Measuring the statistics of pleiotropic and epistatic fitness effects of beneficial mutations during microbial adaptation across environments

测量微生物跨环境适应过程中有益突变的多效性和上位适应性效应的统计数据

基本信息

批准号：
9396806
负责人：
Parris Humphrey
金额：
$ 5.71万
依托单位：
HARVARD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2019-06-30
项目状态：
已结题

项目摘要

Project Summary. Two main sources of uncertainty can combine to make the outcome of adaptive evolution unpredictable: how the effects of mutations on evolutionary fitness depend on either the environment (i.e. pleiotropy) or genotype (i.e. epistasis) of the organism. By conferring substantial context dependence to the fitness effects of mutations, epistasis and pleiotropy independently and jointly impact the repeatability—and thus predictability—of evolution at phenotypic and genotypic levels. Yet we lack an empirical and conceptual basis for predicting their general importance in evolution, leaving a fundamental question unanswered: how much of the future depends on the past? The answers may be as pertinent to the causes of biological diversification and speciation as they are to predicting cancer progression, parasite host switches, and the emergence of drug resistant pathogens— adaptive processes for which predictability is greatly needed. When studied in detail, pleiotropy and epistasis reveal important facets of how the genetic architecture of organismal traits interacts with the environment to affect the outcomes of adaptation. This work moves beyond case-studies by proposing to systematically measure the distribution of fitness effects of beneficial mutations across environments, as well as how these effects change when paired with other mutations in the genome. Progress on these fronts has been impeded by practical combinatorial limits to the gene–gene and gene– environment interactions that can be assayed at once, as well as by the limited availability of study systems allowing controlled manipulation of recombination to isolate its effect on the evolution of such interactions. Using budding yeast as a model for microbial adaptation, the proposed work removes the combinatorial upper limit that has for so long prevented estimation of the statistics of epistatic and pleiotropic mutational effects, and permits an analysis of how recombination affects their role in evolution. This work uses innovative sequencing-based approaches to track the fitness of thousands of genotypes via DNA barcodes in bulk across a wide range of environments, and will reveal the statistical context in which important forms of pleiotropy (e.g. trade-offs) actually tend to arise. Additionally, this work tests how the effects, and genetic basis, of epistasis and pleiotropy evolve differently in sexual versus asexual populations, by using DNA barcode fitness tracking of thousands of recombinant offspring across multiple environments. Together, this work will provide a comprehensive study of classic but poorly measured evolutionary quantities that impact the genetic and phenotypic dynamics of adaptation and its predictability.

项目摘要。不确定性的两个主要来源可以结合起来，使适应性进化的结果无法预测：如何突变对进化适应性的影响取决于环境（即多效）或基因型（即上述）生物体。通过赋予突变的适应性效果的实质性上下文依赖性，上毒和多效性独立并共同影响可重复性，从而可以预测性的进化性在表型和基因型水平上。然而，我们缺乏预测其一般的经验和概念基础进化中的重要性，留下一个基本问题未解决：未来的多少取决于过去的？答案可能与生物多元化和规格的原因一样，与之相关预测癌症的进展，寄生虫宿主开关和耐药病原体的出现 - 非常需要可预测性的自适应过程。详细研究时，多效性和上科学揭示了重要方面有机体特征与环境相互作用，以影响适应结果。这项工作超越通过提议系统地测量有益突变的适应性效应的分布来研究案例研究跨环境以及与基因组中其他突变配对时这些效果的变化。这些方面的进展受到了基因和基因 - 基因 - 基因的实用组合限制的阻碍可以立即分配的环境互动以及研究系统的有限可用性允许对重组的控制操纵以隔离其对这种相互作用进化的影响。使用芽酵母作为微生物适应的模型，提出的工作消除了组合鞋面长期以来一直以来的限制阻止了估计并发性和多效突变效应的统计数据，并允许分析重组如何影响其在进化中的作用。这项工作使用创新基于测序基于测序的方法，通过跨越跨越的DNA条形码跟踪数千种基因型的适应性各种环境，并将揭示重要形式的多效性形式的统计环境（例如，更折衷）实际上倾向于出现。此外，这项工作还测试了上毒的效果和遗传基础通过使用DNA条形码健身跟踪在多种环境中成千上万的重组后代。这项工作一起将提供全面研究经典但测量不佳的进化量，影响遗传和适应性及其可预测性的表型动力学。