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

Leila B. Giron, Clovis S. Palmer, Qin Liu, Xiangfan Yin, Emmanouil Papasavvas, Radwa Sharaf, Behzad Etemad, Mohammad Damra, Aaron R. Goldman, Hsin-Yao Tang, Rowena Johnston, Karam Mounzer, Jay R. Kostman, Pablo Tebas, Alan Landay, Luis J. Montaner, Jeffrey M. Jacobson, Jonathan Z. Li and Mohamed Abdel-Mohsen ()
Additional contact information
Leila B. Giron: The Wistar Institute
Clovis S. Palmer: The Burnet Institute
Qin Liu: The Wistar Institute
Xiangfan Yin: The Wistar Institute
Emmanouil Papasavvas: The Wistar Institute
Radwa Sharaf: Brigham and Women’s Hospital, Harvard Medical School
Behzad Etemad: Brigham and Women’s Hospital, Harvard Medical School
Mohammad Damra: The Wistar Institute
Aaron R. Goldman: The Wistar Institute
Hsin-Yao Tang: The Wistar Institute
Rowena Johnston: amfAR, The Foundation for AIDS Research
Karam Mounzer: Philadelphia FIGHT
Jay R. Kostman: Philadelphia FIGHT
Pablo Tebas: University of Pennsylvania
Alan Landay: Rush University
Luis J. Montaner: The Wistar Institute
Jeffrey M. Jacobson: Case Western Reserve University School of Medicine
Jonathan Z. Li: Brigham and Women’s Hospital, Harvard Medical School
Mohamed Abdel-Mohsen: The Wistar Institute

Nature Communications, 2021, vol. 12, issue 1, 1-15

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-24077-w

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