# A Big Data Approach to Identify Epigenetic, Transcriptomic, and Network Dynamics as Immune Dysfunction Drivers Associated with HIV Infection and Substance Use Disorder

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $567,700

## Abstract

PROJECT ABSTRACT
 The opioid crisis was declared a public health emergency in 2017. It has led to an
increased incidence of opioid overdose, injection substance use, and, eventually, HIV
transmission. More than 171,000 people in the United States are living with HIV as a result of
substance use disorder (SUD). Despite the known fact that both HIV and SUD significantly
disturbs both innate immunity and adaptive immunity, their underlying molecular mechanisms,
and interplay to immune dysfunction remain unexplored. Comprehensive functional
characterization at a single-cell resolution is essential to provide new molecular insights and
discover therapeutic targets.
 Recent advances in novel sequencing technologies and community efforts to share
genomic data provide unprecedented opportunities to understand the molecular dynamics of
immune dysfunction up HIV infection and SUD. This application describes the development of
integrative strategies and machine learning methods to combine novel assays (such as STARR-
seq) with high-dimensional, multi-scale genomic profiles to elucidate the transcriptional,
epigenetic, and network alterations and to key immune dysfunction drivers associated with HIV
and SUD. Specifically, we will (1) Integrate novel functional genomics assays with single-cell
multi-omics data to construct cell-type-specific multi-modal gene regulatory network (GRNs) in
healthy individuals, (2) build a comprehensive immune profiling data hub for HIV/SUD-affected
individuals and construct disease- and cell-type-specific GRNs, (3) uncover how key network
changes and aberrant behaviors of TFs upon HIV infection and/or SUD can lead to immune
dysfunction. Distinct from existing efforts focusing on transcriptome analyses, this proposed work
presents a genuinely novel big-data approach for both modeling gene regulation and investigating
disease-risk factors by incorporating heterogeneous multi-omics profiles at a single-cell resolution.
The resultant comprehensive list of cis-regulatory elements at a single-cell resolution will expand
the number of known functional regions. The constructed immune cell atlas, GRNs, and identify
key drivers of immune dysfunction will be accessible to the public via web services and annotation
databases. Our integrative computational efforts will be released distributed open-source
programs. Altogether, our released resource will accelerate research in the broader scientific
community by providing essential tools to investigate immune function, which will benefit other
investigators exploring the genetic underpinnings of immune system function of HIV and/or SUD.

## Key facts

- **NIH application ID:** 10055913
- **Project number:** 1R01DA051906-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Mark Bender Gerstein
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $567,700
- **Award type:** 1
- **Project period:** 2020-07-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10055913

## Citation

> US National Institutes of Health, RePORTER application 10055913, A Big Data Approach to Identify Epigenetic, Transcriptomic, and Network Dynamics as Immune Dysfunction Drivers Associated with HIV Infection and Substance Use Disorder (1R01DA051906-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10055913. Licensed CC0.

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