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

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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.