# Microbial Adaptation and the Statistics of Epistasis and Pleiotropy

> **NIH NIH R01** · HARVARD UNIVERSITY · 2020 · $351,084

## Abstract

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 pleiotro...

## Key facts

- **NIH application ID:** 9936208
- **Project number:** 5R01GM104239-08
- **Recipient organization:** HARVARD UNIVERSITY
- **Principal Investigator:** Michael M Desai
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $351,084
- **Award type:** 5
- **Project period:** 2013-07-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9936208

## Citation

> US National Institutes of Health, RePORTER application 9936208, Microbial Adaptation and the Statistics of Epistasis and Pleiotropy (5R01GM104239-08). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9936208. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*