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: 10055913
• 负责人: Mark Bender Gerstein
• 金额: $ 56.77万
• 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/10055913
• 关键词: ATAC-seq; Affect; Alcohol or Other Drugs use; 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; Human Cell Line; Humoral Immunities; Immune; Immune System Diseases; Incidence; Individual; Injections; Lead; Link; Machine Learning; Maps; Methods; Modeling; Molecular; Molecular Profiling; Multiomic Data; Natural Immunity; Pathogenesis; Pharmacology; Phenotype; Physiological; Prevalence; Psychological Transfer; PubMed; Quantitative Trait Loci; Regulation; Regulator Genes; 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; genomic data; genomic profiles; high dimensionality; immune function; immune system function; in vivo; insight; machine learning method; molecular dynamics; multidimensional data; multimodality; multiple omics; novel; novel sequencing technology; open source; opioid epidemic; opioid overdose; opioid use disorder; programs; promoter; public health emergency; single-cell RNA sequencing; text searching; therapeutic target; tool; transcription factor; transcriptome; transcriptomics; 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 2017年，阿片类药物危机被宣布为公共卫生紧急状态。它导致了一场 阿片类药物过量、注射药物使用以及最终感染艾滋病毒的发生率增加 变速箱。美国有超过17.1万人携带艾滋病毒，原因是 物质使用障碍(SUD)。尽管已知的事实是艾滋病毒和性病都显著 干扰先天免疫和获得性免疫，它们的基本分子机制， 而与免疫功能障碍的相互作用仍未被探索。综合功能 单细胞分辨率的表征对于提供新的分子洞察力和 发现治疗靶点。 新测序技术的最新进展和社区共享的努力 基因组数据为理解分子动力学提供了前所未有的机会 免疫功能障碍会增加HIV感染和SUD。此应用程序描述了 综合策略和机器学习方法，以结合新的分析方法(如STARR- SEQ)用高维、多尺度的基因组图谱来阐明转录， 表观遗传和网络改变以及与艾滋病毒相关的关键免疫功能障碍驱动因素 还有苏德。具体地说，我们将(1)将新的功能基因组学分析与单细胞 多组学数据构建细胞类型特异性多模式基因调控网络(GRN) 健康人，(2)为受艾滋病毒/SUD影响的人建立全面的免疫概况数据中心 个人和构建特定于疾病和细胞类型的GRN，(3)揭示关键网络 转录因子在HIV感染和/或SUD时的变化和异常行为可导致免疫 功能障碍。与专注于转录组分析的现有工作不同，这项拟议的工作 提出了一种真正新颖的大数据方法，用于建模基因调控和研究 通过在单细胞分辨率下整合不同种类的多组学图谱，研究疾病风险因素。 由此产生的单一单元格分辨率的顺式调控要素的全面清单将扩大 已知功能区的数量。构建的免疫细胞图谱、GRN和鉴定 免疫功能障碍的关键驱动因素将通过网络服务和注释向公众开放 数据库。我们的综合计算成果将以分布式开源方式发布 程序。总之，我们发布的资源将加速更广泛的科学领域的研究 通过提供必要的工具来研究免疫功能，这将使其他人受益 研究人员探索HIV和/或SUD免疫系统功能的遗传基础。