . For both species, the pattern of IBD within population units was tested as previously described for allozymes. To test if Fst values obtained between sampling locations from different groups were significantly higher than those obtained between sampling locations from the same group, randomisation tests were performed as previously described.Sequences from the mtDNA gene cytochrome C oxidase subunit I were trimmed to a 600 bp fragment and aligned manually with Bioedit 7.0.9.0 P. rustica an average of 4.7±2.2 alleles were found per locus, with a maximum of eight alleles in PGM1 and a minimum of two for both PGD and MDH. For P. ulyssiponensis 5.9±2.3 alleles were found on average per locus, with a maximum of nine alleles at the GPI locus and a minimum of 3 at ME.For P. ulyssiponensis and three for P. rustica, in both spatial and non-spatial analyses. The clustering differences between the individual and locality levels are consistent with the gain of information described by Corander et al.After applying Bonferroni's correction P. rustica were the same for spatial  and non-spatial analyses (LML: −2436.1). An “Atlantic” cluster (AtC) includes all the Atlantic sampling locations and Estepona; A “Western Mediterranean” cluster groups samples from Valência, Vibo Valentia, Crotone and Taranto; and an “Eastern Mediterranean” cluster unites samples from Savelletri and Peschici. The smallest change in the LML values when a sampling location is moved from the cluster to which it was assigned to another cluster was −10.9 for spatial analysis and −7.6 for non spatial analysis. These values are both of much higher magnitude than the significance limit of 2.3 proposed by Kass and Raftery Fst values ranging from 0.08 to 0.20  and non-spatial (LML: −2902.2) BAPS analyses. An “Atlantic” cluster (AtC) groups all the samples from the Atlantic together with Tarifa and Estepona. A “Mediterranean” cluster groups the remaining Mediterranean samples. The smallest change in the LML when a sampling location is moved from the cluster to which it was assigned to another cluster was −8.6 for spatial analysis and −7.5 for non-spatial analysis. Significant differences in allelic frequencies between these clusters were detected for ME, GPI and PEPD  are also observed for this species  H1 with ΔLL = 35.12, ΔDF = 3, p<0.001 and H1 is rwrt H2 with LL = 3.91, ΔDF = 3, p<0.05; for P. rustica H0 is rwrt H1 with ΔLL = 21.49, ΔDF = 3, p<0.001 and H1 is rwrt H2 with ΔLL = 23.55, ΔDF = 3, p<0.001). When H2 for each species is compared to the simplified hypothesis (H3) of cline shapes being shared between species, the complexity of H2 (species differences in cline shape) is not justified . Under the H3 hypothesis, the centre of the first cline coincides with the Almeria-Oran Front  while the second lies off South Italy , independently of the distance between them (p = 0.87). The Fst values between Agadir or Estepona and the remaining sampling locations from the AtC are significantly higher than those obtained for other intra-cluster comparisons , a pattern that cannot be explained by IBD . The Fst values obtained in comparisons involving Agadir or Estepona and the remaining AtC sampling locations were significantly higher than those obtained for the comparisons within each of these two groups (p<0.05), a pattern not explained by IBD.A total of 32 haplotypes were found for the rest . IntermeP. ulyssiponensis 27 haplotypes were detected, 13 of which had already been reported Fst values for comparisons involving Agadir, Estepona and Tarifa with all other Atlantic samples .For  p<0.01; . Within P. rustica and P. ulyssiponensis show evidence of coincident barriers to gene flow within their ranges. Three lines of evidence favour the importance of these transpecific barriers in contrast to hypotheses of range-wide panmixis or IBD: i) clustering analyses detected more than one genetic cluster for both species; ii) significant sharp changes in the genetic composition of individuals were observed for both species across short geographic distances (iii) Fst comparisons of samples from different clusters were significantly higher than those obtained between samples from the same cluster  F cluster . The sha cluster . On seco cluster . Disting cluster  barriersPatella depressa Pennant, 1777, Patella ferruginea Gmelin, 1791 and Patella caerulea Linnaeus, 1758 P. depressa is restricted to Atlantic shores while its closely related species P. caeruleaP. ferruginea are limited to Mediterranean shores P. ulyssiponensis and P. rustica. However, clinal analyses show the transition between the Atlantic and Mediterranean clusters is not located at the Gibraltar Strait  but most probably at the Almeria-Oran Front, with samples from Tarifa  and Estepona  being included in the Atlantic cluster. The Almeria-Oran Front  ecological shifts in the biotic and abiotic environment that restrict effective dispersal to unsuitable habitats or to a competitor's range (reviewed in ii) barriers to dispersal that prevent/slow down further dispersal P. caerulea and P. ferruginea, causing divergence between populations from the Alboran Sea and the remaining species range. Further research involving ecological modelling of species densities and larval dispersal would also contribute to an understanding of why the above mentioned species boundaries are close but not coincident with intraspecific barriers to gene flow.Several of the factors that restrict gene flow between populations of a given species are also expected to impose limits to species' ranges. These include: P. rustica mirror those of Sá-Pinto et al.P. ulyssiponensis, the clinal analyses revealed a significant increase in the average proportion of nuclear genome assigned to the Mediterranean cluster  are congruent, this would argue for a common barrier promoting diversification and speciation in Mediterranean limpets by allowing population differentiation (as may be the case for P. ulyssiponensis) and/or restricting introgression between differentiated clades (as may be the case for P. rustica).The transition between the Eastern and Western Mediterranean basins is also the limit of distribution for P. ulyssiponensis) and the other including the remaining Atlantic sampling locations. In P. ulyssiponensis the distribution of the mtDNA haplotype Pu3 drives the high and significant Fst values obtained in comparisons of samples from the two areas. For P. rustica, comparisons also result in consistently higher Fst values for both allozymes and mtDNA.Within the AtC, no significant IBD was detected for either species. Our data suggest that there is, however, genetic differentiation, for both species, between two areas - one including Agadir and Estepona Click here for additional data file.Table S2Fst values obtained in comparisons between clusters obtained for Patella ulyssiponensis.(DOC)Click here for additional data file.Table S3Fst values obtained in comparisons between the three geographic areas delimited by the barriers identified by clinal analyses for Patella ulyssiponensis.(DOC)Click here for additional data file.Table S4mtDNA haplotype frequencies in each sampling location for Patella rustica.(DOC)Click here for additional data file.Table S5mtDNA haplotype frequencies in each sampling location for Patella ulyssiponensis.(DOC)Click here for additional data file.


Procapra przewalskii), which inhabits a human-altered area on Qinghai-Tibet Plateau, is one of the most endangered antelope species in the world. Here, we report a landscape genetic study on Przewalski's gazelle. We used skin and fecal samples of 169 wild gazelles collected from nine populations and thirteen microsatellite markers to assess the genetic effect of anthropogenic landscape features on this species. For comparison, the genetic effect of geographical distance and topography were also evaluated. We found significant genetic differentiation, six genetic groups and restricted dispersal pattern in Przewalski's gazelle. Topography, human settlement and road appear to be responsible for observed genetic differentiation as they were significantly correlated with both genetic distance measures [STF/(1−STF) and STF′/(1−STF′)] in Mantel tests. IBD (isolation by distance) was also inferred as a significant factor in Mantel tests when genetic distance was measured as STF/(1−STF). However, using partial Mantel tests, AICc calculations, causal modeling and AMOVA analysis, we found that human settlement was the main factor shaping current genetic differentiation among those tested. Altogether, our results reveal the relative influence of geographical distance, topography and three anthropogenic landscape-type on population genetic differentiation of Przewalski's gazelle and provide useful information for conservation measures on this endangered species.Anthropogenic landscapes influence evolutionary processes such as population genetic differentiation, however, not every type of landscape features exert the same effect on a species, hence it is necessary to estimate their relative effect for species management and conservation. Przewalski's gazelle ( Procapra przewalskii), which inhabits a human-altered area on the Qinghai-Tibet Plateau in central Asia, to investigate the relative genetic effect of different types of landscape features.The potential effect of landscape features on dispersal and population differentiation of wild animals has been well recognized 2 under human disturbance The Przewalski's gazelle is one of the most endangered antelope species in the world In this study, we used 13 nuclear microsatellite loci and landscape genetic methods to test our hypothesis. The aims of this study are: (1) to estimate population genetic differentiation and dispersal pattern of the gazelle; (2) to identify the factors responsible for observed genetic differentiation and to reveal their relative effect. The results provide useful information for conservation of Przewalski's gazelle and, more broadly, yield empirical data for understanding the genetic effect of anthropogenic landscape features on wild ungulates across Eurasia grasslands.Our research on Przewalski's gazelle in the Gonghe Basin and Qinghai Lake Basin was approved by the Chinese Wildlife Management Authority and conducted under the Wildlife Protection Law of the People's Republic of China.2 and an average water depth of 16 m (max. 28 m). The study area is 2900–3800 m above sea level, surrounded by several mountains. There are three main types of man-made landscapes fragmenting the distribution and habitat of the gazelle: human settlements , roads  and railways . Qinghai Lake is the largest inland saline lake in China, and a perennial lake that freezes in winter. The lake has an area of 4300 kmrailways . Their crailways . Samplesrailways , includirailways . Skin saGenomic DNA from skin samples was isolated using standard proteinase K digestion and phenol/chloroform extraction procedures IDP or ID(Sibs)P]. Fecal samples with identity in all alleles or with only a single mismatch were considered to be deposited by the same individual, and duplicates were removed from the data set. The program MICRO-CHECKER version 2.2.3 We conducted two replicate PCRs for skin samples. Fecal samples were amplified using a modified multiple-tube procedure F-statistics STF between populations and overall STF across the study area. The significance of STF values were assessed via 10 000 permutation procedure using FSTAT version 2.9.3.2 STF′ (estimated using STG″) to control for the influence of within population heterozygosity STF′ estimates were obtained through jackknifing, and the significance of STF′ values were assessed by permutation test (10 000 permutations) SH , were obtained from GenoDive Deviations from Hardy–Weinberg Equilibrium (HWE) for each population and genotypic linkage disequilibrium (LD) across all pairs of loci were estimated in GENEPOP version 4.0 STF or STF′ matrix. To test the robustness of estimated barriers, we used MICROSAT The program Barrier version 2.2 K (number of genetic groups) = 1–9 were performed, using correlated allele frequencies K was identified using the maximum values of Ln P(D) (the posterior probability of the data for a given K) and ΔK  The Bayesian clustering method implemented in software STRUCTURE To assess the genetic variance within and among several possible population groupings, a hierarchical AMOVA  was performed using Arlequin version 3.11 homeL and home/LmaxL) implemented in GeneClass version 2.0.h 0). homeL is the likelihood of drawing an individual from the population in which it was sampled, it is appropriate to use when some source populations for immigrants are not sampled home/LmaxL is the ratio of homeL to the greatest likelihood observed in all sampled populations, it is suitable when all source populations are sampled r of each distance class r was tested by comparing the estimated r with the 95% confidence interval about the null hypothesis of a random distribution.For migration rate estimation, we used two types of likelihood computation (STF/(1−STF) and STF′/(1−STF′), respectively] To assess which factors influenced genetic differentiation in Przewalski's gazelle, Mantel tests c values for relatively low number of pairwise comparisons in our data c scores imply closer approximation to the true model, and generally, models with AICc values greater than 10 compared to the model with the lowest AICc value are not supported STF/(1−STF) or STF′/(1−STF′)]. This involved computing Mantel and partial Mantel correlation coefficients for each organizational model. Then, the observed correlation coefficients  were compared with the expectations of organizational models, and the organizational model with the greatest support was identified.To choose the best model interpreting genetic differentiation based on Mantel results, Akaike's information criterion (AIC) which represents how far a tentative model is from the true model was used STF values for each population and relates them to the factors tested using a generalized linear model, and provides posterior probabilities for each model using a reversible jump Markov Chain Monte Carlo (MCMC) approach. Three independent replicates were executed with default parameter values of the program to ensure consistency of results.We also used the hierarchical Bayesian method implemented in GESTE version 2.0 IDP of 3.48×10−9 and ID(Sibs)P of 2.63×10−4 indicates high reliability of the 13 microsatellite markers for distinguishing fecal samples from the same individual. After removing duplicate fecal samples, we obtained genotype data for 169 individuals . The total genotyping error rate was 0.328%, indicating high reliability of the data. MICRO-CHECKER did not detect the presence of null alleles or scoring errors.We collected 28 skin samples and 182 fresh fecal samples from the field. We amplified 25 skin and 161 fecal samples with more than ten loci. The unbiased At the 0.05 significant level, all microsatellite loci were in HWE for each population and the entire sample. Thirty seven out of 702 loci pairs for each population  and seven out of 78 loci pairs for the entire sample showed significant LD. After Bonferroni correction, genotypic linkage disequilibrium was not significant.STF and STF′ across the study area was 0.072 (P<0.001) and 0.147 (P<0.001) respectively. Pairwise STF between sampling populations ranged from 0.0002 to 0.1152, and the range of pairwise STF′ were from 0.000 to 0.257 (STF or STF′), the results indicated significant genetic differentiation between the population pairs except P2–P3 pair, P5–P6 pair, and pairs involving P4 or P7 (SH) ranged from 0.462 in P3 to 0.641 in P7 (STF or STF′) was used, which separated the nine populations of Przewalski's gazelle into seven groups: P1, P2–P3, P4, P5–P6, P7, P8, P9 (Ln P(D) maximized at K = 6 genetic groups consisting of: G1 (P1), G2 (P2–P3–P4), G3 (P5–P6), G4 (P7), G5 (P8) and G6 (P9) , G′2  and G′3 (P9) (P<0.001) and genetic discontinuities from Barrier  were much higher  , whereasG′3 (P9) . BecauseG′3 (P9) . Overallh higher . The groh higher .home/LmaxL. They were Hudong4 (in P2) from P3, Ketu10 (in P3) from P2, Shadao2 (in P4) from P3 and HerG8 (in P6) from P5. Using homeL, in addition to the same four F0 being identified, another first generation migrant (Hudong7) in P2 was detected from P1. Spatial autocorrelation analysis displayed positive and significant r values for the first five distance classes , and the highest r value was observed within two kilometers . The correlogram flattened out after 20 km, then r values remained slightly negative up to 80 km, followed by oscillation  or STF′/(1−STF′)] were largely consistent. Significant positive associations were found between genetic differentiation and human settlement, road or topography, but genetic differentiation was not significantly correlated with the presence of railway (STF/(1−STF), Mantel tests also indicated that 17.04% of the genetic differentiation could be explained by IBD (isolation by distance) ], indicating that GESTE failed to identify the true model In Mantel tests, the results from two genetic distance measures  (STF/(1−STF) ] . Geograp/(1−FST) . The resn factor  and S3. n factor , thus hu2), it is much higher within human settlement areas (about 1000 to 1600 persons per km2).The primary role of human settlement could be corroborated by following observations. First, P1, P2 and P3 provide a good example showing the large genetic effect of human settlement . These pBesides human settlement, attention should also be given to the effect of geographical distance, road and topography. As a restricted dispersal pattern was revealed in Przewalski's gazelle, geographical distance seems to be an important influencing factor for distant populations. The study area is fragmented by roads , and a rThe railway showed no influence on genetic differentiation, probably because of its short history, limited distribution and infrequent traffic . MoreoveBased on our findings in this study, we propose the following measures to conserve Przewalski's gazelle. First, since human settlement has large effect on Przewalski's gazelle, measures mitigating its negative influence should be carried out. One sensible strategy is collaboration with local community to protect the gazelle, through conservation communication et al. This proposal is probably realistic, given that local people are in favor of protecting the gazelle In conclusion, our results indicated significant genetic differentiation, five genetic discontinuities, six genetic groups and restricted dispersal pattern in Przewalski's gazelle, and showed that, under realistic circumstance with compound effect, IBD, topography and some anthropogenic landscape features (human settlements and roads) have been affecting the process of population genetic differentiation. Furthermore, using multiple methods, we demonstrated that human settlement is the main factor shaping current genetic differentiation among those tested. In conservation context, we propose several conservation strategies, such as habitat protection and restoration between close populations, and collaboration with local community. It is possible that other factors, biotic as well as abiotic, may have contributed to the genetic structure of this species, although the analyses presented here seem to point clearly at human settlements being the primary agent responsible for differentiation. To our knowledge, this is the first study assessing the genetic effect of anthropogenic landscape features on wild ungulate in an alpine grassland ecosystem on the Qinghai-Tibet Plateau. Because Przewalski's gazelle is a flagship species Figure S1Output of Structure analysis according to ΔK values. (a) Plot of ΔK indicates that there are most likely three genetic groups. (b) Bar plot of three genetic groups. The sampling populations for individuals are shown as P1–P9, and the genetic groups assigned (G′1–G′3) are shown above: G′1 including P1, P5, P6, P7 and P8–red; G′2 including P2, P3 and P4–blue; G′3 including P9–green.(TIF)Click here for additional data file.Figure S2Output of Structure analysis using the data of G′1. (a) Average values of Ln P(D) show that the highest log likelihood occurs at K = 4 genetic groups. (b) Plot of ΔK indicates that there are most likely four genetic groups. (c) Bar plot of four genetic groups. The sampling populations for individuals are shown as P1, P5, P6, P7 and P8, and the genetic groups assigned are shown above: G1 including P1–red; G3 including P5 and P6–blue; G4 including P7–yellow; G5 including P8–green.(TIF)Click here for additional data file.Table S1Matrix of categorical distance of railway  between nine populations.(DOC)Click here for additional data file.Table S2The sample size and genetic diversity within each population.(DOC)Click here for additional data file.Table S3STF′/(1−STF′) in Mantel tests.Evaluation of seven organizational models when using the genetic distance (DOC)Click here for additional data file.


Genomic medicine seeks to exploit an individual’s genomic information in the context of guiding the clinical decision-making process. In the post-genomic era, a range of novel molecular genetic testing methodologies have emerged, allowing the genetic testing industry to grow at a very rapid pace. As a consequence, a considerable number of different private diagnostic testing laboratories now provide a wide variety of genetic testing services, often employing a direct-to-consumer (DTC) business model to identify mutations underlying (or associated with) common Mendelian disorders, to individualize drug response, to attempt to determine an individual’s risk of a multitude of complex  diseases, or even to determine a person’s identity. Recently, we have noted a novel trend in the provision of private molecular genetic testing services, namely saliva and buccal swab collection kits (for deoxyribonucleic acid (DNA) isolation) being offered for sale over the counter by pharmacies. This situation is somewhat different from the standard DTC genetic testing model, since pharmacists are healthcare professionals who are supposedly qualified to give appropriate advice to their clients. There are, however, a number of issues to be addressed in relation to the marketing of DNA collection kits for genetic testing through pharmacies, namely a requirement for regulatory clearance, the comparative lack of appropriate genetics education of the healthcare professionals involved, and most importantly, the lack of awareness on the part of both the patients and the general public with respect to the potential benefits or otherwise of the various types of genetic test offered, which may result in confusion as to which test could be beneficial in their own particular case. We believe that some form of genetic counseling should ideally be integrated into, and made inseparable from, the genetic testing process, while pharmacists should be obliged to receive some basic training about the genetic tests that they offer for sale. Genomic or personalized medicine seeks to exploit an individual’s genetic or genomic information in the context of guiding the clinical decision-making process . Examinahttp://www.gao.gov/new.items/d10847t.pdf).At the same time, molecular genetic testing technology has evolved such that low- and medium-throughput methods have been largely replaced by high-throughput genome-wide microarray-based assays, an important advance which has allowed the genetic testing industry to grow at a very rapid pace . Indeed,Recently, we have noted a novel trend in the provision of private molecular genetic testing services, namely saliva and buccal swab collection kits (for DNA isolation) sold over the counter through pharmacies. These tests are very different from the standard pregnancy or blood sugar test kits that have long been sold over the counter in pharmacies since these latter kits do not involve DNA isolation or genetic analysis.Several genetic testing laboratories in Europe and the USA appear to have entered into partnerships with pharmacies to provide their services to the public. These private genetic laboratories offer the following types of genetic test: (a) genetic tests for the detection of single-gene (Mendelian) disorders; (b) pharmacogenomic tests to individualize drug (including cancer) treatment, e.g., to guide specific mutation-targeted treatment decisions; (c) ‘predictive genomic testing’ for complex disorders and traits, e.g., heart/cardiovascular disease, osteoporosis, diabetes, athletic performance, etc.; (d) nutrigenomic tests, to individualize diet choices with the aim of bringing about weight loss; and (e) identity DNA testing. These tests may be organized into three main categories:CY2D6, CYP2C9, VKORC1, and other pharmacogenes) have been cleared for use in clinical practice.1. Genetic tests designed to diagnose inherited disorders and conditions that can be unambiguously assigned to specific genomic variants, namely (a) single-gene disorders and (b) drug-treatment efficacy or toxicity. For many of these tests, there is strong scientific evidence to support genotype-phenotype correlations, especially in the case of common genetic/genomic disorders. For a number of anticoagulant, psychiatric, and anti-cancer drugs, several pharmacogenomic markers have been identified in genes encoding drug-metabolizing enzymes and transporters that can predict drug efficacy or toxicity with a high degree of reliability, and many of them . In these cases, the parties involved would be guilty of a misdemeanor punishable by law, at least in European countries and several US states .proviso of course that they have the necessary basic genetics training. In all these cases, and in several different countries, pharmacies are in effect acting as middlemen between the genetic testing providers and their customers.This situation is somewhat different from the standard DTC genetic testing model since pharmacists are healthcare professionals who are in principle qualified to give appropriate advice to their clients, with the There are certainly many benefits for the customers to be derived from the genetic tests which have been designed for the diagnosis of single-gene disorders and pharmacogenomics. First of all, by raising awareness of genetic issues, they can empower the individual to make decisions that could improve their health, including the adoption of better lifestyle choices. Moreover, the availability of this type of genetic test may contribute to a general decrease in the cost of genetic testing as a result of commercial competition. There are nevertheless a number of issues to be addressed in relation to selling DNA collection kits for genetic testing in pharmacies to ensure that the process is indeed beneficial to the patients involved and does not mislead the general public.In the USA, regulatory clearance is required before a DNA collection kit  can be commercially distributed. In 2010, the US Food and Drug Administration banned a commercial partnership between Walgreens, a retail pharmacy chain, and Pathway Genomics, a private genetic laboratory, from offering a variety of over-the-counter genetic tests since the company had not previously obtained regulatory clearance for the saliva collection kit to be sold as a medical device . In contcaveats and, if this is not fully appreciated by the user, misunderstanding of the clinical implications of these results is likely to be the rule rather than the exception. When physicians and pharmacists have been surveyed in the USA [Another major area of concern is the comparative lack of appropriate genetics education on the part of the healthcare professionals involved, namely pharmacists and physicians . The gen the USA ,15 and i the USA ,17, manyFor all the reasons discussed above, we believe that some form of genetic counseling should ideally be integrated into, and made inseparable from, the genetic testing process . To thisFinally, we must all understand that our knowledge of the genome remains at a fairly rudimentary stage which can, metaphorically speaking, be likened to the Phaestos disc , where oThe authors declare that they have no competing interests.GPP, DJB, FAM, VV, and DNC conceived and compiled the manuscript. All authors read and approved the final manuscript.


Coregonus albula and the local endemic dwarf-sized spring-spawning C. fontanae, both originating in Lake Stechlin. We have employed molecular cytogenetic tools to identify the genomic differences between the two species of the sympatric pair on the sub-chromosomal level of resolution.Sympatric species pairs are particularly common in freshwater fishes associated with postglacial lakes in northern temperate environments. The nature of divergences between co-occurring sympatric species, factors contributing to reproductive isolation and modes of genome evolution is a much debated topic in evolutionary biology addressed by various experimental tools. To the best of our knowledge, nobody approached this field using molecular cytogenetics. We examined chromosomes and genomes of one postglacial species pair, sympatric European winter-spawning in situ hybridization (FISH) experiments consistently revealed a distinct variation in the copy number of loci of the major ribosomal DNA (the 45S unit) between C. albula and C. fontanae genomes. In C. fontanae, up to 40 chromosomes were identified to bear a part of the major ribosomal DNA, while in C. albula only 8–10 chromosomes possessed these genes. To determine mechanisms how such extensive genome alternation might have arisen, a PCR screening for retrotransposons from genomic DNA of both species was performed. The amplified retrotransposon Rex1 was used as a probe for FISH mapping onto chromosomes of both species. These experiments showed a clear co-localization of the ribosomal DNA and the retrotransposon Rex1 in a pericentromeric region of one or two acrocentric chromosomes in both species.Fluorescence We demonstrated genomic consequences of a rapid ecological speciation on the level undetectable by neither sequence nor karyotype analysis. We provide indirect evidence that ribosomal DNA probably utilized the spreading mechanism of retrotransposons subsequently affecting recombination rates in both genomes, thus, leading to a rapid genome divergence. We attribute these extensive genome re-arrangements associated with speciation event to stress-induced retrotransposons (re)activation. Such causal interplay between genome differentiation, retrotransposons (re)activation and environmental conditions may become a topic to be explored in a broader genomic context in future evolutionary studies. Coregonus, based on extensive genetic and population genetic [Intra-lacustrine fish speciation as an example of ecological speciation is a much debated topic in evolutionary biology addressed by various experimental tools, mostly in complex systems with a number of species, in particular in ancient freshwater lakes . In Euro genetic , phyloge genetic . However genetic ). Such a genetic , explainCoregonus albula and C. fontanae in the dimictic Lake Stechlin, northern Germany to test whether the above outlined parallelism on karyotype differentiation in intralacustrine species pairs can also be observed in incipient speciation processes in young postglacial lakes. Both species are pelagic zooplanktivores, but they differ considerably in their size, spawning time [C. fontanae has not yet been subjected to any cytogenetic analysis as opposed to C. albula  between these two species when compared with another sympatric species pair C. albula and C. lucinensis[C. albula complex and suggested a rather complex colonization history than simple sympatric speciation [In this study, we examined chromosomes and genomes of the sympatric species ing time  and temping time . Up to nula (see  and refeucinensis. Furthereciation . Therefo3 and DAPI fluorescence). At the second stage, we have performed molecular cytogenetic analyses (CGH and FISH with various rDNA fragments and non-LTR retrotransposons as probes) to identify any differences between chromosomal complements of these two species on the sub-chromosomal level of resolution since the karyotype analyses showed no significant differences. At the third stage, we performed molecular biological analyses of the 45S ribosomal RNA genes and the Rex1 non-LTR retrotransposon. Furthermore, we discuss these results in the context of populations of small effective sizes under extreme stress conditions under which retrotransposons (re)activation could have contributed to accelerated speciation.The major cluster of ribosomal RNA genes is expressed as the 45S transcriptional unit , The NF was 96 in both species  and DAPI (specific for AT rich regions) stainings revealed in both species a varying number of 6–8 sites with CMA3+/DAPI- signals. The signals occurred at telomeric regions of 3–4 metacentric chromosomes and at pericentromeric regions of 3–4 acrocentric/submetacentric chromosomes  and both belong to the karyotype category A sensu. They boin situ hybridization (FISH) with 28S ribosomal DNA (rDNA) probes derived from two non-overlapping regions of the 28S rRNA gene of both species (an 800 bp region adjacent towards the 5′-end of the 28S rDNA gene and a 300 bp region adjacent towards the 3′-end) showed strikingly different results. FISH using the shorter fragment as a probe revealed the presence of 6–10 chromosomes in both C. albula and C. fontanae bearing such sequences distributed similarly as the CMA3-/DAPI- . FISH with the longer fragment revealed bright signals on 6–10 chromosomes in C. albula  centromeric or pericentromeric regions of acrocentric chromosomes. Two signals of the 800 bp 28S rDNA probe corresponded to the major NOR sites evidenced also by the 300 bp rDNA and the CMA3/DAPI staining that were localized in telomeric regions of two large metacentric chromosomes  experiments. A mixture of the whole genome DNA (gDNA) of both C. albula and C. fontanae was hybridized simultaneously to both C. fontanae and C. albula chromosomes. This resulted in nearly no significant differences on C. albula chromosomes, i.e. a balanced hybridization of both gDNA probes was observed . The typical chromosomes bearing rDNA signals in the unamplified condition  are shown in Figures C. fontanae  related to a distinct DAPI+ band on a large metacentric chromosome pair  as probes amplified from both of the genomes were performed to C. fontanae might have arisen, a PCR screening for non-LTR retrotransposons in genomic DNA of both species was performed. Retrotransposons of the Rex family are known to have invaded fish genomes in multiple lineages [Rex1, Rex3 and Rex6 were tested in this study. FISH with the Rex3 and Rex6 retroelements yielded inconclusive results. The Rex1 element, as a probe hybridized to chromosomes of C. albula and C. fontanae, typically showed a dispersed pattern of signals on all chromosomes with a distinct accumulation in a pericentromeric region of one single acrocentric chromosome. Co-hybridization of the Rex1 element with the 800 bp 28S rDNA probe in a double-FISH experiment showed co-localization of these two probes typically on one , mostly acrocentric chromosomes in both C. albula and C. fontanae . The Rex1 signal with a distinctly weaker intensity occurred dispersed also on other sites corresponding to the NOR loci in both genomes. The sequences of the Rex1 derived from the C. albula and C. fontanae genome were deposited in GenBank under the accession numbers JQ731754 and JQ731760, respectively.To determine mechanisms how such extensive multiplication of parts of rRNA genes in lineages  and to alineages . TherefoC. albula and C. fontanae.Sequencing of the 18S and 28S rDNA, as well as ITS1 and ITS2 (deposited in GenBank under accession numbers JQ731749-JQ731753 and JQ731755-JQ731759) yielded no significant differences in these genes between C. fontanae genome when compared with the situation in C. albula are in strong contrast with previously reported low genetic differentiation between these two species [Our findings of extensive genomic re-arrangements of a substantial fraction of the 45S rDNA unit in the  species ,18.C. fontanae next to the complete 6–10 NOR loci corresponding to similar number of NOR-bearing chromosomes in C. albula, up to 30 supernumerary and incomplete NOR loci occur. This is supported by results of the sequential fluorescent staining (CMA3 and DAPI), showing about 6–8 signals in karyotypes of both species, although on chromosomes of C. fontanae the signals were slightly weaker. However, these supernumerary sites in C. fontanae were not represented by repeating of the complete 45S rDNA unit n but only by a part including probably complete ITS1 and ITS2 and a part of the 28S rDNA adjacent to the ITS2, i.e. the 5′ end of the 28S rDNA gene (the region of 5.8S rDNA was not investigated separately).Our results indicate that in the genome of C. fontanae were localized in the AT rich pericentromeric regions as well as the major accumulation of the Rex1 retrotransposon on both C. albula and C. fontanae chromosomes. This is in accordance with findings of other authors describing accumulations of transposable elements in centromeric heterochromatin e.g. in genome of humans [Cichla kelberi[Drosophila melanogaster and Bombyx mori[Erythrinus erythrinus, where Rex3 retrotransposons were found in the 5S rRNA genes [Most of the supernumerary signals of the 45S rDNA in chromosomes of f humans  and in aa kelberi,28. TEs a kelberi; reviewea kelberi). There a kelberi), in insmbyx mori or in thNA genes .E. erythrinus fish, a similar multiplication of rRNA genes was described [E. erythrinus differ in their chromosomal number, karyotype, presence or absence of heteromorphic sex chromosomes and numbers of 5S rDNA loci. The karyomorph A in E. erythrinus showed only two 5S rDNA loci, while in the karyomorph D, 21–22 5S rDNA loci could be observed. All 5S rDNA sites co-localized with the Rex3 retrotransposon. On the other hand, no changes in the heterochromatin and 18S rDNA patterns were found between these two karyomorphs [E. erythrinus may be seen as an incipient stage of a speciation event. This situation can thus represent an initial stage, later resulting in the condition observed in morphologically [C. fontanae and C. albula described in this study. A similar observation of extremely multiplied NOR sites , however, without any further detailed analysis, were reported in brook char Salvelinus fontinalis  [In the above-mentioned escribed . In thatyomorphs . Such twogically , ecologiogically ,34 divermonidae) .C. albula are far behind the other populations of this species in adjacent lakes in northern Germany [C. fontanae is the smallest species of the genus Coregonus in Europe [C. fontanae, live in an extreme permanent starvation in the Lake Stechlin. It can be also hypothesized that the spring-spawning habit of C. fontanae might have originated simply by the shift of sexual maturity in the part of the population that has not been able to attain sexual maturity in autumn due to the lower food intake and hence environmental starvation stress.In salmonid fishes, TEs have been studied intensively ,36,37. M Germany . C. fontn Europe . RaisingEffects of stress on the genome can result in important perturbations creating new combinations better compatible with survival . rosophila providesCoregonus spp.), [In a very similar, but North American study system ,  next genC. albula and C. fontanae, we encounter a complex situation involving several evolutionary phenomena and factors. Firstly, a rapid ecological speciation event with an unclear sympatric scenario, i.e. the derived species C. fontanae fully differentiated from C. albula physiologically, ecologically and morphologically within about 12 – 14 kyrs in the newly colonized Stechlin Lake after the last glacier retreated [C. fontanae when compared with its most likely ancestral species – C. albula. The genome re-arrangements are exhibited as a distinct loci number differences and relocation of variable number (about 30) AT rich pericentromeric regions in C. fontanae. The molecular mechanism behind these re-arrangements might be a retrotransposition of a part of the 45S rDNA unit mediated by retrotransposons. Retrotransposonal activity can be mobilized under certain conditions  and cause rapid and extensive structural changes to the host genome. These structural genomic differences in C. fontanae accumulated to pericentromeric heterochromatin in almost half of the chromosome complement. This might then have been acting as a partial but permanent reproductive barrier by hampering recombination, thus, enabling and accelerating the morphological, ecological and physiological differentiation of C. fontanae. Moreover, interspecific hybridization between the old and the newly arising species might have activated retrotransposonal activity in hybrids resulting in hybrid sterility or unviability as reviewed by [In the sympatric species pair etreated . Secondletreated  showed, iewed by . The popCoregonus albula  and 16 individuals of C. fontanae[C. albula, 3 individuals  yielded metaphases usable for down-stream FISH and CGH experiments. In C. fontanae, 3 individuals also  yielded usable chromosome preparations. Of all studied individuals, we isolated genomic DNA from fin clips and muscles. All tissue and DNA samples, including cell suspensions and chromosome preparations, are deposited in the Laboratory of Fish Genetics of the Institute of Animal Physiology and Genetics (IAPG). This study was covered by the “Valid Animal Use Protocols” Nr. CZ 00221 at the IAPG issued by the Czech Ministry of Agriculture on 10 June 2009.For this study, we had 12 individuals of  fontanae, both fr fontanae,34. In CMetaphases were prepared according to  with sliin situ hybridization experiments were produced either by PCR (FISH probes) or directly from the genomic DNA (CGH probes). Probes were indirectly labelled with haptens (biotin and digoxigenin) by means of nick translation (whole genomic DNA and FISH probe longer than 600 bp) using the Roche Nick Translation Mix  according to the manufacturer’s instructions. Shorter DNA fragments were labelled by PCR using the Roche PCR DIG Labeling Mix (Cat.No. 11585550910). The biotin-dUTP labelled probes  were detected by either the Invitrogen Cy™3-Streptavidin  or by the FITC-Streptavidin (Cat.No. 43–4311). The digoxigenin-dUTP labelled probes  were detected by either the Roche Anti-Digoxogenin-Fluorescein (Cat.No. 11207741910) or by the Anti-Digoxigenin-Rhodamin (Cat.No. 11207750910). An unlabelled DNA competitor for suppression of nonspecific hybridization of fragment size of 100–200 bp was added with 20-fold the concentration of the DNA probe in CGH experiments. The CGH DNA probe concentration was 1 ug per reaction for both genomes compared. An aging of chromosome preparations at 37°C for 3 hours was carried out prior to each of the hybridization experiment. Pepsinization, hybridization and detection were carried out under conditions as described by [Probes for ribed by .All rDNA FISH probes were constructed using published, mostly generally used PCR primer sets of the 45S rDNA unit to cover its major regions and to map them physically onto chromosomes.All primer sets used in this study are summarized in Table PCR products were cloned using the QIAGEN PCR Cloning Kit and QIAGEN EZ Competent Cells ; the plasmids were isolated from the cells with Qia PREP Spin Miniprep Kit according to the manufacturer’s instructions. The primary PCR products were first sequenced on the ABI 3130 Genetic Analyzer  using the BigDye Terminator Cycle Sequencing Kit (Applied Biosystems). Furthermore, cloned DNA fragments that were later applied as FISH probes were commercially sequenced by Macrogen . The commercially obtained sequences were subjected to online megablast or discontiguous megablast  searchesChromosome preparations were analysed with the Provis AX70 Olympus microscope equipped with standard fluorescence filter sets. Gray-scale hybridization signals on chromosomes and/or DAPI counterstained chromosomes were captured by the CCD camera (DP30W Olympus). Using the Olympus Acquisition Software, black and white images were pseudo-coloured and superimposed with the software MicroImage. The colour images have been analyzed and processed with Adobe Photoshop, Version CS5. The chromosomes were classified using the nomenclature proposed by . Karyoty3;DAPI: 4′, 6-diamidino-2-phenylindole;DAPI/CMA3+/−: DAPI/CMA3 positive/negative signals;FISH: Fluorescence in situ hybridization;IAPG: Institute of animal physiology and genetics;kyrs BP: Thousand years before presence;NF: “Nombre fundamental” chromosome arm number;NGS: Next generation sequencing;NOR: Nucleolar organizer DNA;rDNA: Ribosomal DNA;TE: Transposable elements;2n: Diploid chromosome numberAFLP: Amplified fragment length polymorphism;CGH: Comparative genomic hybridization;CMA3: Chromomycin AThere are no competing interests to declare.RS designed and performed experiments FISH and CGH and co-drafted the manuscript, ZM and AS performed FISH experiments, ZM performed PCRs and sequenced PCR products and co-drafted the manuscript, GS and JF collected and raised material, JB and RS prepared chromosome preparations, MR partly contributed to digital processing of images, PR designed the study and co-drafted the manuscript. All authors read and approved the final manuscript.