# Alcohol-Related Changes in Gene Expression and Structure Using Next Generation Sequencing

> **NIH NIH U01** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $428,932

## Abstract

PROJECT SUMMARY
Data from our previous funding period and supporting evidence from other INIA-Neuroimmune (INIA-N)
laboratories provide the foundation for the overall hypothesis of this proposal: Behavioral changes
characteristic of excessive alcohol consumption and alcohol use disorder (AUD) are due, in part, to
neuroimmune mechanisms that significantly reshape the transcriptome. Drug repurposing algorithms
can analyze cross-species transcriptome changes to predict compounds that reduce alcohol
consumption. We propose that brain dysfunction in alcoholics is the result of dysregulation in both the protein
coding and non-coding transcriptome. Advances in genomic studies by our research team and INIA-N
collaborators have enabled us to generate transcriptome data on an unprecedented scale. Devising methods
to visualize and analyze these data to produce meaningful biological interpretation is one of the next important
steps. We aim to take full advantage of the emerging technologies, allowing us to move forward in the post-
genomic, “big data” era. We previously used RNA sequencing (RNA-Seq) to define the transcriptome in four
brain regions of 90 human cases (alcoholics and matched controls). In addition to identifying novel splice
variants and co-variation with lifetime alcohol consumption, we identified novel changes in long non-coding
RNAs (lncRNAs) in human brain and propose that these play an important role in gene expression changes.
Differentially expressed lncRNAs that are correlated with alcohol consumption and have syntenic conservation
between humans and mice will be prioritized for genetic and behavioral testing in mice in collaboration with
INIA-N investigators. Other interactions involve mining the RNA-Seq transcriptome profiles in human subjects
and macaque to link expression changes with genetic differences found in the Collaborative Studies on
Genetics of Alcoholism (COGA). This will allow us to define, in unprecedented detail, changes in RNA splicing
and pathways constructed from detected single-nucleotide polymorphisms. Finally, we will study the
convergence of transcriptome changes in human, macaque, and rodent brain to determine which rodent
models reflect the gene expression changes seen in human AUD. The overlapping changes in expression and
associated networks produced by excessive alcohol consumption can be used to predict drugs that will
normalize the network using cutting-edge computational analyses. Thus, we and INIA-N collaborators will
utilize our combined transcriptome data to predict novel therapeutics based on cross-species convergence of
gene networks and then test FDA-approved drugs predicted to target the affected networks in mouse and rat
models of excessive alcohol consumption. The most promising drugs will then be selected for testing in human
alcoholics. These efforts integrate advanced computational, genomic, molecular, functional system, and
behavioral studies to be integrated into a systematic framework desi...

## Key facts

- **NIH application ID:** 10087794
- **Project number:** 5U01AA020926-10
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** R. DAYNE MAYFIELD
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $428,932
- **Award type:** 5
- **Project period:** 2011-09-05 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10087794

## Citation

> US National Institutes of Health, RePORTER application 10087794, Alcohol-Related Changes in Gene Expression and Structure Using Next Generation Sequencing (5U01AA020926-10). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10087794. Licensed CC0.

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