Biology subjects

Dutheil, J. Y.

Publications and source records attributed to Dutheil, J. Y..

3 recordsLinked to original sources

bioRxiv2017

The genomic rate of adaptation in the fungal wheat pathogen Zymoseptoria tritici

Antagonistic host-pathogen co-evolution is a determining factor in the outcome of infection and shapes genetic diversity at the population level of both partners. While the molecular function of an increasing number of genes involved in pathogenicity is being uncovered, little is known about the molecular bases and genomic impact of hst-pathogen coevolution and rapid adaptation. Here, we apply a population genomic approach to infer genome-wide patterns of selection among thirteen isolates of the fungal pathogen Zymoseptoria tritici. Using whole genome alignments, we characterize intragenic polymorphism, and we apply different test statistics based on the distribution of non-synonymous and synonymous polymorphisms (pN/pS) and substitutions (dN/dS) to (1) characterise the selection regime acting on each gene, (2) estimate rates of adaptation and (3) identify targets of selection. We correlate our estimates with different genome variables to identify the main determinants of past and ongoing adaptive evolution, as well as purifying and balancing selection. We report a negative relationship between pN/pS and fine-scale recombination rate and a strong positive correlation between the rate of adaptive non-synonymous substitutions ({omega}a) and recombination rate. This result suggests a pervasive role of Hill-Robertson interference even in a species with an exceptionally high recombination rate (60 cM/Mb). Moreover, we report that the genome-wide fraction of adaptive non-synonymous substitutions () is ~ 44%, however in genes encoding determinants of pathogenicity we find a mean value of alpha ~ 68% demonstrating a considerably faster rate of adaptive evolution in this class of genes. We identify 787 candidate genes under balancing selection with an enrichment of genes involved in secondary metabolism and host infection, but not predicted effectors. This suggests that different classes of pathogenicity-related genes evolve according to distinct selection regimes. Overall our study shows that sexual recombination is a main driver of genome evolution in this pathogen.

evolutionary biology↗

bioRxiv2017

Comparison of fine-scale recombination maps in fungal plant pathogens reveals dynamic recombination landscapes and intragenic hotspots

Meiotic recombination is an important driver of evolution. Variability in the intensity of recombination across chromosomes can affect sequence composition, nucleotide variation and rates of adaptation. In many organisms recombination events are concentrated within short segments termed recombination hotspots. The variation in recombination rate and positions of recombination hotspot can be studied using population genomics data and statistical methods. In this study, we conducted population genomics analyses to address the evolution of recombination in two closely related fungal plant pathogens: the prominent wheat pathogen Zymoseptoria tritici and a sister species infecting wild grasses Zymoseptoria ardabiliae. We specifically addressed whether recombination landscapes, including hotspot positions, are conserved in the two recently diverged species and if recombination contributes to rapid evolution of pathogenicity traits. We conducted a detailed simulation analysis to assess the performance of methods of recombination rate estimation based on patterns of linkage disequilibrium, in particular in the context of high nucleotide diversity. Our analyses reveal overall high recombination rates, a lack of suppressed recombination in centromeres and significantly lower recombination rates on chromosomes that are known to be accessory. The comparison of the recombination landscapes of the two species reveals a strong correlation of recombination rate at the megabase scale, but little correlation at smaller scales. The recombination landscapes in both pathogen species are dominated by frequent recombination hotspots across the genome including coding regions, suggesting a strong impact of recombination on gene evolution. A significant but small fraction of these hotspots co-localize between the two species, suggesting that hotspots dynamics contribute to the overall pattern of fast evolving recombination in these species.

evolutionary biology↗

bioRxiv2017

Selection at the pathway level drives the evolution of gene-specific transcriptional noise

Biochemical reactions within individual cells result from the interactions of molecules, often in small numbers. Consequently, the inherent stochasticity of binding and diffusion processes generate noise along the cascade that leads to the synthesis of a protein from its encoding gene. As a result, isogenic cell populations display phenotypic variability even in homogeneous environments. The extent and consequences of this stochastic gene expression have only recently been assessed on a genome-wide scale, in particular owing to the advent of single cell transcriptomics. However, the evolutionary forces shaping this stochasticity have yet to be unraveled. We took advantage of two recently published data sets of the single-cell transcriptome of the domestic mouse Mus musculus in order to characterize the effect of natural selection on gene-specific transcriptional stochasticity. We showed that noise levels in the mRNA distributions (a.k.a. transcriptional noise) significantly correlate with three-dimensional nuclear domain organization, evolutionary constraint on the encoded protein and gene age. The position of the encoded protein in biological pathways, however, is the main factor that explains observed levels of transcriptional noise, in agreement with models of noise propagation within gene networks. Because transcriptional noise is under widespread selection, we argue that it constitutes an important component of the phenotype and that variance of expression is a potential target of adaptation. Stochastic gene expression should therefore be considered together with mean expression level in functional and evolutionary studies of gene expression.

evolutionary biology↗