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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM104239-02
Application #
8683196
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Eckstrand, Irene A
Project Start
2013-07-01
Project End
2018-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
2
Fiscal Year
2014
Total Cost
$321,100
Indirect Cost
$131,100
Name
Harvard University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
082359691
City
Cambridge
State
MA
Country
United States
Zip Code
02138
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 :
Good, Benjamin H; Desai, Michael M (2015) The impact of macroscopic epistasis on long-term evolutionary dynamics. Genetics 199:177-90
Ochs, Ian E; Desai, Michael M (2015) The competition between simple and complex evolutionary trajectories in asexual populations. BMC Evol Biol 15:55
Frenkel, Evgeni M; McDonald, Michael J; Van Dyken, J David et al. (2015) Crowded growth leads to the spontaneous evolution of semistable coexistence in laboratory yeast populations. Proc Natl Acad Sci U S A 112:11306-11
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
Cvijović, Ivana; Good, Benjamin H; Jerison, Elizabeth R et al. (2015) Fate of a mutation in a fluctuating environment. Proc Natl Acad Sci U S A 112:E5021-8
Rice, Daniel P; Good, Benjamin H; Desai, Michael M (2015) The evolutionarily stable distribution of fitness effects. Genetics 200:321-9
Baym, Michael; Kryazhimskiy, Sergey; Lieberman, Tami D et al. (2015) Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS One 10:e0128036
Good, Benjamin H; Walczak, Aleksandra M; Neher, Richard A et al. (2014) Genetic diversity in the interference selection limit. PLoS Genet 10:e1004222

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