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 阿片类药物危机在2017年被宣布为公共卫生紧急情况。这导致了 阿片类药物过量的发生率增加，注射药物的使用，最终是HIV 传播。由于 药物使用障碍（SUD）。尽管已知艾滋病毒和SUD都显着 干扰先天免疫​​和适应性免疫，即它们的基本分子机制， 与免疫功能障碍的相互作用仍未开发。全面的功能 单细胞分辨率的表征对于提供新的分子见解至关重要 发现治疗靶标。 新颖的测序技术和社区努力分享的最新进展 基因组数据提供了前所未有的机会来了解分子动力学 免疫功能障碍可在HIV感染和SUD上。该应用程序描述了 集成策略和机器学习方法结合新的测定法（例如Starr- SEQ）具有高维，多尺度基因组谱，以阐明转录， 表观遗传和网络变化，以及与HIV相关的关键免疫功能障碍驱动因素 和SUD。具体而言，我们将（1）与单细胞整合新型功能基因组学测定 在构建细胞类型特异性多模式基因调节网络（GRNS）的多摩学数据中 健康的个体，（2）为受艾滋病毒/受SUD影响的全面免疫分析数据中心 个体并构建疾病和细胞类型特异性GRN，（3）发现关键网络如何 HIV感染和/或SUD时TF的变化和异常行为会导致免疫 功能障碍。这项拟议的工作与现有的专注于转录组分析的努力不同 提出了一种真正新颖的大数据方法，用于建模基因调节和研究 通过以单细胞分辨率纳入异质的多媒体轮廓，疾病风险因素。 由单细胞分辨率下的顺式调节元素的最终综合列表将扩展 已知功能区域的数量。构造的免疫细胞图集，GRNS，并识别 免疫功能障碍的关键驱动因素将通过Web服务和注释访问 数据库。我们的集成计算工作将被发布分布式开源 程序。总之，我们发布的资源将加速更广泛的科学研究 通过提供研究免疫功能的基本工具，社区将受益其他 研究人员探索HIV和/或SUD的免疫系统功能的遗传基础。