# Study of Pathway-Dependent Effects of Luminal Microbial Metabolites Including Short Chain Fatty Acids and Bile Acids in Irritable Bowel Syndrome Through Meta-omics Analysis of Fecal Specimens

> **NIH NIH R03** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $116,551

## Abstract

PROJECT SUMMARY
The gut microbiome and its metabolites including short chain fatty acids (SCFA) and bile acids (BA) regulate
gastrointestinal (GI) physiology and carry immense potential as diagnostic and therapeutic tools for irritable
bowel syndrome (IBS), a common and chronic disorder of gut-brain interaction (or functional GI disorder).
However, the precise mechanisms by which the gut microbiome and its intermediaries contribute to IBS
symptoms are unclear. A mechanistically-informed understanding of microbiome-metabolome-host interactions
will be essential to developing novel and targeted strategies to enhance the care of individuals with IBS. This
R03 application is submitted in response to PAR-19-365. In this application, the PI proposes a hypothesis-driven
research strategy to (1) identify functional pathways (genes) associated with fecal SCFA and BA levels/profiles
and physiological traits in IBS and control volunteers and (2) confirm functional pathway analysis through
untargeted fecal metabolite profiling. This study will complement the objectives of the PI's K23 research study
which are to (1) identify changes in fecal microbiota composition that are associated with SCFA and BA profiles
in IBS through 16S rRNA gene sequencing and targeted metabolite analysis, (2) establish SCFA as actionable
IBS biomarkers, and (3) investigate interactions between SCFA and BA in IBS. The specific aims of this R03
proposal are to (1) identify differentially abundant metabolic pathways (genes) of SCFA production and BA
biotransformation in IBS (IBS with diarrhea [IBS-D], IBS with constipation [IBS-C]) and control volunteers through
functional profiling of metagenomic sequencing data and (2) compare if/how the end-products of the genomically-
encoded functions of key microbial taxa differ in IBS-D, IBS-C, and controls through untargeted metabolomics.
To achieve these aims, the PI will leverage her existing K23 cohort of prospectively-recruited and well-
phenotyped IBS and matched-control volunteers. As part of the K23-funded study, all participants have submitted
2-day stool samples using standardized collection procedures for assessment of the fecal microbiota, fecal
SCFA, and fecal BA. Residual specimens are archived and available for further analysis as described in this R03
proposal. The strategies proposed in this application will complement the PI's current career development
activities and benefit from the continued mentorship from a multidisciplinary K23 mentorship panel. Findings will
guide the approach for a subsequent R01 by identifying which features of the fecal microbiota could be refined
into practical microbiota-based tools (e.g. third generation, long-read technology) that could be tested in a larger
IBS and control volunteer cohort. Alternatively, if the genomically-encoded metabolic potential cannot be
confirmed by metabolomics, findings will inform the need measure gene expression (i.e. metatranscriptomics)
or quantitative microbial pr...

## Key facts

- **NIH application ID:** 10993051
- **Project number:** 7R03DK132446-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Andrea Shin
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $116,551
- **Award type:** 7
- **Project period:** 2022-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10993051

## Citation

> US National Institutes of Health, RePORTER application 10993051, Study of Pathway-Dependent Effects of Luminal Microbial Metabolites Including Short Chain Fatty Acids and Bile Acids in Irritable Bowel Syndrome Through Meta-omics Analysis of Fecal Specimens (7R03DK132446-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10993051. Licensed CC0.

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