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

利用大数据方法识别表观遗传、转录组和网络动态作为与 HIV 感染和药物滥用障碍相关的免疫功能障碍驱动因素

• 批准号: 10408130
• 负责人: Mark Bender Gerstein
• 金额: $ 56.09万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10408130
• 关键词: ATAC-seq; Affect; Atlases; Behavior; Big Data; Binding; Biological Assay; Biological Process; CD4 Positive T Lymphocytes; Cells; Cellular Immunity; Communities; Companions; Complex; Computing Methodologies; Data; Data Set; Databases; Development; Disease; Distal; Enhancers; Epigenetic Process; Event; Gene Expression Regulation; General Population; Genes; Genetic; Genetic Transcription; HIV; HIV Infections; Hi-C; Human Cell Line; Humoral Immunities; Immune; Immune System Diseases; Incidence; Individual; Injections; Lead; Link; Maps; Methods; Modeling; Molecular; Molecular Profiling; Multiomic Data; Natural Immunity; Pathogenesis; Persons; Pharmacology; Phenotype; Physiological; Prevalence; PubMed; Quantitative Trait Loci; Regulation; Regulatory Element; Reporting; Research; Research Personnel; Resolution; Resources; Risk Factors; Role; Series; Substance Use Disorder; Technology; Time; Twitter; United States; Viral; Work; adaptive immunity; cell type; computer framework; data hub; disorder risk; epigenome; experience; experimental study; functional genomics; gene network; gene regulatory network; genomic data; genomic profiles; high dimensionality; immune function; immune system function; in vivo; insight; machine learning method; machine learning model; molecular dynamics; multidimensional data; multimodality; multiple omics; novel; novel sequencing technology; open source; opioid epidemic; opioid overdose; opioid use disorder; programs; promoter; public database; public health emergency; single-cell RNA sequencing; substance use; text searching; therapeutic target; tool; transcription factor; transcriptome; transcriptomics; transfer learning; transmission process; web services

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.

项目摘要