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伏性储存库， 被感染的细胞抗逆转录病毒疗法（ART）不能消除潜伏的HIV储存库， 中断导致病毒在数周内不受控制地反弹。尽管这个水库很重要， 关于影响它的生物学参数，或者娱乐性药物使用的影响， 了虽然艾滋病毒库的大小在个体内相当稳定，但差异很大（高达1000倍） 个体之间，这表明宿主因素影响其大小。这些因素可能包括 复杂的基因组、转录途径、免疫细胞群体和环境 影响，包括滥用药物。大麻（CB）的使用，特别是在人群中流行， 艾滋病毒感染者（PWH），高达49%的PWH报告经常使用。然而，CB对HIV的影响 水库尚未得到充分调查。CB具有免疫调节和抗炎活性 通过激活免疫细胞（包括CD 4 T细胞）中广泛表达的CB 2受体 是艾滋病病毒的主要来源。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规模的因素。由于CB相互作用的复杂性， 对于宿主免疫系统，需要新的计算工具来深入了解 CB如何影响宿主免疫系统和HIV宿主。 我们的目标是开发一个新的异构数据集成框架，包括新的 降维和可解释机器学习的工具，并将其应用于三个数据库中的数据。 HIV队列研究（US-UBM、瑞士和US-Duke）。这种方法将揭示关系 宿主特征和HIV储存库大小之间的关系，无论是在存在还是不存在CB使用的情况下。在 目标1，我们开发了降维（DR）工具，并应用于异构数据， 美国-美国PWH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（adtrees）-并将其应用于一项大型PWH瑞士队列研究的数据， 揭示了决定艾滋病毒储存库大小的因素。在目标3中，这两种工具都将应用于来自 在杜克大学使用CB-PWH的独特队列，以解释大麻对 免疫系统和潜伏的HIV宿主。

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.