Microbial adaptation and the statistics of epistasis and pleiotropy

微生物适应以及上位性和多效性的统计

• 批准号: 8683196
• 负责人: Michael M Desai
• 金额: $ 32.11万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8683196
• 关键词: Affect; Antibiotic Resistance; Biological Assay; Chromosomes; Complex; Drug resistance; Environment; Ethanol; Evolution; Face; Frequencies; Genetic Epistasis; Genetic Recombination; Genetic Variation; Glucose; Goals; Health; Human; Immune; Individual; Laboratories; Libraries; Link; Maintenance; Malignant Neoplasms; Measures; Microbe; Mutation; Natural Selections; Nitrogen; Nutrient; Pattern; Pharmaceutical Preparations; Phenotype; Plant Roots; Population; Population Characteristics; Process; Public Health; Rest; Role; Saccharomycetales; Sodium Chloride; Stress; Structure; Surveys; System; Testing; Variant; Viral; Virus; Work; Yeasts; abstracting; asexual; base; fitness; knockout gene; mathematical model; microbial; novel; novel strategies; pathogen; pleiotropism; public health relevance; response; statistics; theories

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.

描述(由申请人提供)： 项目摘要/摘要这项工作的总体目标是结合数学建模和芽殖酵母高通量实验进化，了解微生物种群的适应情况。具体地说，我们的目标是预测进化如何在这些种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度效应的分布。然而，在更长的时间尺度上，突变之间的上位性相互作用对于适应可能是至关重要的。同样，突变通常在不同的环境中具有不同的适应效果(“多向适应”)。这对于在多变的环境中进行长期适应是至关重要的。最近的工作表明，在许多微生物和病毒系统的特定突变集合中，上位性和适合度的多效性是强烈和常见的。然而，这些对特定有限突变集合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中适应的速度、可重复性或动态。即使给出了一套完整的上位性和多效性相互作用，除了少数特别简单的情况外，我们也无法预测进化将如何发挥作用。这严重限制了我们预测复杂表型进化的能力，例如补偿的抗生素耐药性，免疫逃逸所需的多个突变，或导致癌症进化的多个基因敲除。这项建议的中心目标是研究上位性和多向适合性在微生物种群进化中的作用。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数据，而不是描述具体的例子。然后，我们将预测这种上位性和多效性如何改变进化如何在可能的突变轨迹中进行选择。在目标1中，我们将使用一种新的高通量实验进化策略来测量上位性的统计信息。具体地说，我们将确定不同突变轨迹在其长期前景中发散的统计趋势。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，并用发芽酵母的实验室进化来检验这些预测。最后，在目标3中，我们将测量突变的适应效果如何在相关环境中变化，并预测这如何改变微生物适应的过程。我们将把重点放在微生物种群进化中特别常见的环境波动上，例如对波动的营养浓度和不同强度的环境压力的适应。与最近探索小突变和特定突变集之间的上位性和多效性的工作不同，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景。