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

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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.