# Quantitative approaches for mapping the real-time evolution of the gut microbiota

> **NIH NIH R35** · STANFORD UNIVERSITY · 2023 · $392,125

## Abstract

Microbial communities drive important biogeochemical cycles, from the ocean to the soil to the human gut. High
rates of cell turnover provide these ecosystems with an enormous potential for rapid evolutionary change: billions
of new mutations are produced within each of our gut microbiota every day. We and others have recently shown
that these genetic changes can sweep through resident populations of gut bacteria on timescales ranging from
a few months to a few days. This rapid pace of evolution is hypothesized to have important practical
consequences, from the spread of antibiotic resistance mutations to the success of fecal microbiome transplants
and other personalized therapies. Despite the potential importance of these effects, we currently know very little
about the evolutionary forces that operate within complex communities like the gut microbiota, or how they
influence (or are influenced by) the composition of the surrounding community. A central challenge is that we
lack a population genetic framework for predicting how mutations spread in communities with large numbers of
ecologically interacting strains. This limits our ability to predict how microbiota will evolve in response to
environmental perturbations such as drugs or diet, or to interpret functional changes that we observe in these
communities over time. My lab aims to address this gap in our understanding by combining biophysical and
population genetic modeling with the development of computational tools for measuring in situ evolution in both
natural and synthetic gut communities. Our long-term goal is to decipher the population genetic “rules” that
govern the short-term evolution of the gut microbiota, and to use these insights to guide future experimental and
therapeutic efforts. In the next five years, we will pursue this goal through a multi-pronged strategy: First, we will
develop new time-series methods for analyzing the trajectories of linked mutations in longitudinally sampled
human gut metagenomes. These methods will allow us to address key open questions about the strength and
duration of natural selection on sweeping genetic variants, and whether they are correlated with broader shifts
in taxonomic or functional composition. Second, we will develop new methods for leveraging widely deployed
transposon insertion libraries to measure the rates and fitness effects of spontaneous beneficial mutations in
vivo in ex-germ-free mice, and we will quantify for the first time how this landscape varies across species, diets,
and community contexts. Finally, to interpret these new data and to craft driving hypotheses, we will develop a
mechanistic modeling framework for predicting how ecological diversity influences short-term evolutionary
dynamics in highly diverse communities that compete for common metabolites. Together, this work will provide
unprecedented insight into the short-term evolution of our gut microbiota, and will constitute a crucial step toward
the development of...

## Key facts

- **NIH application ID:** 10684953
- **Project number:** 5R35GM146949-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Benjamin H Good
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $392,125
- **Award type:** 5
- **Project period:** 2022-09-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10684953

## Citation

> US National Institutes of Health, RePORTER application 10684953, Quantitative approaches for mapping the real-time evolution of the gut microbiota (5R35GM146949-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10684953. Licensed CC0.

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