SARS-CoV-2 viremia is associated with distinct proteomic pathways and predicts COVID-19 outcomes
Yijia Li, … , Michael R. Filbin, Jonathan Z. Li
Yijia Li, … , Michael R. Filbin, Jonathan Z. Li
Published July 1, 2021
Citation Information: J Clin Invest. 2021;131(13):e148635. https://doi.org/10.1172/JCI148635.
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Clinical Research and Public Health Infectious disease

SARS-CoV-2 viremia is associated with distinct proteomic pathways and predicts COVID-19 outcomes

Abstract

BACKGROUND SARS-CoV-2 plasma viremia has been associated with severe disease and death in COVID-19 in small-scale cohort studies. The mechanisms behind this association remain elusive.METHODS We evaluated the relationship between SARS-CoV-2 viremia, disease outcome, and inflammatory and proteomic profiles in a cohort of COVID-19 emergency department participants. SARS-CoV-2 viral load was measured using a quantitative reverse transcription PCR–based platform. Proteomic data were generated with Proximity Extension Assay using the Olink platform.RESULTS This study included 300 participants with nucleic acid test–confirmed COVID-19. Plasma SARS-CoV-2 viremia levels at the time of presentation predicted adverse disease outcomes, with an adjusted OR of 10.6 (95% CI 4.4–25.5, P < 0.001) for severe disease (mechanical ventilation and/or 28-day mortality) and 3.9 (95% CI 1.5–10.1, P = 0.006) for 28-day mortality. Proteomic analyses revealed prominent proteomic pathways associated with SARS-CoV-2 viremia, including upregulation of SARS-CoV-2 entry factors (ACE2, CTSL, FURIN), heightened markers of tissue damage to the lungs, gastrointestinal tract, and endothelium/vasculature, and alterations in coagulation pathways.CONCLUSION These results highlight the cascade of vascular and tissue damage associated with SARS-CoV-2 plasma viremia that underlies its ability to predict COVID-19 disease outcomes.FUNDING Mark and Lisa Schwartz; the National Institutes of Health (U19AI082630); the American Lung Association; the Executive Committee on Research at Massachusetts General Hospital; the Chan Zuckerberg Initiative; Arthur, Sandra, and Sarah Irving for the David P. Ryan, MD, Endowed Chair in Cancer Research; an EMBO Long-Term Fellowship (ALTF 486-2018); a Cancer Research Institute/Bristol Myers Squibb Fellowship (CRI2993); the Harvard Catalyst/Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences, NIH awards UL1TR001102 and UL1TR002541-01); and by the Harvard University Center for AIDS Research (National Institute of Allergy and Infectious Diseases, 5P30AI060354).

Authors

Yijia Li, Alexis M. Schneider, Arnav Mehta, Moshe Sade-Feldman, Kyle R. Kays, Matteo Gentili, Nicole C. Charland, Anna L.K. Gonye, Irena Gushterova, Hargun K. Khanna, Thomas J. LaSalle, Kendall M. Lavin-Parsons, Brendan M. Lilley, Carl L. Lodenstein, Kasidet Manakongtreecheep, Justin D. Margolin, Brenna N. McKaig, Blair A. Parry, Maricarmen Rojas-Lopez, Brian C. Russo, Nihaarika Sharma, Jessica Tantivit, Molly F. Thomas, James Regan, James P. Flynn, Alexandra-Chloé Villani, Nir Hacohen, Marcia B. Goldberg, Michael R. Filbin, Jonathan Z. Li

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Figure 5

Temporal trends of differentially expressed proteins between viremic and aviremic groups.

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Temporal trends of differentially expressed proteins between viremic and...
(A) Volcano plots showing linear mixed model (LMM) of differentially expressed proteins at different time points (P values indicate group differences calculated by Tukey’s post hoc method, n = 103 at each time point). Venn diagrams demonstrate the overlap of differentially expressed proteins at different time points. (B) Selected proteins differentially expressed in the viremic group later in hospitalization (only at day 7 or only at day 3 + day 7). Underlines indicate statistical significance after adjustment for severe disease. n = 103.