# Multi-omic Functional Integration Using Networks

> **NIH NIH R01** · DARTMOUTH COLLEGE · 2020 · $352,350

## Abstract

PROJECT SUMMARY / ABSTRACT
The human-associated microbiota has recently been established as a critical determinant of health and
disease. It has attracted wide interest across medical disciplines with the promise of novel microbiota-targeted
therapies that can effectively shift the microbiota toward a health-promoting state. The microbiota is noted for
its exceptional complexity, with intricate metabolic networks governing microbial symbiotic and competitive
interactions. The simultaneous use of multiple ‘omics technologies for characterizing the microbiota has been
recognized broadly by the research community as a powerful approach because it can expose the interactions
between the various components of the microbiota and link microbiota composition and function, a key
requirement of targeted therapy development. However, the success of this approach depends on the
development of computational and statistical methods for identifying the high-order interactions in the
microbiota that are relevant for influencing disease risk and for bridging multiple high-dimensional ‘omics data
streams. We aim to address this methodological gap by developing, evaluating, applying and distributing a new
set of tools for performing meaningful analysis and integration of ‘omics data for human microbiota studies. We
will frame the development of our tools around two of the most powerful and widely-used technologies for
characterizing the human-associated microbiota: (1) sequence-based microbial taxonomic profiling for
characterizing microbial community composition and (2) spectroscopy-based untargeted metabolomic profiling
for characterizing microbial community function. We will first develop and evaluate a method for integrating
multi-omic data streams that leverages publicly available databases of microbial metabolic pathways. Next, we
will develop a method for mapping associations between composition and clinical outcomes through the lens of
microbiota functional profiles. We will then apply our methods for identifying microbial communities and their
phenotypes associated with clinical endpoints in a large cohort. Finally, our methods will be released to the
human microbiota research community as an open-source software package. The work we propose will build
analytic tools that perform functional integration of multiple ‘omics data streams and evaluate complex
relationships with clinical outcomes. This represents a novel framework for identifying microbiota-health
associations and their functional underpinnings in a manner that embraces the complexity of this system. The
significance of this work lies in its potential to help translate experimental and human subjects studies of the
microbiota into targeted therapies that shift the microbiota toward a health-promoting state.

## Key facts

- **NIH application ID:** 9986900
- **Project number:** 5R01LM012723-04
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Anne Gatewood Hoen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $352,350
- **Award type:** 5
- **Project period:** 2017-09-05 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9986900

## Citation

> US National Institutes of Health, RePORTER application 9986900, Multi-omic Functional Integration Using Networks (5R01LM012723-04). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/9986900. Licensed CC0.

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