# Democratizing Multi-Omics to Expedite Discovery of Hidden Metabolic Pathways

> **NIH NIH R35** · CEDARS-SINAI MEDICAL CENTER · 2023 · $417,500

## Abstract

PROJECT SUMMARY/ABSTRACT
There is a fundamental gap in our understanding of how metabolism changes in many diseases because we
lack methods for high-throughput, unbiased discovery of indirect metabolite-protein connections. Continued ex-
istence of this knowledge gap represents a major issue for public health and the mission of the NIH because,
until it is filled, development of treatments for many diseases will remain largely intractable. Multi-omic analysis
of proteomes and metabolomes from the same system offers a promising path to discover hidden metabolic
pathways, but the requirement for human expert interpretation is a critical barrier that prevents complete value
extraction from multi-omic experiments. The long-term goal of the Meyer Research Group at Medical College of
Wisconsin is to reveal previously hidden metabolic pathways. The overall objective here, which is the first step
in realizing this vision, is to democratize multi-omic data collection and data interpretation, thereby increasing
the pace of metabolic pathway discovery. The central hypothesis is that artificial intelligence models can learn
to draw new metabolic connections between metabolites and proteins. This hypothesis is based on preliminary
data generated by the applicant and published literature, which shows how the strategy reveals known and new
connections between metabolites and proteins. The rationale for the proposed research is that unbiased, data-
driven discovery of new metabolic connections with AI algorithms (such as deep neural networks) will result in
new and innovative therapeutic targets that can be manipulated positively or negatively to prevent or treat dis-
ease. Guided by preliminary data and literature, this hypothesis will be tested by pursuing two complementary
focus areas: (1) multi-omic data integration, and (2) multi-omic data collection. The multi-omic data integration
focus uses AI models, already established as feasible in the applicant’s lab, to predict metabolite-protein inter-
actions. AI models will be optimized with existing public data, models will be validated with newly collected data,
and then novel metabolic connections will be validated using classic genetic and biochemical techniques. The
second focus area builds new, fast methods for multi-omic data collection to feed data into AI models, starting
from a recent advancement published by the applicant (Meyer et al., ChemRxiv 2020, accepted at Nature Meth-
ods). The applicant’s lab will further develop this method to quantify the full yeast proteome, and also extend the
method to enable multi-omic analysis on a single platform. This approach is innovative because it departs from
the status quo of slow multi-omic data interpretation requiring expert humans by building and validating a new,
automated AI method for metabolite pathway discovery. The multi-omic data collection focus is innovative be-
cause it departs from the status quo of slow multi-omic data collection requiring multi...

## Key facts

- **NIH application ID:** 10690460
- **Project number:** 5R35GM142502-04
- **Recipient organization:** CEDARS-SINAI MEDICAL CENTER
- **Principal Investigator:** Jesse Meyer
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $417,500
- **Award type:** 5
- **Project period:** 2022-03-28 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10690460

## Citation

> US National Institutes of Health, RePORTER application 10690460, Democratizing Multi-Omics to Expedite Discovery of Hidden Metabolic Pathways (5R35GM142502-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10690460. Licensed CC0.

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