Raphaël Leblois

Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity

Pandemic infectious diseases have accompanied humans since their origins1, and have shaped the form of civilizations2. Of these, plague is possibly historically the most dramatic. We reconstructed historical patterns of plague transmission through sequence variation in 17 complete genome sequences and 933 single nucleotide polymorphisms (SNPs) within a global collection of 286 Yersinia pestis isolates. Y. pestis evolved in or near China, and has been transmitted via multiple epidemics that followed various routes, probably including transmissions to West Asia via the Silk Road and to Africa by Chinese marine voyages. In 1894, Y. pestis spread to India and radiated to diverse parts of the globe, leading to country-specific lineages that can be traced by lineage-specific SNPs. All 626 current isolates from the U.S.A. reflect one radiation and 82 isolates from Madagascar represent a second. Subsequent local microevolution of Y. pestis is marked by sequential, geographically-specific SNPs.Plague is a pandemic human invasive disease caused by the bacterial agent Yersinia pestis. We here report a comparison of 17 whole genomes of Y. pestis isolates from global sources. We also screened a global collection of 286 Y. pestis isolates for 933 SNPs using Sequenom MassArray SNP typing. We conducted phylogenetic analyses on this sequence variation dataset, assigned isolates to populations based on maximum parsimony and, from these results, made inferences regarding historical transmission routes. Our phylogenetic analysis suggests that Y. pestis evolved in or near China and spread through multiple radiations to Europe, South America, Africa and Southeast Asia, leading to country-specific lineages that can be traced by lineage-specific SNPs. All 626 current isolates from the United States reflect one radiation, and 82 isolates from Madagascar represent a second radiation. Subsequent local microevolution of Y. pestis is marked by sequential, geographically specific SNPs.

Statistical methods in spatial genetics

The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only the potential of various approaches but also methodological pitfalls.

Supplementary information to support : 'Yersinia pestis genome sequencing identifies patterns of global phylogenetic diversity'

Pandemic infectious diseases have accompanied humans since their origins1, and have shaped the form of civilizations2. Of these, plague is possibly historically the most dramatic. We reconstructed historical patterns of plague transmission through sequence variation in 17 complete genome sequences and 933 single nucleotide polymorphisms (SNPs) within a global collection of 286 Yersinia pestis isolates. Y. pestis evolved in or near China, and has been transmitted via multiple epidemics that followed various routes, probably including transmissions to West Asia via the Silk Road and to Africa by Chinese marine voyages. In 1894, Y. pestis spread to India and radiated to diverse parts of the globe, leading to country-specific lineages that can be traced by lineage-specific SNPs. All 626 current isolates from the U.S.A. reflect one radiation and 82 isolates from Madagascar represent a second. Subsequent local microevolution of Y. pestis is marked by sequential, geographically-specific SNPs.Plague is a pandemic human invasive disease caused by the bacterial agent Yersinia pestis. We here report a comparison of 17 whole genomes of Y. pestis isolates from global sources. We also screened a global collection of 286 Y. pestis isolates for 933 SNPs using Sequenom MassArray SNP typing. We conducted phylogenetic analyses on this sequence variation dataset, assigned isolates to populations based on maximum parsimony and, from these results, made inferences regarding historical transmission routes. Our phylogenetic analysis suggests that Y. pestis evolved in or near China and spread through multiple radiations to Europe, South America, Africa and Southeast Asia, leading to country-specific lineages that can be traced by lineage-specific SNPs. All 626 current isolates from the United States reflect one radiation, and 82 isolates from Madagascar represent a second radiation. Subsequent local microevolution of Y. pestis is marked by sequential, geographically specific SNPs.

Four years of DNA barcoding: Current advances and prospects

Research using cytochrome c oxidase barcoding techniques on zoological specimens was initiated by Hebert et al. [Hebert, P.D.N., Ratnasingham, S., deWaard, J.R., 2003. Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc. R. Soc. Lond. B 270, S96-S99]. By March 2004, the Consortium for the Barcode of Life started to promote the use of a standardized DNA barcoding approach, consisting of identifying a specimen as belonging to a certain animal species based on a single universal marker: the DNA barcode sequence. Over the last 4 years, this approach has become increasingly popular and advances as well as limitations have clearly emerged as increasing amounts of organisms have been studied. Our purpose is to briefly expose DNA Barcode of Life principles, pros and cons, relevance and universality. The initially proposed Barcode of life framework has greatly evolved, giving rise to a flexible description of DNA barcoding and a larger range of applications.

A timescale for evolution, population expansion, and spatial spread of an emerging clone of methicillin-resistant Staphylococcus aureus.

Due to the lack of fossil evidence, the timescales of bacterial evolution are largely unknown. The speed with which genetic change accumulates in populations of pathogenic bacteria, however, is a key parameter that is crucial for understanding the emergence of traits such as increased virulence or antibiotic resistance, together with the forces driving pathogen spread. Methicillin-resistant Staphylococcus aureus (MRSA) is a common cause of hospital-acquired infections. We have investigated an MRSA strain (ST225) that is highly prevalent in hospitals in Central Europe. By using mutation discovery at 269 genetic loci (118,804 basepairs) within an international isolate collection, we ascertained extremely low diversity among European ST225 isolates, indicating that a recent population bottleneck had preceded the expansion of this clone. In contrast, US isolates were more divergent, suggesting they represent the ancestral population. While diversity was low, however, our results demonstrate that the short-term evolutionary rate in this natural population of MRSA resulted in the accumulation of measurable DNA sequence variation within two decades, which we could exploit to reconstruct its recent demographic history and the spatiotemporal dynamics of spread. By applying Bayesian coalescent methods on DNA sequences serially sampled through time, we estimated that ST225 had diverged since approximately 1990 (1987 to 1994), and that expansion of the European clade began in 1995 (1991 to 1999), several years before the new clone was recognized. Demographic analysis based on DNA sequence variation indicated a sharp increase of bacterial population size from 2001 to 2004, which is concordant with the reported prevalence of this strain in several European countries. A detailed ancestry-based reconstruction of the spatiotemporal dispersal dynamics suggested a pattern of frequent transmission of the ST225 clone among hospitals within Central Europe. In addition, comparative genomics indicated complex bacteriophage dynamics.

Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: analysis of 'neighbourhood size' using a more precise estimator.

Genetic and demographic estimates of dispersal are often thought to be inconsistent. In this study, we use the damselfly Coenagrion mercuriale (Odonata: Zygoptera) as a model to evaluate directly the relationship between estimates of dispersal rate measured during capture–mark–recapture fieldwork with those made from the spatial pattern of genetic markers in linear and two‐dimensional habitats. We estimate the ‘neighbourhood size’ (Nb) — the product of the mean axial dispersal rate between parent and offspring and the population density — by a previously described technique, here called the regression method. Because C. mercuriale is less philopatric than species investigated previously by the regression method we evaluate a refined estimator that may be more applicable for relatively mobile species. Results from simulations and empirical data sets reveal that the new estimator performs better under most situations, except when dispersal is very localized relative to population density. Analysis of the C. mercuriale data extends previous results which demonstrated that demographic and genetic estimates of Nb by the regression method are equivalent to within a factor of two at local scales where genetic estimates are less affected by habitat heterogeneity, stochastic processes and/or differential selective regimes. The corollary is that with a little insight into a species’ ecology the pattern of spatial genetic structure provides quantitative information on dispersal rates and/or population densities that has real value for conservation management.

Fine-scale genetic structure of two carabid species with contrasted levels of habitat specialization

Using microsatellite markers, we compared the genetic structure of populations of two carabid species, one described as a generalist (commonly found in forest and in open habitats) and the other known as a forest specialist. Both species were sampled in the same forest plots, which were separated from each other by either open or forested areas. At the local scale considered (13.6 km separating the most distant plots), genetic differentiation was substantial for both species studied, but populations of the forest specialist Carabus punctatoauratus appeared to be more spatially structured than those of C. nemoralis. Isolation by distance analyses showed that nonforested areas are partial barriers to gene flow for both species studied, although more clearly for the forest specialist. Between and within forests, dispersal capacity of the generalist C. nemoralis was shown to be higher than that of the specialist C. punctatoauratus.

Genetics of recent habitat contraction and reduction in population size: does isolation by distance matter?

Fragmentation and loss of natural habitats are recognized as major threats to contemporary flora and fauna. Detecting past or current reductions in population size is therefore a major aim in conservation genetics. Statistical methods developed to this purpose have tended to ignore the effects of spatial population structure. However in many species, individual dispersal is restricted in space and fine‐scale spatial structure such as isolation by distance (IBD) is commonly observed in continuous populations. Using a simulation‐based approach, we investigated how comparative and single‐point methods, traditionally used in a Wright–Fisher (WF) population context for detecting population size reduction, behave for IBD populations. We found that a complex ‘quartet’ of factors was acting that includes restricted dispersal, population size (i.e. habitat size), demographic history, and sampling scale. After habitat reduction, IBD populations were characterized by a stronger inertia in the loss of genetic diversity than WF populations. This inertia increases with the strength of IBD, and decreases when the sampling scale increases. Depending on the method used to detect a population size reduction, a local sampling can be more informative than a sample scaled to habitat size or vice versa. However, IBD structure led in numerous cases to incorrect inferences on population demographic history. The reanalysis of a real microsatellite data set of skink populations from fragmented and intact rainforest habitats confirmed most of our simulation results.

Habitat continuity and geographic distance predict population genetic differentiation in giant kelp

Isolation by distance (IBD) models are widely used to predict levels of genetic connectivity as a function of Euclidean distance, and although recent studies have used GIS-landscape ecological approaches to improve the predictability of spatial genetic structure, few if any have addressed the effect of habitat continuity on gene flow. Landscape effects on genetic connectivity are even less understood in marine populations, where habitat mapping is particularly challenging. In this study, we model spatial genetic structure of a habitat-structuring species, the giant kelp Macrocystis pyrifera, using highly variable microsatellite markers. GIS mapping was used to characterize habitat continuity and distance between sampling sites along the mainland coast of the Santa Barbara Channel, and their roles as predictors of genetic differentiation were evaluated. Mean dispersal distance (sigma) and effective population size (Ne) were estimated by comparing our IBD slope with those from simulations incorporating habitat continuity and spore dispersal characteristics of the study area. We found an allelic richness of 7-50 alleles/locus, which to our knowledge is the highest reported for macroalgae. The best regression model relating genetic distance to habitat variables included both geographic distance and habitat continuity, which were respectively, positively and negatively related to genetic distance. Our results provide strong support for a dependence of gene flow on both distance and habitat continuity and elucidate the combination of Ne and a that explained genetic differentiation.

IBDSim: a computer program to simulate genotypic data under isolation by distance

IBDSim is a package for the simulation of genotypic data under isolation by distance. It is based on a backward ‘generation by generation’ coalescent algorithm allowing the consideration of various isolation by distance models with discrete subpopulations as well as continuous populations. Many dispersal distributions can be considered as well as heterogeneities in space and time of the demographic parameters. Typical applications of our program include (i) the study of the effect of various sampling, mutational and demographic factors on the pattern of genetic variation; and (ii) the production of test data sets to assess the influence of these factors on inferential methods available to analyse genotypic data.