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

项目概要/摘要 这项研究的目的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）和潜伏储存库（LR）的潜在变化，以及可卡因如何 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战略的成功， 由于艾滋病的发病率很高，发达国家的艾滋病毒负担现在大部分是一种慢性疾病，包括吸毒者， 用户.管理HIV进展（HP）和寻找HIV治愈方法至关重要。更高 治疗前VL与HP相关，并与较大的LR相关。艾滋病的治愈依赖于 取消LR。可卡因是艾滋病毒阳性者中最常滥用的非法药物之一， 已知增加VL，恶化HP，减缓cART后病毒产生的下降，我们假设，影响 LR的数量。因此，VL、LR和CA之间存在复杂的关系网，这些关系网部分地 由遗传易感性和基因调控驱动和介导。正如Le Cleric et al. （2019）在他们最近的评论中：“只有综合方法，联合收割机结合所有大数据的结果，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片。这本小说 挑战将需要大量的合作努力，并代表了一个巨大的开放领域的创新 生物信息学方法”。 我们提出了基因网络识别和整合（GNetii）作为一个多方法，多组学框架 发现和理解艾滋病毒结果和CA影响的生物学基础。我们将应用 可解释的人工智能，网络映射和证据线整合到现有的基因组， 甲基化组和转录组范围的数据，在以下目的中跨越多个队列： 目标1：应用GNetii确定HIV VL和LR变异的基因网络。 目的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影响的关键艾滋病毒结果， 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。我们的多个主要研究者团队包括 艾滋病、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的 深入了解艾滋病毒作为一种慢性病的关键要素：为针对CA的独特特征提供基础 这影响了VL和LR，使艾滋病毒管理和治愈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.