# Systems Metabolomics for Biomarker Discovery

> **NIH NIH R35** · GEORGETOWN UNIVERSITY · 2021 · $390,000

## Abstract

PROJECT SUMMARY
Metabolomics offers a comprehensive analysis of thousands of small molecules in biological samples. It can
play an indispensable role in the growing systems biology approaches to unravel the relationships between
metabolites and diseases. Liquid chromatography coupled to mass spectrometry (LC-MS) and gas
chromatography coupled to mass spectrometry (GC-MS) have been used for high-throughput analysis of
thousands of metabolites. However, the potential values of many disease-associated metabolites discovered by
using these platforms have been inadequately explored in systems biology approaches for biomarker discovery
due to lack of computational tools and resources to: (1) accurately determine the identity of most of the
metabolites; (2) investigate the rewiring interactions among the metabolites due to diseases; and (3) integrate
metabolite profiles with those from other omics studies to evaluate the relationships between the metabolites
and the diseases at the systems level. Partly due to these limitations, poor generalizability of previously identified
metabolite biomarker candidates has been observed, especially when they are evaluated through independent
platforms and validation sets. Therefore, new methods are sought to find more generalizable metabolite
biomarker candidates. The goal of this research program is to fill the gaps in metabolite identification and multi-
omics integration by using systems metabolomics approaches that will enhance the role of metabolomics in
systems biology approaches for biomarker discovery. Specifically, the proposed research program will utilize
multiple resources (biological databases, spectral libraries, etc.) and innovative statistical, machine learning, and
network-based methods for: (1) developing a comprehensive workflow for ranking putative metabolite IDs; (2)
differential analysis of metabolite profiles based on changes in the levels of individual metabolites and pairwise
interactions in disease vs. control groups; and (3) integration of metabolomics data with genomics,
transcriptomics, proteomics, and glycoproteomics data to identify highly promising metabolite biomarker
candidates. Our recent progress has led to acquisition of multi-omics data and development of computational
tools for metabolite identification and integrative analysis. The performance of the proposed metabolite
identification workflow in ranking putative metabolite IDs will be evaluated through experimental methods using
reference compounds. The differential and integrative analysis methods will be used for selection of candidate
biomarkers via multi-omics data acquired in biomarker discovery studies. The selected candidates will be
evaluated by targeted quantitation using independent samples and platforms compared to those used for
discovery. The outcomes of these experimental evaluations will be used not only to help refine the computational
methods but also to identify promising biomarker candidates. In summary, th...

## Key facts

- **NIH application ID:** 10206465
- **Project number:** 1R35GM141944-01
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** Habtom W Ressom
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $390,000
- **Award type:** 1
- **Project period:** 2021-09-22 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10206465

## Citation

> US National Institutes of Health, RePORTER application 10206465, Systems Metabolomics for Biomarker Discovery (1R35GM141944-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10206465. Licensed CC0.

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