ACCOMPLISH (Applied Coaching of COMPutational Learners In Substance use and HIV) Mentoring Program

About This Grant

PROJECT SUMMARY/ ABSTRACT Methamphetamine (MA) use has been increasingly recognized as a significant contributor to the worsening health outcomes of individuals with HIV (PWH), with severe comorbidities including immune dysfunction, cardiovascular disease, and pulmonary hypertension. Even in the presence of effective antiretroviral therapy (ART) that maintains viral suppression, MA use exacerbates HIV pathogenesis and complicates efforts to achieve an HIV cure. Despite the high prevalence of MA use among PWH, its direct effects on HIV cure efforts, particularly how it alters HIV reservoirs and immune function, remain poorly understood. The overarching goal of this proposal is to investigate the complex intersection of HIV, methamphetamine use, and immune dysfunction. In addition, this career mentorship award will provide protected time for Dr. Lee to expand her mentoring capacity of early-stage data science investigators in the interdisciplinary field of clinical translational HIV and substance use research. This proposal outlines a comprehensive, data-driven approach to investigate how MA exposure influences HIV reservoir transcription and host immune responses, with the goal of developing computational models that can predict the biological consequences of MA exposure, and ultimately, inform HIV cure strategies. The specific aims of this proposal will employ advanced statistical and computational methods to examine the causal relationships between MA exposure and immune dysfunction, quantify the pharmacologic effects of MA in individuals with HIV, and identify the genetic factors that mediate these effects. In Aim 1, we will apply causal inference methodologies to longitudinal data from Dr. Lee’s ongoing Effect of Methamphetamine on Residual Latent HIV Disease (EMRLHD) cohort. This analysis will focus on determining whether MA exposure causally influences host immune dysfunction, including the activation of the NLRP3 inflammasome and elevated IL-1β signaling, as well as whether it contributes to persistent HIV transcription in CD4+ T cells of individuals on ART. In Aim 2, we will use pharmacokinetic/pharmacodynamic (PK/PD) modeling to explore the dose- and host- specific effects of MA on HIV reservoir transcription and immune function. Using data from a randomized placebo-controlled trial of PWH on ART receiving oral MA, we will apply nonlinear mixed-effects PK/PD modeling to assess how varying doses of MA impact host immune responses (such as cytokine levels and gene expression) and HIV reservoir transcription. Finally, in Aim 3, we will prioritize genes that are functionally relevant to MA exposure using a transcriptome-wide association study (TWAS) approach, combined with machine learning algorithms, integrating genetic data with transcriptome data to identify genes whose expression is influenced by MA exposure. This work will address critical gaps in our understanding of how MA exposure impairs immune function and maintains HIV reservoirs, even in individuals on ART. Ultimately, this work will not only advance our understanding of MA’s role in HIV pathogenesis but also help lay the foundation for innovative, data-driven therapeutic strategies to address both HIV and substance use comorbidities in the future.