Gene Network Identification and Integration (GNetii) Approach to Understanding the Biology Underlying HIV and Drug Abuse.

基因网络识别和整合 (GNetii) 方法用于了解艾滋病毒和药物滥用背后的生物学。

• 批准号: 10056018
• 负责人: Daniel A Jacobson
• 金额: $ 63.15万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10056018
• 关键词: Address; Affect; Algorithms; Artificial Intelligence; Big Data; Bioinformatics; Biological; Biological Process; Biology; Chromosome Mapping; Chronic Disease; Cocaine; Cocaine Abuse; Cocaine Users; Complex; Computational Biology; Custom; DNA Methylation; Data; Data Set; Developed Countries; Development; Disease; Drug abuse; Drug user; Elements; Gene Expression Regulation; Genes; Genetic Epistasis; Genetic Predisposition to Disease; Genome; Genotype; Genotype-Tissue Expression Project; Goals; HIV; HIV Infections; Illicit Drugs; Incidence; Individual; Information Networks; Internet; Joints; Laboratories; Maps; Mediating; Methods; Molecular; Multiomic Data; Network-based; Oranges; Outcome; Pathogenesis; Pathway interactions; Pharmaceutical Preparations; Population; Principal Investigator; Production; Public Health; Regulator Genes; Research Design; Resources; Sampling; Signal Transduction; Source; Surface; Techniques; Variant; Viral; Viral Load result; Viral reservoir; Virus Latency; Woman; antiretroviral therapy; cohort; genetic variant; genome wide association study; genome-wide; improved; innovation; insight; mRNA Expression; methylome; multiple omics; novel; random forest; response; success; supercomputer; symposium; transcriptome; transcriptome sequencing

PROJECT SUMMARY/ABSTRACT The goal of the proposed study is to advance our understanding of the complex networks of biology underlying variation in HIV viral load (VL) and latent reservoir (LR) among HIV+ individuals, and how cocaine abuse (CA) affects identified biological networks. With the success of combination antiretroviral therapy (cART) and public health strategies to reduce HIV incidence, much of the HIV burden in developed countries is now as a chronic disease, including among drug users. Managing HIV progression (HP) and searching for an HIV cure are of paramount importance. Higher pretreatment VL is associated with HP and is associated with a larger LR. An HIV cure is dependent on eliminating the LR. Cocaine is one of the most frequently abused illicit drugs among HIV+ individuals and is known to increase VL, worsen HP, slow decline of viral production after cART, and, we hypothesize, affect the quantity of LR. Thus, there is a complex web of relationships among VL, LR, and CA, which are partially driven by and mediated through genetic susceptibility and gene regulation. As concluded by Le Cleric et al. (2019) in their recent review: “Only integrative approaches that combine all big data results and consider their complex interactions will allow us to capture the global picture of HIV molecular pathogenesis. This novel challenge will require large collaborative efforts and represents a huge open field for innovative bioinformatics approaches.” We propose Gene Network Identification and Integration (GNetii) as a multi-method, multi-omic framework for discovering and understanding the biology underlying HIV outcomes and the effect of CA. We will apply Explainable Artificial Intelligence, network mapping, and Lines-of-Evidence integration to existing genome-, methylome-, and transcriptome-wide data across a number of cohorts in the following aims:  Aim 1: Identify gene networks underlying variation in HIV VL and LR applying GNetii.  Aim 2: Identify differences in HIV associated gene networks by CA. This robustly designed study is significant and innovative: targeting key HIV outcomes affected by CA, applying big data techniques to identify gene networks across multiple omics (enhancing discovery and biological interpretation), and leveraging unique LR data. Our multiple Principal Investigator team includes expertise in HIV, drug abuse, and computational biology. Thus, we are likely to produce important new insights into key elements of HIV as a chronic disease: providing a basis for targeting unique features of CA that impact VL and LR, which make HIV management and a cure more challenging in this population.

项目概要/摘要 拟议研究的目标是增进我们对复杂生物学网络的理解 HIV+ 个体中 HIV 病毒载量 (VL) 和潜伏病毒库 (LR) 的潜在差异，以及可卡因如何影响 滥用（CA）影响已识别的生物网络。 随着联合抗逆转录病毒疗法 (cART) 和减少艾滋病毒的公共卫生策略的成功 发，发达国家的艾滋病毒负担大部分已成为一种慢性疾病，其中包括药物 用户。管理 HIV 进展 (HP) 和寻找 HIV 治疗方法至关重要。更高 预处理 VL 与 HP 相关，并且与较大的 LR 相关。 HIV 治愈取决于 消除LR。可卡因是艾滋病病毒感染者中最常滥用的非法药物之一， 已知会增加 VL、恶化 HP、减缓 cART 后病毒产量的下降，并且我们假设会影响 LR的数量。因此，VL、LR 和 CA 之间存在一个复杂的关系网，它们部分地 由遗传易感性和基因调控驱动并介导。正如 Le Cleric 等人的结论。 （2019）在他们最近的评论中：“只有结合所有大数据结果并考虑其 复杂的相互作用将使我们能够全面了解艾滋病毒分子发病机制。这部小说 挑战需要大量的协作努力，并且代表着一个巨大的创新领域 生物信息学方法。” 我们提出基因网络识别和整合（GNetii）作为多方法、多组学框架 发现和理解 HIV 结果的生物学原理以及 CA 的影响。我们将申请 可解释的人工智能、网络映射和证据线与现有基因组的集成， 多个队列的甲基化组和转录组范围的数据，目标如下： 目标 1：应用 GNetii 识别 HIV VL 和 LR 变异背后的基因网络。  目标 2：通过 CA 识别 HIV 相关基因网络的差异。 这项设计严谨的研究具有重要意义和创新性：针对受 CA 影响的关键 HIV 结果， 应用大数据技术来识别跨多个组学的基因网络（增强发现和 生物学解释），并利用独特的 LR 数据。我们的多名首席研究员团队包括 艾滋病毒、药物滥用和计算生物学方面的专业知识。因此，我们可能会产生重要的新产品 深入了解艾滋病毒作为一种慢性疾病的关键要素：为针对 CA 的独特特征提供基础 影响 VL 和 LR，这使得该人群的艾滋病毒管理和治疗更具挑战性。