A machine learning framework for understanding impacts on the HIV latent reservoir size, including drugs of abuse

治愈艾滋病毒感染的主要障碍是持久和持久的潜在蓄水池 被感染的细胞。潜伏的艾滋病毒蓄积物不能通过抗逆转录病毒疗法(ART)和ART消除 中断会导致病毒在几周内失控反弹。尽管这个水库很重要， 对影响它的生物参数或娱乐性吸毒对 它。虽然艾滋病毒携带者在个体内的大小相当稳定，但差异很大(高达1000倍) 个体之间的差异，表明寄主因素影响其大小。这些因素可能包括 一组复杂的基因、转录途径、免疫细胞群和环境 影响，包括滥用药物。尤其是大麻素(CB)的使用在人群中很普遍 报告经常使用艾滋病毒(PWH)的患者高达49%。然而，哥伦比亚广播公司对艾滋病毒的影响 储集层尚未得到充分调查。CBS具有免疫调节和抗炎活性 通过激活在免疫细胞中广泛表达的CB2受体，包括CD4T细胞 那里是大部分艾滋病病毒携带者的港湾。我们的假设是CB与宿主途径相互作用 影响艾滋病毒储存库大小的因素。由于CB相互作用的复杂性 有了宿主免疫系统，就需要新的计算工具来实现对 炭疽是如何影响宿主免疫系统和艾滋病毒宿主的。 我们的目标是开发一个新的异构数据集成框架，包括新的 用于降维和可解释机器学习的工具，并将其应用于来自三个 艾滋病毒队列研究(美国-北卡罗来纳大学、瑞士和美国-杜克大学)。这一方法将揭示关系 在存在和不存在CB使用的情况下，宿主特征和HIV储存库大小之间的关系。在……里面 目标1，我们开发降维(DR)工具，并将其应用于来自 美国-北卡罗来纳大学威斯康星医院队列。在目标2中，我们开发了一种新的强大的可解释机器技术-- 交替决策树(Adtree)-并将其应用于来自PWH瑞士大型队列研究的数据 揭示了决定艾滋病毒储存库大小的因素。在AIM 3中，这两个工具都将应用于来自 独一无二的CB队列--在杜克大学使用PWH解释大麻对 威尔斯亲王的免疫系统和潜伏的HIV蓄水池。

The major obstacle to curing HIV infection is a durable and persistent latent reservoir of infected cells. The latent HIV reservoir is not eliminated by antiretroviral therapy (ART), and ART interruption results in uncontrolled virus rebound within weeks. Despite the importance of this reservoir, little is known about the biological parameters that influence it, or the effects of recreational drug use on it. While the size of the HIV reservoir is fairly stable within individuals, it varies greatly (up to 1000-fold) between individuals, suggesting that host factors influence its size. These factors likely include a complex set of genes, transcriptional pathways, immune cell populations, and environmental influences, including drugs of abuse. Cannabinoid (CB) use, in particular, is prevalent amongst persons with HIV (PWH) with up to 49% PWH reporting regular use. However, the impact of CBs on the HIV reservoir has not been fully investigated. CBs have immuno-modulatory and anti-inflammatory activities through activation of the CB2 receptor that is widely expressed in immune cells, including CD4 T cells that harbor most of the HIV reservoir. Our hypothesis is that CB interacts with host pathways and factors that impact the size of the HIV reservoir. Due to the complex nature of the interaction of CB with the host immune system, new computational tools are required lo achieve a deep understanding of how CB impacts both the host immune system and the HIV reservoir. Our goal is to develop a novel framework for heterogeneous data integration, including new tools for dimension reduction and interpretable machine learning, and apply it to data from three HIV cohort studies (US-UNC, Switzerland, and US-Duke). This approach will reveal relationships between host characteristics and HIV reservoir size, both in the presence and absence of CB use. In Aim 1, we develop dimension reduction {DR) tools, with application to heterogeneous data from the US-UNC PWH cohort. In Aim 2, we develop a new powerful interpretable machine technique - alternating decision trees (adtrees) - and apply it to data from a large PWH Swiss cohort study to reveal factors that determine HIV reservoir size. In Aim 3, both tools will be applied to data from a unique cohort of CB-using PWH at Duke University, to explain the effects of cannabis on the immune system of PWH and on the latent HIV reservoir.