Investigating the impact and patterns of homologous recombination and adaptive evolution on bacterial genomes

项目总结 与有性生物体相比，细菌的群体遗传学机制要少得多 明白了。细菌群体遗传学的两个基本方面仍然存在 研究不足：i)DNA交换对细菌基因组进化的影响和 种群数量在很大程度上是未知的。二)适应性进化的突出之处还没有 在细菌中进行了全面评估。决定重组和适应性进化如何 影响细菌是理解这些生物的生物学和开发相关的 它们进化的模型。尽管细菌可以克隆繁殖，但越来越多的证据表明 这些生物中的绝大多数能够通过交换进行同源重组 DNA片段在类似于动植物基因转换的过程中。这一过程 提高微生物适应压力或不断变化的环境的能力以及 细菌菌株之间的DNA是人类健康的一个主要问题，例如 毒力和抗生素抗性基因的转移。尽管这一进程发挥了核心作用，但 在细菌中，重组的速度和模式仍未解决。重组的程度 通常在不同的研究中差异很大，因此，相同的细菌种类可以 在一项研究中被认为是克隆人，在另一项研究中被视为高度重组。在这个项目中，我们 建议重新评估沿基因组的重组率和模式的格局 数百种细菌。使用基于近似的新方法框架 贝叶斯计算和深度学习，我们将确定影响变化的因素 细菌间的重组率。我们还将发现重组率的差异 细菌染色体(即热点和冷点)。我们的费率估计还将使我们能够 研究重组如何推动细菌基因组结构的进化，包括 基因含量的周转。最后，我们将量化细菌适应性进化的影响， 由于大型细菌的有效作用，它可能比其他生物体大得多 人口规模。我们还将调查适应和 重组，并确定负责适应的基因/途径。总而言之，这是 这项研究将评估数百个物种的重组速度和模式， 确定推动重组过程演变的因素，揭示 细菌中的适应性进化，以及重组和适应之间的相互作用。

Project summary 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.