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.

Public Health Relevance

PUBLIC HEALTH RELEVANCE STATEMENT/NARRATIVE Interactions between mutations (epistasis) and between mutations and changing environments (pleiotropy) are central to the evolution of many complex microbial phenotypes that directly affect human health, such as compensated antibiotic resistance and the adaptation of pathogens to immune selection. This project will provide a broad survey of the statistics of epistasis and pleiotropy, and will explain how these effects constrain the evolution of microbial populations. This will provide a basis for predicting the conditions under which complex microbial phenotypes relevant for public health are likely to evolve.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Reddy, Michael K
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard University
Schools of Arts and Sciences
United States
Zip Code
Cvijovi?, Ivana; Good, Benjamin H; Desai, Michael M (2018) The Effect of Strong Purifying Selection on Genetic Diversity. Genetics 209:1235-1278
Kosheleva, Katya; Desai, Michael M (2018) Recombination Alters the Dynamics of Adaptation on Standing Variation in Laboratory Yeast Populations. Mol Biol Evol 35:180-201
Jerison, Elizabeth R; Kryazhimskiy, Sergey; Mitchell, James Kameron et al. (2017) Genetic variation in adaptability and pleiotropy in budding yeast. Elife 6:
Good, Benjamin H; McDonald, Michael J; Barrick, Jeffrey E et al. (2017) The dynamics of molecular evolution over 60,000 generations. Nature 551:45-50
McDonald, Michael J; Rice, Daniel P; Desai, Michael M (2016) Sex speeds adaptation by altering the dynamics of molecular evolution. Nature 531:233-6
Good, Benjamin H; Desai, Michael M (2016) Evolution of Mutation Rates in Rapidly Adapting Asexual Populations. Genetics 204:1249-1266
Baym, Michael; Kryazhimskiy, Sergey; Lieberman, Tami D et al. (2015) Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS One 10:e0128036
Ochs, Ian E; Desai, Michael M (2015) The competition between simple and complex evolutionary trajectories in asexual populations. BMC Evol Biol 15:55
Rice, Daniel P; Good, Benjamin H; Desai, Michael M (2015) The evolutionarily stable distribution of fitness effects. Genetics 200:321-9
Jerison, Elizabeth R; Desai, Michael M (2015) Genomic investigations of evolutionary dynamics and epistasis in microbial evolution experiments. Curr Opin Genet Dev 35:33-9

Showing the most recent 10 out of 20 publications