Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People With HIV on Suppressive Antiretroviral Therapy.

BACKGROUND: Human immunodeficiency virus (HIV) infection remains incurable due to the persistence of a viral reservoir despite antiretroviral therapy (ART). Cannabis (CB) use is prevalent amongst people with HIV (PWH), but the impact of CB on the latent HIV reservoir has not been investigated. METHODS: Peripheral blood cells from a cohort of PWH who use CB and a matched cohort of PWH who do not use CB on ART were evaluated for expression of maturation/activation markers, HIV-specific T-cell responses, and intact proviral DNA. RESULTS: CB use was associated with increased abundance of naive T cells, reduced effector T cells, and reduced expression of activation markers. CB use was also associated with reduced levels of exhausted and senescent T cells compared to nonusing controls. HIV-specific T-cell responses were unaffected by CB use. CB use was not associated with intact or total HIV DNA frequency in CD4 T cells. CONCLUSIONS: This analysis is consistent with the hypothesis that CB use reduces activation, exhaustion, and senescence in the T cells of PWH, and does not impair HIV-specific CD8 T-cell responses. Longitudinal and interventional studies with evaluation of CB exposure are needed to fully evaluate the impact of CB use on the HIV reservoir.

Automated microarray platform for single-cell sorting and collection of lymphocytes following HIV reactivation.

A promising strategy to cure HIV-infected individuals is to use latency reversing agents (LRAs) to reactivate latent viruses, followed by host clearance of infected reservoir cells. However, reactivation of latent proviruses within infected cells is heterogeneous and often incomplete. This fact limits strategies to cure HIV which may require complete elimination of viable virus from all cellular reservoirs. For this reason, understanding the mechanism(s) of reactivation of HIV within cellular reservoirs is critical to achieve therapeutic success. Methodologies enabling temporal tracking of single cells as they reactivate followed by sorting and molecular analysis of those cells are urgently needed. To this end, microraft arrays were adapted to image T-lymphocytes expressing mCherry under the control of the HIV long terminal repeat (LTR) promoter, in response to the application of LRAs (prostratin, iBET151, and SAHA). In response to prostratin, iBET151, and SAHA, 30.5%, 11.2%, and 12.1% percentage of cells, respectively. The arrays enabled large numbers of single cells (>25,000) to be imaged over time. mCherry fluorescence quantification identified cell subpopulations with differing reactivation kinetics. Significant heterogeneity was observed at the single-cell level between different LRAs in terms of time to reactivation, rate of mCherry fluorescence increase upon reactivation, and peak fluorescence attained. In response to prostratin, subpopulations of T lymphocytes with slow and fast reactivation kinetics were identified. Single T-lymphocytes that were either fast or slow reactivators were sorted, and single-cell RNA-sequencing was performed. Different genes associated with inflammation, immune activation, and cellular and viral transcription factors were found.

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

Antiretroviral therapy (ART) halts HIV replication; however, cellular / immue cell viral reservoirs persist despite ART. Understanding the interplay between the HIV reservoir, immune perturbations, and HIV-specific immune responses on ART may yield insights into HIV persistence. A cross-sectional study of peripheral blood samples from 115 people with HIV (PWH) on long-term ART was conducted. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and the intact proviral DNA assay (IPDA) were performed. Total and intact HIV DNA was positively correlated with T cell activation and exhaustion. Years of ART and select bifunctional HIV-specific CD4 T cell responses were negatively correlated with the percentage of intact proviruses. A Leave-One-Covariate-Out (LOCO) inference approach identified specific HIV reservoir and clinical-demographic parameters that were particularly important in predicting select immunophenotypes. Dimension reduction revealed two main clusters of PWH with distinct reservoirs. Additionally, machine learning approaches identified specific combinations of immune and clinical-demographic parameters that predicted with approximately 70% accuracy whether a given participant had qualitatively high or low levels of total or intact HIV DNA. The techniques described here may be useful for assessing global patterns within the increasingly high-dimensional data used in HIV reservoir and other studies of complex biology.