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Trait-Based Metagenomics for Predicting Microbiome Dynamics

Award Number: R35GM165706
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2026
PERIOD OF PERFORMANCE END DATE: 08/31/2031

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Trait-Based Metagenomics for Predicting Microbiome Dynamics - A microbiome is more than the sum of its constituent organisms. Microbes live and grow in diverse and highly complex communities, and through dense networks of interactions as a community produce system-level outputs relevant to human health (e.g., through control of metabolites in the human gut). These interactions occur on the basis of the traits or “functions” particular to each organism. The next generation of bioinformatic software for probing microbiome composition and dynamics will need to resolve complex microbial traits, beyond gene presence or absence, in order to build a comprehensive understanding of how microbiomes impact human health. Unfortunately, our ability to genomically survey complex microbial traits in situ for human microbiomes is limited at best and is largely restricted to analyzing the presence or absence of particular metabolic pathways across metagenomes. Microbes, though, are not reducible to a set of metabolic pathways, and many complex traits are not reducible to a simple problem of gene presence or absence. The long-term goal of my group’s work is to reliably link genomic information to emergent microbial community properties via inference of complex traits and life-history strategies at the community level. In doing so, I aim to address a major challenge in microbiome sciences, that of developing a mechanistic understanding of the dynamics of host-associated microbiomes. Increasingly, researchers studying the human microbiome are generating high-resolution time-series data to capture community turnover in response to environmental perturbations (e.g., antibiotic use, infection, cancer treatment). Yet, the high-dimensional nature of the microbiome, which comprises hundreds or thousands of taxa, resists easy description by existing ecological modeling frameworks. To bridge this gap, I propose to develop a mechanistic, trait-based understanding of microbial dynamics, asking whether differences in the predicted growth strategies and environmental preferences of individual organisms in a community can be used to inform ecological models of bacterial community dynamics in response to perturbation. My group is actively engaged in this research on three primary fronts. First, we are working to develop AI/ML- based methods for (meta)genomic trait inference to determine the distribution of complex microbial traits in a community directly from sequencing data. Second, we are using our trait-inference approaches to identify natural structuring of microbial communities to inform model development for host-associated microbiomes. Third, we are integrating our taxon-specific inferences of growth strategies into dynamic models of community succession that can be ground-truthed against high-resolution microbiome time-series data. Together, our mechanistic metagenomes-to-dynamics approach will yield predictive models of future community dynamics that we will then apply to large, publicly available metagenomic time series to provide insight into assembly processes of the human microbiome in response to environmental perturbations.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $396,667 )
2026	2026	THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK	W5510 FRANKS MELVILLE MEMORIAL LIBRARY	STONY BROOK	NY	11794	SUFFOLK	USA	Biomedical Research and Research Training	000	1	8/10/2026	NEW	$396,667
														Subtotal = $396,667

Grand Total All Awards = $396,667

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Trait-Based Metagenomics for Predicting Microbiome Dynamics

Award Number: R35GM165706

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2026

PERIOD OF PERFORMANCE END DATE: 08/31/2031

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