# Environmental modulation of metabolic function in microbial communities

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2024 · $337,089

## Abstract

Microbial communities are complex systems whose emergent metabolic properties play a key role in
determining human health. Metabolic processes enabled by host-associated microbiota play a defining role in
individual health outcomes, and the emergent metabolism of microbial consortia affect environmental
processes from eutrophication to climate change, impacting human health on a global scale. Therefore,
humanity would benefit from a quantitative understanding of the rules by which the genomic composition of a
microbial community, and the environment in which it resides, determines its emergent metabolism.
Discovering the principles by which environmental variation alters community structure and determines
metabolic function is a necessity if we are to manipulate or design communities to improve health outcomes.
However, this task is challenging for existing methods.
 In preliminary work, we establish a new quantitative framework for predicting the emergent metabolism
of a bacterial community from its genomic composition using denitrification as a model metabolic process.
Combining quantitative bacterial phenotyping, modeling, and a simple statistical approach we demonstrated a
method that quantitatively maps gene content to metabolite dynamics in microbial communities. This insight
provides a route to quantitatively connecting the genes present in a community to metabolite dynamics. The
next challenge is to use this insight to understand how community function and structure depend on the
environment.
 We propose to extend this success by understanding how environmental gradients, complexity, and
dynamics impact community structure and function. We accomplish this by developing denitrification as a
model metabolic process. The outcomes of the proposed work will be three-fold. First, microbiome studies
have documented ubiquitous associations between environmental conditions and community composition, but
we do not understand the ecological or physiological origins of these emergent patterns or their metabolic
consequences. Using denitrifying communities across a pH gradient I will show that such patterns emerge from
ecological interactions. I will show that these interactions arise generically from the presence of physiological
trade-offs on microbial traits, providing a generalizable route to understanding the functional impact of
environmental variation on communities. Second, our preliminary study connected genomes to community
metabolism for a simple metabolic pathway acting. I will extend this success to complex pathways and
environmental conditions by constructing a method for predicting carbon utilization by communities in complex
nutrient conditions directly from genomes. I will utilize a powerful blend of genome-scale metabolic modeling
and multi-view machine learning, with impacts from host physiology to climate change. Third, I will use
denitrifying communities to test the idea that, like cells and organisms, microbial communities exhibi...

## Key facts

- **NIH application ID:** 10898908
- **Project number:** 5R01GM151538-02
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Seppe Kuehn
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $337,089
- **Award type:** 5
- **Project period:** 2023-08-03 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10898908

## Citation

> US National Institutes of Health, RePORTER application 10898908, Environmental modulation of metabolic function in microbial communities (5R01GM151538-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10898908. Licensed CC0.

---

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