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Mounzer, K.

Publications and source records attributed to Mounzer, K..

2 recordsLinked to original sources

Cognate Antigen Engagement Induces HIV-1 Expression In CD4+ T Cells From People On Long-Term ART

Despite antiretroviral therapy (ART), HIV-1 persists in latently-infected CD4+ T cells, preventing cure. Antigens drive the proliferation of infected cells, precluding latent reservoir decay. However, the relationship between antigen recognition and HIV-1 gene expression is poorly understood since most studies of latency reversal use agents that induce non-specific global T cell activation. Here, we isolated rare CD4+ T cells responding to cytomegalovirus (CMV) or HIV-1 Gag antigens from participants on long-term ART and assessed T cell activation and HIV-1 RNA expression upon co-culture with autologous dendritic cells (DCs) presenting cognate antigens. Physiological presentation of cognate antigens induced broad T cell activation (median 42-fold increase in CD154+CD69+ cells) and significantly increased HIV-1 transcription (median 4-fold), mostly through the induction of rare cells with higher viral expression. Thus, despite low proviral inducibility, physiologic antigen recognition can promote HIV-1 expression, potentially contributing to spontaneous reservoir activity on ART and viral rebound upon ART interruption.

immunology↗

Non-Invasive Plasma Glycomic and Metabolic Biomarkers of Post-treatment Control of HIV

Non-invasive biomarkers that predict HIV remission after antiretroviral therapy (ART) interruption are urgently needed. Such biomarkers can improve the safety of analytic treatment interruption (ATI) and provide mechanistic insights into the pathways involved in post-ART HIV control. We identified plasma glycomic and metabolic signatures of time-to-viral-rebound and probability-of-viral-rebound using samples from two independent cohorts. These samples include a large number of post-treatment controllers, a rare population demonstrating sustained virologic suppression after ART-cessation. The signatures remained significant after adjusting for key demographic and clinical confounders. We also confirmed a mechanistic link between biomarkers and HIV latency reactivation and myeloid inflammation in vitro. Finally, machine learning algorithms selected sets of biomarkers that predict time-to-viral-rebound with 74-76% capacity and probability-of-viral-rebound with 97.5% capacity. In summary, we fill a major gap in HIV cure research by identifying non-invasive biomarkers, with potential functional significance, that predict duration and probability of viral remission after treatment interruption.

microbiology↗