# Integrating metagenomics data into accurate mass stool metabolite identifications

> **NIH NIH R03** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2022 · $319,166

## Abstract

Project Summary
Prof. Oliver Fiehn will work with his key persons, statistician Dr. Christopher Brydges, bioinformatics specialist
Dr. Yuanyue Li and programmer Gert Wohlgemuth (all UC Davis) to generate new pipelines that integrate stool
microbial metagenomics data and stool mass spectrometry data to better associate metabolites with disease
progression in inflammatory bowel disease.
We will work in consultation with Dr. Clary Clish (Broad Institute) who generated and deposited the data to the
NIH Common Funds MetabolomicsWorkbench and the iHMP integrated human microbiome data. We will
prioritize the enormous set of more than 80,000 yet unidentified stool metabolic signals using longitudinal disease
progression over 1 year in subjects with inflammatory bowel disease, in comparison to healthy subjects. For this
limited set of not more than 1,000 metabolites that will show significant association with health outcomes, we will
use all available accurate mass MS/MS data and all stool microbiome data to obtain metabolite class information
and likely metabolite structures or substructures. Dr. Clish will review our results and share new annotations that
his group will release.
To this end, we will develop the tools for metabolome predictions that have been built by the KBase collaborative
research consortium over the past 10 years. KBase uses microbial genomic sequences (or even transcriptomics
data) to automatically build metabolic pathways through enzyme predictions and gap filling. KBase also
empowers utilization of microbial communities, modeling import and export of metabolites that other microbes
can use as carbon sources. In consultation with Dr. Chris Henry (Argonne National Lab) from the KBase
consortium, we will then build pipelines within the KBase environment to include mass spectrometry tools that
the Fiehn laboratory has built through its past NIH funding, specifically formula predictions and substructure
predictions (in MS-FINDER), retention time predictions (in Retip.app), hybrid-shift MS/MS similarity matching (in
NIST search), and entropy similarity MS/MS matching (in MassBank.us).
This specific project will have large impact on other, similar microbiome/metabolome projects that will be
uploaded to the NIH Common Funds databases in the future. The project addresses the huge complexity in stool
metagenomics and stool metabolomics data, and delivers key pipelines (called ‘narratives’ in KBase) that can
be used by the research community at large.

## Key facts

- **NIH application ID:** 10576770
- **Project number:** 1R03OD034497-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Oliver Fiehn
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $319,166
- **Award type:** 1
- **Project period:** 2022-09-20 → 2024-09-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10576770

## Citation

> US National Institutes of Health, RePORTER application 10576770, Integrating metagenomics data into accurate mass stool metabolite identifications (1R03OD034497-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10576770. Licensed CC0.

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