Microbial adaptation and the statistics of epistasis and pleiotropy

描述（由申请人提供）： 项目概要/摘要这项工作的总体目标是了解微生物种群的适应性，使用数学建模和高通量实验进化芽殖酵母的组合。具体来说，我们的目标是预测进化如何在这些人群中可能的突变轨迹谱中进行概率选择。在短期内，进化主要取决于个体突变的适应性效应的分布。然而，在较长的时间尺度上，突变之间的上位相互作用对适应至关重要。类似地，突变在不同的环境中通常具有不同的适应性效应（“适应性多效性”）。这对于在波动环境中长期适应至关重要。最近的研究表明，上位性和适应性的多效性在许多微生物和病毒系统中的特定突变组中是很强和常见的。然而，这些特定的有限的突变集的研究不能完全解释上位性和多效性如何限制微生物种群的适应率，可重复性或动态。即使有一套完整的上位性和多效性相互作用，我们也无法预测进化在所有情况下的作用，只有少数特别简单的情况除外。这严重限制了我们预测复杂表型演变的能力，例如补偿性抗生素耐药性、免疫逃逸所需的多个突变或使癌症演变成为可能的多个基因敲除。这个提议的中心目标是研究上位性和多效性在微生物种群进化中对适应性的作用。而不是表征具体的例子，我们建议调查的上位性和多效性，是相关的约束微生物的适应性的总体统计。然后，我们将预测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，我们将使用高通量实验进化的新策略来测量上位性的统计数据。具体来说，我们将确定不同突变轨迹在其长期前景中的统计趋势。在目标2中，我们将预测上位性如何与遗传变异相互作用以限制微生物种群的进化，并在芽殖酵母的实验室进化中测试这些预测。最后，在目标3中，我们将测量突变的适应性效应如何在相关环境中变化，并预测这如何改变微生物的适应过程。我们将重点关注在微生物种群进化中特别常见的环境波动，例如适应波动的营养浓度和不同强度的环境压力。与最近探索小的和特定的突变集之间的上位性和多效性的工作相反，我们的方法将提供这些因素改变微生物进化过程的程度的全面图片。

DESCRIPTION (provided by applicant): PROJECT SUMMARY/ABSTRACT The overall goal of this work is to understand adaptation in microbial populations, using a combination of mathematical modeling and high-throughput experimental evolution in budding yeast. Specifically, we aim to predict how evolution chooses probabilistically among the spectrum of possible mutational trajectories in these populations. 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 for adaptation. Similarly, mutations often have different fitness effects in different environments ("pleiotropy for fitness"). This is essential to long-term adaptation in fluctuating environments. Recent work shows that epistasis and pleiotropy for fitness are strong and common among specific sets of mutations in many microbial and viral 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 in microbial populations. And even given a complete set of epistatic and pleiotropic interactions, we cannot predict how evolution will act in all but a few particularly simple cases. This severely limits our ability to predict th 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. We will then predict how this epistasis and pleiotropy alters how evolution chooses among possible mutational trajectories. In Aim 1, we will measure the statistics of epistasis using a novel strategy for high-throughput experimental evolution. Specifically, we will determine the statistical tendency of different mutational trajectories to diverge in their long-tem prospects. In Aim 2, we will predict how epistasis interacts with genetic variation to constrain th evolution of microbial populations, and test these predictions with laboratory evolution in budding yeast. Finally, in Aim 3, we will measure how the fitness effects of mutations change across related environments and predict how this alters the course of microbial adaptation. We will focus on environmental fluctuations that are particularly common in the evolution of microbial populations, such as adaptation to fluctuating nutrient concentrations and varying intensities of environmental stresses. In contrast to recent work probing epistasis and pleiotropy between small and specific sets of mutations, our approach will provide a comprehensive picture of the degree to which these factors alter the course of microbial evolution.