Microbial Adaptation and the Statistics of Epistasis and Pleiotropy

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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.