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

项目摘要/摘要 这项拟议的研究的目标是促进我们对生物学复杂网络的理解 HIV个体中HIV病毒载量(VL)和潜伏期(LR)的潜在差异以及可卡因是如何 滥用(CA)影响已确定的生物网络。 随着抗逆转录病毒疗法(CART)和公共卫生战略相结合以减少艾滋病毒的成功 发达国家的艾滋病毒负担现在大多是一种慢性病，包括毒品。 用户。控制艾滋病毒进展(HP)和寻找治疗艾滋病毒的方法是至关重要的。更高 治疗前VL与HP相关，并与较大的LR相关。HIV的治愈依赖于 消除了LR。可卡因是艾滋病毒感染者中滥用最频繁的非法药物之一， 已知的是VL增加，HP恶化，CART后病毒产生缓慢下降，我们假设，影响 LR的数量。因此，在VL、LR和CA之间有一个复杂的关系网络，它们部分地 由遗传易感性和基因调控驱动并通过其中介。正如勒克雷尔等人得出的结论。 (2019)在他们最近的回顾中：“只有将所有大数据结果结合在一起并考虑其 复杂的相互作用将使我们能够捕捉到艾滋病毒分子发病机制的全球图景。这部小说 挑战将需要大量的协作努力，并代表着创新的巨大开放领域 生物信息学的方法。 我们提出了基因网络识别和整合(GNetii)作为一个多方法、多基因组的框架 用于发现和了解艾滋病毒后果的生物学基础和CA的影响。我们会申请 可解释的人工智能、网络映射和与现有基因组的证据线整合-- 以下目标中若干队列的甲基组和转录组范围数据： 目标1：应用Gnetii识别HIV VL和LR变异的基因网络。 目标2：通过CA识别艾滋病毒相关基因网络的差异。 这项设计稳健的研究具有重大意义和创新性：针对受CA影响的关键艾滋病毒结果， 应用大数据技术识别跨多个组学的基因网络(增强发现和 生物解释)，并利用独特的LR数据。我们的多名首席调查员团队包括 在艾滋病毒、药物滥用和计算生物学方面的专业知识。因此，我们很可能会产生重要的新的 对HIV作为慢性病的关键因素的洞察：为针对CA的独特特征提供基础 这影响了VL和LR，这使得艾滋病毒的管理和治疗在这一人群中更具挑战性。

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.