The role of fitness epistasis and gene network interactions in bacterial evolution

项目总结/摘要 自然选择通过在种群中传播有益的突变来驱动适应性进化。然而，在这方面， 选择作用于不同基因座突变之间上位相互作用的能力取决于 重组由于高水平的重组，例如在有性真核生物中观察到的重组， 随机地相互联系，这样自然选择就不能有效地作用于特定的相互作用。 组合，而是作用于所有遗传背景中每个基因的平均效应。细菌做 不是有性繁殖，但仍然通过称为同源重组的过程进行重组， 通常涉及比真核生物中的重组更短的DNA片段。在这些高度关联的 细菌基因组，选择可以有利地与重组竞争，以促进有益的传播。 相互作用的突变该项目的目标是通过以下方式来推进我们对细菌进化的理解： 量化选择作用于细菌基因组中上位适应性效应的能力，以及这种选择性 这一过程在基因组数据中留下了可观察到的特征。此外，该项目将涉及开发 新的基因组分析，以研究基因网络的进化，可能窝藏上位相互作用 突变。这项提议的第一个目的是使用新的种群遗传计算机模拟来研究如何 上位性驱动细菌进化。由于上位性的相对重要性取决于 上位性相互作用相比，个别加性基因的影响和重组，这些数量将是 不同的模拟。细菌基因组对上位性的敏感性将通过它们的倾向来衡量 形成有益的等位基因组合，在种群中扩展。一旦这些克隆人组成的 等位基因的有益组合的频率增加并且群体达到局部适合度最佳值， 重组动力学可能由于重组体具有较低的适应性而改变，除非足够大的重组体具有足够的适应性。 改变使得能够殖民化另一个局部适应度最优值。将采用类似的模拟框架 研究不同适应度最优种群之间的重组动力学，并量化 选择和重组之间的相互作用可能会在观察到的同源模式中产生异质性。 重组，在所观察到的速率和道长度分布方面。这些模拟将测试 异质性随着上位性与加性适合度效应的相对强度而增加的假设， 从而选择特定的等位基因组合。第二个目的是探索高度互动的进化 潜在的上位相互作用突变的基因。使用已知的交互网络， 特征的代谢基因，新的基因组分析将创建研究如何结构和 网络相互作用的连通性解释了细菌病原体的基因组变异模式 肺炎链球菌。初步分析表明，选择可能是保持某些等位基因， 代谢基因的组合，正如在适应性上位性模型下所预期的那样。

Project Summary/Abstract Natural selection drives adaptive evolution by spreading beneficial mutations through populations. However, the ability of selection to act on epistatic interactions between mutations at different loci depends on recombination. With high levels of recombination, such as those observed in sexual eukaryotes, genes associate with one another randomly, such that natural selection cannot effectively act on particular interacting combinations but instead acts on the average effect of each gene across all genetic backgrounds. Bacteria do not sexually reproduce but still recombine through a process called homologous recombination that occurs less frequently and involves shorter segments of DNA than recombination in eukaryotes. Within these highly linked bacterial genomes, selection may compete favorably with recombination to promote the spread of beneficially interacting mutations. The goal of this project is to advance our understanding of bacterial evolution by quantifying the ability of selection to act on epistatic fitness effects in bacterial genomes and how this selective process leaves observable signatures in genomic data. Additionally, this project will involve the development of novel genomic analyses to study the evolution of gene networks that likely harbor epistatically interacting mutations. The first aim of this proposal uses novel population genetic computer simulations to study how epistasis drives bacterial evolution. Since the relative importance epistasis depends on the fitness effects of epistatic interactions compared to individual additive gene effects and recombination, these quantities will be varied across simulations. The sensitivity of bacterial genomes to epistasis will be measured by their tendency to form beneficial combinations of alleles that expand in the population. Once these clones consisting of beneficial combinations of alleles increase in frequency and the population reaches a local fitness optimum, recombination dynamics may change owing to recombinants having lower fitness unless a sufficiently large change enables colonization of another local fitness optimum. A similar simulation framework will be employed to study these recombination dynamics between populations at different fitness optima and quantify how the interplay between selection and recombination may create heterogeneity in observed patterns of homologous recombination, in terms of both the observed rate and tract length distributions. These simulations will test the hypothesis that heterogeneity increases with the relative strength of epistatic to additive fitness effects, and thus selection for particular allelic combinations. The second aim explores the evolution of highly interacting genes that potentially harbor epistatically interacting mutations. Using known interaction networks from well- characterized metabolic genes, novel genomic analyses will be created to study how the structure and connectivity of network interactions explains patterns of genomic variation in the bacterial pathogen Streptococcus pneumoniae. Preliminary analyses indicate that selection may be maintaining certain allele combinations of metabolic genes, as expected under a model of fitness epistasis.