Saturday, April 20, 2024
4/20/2024
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 quantify statistical patterns of epistasis among both natural variants and mutations relevant to adaptation in laboratory budding yeast populations. We will use our data to test recent theoretical predictions describing how overall statistical patterns of epistasis emerge from individual idiosyncratic interactions. In Aim 2, we will measure patterns of pleiotropy across hundreds of environmental conditions, and use our data as the basis for a novel computational method to infer lower-dimensional statistical structure in the underlying phenotypic space. Finally, in Aim 3, we will track evolutionary dynamics in fluctuating conditions in both clonally evolving and outcrossed recombining laboratory budding yeast populations, using genetic systems we have developed to control mating and to continuously barcode lineages. We will interpret these results within the context of novel population genetic theory we will develop to predict how epistasis and pleiotropy affect evolutionary dynamics in fluctuating environments. 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.
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