Microbial Adaptation and the Statistics of Epistasis and Pleiotropy

项目总结/摘要 我的研究计划的总体目标是了解微生物种群的适应性，使用 结合数学建模和高通量实验进化芽殖酵母。从根本上说， 我们的目标是预测进化如何在不同的突变轨迹中进行概率选择，以确定 适应的速度和结果。在短期内，进化主要取决于适应度的分布 个别突变的影响。然而，在较长的时间尺度上，突变之间的上位性相互作用可能是 至关重要的.类似地，突变在不同的环境中通常具有不同的适应性效应（"基因多效性"）。 健身"）。这对于在波动环境中的进化至关重要。最近的研究表明，上位性和 多效性在许多微生物系统中的特定突变组中是强的和常见的。然而，在这方面， 这些对特定的有限突变集的研究不能完全解释上位性和多效性是如何限制 适应的速度、重复性或动态。即使有一整套上位性和多效性 尽管如此，我们仍然经常无法预测进化将如何行动。这严重限制了我们 了解复杂表型的演变，如补偿性抗生素耐药性，多种 免疫逃逸所需的突变，或使癌症演变成为可能的多个基因敲除。 这项建议的中心目标是研究上位性和多效性在适应性中的作用。 微生物种群的进化。而不是描述具体的例子，我们建议调查 与限制微生物适应相关的上位性和多效性的总体统计，以及 分析这种上位性和多效性如何改变进化在可能的突变轨迹中的选择。 在目标1中，我们将测量上位性和多效性如何改变适应性谱系的进化潜力 随着时间的推移和环境条件的变化。具体来说，我们将衡量个人 突变改变了未来进化轨迹的频谱，使用一种新的“可再生条形码”方法 我们已经开发出在实验室酵母中以高分辨率追踪谱系。在目标2中，我们将量化统计 在适应性微生物种群中积累的突变之间的上位性和多效性模式，以及 分析群体遗传因素如重组率和群体大小如何与 上位性来确定哪些突变随着时间的推移而积累。最后，在目标3中，我们将联合收割机 使用基于CRISPR-Cas9的基因驱动系统的分子条形码化方法来创建组合文库 特定的突变集合。我们将使用这个系统来分析上位性的总体统计模式， 基因多效性来自适应性种群中出现的特定突变之间的相互作用。相比 最近的工作探索上位性和多效性之间的限制套个别突变，我们的方法将 提供了这些因素改变微生物进化过程的程度的全面图片。

PROJECT SUMMARY/ABSTRACT The overall goal of my research program is to understand adaptation in microbial populations, using a combination of mathematical modeling and high-throughput experimental evolution in budding yeast. At root, we aim to predict how evolution chooses probabilistically among different mutational trajectories, to determine the rate and outcomes of adaptation. In the short term, evolution depends primarily on the distribution of fitness effects of individual mutations. However, on longer timescales epistatic interactions between mutations can be crucial. Similarly, mutations often have different fitness effects in different environments (“pleiotropy for fitness”). This is essential to evolution in fluctuating environments. Recent work shows that epistasis and pleiotropy are strong and common among specific sets of mutations in many microbial systems. However, these studies of specific limited sets of mutations cannot fully explain how epistasis and pleiotropy constrain the rate, repeatability, or dynamics of adaptation. And even given a complete set of epistatic and pleiotropic interactions, we are still often unable to predict how evolution will act. This severely limits our ability to understand the evolution of complex phenotypes, such as compensated antibiotic resistance, multiple mutations required for immune escape, or multiple gene knockouts enabling cancer evolution. The central objective of this proposal is to examine the role of epistasis and pleiotropy for fitness in the evolution of microbial populations. Rather than characterizing specific examples, we propose to survey the overall statistics of epistasis and pleiotropy that are relevant for constraining microbial adaptation, and to analyze how this epistasis and pleiotropy alters how evolution chooses among possible mutational trajectories. In Aim 1, we will measure how epistasis and pleiotropy change the evolutionary potential of adapting lineages over time and across fluctuating environmental conditions. Specifically, we will measure how individual mutations change the spectrum of future evolutionary trajectories, using a novel “renewable barcoding” method we have developed to track lineages at high resolution in laboratory yeast. In Aim 2, we will quantify statistical patterns of epistasis and pleiotropy among mutations that accumulate in adapting microbial populations, and analyze how population genetic factors such as recombination rate and population size interact with patterns of epistasis to determine which mutations accumulate over time. Finally, in Aim 3, we will combine our renewable molecular barcoding methods with a CRISPR-Cas9 based gene drive system to create combinatorial libraries of specific sets of mutations. We will use this system to analyze how overall statistical patterns of epistasis and pleiotropy emerge from interactions among specific mutations that arise in adapting populations. In contrast to recent work probing epistasis and pleiotropy between restricted sets of individual mutations, our approach will provide a comprehensive picture of the degree to which these factors alter the course of microbial evolution.