Biology subjects

Murdoch, D. M.

Publications and source records attributed to Murdoch, D. M..

3 recordsLinked to original sources

bioRxiv2023

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

Understanding the interplay between the HIV reservoir and the host immune system may yield insights into HIV persistence during antiretroviral therapy (ART) and inform strategies for a cure. Here, we applied machine learning approaches to cross-sectional high-parameter HIV reservoir and immunology data in order to characterize host-reservoir associations and generate new hypotheses about HIV reservoir biology. High-dimensional immunophenotyping, quantification of HIV-specific T cell responses, and measurement of genetically intact and total HIV proviral DNA frequencies were performed on peripheral blood samples from 115 people with HIV (PWH) on long-term ART. Analysis demonstrated that both intact and total proviral DNA frequencies were 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, such as age and biological sex, that were particularly important in predicting immunophenotypes. Overall, immune parameters were more strongly associated with total HIV proviral frequencies than intact proviral frequencies. Uniquely, however, expression of the IL-7 receptor alpha chain (CD127) on CD4 T cells was more strongly correlated with the intact reservoir. Unsupervised dimension reduction analysis identified two main clusters of PWH with distinct immune and reservoir characteristics. Using reservoir correlates identified in these initial analyses, decision tree methods were employed to visualize relationships among multiple immune and clinical-demographic parameters and the HIV reservoir. Finally, using random splits of our data as training-test sets, machine learning algorithms 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.

microbiology↗

bioRxiv2022

Impact of cannabis use on immune cell populations and the viral reservoir in people with HIV on suppressive antiretroviral therapy.

HIV infection remains incurable due to the persistence of a viral reservoir during antiretroviral therapy. Cannabis (CB) use is prevalent amongst people with HIV (PWH), but the impact of CB on the latent HIV reservoir has not been investigated. Peripheral CD4 and CD8 T cells from a cohort of CB-using PWH and a matched cohort of non-users on antiretroviral therapy were evaluated for expression of maturation/activation markers, HIV-specific T cell responses, and the frequency of intact proviral DNA. CB use was associated with increased abundance of naive T cells, reduced effector T cells, and reduced expression of activation markers. CB users also exhibited reduced levels of exhausted and senescent T cells compared to non-using controls. HIV-specific CD8 T cell responses were unaffected by CB use. While the abundance of intact proviruses was not significantly affected by CB use across the whole cohort, we observed that, for participants with high frequency of NKG2A or CD16 expression in NK cells, CB use was associated with a smaller intact HIV reservoir. This analysis is consistent with the hypothesis that CB use reduces activation, exhaustion and senescence in the T cells of PWH and may influence the size of the HIV reservoir.

microbiology↗

bioRxiv2022

Integrated single-cell multiomic analysis of HIV latency reversal reveals novel regulators of viral reactivation.

Despite the success of antiretroviral therapy, HIV cannot be cured because of a reservoir of latently infected cells that evades therapy. To understand the mechanisms of HIV latency, we employed an integrated single-cell RNA-seq/ATAC-seq approach to simultaneously profile the transcriptomic and epigenomic characteristics of ~4000 latently infected cells after reactivation using three different latency-reversing agents (LRAs). Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor (TF) activities across the cell population. We identify cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrate that a machine learning model trained on these data was 68% accurate at predicting viral reactivation. Finally, we validate the role of a new candidate HIV-regulating factor, GATA3, in the viral response to prostratin stimulation. These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.

microbiology↗