Journal Article

Non-invasive plasma glycomic and metabolic biomarkers of post-treatment control of HIV

Liu, Qin, Etemad, Behzad, Goldman, Aaron R., Johnston, Rowena, Tebas, Pablo, Landay, Alan, Sharaf, Radwa, Tang, Hsin-Yao, Yin, Xiangfan, Abdel-Mohsen, Mohamed, Damra, Mohammad, Mounzer, Karam, Palmer, Clovis S., Papasavvas, Emmanouil, Montaner, Luis J., Giron, Leila B., Kostman, Jay R., Li, Jonathan Z., Jacobson, Jeffrey M.

2021

Overview

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 host pathways involved in post-ART HIV control. Here we report plasma glycomic and metabolic signatures of time-to-viral-rebound and probability-of-viral-remission 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. These signatures remain significant after adjusting for key demographic and clinical confounders. We also report mechanistic links between some of these biomarkers and HIV latency reactivation and/or myeloid inflammation in vitro. Finally, machine learning algorithms, based on selected sets of these biomarkers, predict time-to-viral-rebound with 74% capacity and probability-of-viral-remission with 97.5% capacity. In summary, we report non-invasive plasma biomarkers, with potential functional significance, that predict both the duration and probability of HIV remission after treatment interruption. Current HIV cure-focused clinical trials rely on analytic treatment interruption (ATI) to evaluate post-treatment control (PTC). Here, combining untargetted metabolomics and glycomics in two HIV clinical cohorts, in vitro assays, and machine learning, the authors identify and validate metabolic and glycomic biomarkers linked to inflammatory pathways and HIV latency reactivation associated with PTC, suggesting non-invasive biomarkers as an alternative to predict HIV remission.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 49/61 / 631/326/596/1787 / 631/326/596/2557 / 631/326/596/2564 / 692/699/255/1901 / 82/58 / Algorithms / Antiretroviral agents / Antiretroviral drugs / Antiretroviral therapy / Biomarkers / Clinical trials / HIV / Human immunodeficiency virus / Humanities and Social Sciences / Inflammation / Latency / Learning algorithms / Machine learning / Metabolism / Metabolomics / multidisciplinary / Remission / Science / Science (multidisciplinary)