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

项目摘要/摘要 自然选择通过在种群中传播有益的突变来推动适应性进化。然而， 选择作用于不同基因座突变之间的上位性相互作用的能力取决于 重组。具有高水平的重组，如在有性真核生物中观察到的，基因 相互随机关联，使得自然选择不能有效地作用于特定的相互作用 而是作用于所有遗传背景中每个基因的平均效应。细菌可以 不是有性繁殖，但仍然通过一种称为同源重组的过程进行重组，这种过程发生的次数较少 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、选择可能与重组有利竞争，以促进有益的传播 相互作用的突变。这个项目的目标是通过以下方式促进我们对细菌进化的理解 量化选择作用于细菌基因组上位性适合度效应的能力以及这种选择是如何 这个过程在基因组数据中留下了可观察到的签名。此外，该项目还将涉及开发 新的基因组分析用于研究可能存在上位性相互作用的基因网络的进化 突变。这项提议的第一个目的是使用新的群体遗传计算机模拟来研究如何 上位性疾病推动细菌进化。由于上位性的相对重要性取决于 上位性交互作用与个体基因的加性效应和重组相比，这些量将是 在不同的模拟中有所不同。细菌基因组对上位性的敏感性将通过它们的趋势来衡量。 以形成在种群中扩展的有益的等位基因组合。一旦这些克隆人由 有益的等位基因组合频率增加，种群达到局部适应度最优， 重组动力学可能会由于重组子具有较低的适应度而改变，除非足够大的 改变使另一个局部适应值达到最优。将采用类似的模拟框架 研究处于不同适应度最优值的种群之间的重组动态，并量化 选择和重组之间的相互作用可能在观察到的同源模式中造成异质性 重组，就观察到的速度和轨道长度分布而言。这些模拟将测试 假设异质性随着上位性对加性适合度效应的相对强度而增加，以及 从而选择特定的等位基因组合。第二个目标是探索高度互动的 潜在存在上位性相互作用突变的基因。使用来自油井的已知交互网络- 新的基因组分析将被创造出来，以研究结构和 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，选择可能保持了某些等位基因 代谢基因的组合，正如在适应上位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.