Plasma Proteomics of COVID-19-Associated Cardiovascular Complications: Implications for Pathophysiology and Therapeutics

Abstract

To gain insights into the mechanisms driving cardiovascular complications in COVID-19, we performed a case-control plasma proteomics study in COVID-19 patients. Our results identify the senescence-associated secretory phenotype, a marker of biological aging, as the dominant process associated with disease severity and cardiac involvement. FSTL3, an indicator of senescence-promoting Activin/TGFβ signaling, and ADAMTS13, the von Willebrand Factor-cleaving protease whose loss-of-function causes microvascular thrombosis, were among the proteins most strongly associated with myocardial stress and injury. Findings were validated in a larger COVID-19 patient cohort and the hamster COVID-19 model, providing new insights into the pathophysiology of COVID-19 cardiovascular complications with therapeutic implications.

Keywords: ADAMTS13, A Disintegrin And Metalloproteinase with a Thrombospondin type 1 motif, member 13; COVID-19; FSTL3, follistatin-like 3; NT-proBNP, N-terminal pro–B-type natriuretic peptide; SASP, senescence associated secretory phenotype; TGFβ, transforming growth factor beta; hsTnT, high sensitivity troponin T; myocardial injury; proteomics; senescence.

Graphical abstract

Figure 1

Senescence Associated Secretory Phenotype Is Associated With COVID-19 Severity and Cardiac Involvement (A) Unsupervised clustering of the 80 patient plasma samples based on principle component 1 (PC1). (B) Venn diagram displaying overlapping pathways associated with increasing COVID-19 disease severity in the discovery and validation cohorts. In the discovery cohort, enrichment analysis was done with PC1 proteins (Supplemental Table 3). In the validation cohort, pathway analysis was performed using Day 0 proteomic profiles regressed on maximum World Health Organization disease severity per patient (Supplemental Table 8). (C) Heat maps displaying differential expression of the 50 most significantly regulated genes in the SASP-1 or -2 gene sets in lungs from hamsters infected with SARS-CoV-2 vs naive control subjects. Tissue samples were collected 4 days after infection. n = 3/group. SASP = senescence associated secretory phenotype.

Figure 2

Differentially Expressed Plasma Proteins Associated With COVID-19 Infection and Disease Severity (A) Volcano plot displaying differentially expressed plasma proteins in the discovery study when comparative analysis was done between COVID-19 patients (n = 54) vs control subjects (n = 26). (B) Volcano plot displaying differentially expressed plasma proteins in the discovery study when comparing COVID-19 patients with moderate (n = 25) vs severe (n = 29) disease. Proteins with significant (P_adj < 0.05) differences in abundance (nonsignificant proteins colored black) with an absolute change >1.5-fold are colored red (proteins with more modest fold-change are colored blue). For all analyses, P values were adjusted for multiple hypothesis testing using the Benjamini and Hochberg method.

Figure 3

Inverse Association of ADAMTS13 With Myocardial Injury in COVID-19 (A) Volcano plot displaying differentially expressed plasma proteins in the discovery study after regression on cardiac TnT levels. Beta coefficient is plotted on the X-axis showing magnitude and direction of correlation with TnT. Red = significant (P_adj < 0.05). Green = nonsignificant. P values were adjusted for multiple hypothesis testing using the Benjamini and Hochberg method. (B) Boxplots displaying median and 25th and 75th percentiles of plasma ADAMTS13 levels according to disease severity and cardiac involvement in the discovery cohort. (C) Pearson correlations of ADAMTS13 levels with cardiac biomarkers of myocardial injury (cardiac TnT) and stress (NT-proBNP) in COVID-19 patients in the discovery cohort. Solid line represents best fit line after simple linear regression, and dashed lines represent the 95% CI. NT-proBNP = N-terminal pro–B-type natriuretic peptide; TnT = troponin T.

Figure 4

Association of Lower ADAMTS13 Levels With Disease Severity and Mortality in COVID-19 Boxplots of ADAMTS13 plasma levels in the MGH Emergency Department validation cohort (n = 305 COVID-19 patients) according to (A) days from initial presentation or at event, E, marking significant clinical deterioration; (B) maximum COVID-19 severity (World Health Organization classification); or (C) 28-day survival. Boxplots display median and 25th and 75th percentiles. NPX = normalized protein expression.

Figure 5

Mendelian Randomization Analysis of cis-pQTLs in ADAMTS13 Gene Infers Causality in Myocardial Injury Scatterplot of SNP effects on ADAMTS13 plasma levels (pQTLs) and clinically diagnosed myocardial injury in the UK Biobank general population sample (n = 463,010; 269 cases and 462,741 control subjects). The slope of each line corresponds to the estimated Mendelian randomization effect per method. (Light blue = inverse variance-weighted method; dark blue = weighted median method; green = MR Egger method). This demonstrates that genetically determined decreases in ADAMTS13 levels are quantitatively associated with increasing risk of myocardial injury, and support a causal role of ADAMTS13 in this context.

Figure 6

Pulmonary and Cardiac Vascular Thrombus Formation and Decreased ADAMTS13 Expression in SARS-CoV-2 Infected Syrian Hamsters Hamsters were infected with 5 × 10⁵ TCID₅₀ SARS-CoV-2 and sacrificed on day 4 following challenge. (A) Thrombus, pulmonary vein. (B) Endothelialitis, pulmonary vein. (C) Endothelialitis, pulmonary artery. (D) Fibrin deposition along endothelium, cardiac vein. (E) Fibrin deposition, cardiac artery with adjacent myodegeneration. (F) Focal myocarditis and myodegeneration. (A, D, E) Carstairs stain (magenta = fibrin). (B, C, F) Hematoxylin and eosin. Scale bars = 20 microns. (G) Relative change in mRNA levels of ADAMTS13 and its substrate, vWF, in lung tissue from hamsters infected with SARS-CoV-2 (n = 3) compared with naive uninfected control subjects (n = 3). Tissue samples were collected at 4 days postinfection, and mRNA levels were quantified using bulk RNAseq. P values were adjusted for multiple hypothesis testing using the Benjamini and Hochberg method.

Figure 7

Association of TGFβ Superfamily Signaling With Heart Failure Biomarker in COVID-19 (A) Volcano plot displaying differentially expressed plasma proteins in the discovery study after regression on NT-proBNP levels. Beta coefficient is plotted on the X-axis showing magnitude and direction of correlation with NT-proBNP. Red = significant (P_adj < 0.05). Green = nonsignificant. P values were adjusted for multiple hypothesis testing using the Benjamini and Hochberg method. (B) Boxplots displaying median and 25th and 75th percentiles of plasma FSTL3 levels according to disease severity and cardiac involvement in the discovery cohort. (C) Pearson correlations of FSTL3 levels with biomarkers for myocardial stress (NT-proBNP) and injury (cardiac TnT) in the discovery cohort. Solid line represents best fit line after simple linear regression, and dashed lines represent the 95% CI. TGFβ = transforming growth factor beta; other abbreviations as in Figure 3.

Figure 8

Association of TGFβ Superfamily Signaling With Disease Severity and Mortality in COVID-19 Boxplots of FSTL3 levels in the MGH Emergency Department validation cohort according to (A) time from clinical presentation in days or at event, E, marked by significant clinical deterioration; (B) maximum COVID-19 severity by World Health Organization classification; or (C) 28-day survival. Boxplots display median and 25th and 75th percentiles. NPX = normalized protein expression.