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

Abstract

BACKGROUNDSARS-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.METHODSWe 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.RESULTSThis 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.CONCLUSIONThese 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.FUNDINGMark 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).

Keywords: COVID-19; Fibrosis; Infectious disease; Proteomics; Pulmonary surfactants.

Figure 1. Enrollment and follow-up flow diagram.

MGH, Massachusetts General Hospital; ED, emergency department; VL, viral load.

Figure 2. SARS-CoV-2 viremia at day 0.

(A) Distribution of SARS-CoV-2 viral load (VL). Fifty-three participants had viremia within the quantification range with median viral load 2.68 log copies/mL; 247 participants had viral loads below the range of quantification or detection. We used the Mann-Whitney test to compare 2 groups. (B) Duration between symptom onset and ED presentation was comparable between the viremic (quantifiable) and the aviremic/viremic (unquantifiable) group. The Mann-Whitney test was used for comparison. (C) Pairwise correlation heatmap between viral load and baseline factors (Spearman’s rank correlation coefficient). n = 300. *P < 0.05; **P < 0.01; ***P < 0.001. LDH, lactate dehydrogenase; AST, aspartate transaminase; ALT, alanine transaminase.

Figure 3. Association between baseline SARS-CoV-2 viral load and maximal disease severity (acuity_max).

(A) Disease severity categorized by viral load (VL) above and below the quantification limit (≥2 log₁₀ copies/mL vs. <2 log₁₀ copies/mL). (B) Disease severity categorized by viral load within the quantification range, below the quantification range but detectable, or aviremic. The χ² test or Fisher’s exact test was used for comparison. n = 300.

Figure 4. Plasma proteomic biomarkers and predictors of disease severity.

(A) Unsupervised clustering uniform manifold approximation and projection (UMAP) for COVID-19–positive patients at days 0, 3, and 7. Red dots indicate viremic participants, and blue dots indicate aviremic participants. (B) Volcano plots showing normalized protein expression (NPX) differences in protein levels between viremic and aviremic participants. The left panel is derived from a linear model without severity as a covariate; the right panel is derived from a linear model with severity as a covariate. (C) Representative differentially expressed proteins between viremic and aviremic participants. Adjusted P values are color-coded as indicated. n = 247.

Figure 5. Temporal trends of differentially expressed proteins between viremic and aviremic groups.

(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.

Figure 6. Neutralization level and viremia.

(A) Violin plot of neutralization levels stratified by viremia status. Mann-Whitney U test was used to evaluate the difference between 2 groups. (B) Neutralization rate between viremic and aviremic groups. LOWESS (locally weighted scatterplot smoothing) smooth regression was performed to depict the trajectory of neutralizing rates between 2 groups. (C) Correlation between SDC1/CD138 (a marker for plasmablasts) NPX and neutralizing rate at day 0. Linear regression (solid line) with 95% CIs (dotted lines) is shown. Spearmann’s correlation was used to evaluate the correlation between SDC1/CD138 NPX and neutralizing rates. n = 175.