# Developing novel strategies for personalized treatment and prevention of ALS: Leveraging the global exposome, genome, epigenome, metabolome, and inflammasome with data science in a case/control cohort

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $989,984

## Abstract

ABSTRACT
Genetic heritability incompletely explains amyotrophic lateral sclerosis (ALS), and the pace of ALS genetic dis-
coveries has slowed, meaning entirely new research directions are needed to unravel disease mechanisms
and identify therapies. Our goal is to understand, cure, and prevent ALS. Our overall approach is to identify the
intersection of exposures, genomics, epigenomics, transcriptomics, metabolomics, and inflammation on ALS.
Our rationale is that prior environmental risk scores (ERS) based on even crude plasma measures of limited
classes of pollutants associate with a 7-fold increase in ALS risk and 2-fold decrease in survival, therefore a
detailed understanding of the exposome with other omics can immediately provide new, much needed strate-
gies for both ALS treatment and prevention. We propose 3 aims: 1) comprehensively assess environmental
exposures and polygenic factors in ALS versus control subjects to identify synergistic environment-polygenic
associations that increase ALS risk; 2) define exposome signatures in the ALS epigenome, transcriptome, and
metabolome; and 3) determine how environmental exposures alter ALS immune profiles and identify drug tar-
gets. First, we account for the complex exposure data from self-reports, geospatial analysis, and biospecimens
using component-ERS (cERS) for specific exposure types (e.g. pesticides, metals, air pollution) and a poly-
ERS for combined exposures. We account for genetic risk using polygenic risk scores (PRS) and C9ORF72
status. We will build ALS risk and prediction models based on ERS and PRS. Next, using cERS, poly-ERS,
and PRS, we determine the environmental signature on the DNA methylome, mRNA and microRNA, to identify
exposures that associate with differentially expressed genes and target pathways. Expression quantitative trait
loci (eQTL) analyses will define the relative contribution of polymorphisms vs exposures on gene expression.
High resolution untargeted metabolomics will reveal the environmental signature of the ALS metabolome and
identify new toxicants. All datasets will be integrated using pan-omics techniques to identify gene-metabolite
networks that are disease targets. Finally, we will classify immune profiles that associate with cERS and poly-
ERS to identify therapeutic targets using existing FDA approved drugs. Our proposal is highly innovative; it de-
fines for each patient, (i) their exposome, summarized with cERS/poly-ERS; (ii) their genome summarized by
PRS and its association with ERS to understand the combined gene/environment risk; (iii) their multi-omic en-
vironmental signatures from the epigenome, transcriptome, metabolome, and inflammasome; (iv) their dysreg-
ulated pathways, ranked by their association to ALS risk and progression to identify personalized mechanism-
based drug-targets; and (v) ALS prediction models and preventative strategies via risk factor modification. Our
parallel, multi-omics approach is significantly faster than serial, si...

## Key facts

- **NIH application ID:** 10271663
- **Project number:** 1R01NS127188-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** STUART A BATTERMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $989,984
- **Award type:** 1
- **Project period:** 2021-09-30 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10271663

## Citation

> US National Institutes of Health, RePORTER application 10271663, Developing novel strategies for personalized treatment and prevention of ALS: Leveraging the global exposome, genome, epigenome, metabolome, and inflammasome with data science in a case/control cohort (1R01NS127188-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10271663. Licensed CC0.

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