# Systems Metabolomics for Biomarker Discovery

> **NIH NIH R35** · GEORGETOWN UNIVERSITY · 2024 · $249,999

## Abstract

PROJECT SUMMARY
Metabolomics offers a comprehensive analysis of thousands of small molecules in biological samples. It plays
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 analytes;
(2) investigate the rewiring interactions among the metabolites due to diseases; and (3) integrate metabolite
profiles with 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. The parent award aims to fill the gaps in metabolite identification and multi-omics integration by
using systems metabolomics approaches that enhance the role of metabolomics in systems biology approaches
for biomarker discovery. Specifically, it utilizes multiple resources (biological databases, mass spectral libraries,
etc.) and innovative statistical, deep learning, and network-based methods for: (1) developing a comprehensive
workflow for ranking putative metabolite IDs; (2) differential analysis of metabolite profiles and integration of
metabolomics data with proteomics, glycomics, glycoproteomics. and phosphoproteomics data to identify highly
promising metabolite biomarker candidates. The selected candidates are evaluated by targeted quantitation
using independent samples and platforms compared to those used for discovery. However, the successful
implementation of one of the critical tasks proposed in the parent award relies heavily on analytical platforms
that have high resolution, mass accuracy, and sensitivity for identification and quantitation of various analytes.
To this end, in this administrative supplement application, we request support to purchase the Thermo Scientific
Orbitrap Exploris Mass Spectrometer that offers the resolution, accuracy, sensitivity, and throughput needed by
our multi-omics approach for biomarker discovery. Availability of this instrument will not only provide the much
needed sensitive, robust, and reliable platform but also address unforeseen circumstances that occurred due to
the significant amount of machine time required to develop instrument methods for the identification and
quantitation of biomarker candidates relevant to the parent award.

## Key facts

- **NIH application ID:** 11100665
- **Project number:** 3R35GM141944-04S1
- **Recipient organization:** GEORGETOWN UNIVERSITY
- **Principal Investigator:** Habtom W Ressom
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $249,999
- **Award type:** 3
- **Project period:** 2021-09-22 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11100665

## Citation

> US National Institutes of Health, RePORTER application 11100665, Systems Metabolomics for Biomarker Discovery (3R35GM141944-04S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/11100665. Licensed CC0.

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