# Fitness Effects of Beneficial Mutations

> **NIH NIH R35** · STANFORD UNIVERSITY · 2020 · $428,092

## Abstract

Project Summary/Abstract
 Evolutionary processes underlie the etiology of many human diseases. For example, the progress of
infectious disease, be it chronic or acute, is an evolutionary process, in which pathogens engage in an arms race
with their human hosts. In modern times, vaccines and antibiotics have enabled mankind to skew the outcome
of these contests. However, these bulwarks against contagion are being steadily eroded. Mutation and natural
selection, coupled with the rapid generation times and immense pathogen population sizes, appear to provide
pathogens a decisive advantage in the evolutionary contest. To regain the upper hand we must better understand
the evolutionary process itself, to aid in the development of novel classes of antimicrobials and devise therapeutic
strategies that take into account how these weapons work and how pathogens adaptively evolve to subvert them.
However, until recently, we have been stymied in our efforts to gain a deep understanding of the adaptive
process, because adaptive mutations are rare. My lab has developed a lineage tracking system that uses high
throughput sequencing to allows us to follow the evolutionary process in almost real time. We are able to track
the evolutionary process, readily isolate thousands of adaptive lineages, remeasure fitness of those lineages
across many environments, and cheaply whole genome sequence hundreds to thousands of such mutants.
 I propose an ambitious, integrated program that will take advantage of this lineage tracking system. First, we
will determine how the environment in which a population is evolving – and the ploidy of that population – controls
which mutations are selected and the distribution of their fitness effects. Next, we will identify how mutations
selected in one environment trade-off in others and establish why they do so mechanistically. Lastly, we will
investigate epistasis between adaptive mutations, systematically determining the degree to which the sign and
magnitude of gene interactions depend on their environmental context. To achieve these goals, we will evolve
both haploid and diploid populations of Saccharomyces cerevisiae under a rationally designed set of
experimental conditions, isolate hundreds of adaptive lineages from each of these evolutions, then remeasure
the fitness of these adaptive clones under each of the other conditions. This experimental program will enable
us to describe the “joint distribution of fitness effects”, a comprehensive picture of a genome’s adaptive
possibilities under one condition, the evolutionary constraints on those possibilities others, and the mechanistic
connection between those opportunities and constraints, all viewed through the lenses of ploidy and epistasis.
Executing this program will provide unprecedented insight the adaptive process under alternative forms of
selection and genome structure. Comparative analysis of these mutants will shed light on the underlying genetic
circuitry that allows ce...

## Key facts

- **NIH application ID:** 9913557
- **Project number:** 5R35GM131824-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Gavin J Sherlock
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $428,092
- **Award type:** 5
- **Project period:** 2019-05-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9913557

## Citation

> US National Institutes of Health, RePORTER application 9913557, Fitness Effects of Beneficial Mutations (5R35GM131824-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9913557. Licensed CC0.

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