In contract to sexual organisms, the mechanisms of population genetics in bacteria are far less understood. Two fundamental aspects of bacterial population genetics remain sorely understudied: i) the impact of DNA exchange on the evolution of bacterial genomes and populations is largely unknown. ii) the prominence of adaptive evolution has not been comprehensively assessed in bacteria. Determining how recombination and adaptive evolution impact bacteria is key to understand the biology of these organisms and to develop relevant models of their evolution. Although bacteria reproduce clonally, there is increasing evidence that the vast majority of these organisms are capable of homologous recombination by exchanging pieces of DNA in a process similar to gene conversion in animals and plants. This process enhances microbial capacity to adapt to stresses or changing environments and the exchange of DNA between bacterial strains is a major concern for human health as exemplified by the transfer of virulence and antibiotic resistance genes. Despite the central role of this process, the rates and patterns of recombination remain unresolved in bacteria. The extent of recombination often varies greatly from one study to another and, as a result, the same bacterial species can be perceived as clonal in one study and highly recombining in another. In this project, we propose to re-evaluate the landscape of recombination rates and patterns along the genomes of hundreds of bacterial species. Using new methodological frameworks based on Approximate Bayesian Computation and Deep Learning, we will identify the factors shaping the variation in recombination rate across bacteria. We will also uncover recombination rate variation across bacterial chromosomes (i.e. hot spots and cold spots). Our rate estimates will also allow us to study how recombination drives the evolution of genomic architecture of bacteria, including turnover in gene content. Finally, we will quantify the impact of adaptive evolution in bacteria, which may be substantially larger than in other organisms due to large bacterial effective population sizes. We will also investigate the relationship between adaptation and recombination, and identify the genes/pathways responsible for adaptation. In summary, this study will evaluate the rates and patterns of recombination across hundreds of species, determine the factors driving the evolution of the recombination process, reveal the role of adaptive evolution in bacteria, and the interplay between recombination and adaptation.
Homologous recombination and adaptive evolution are key mechanisms driving bacterial adaptation to new environments and new treatments. The proposed study aims to apply new approaches to determine the rates and patterns of recombination across genomic data in order to identify the factors shaping the rates and landscapes of recombination as well as the impact of adaptive evolution on bacteria. Upon completion, this project will provide a global view of the interplay between recombination and adaptative evolution across hundreds of bacterial species.
