Platelets amplify endotheliopathy in COVID-19

Abstract

Given the evidence for a hyperactive platelet phenotype in COVID-19, we investigated effector cell properties of COVID-19 platelets on endothelial cells (ECs). Integration of EC and platelet RNA sequencing revealed that platelet-released factors in COVID-19 promote an inflammatory hypercoagulable endotheliopathy. We identified S100A8 and S100A9 as transcripts enriched in COVID-19 platelets and were induced by megakaryocyte infection with SARS-CoV-2. Consistent with increased gene expression, the heterodimer protein product of S100A8/A9, myeloid-related protein (MRP) 8/14, was released to a greater extent by platelets from COVID-19 patients relative to controls. We demonstrate that platelet-derived MRP8/14 activates ECs, promotes an inflammatory hypercoagulable phenotype, and is a significant contributor to poor clinical outcomes in COVID-19 patients. Last, we present evidence that targeting platelet P2Y₁₂ represents a promising candidate to reduce proinflammatory platelet-endothelial interactions. Together, these findings demonstrate a previously unappreciated role for platelets and their activation-induced endotheliopathy in COVID-19.

Fig. 1.. Platelets from COVID-19 patients are hyperreactive and induce EC activation.

(A) Platelet P-selectin and (B) platelet CD40 expression measured in whole blood by flow cytometry. (C) Platelet-leukocyte (LPA), platelet-neutrophil (NPA), and platelet-monocyte (MPA) aggregates measured in whole blood by flow cytometry as assessed by CD45⁺CD61⁺ events. (D) Quantification of platelet aggregation in response to phosphate-buffered saline (PBS) [spontaneous aggregation (Spont.)], 0.1 μM adenosine diphosphate (ADP), and 0.1 μM epinephrine (Epi.). Measurements performed in seven controls donors and nine hospitalized COVID-19 patients. (E) Transmission electron microscopy of an autopsy sample from a COVID-19 patient’s lung, highlighting the interconnection between platelets and ECs. (F) CD61 (red) and P-selectin (brown) immunohistochemistry of lung tissue of a COVID-19 patient, ×40 magnification. (G) Schematic of endothelial RNA-seq experiment; microvascular ECs were treated with platelet releasate generated from COVID-19 platelets or controls. (H) Volcano plot of differentially expressed transcripts. Colored dots are adjP < 0.05; red dots are up-regulated genes, and blue dots are down-regulated genes. (I) Unsupervised hierarchical clustering heatmap of differentially expressed transcripts between EC exposed to COVID-19 releasate and control releasate (adjP < 0.05, |log₂FoldChange| > 0.5) and (J) enriched pathways between COVID-19 patients and controls, with the bars depicting the normalized enrichment score. (K) Boxplots of relative eigengene values for each sample for the apical junction pathway, coagulation pathway, and TNFα signaling pathway. (L) Highlighted EC transcripts and their expression levels following exposure to COVID-19 and control patient platelet releasate. EC RNA-seq was performed with platelet releasate from seven controls and seven COVID-19 donors. Reported false discovery rate (FDR)–adjusted P values output from DESeq2. *P < 0.05, **P < 0.01, as determined by a Student’s t-test.

Fig. 2.. COVID-19 platelet gene clusters induce a procoagulant EC phenotype.

(A) Heatmap of differentially expressed endothelial transcripts (adjP < 0.05) that are a part of the coagulation pathway. (B) Heatmap of the correlation values for the expression of each differentially expressed platelet gene (COVID-19 versus controls) with the expression of each of the differentially expressed endothelial genes in the coagulation pathway. Clusters of interest are demarcated by C1, C2, and C3. Insets of three pairwise correlation show endothelial VWF versus platelet S100A8, endothelial MMP2 versus platelet CD151, and endothelial ITGA2 versus platelet HCST. (C to E) GSEA results for C1, C2, and C3. (F) Number of platelet genes from the C1 module across differentially expressed EC pathways, and Venn diagram showing overlap of all C1 sets. Labeled sections show the number of genes unique to each signature and the overlap of all C1 platelet gene modules. (G) GSEA results for the overlapping C1 gene module.

Fig. 3.. SARS-CoV-2 up-regulates platelet and megakaryocyte S100A8/A9 and MRP8/14 release.

(A) Rank analysis of differentially expressed genes in COVID-19 platelets versus control platelets based on baseMean expression, log₂ (FoldChange), and adjusted P value between COVID-19 and control platelets. Subset zooms in on the top 50 candidates, with S100A8 and S100A9 highlighted. (B) Platelet mRNA expression of S100A8 and S100A9. N = 7 samples per group; ***P < 0.001 FDR-adjusted P values output from DESeq2. (C) CD34-derived megakaryocyte mRNA expression of S100A8 and S100A9 following treatment with PBS, SARS-CoV-2, or CoV-OC43 for 24 hours. N = 3 samples per group. **P < 0.005, ***P < 0.001 by one-way analysis of variance (ANOVA). (D and E) Platelet releasate and plasma concentration of MRP8/14. N = 7 to 9 samples per group; means ± SEM; **P < 0.005, ***P < 0.0005, as determined by Student’s t test. (F) Correlation between plasma and platelet releasate MRP8/14.

Fig. 4.. Platelet-released MRP8/14 induce EC activation.

(A) Correlation of platelet S100A8/9 mRNA expression and the expression of identified EC pathways enriched following exposure to COVID-19–derived platelet releasate. (B) Boxplots of platelet MRP8/14 releasate in COVID-19 and control samples (y axis) and boxplots of the relative eigengene values for the apical junction, coagulation, and TNFα signaling pathways (x axis). Correlation of the respective values shown in scatterplots. (C) Dotplot depicting the correlation value of the relative eigengene expression of each pathway to its respective metadata metric. Pathways dysregulated in ECs are shown on the y axis, and various metadata are shown on the x axis. The top annotation shows the Wilcoxon P value for the population difference of the respective metadata metric between COVID-19 samples and controls. Platelet heteroaggregates: MPA, leukocyte platelet aggregates (LPA), NPA, lymphocyte platelet aggregates (LYPA). Platelet aggregation: Spontaneous (PBS), submaximal ADP (0.1 μM), epinephrine (0.1 μM), collagen (0.2 μM). Mean platelet volume (MPV). (D) Quantification of IL8 and IL6 release from microvascular ECs in response to treatment with platelet releasate isolated from COVID-19 patients or controls. Data are means ± SEM; *P < 0.05, as determined by t test. (E) Correlation between released IL6 and IL8 by microvascular ECs and MRP8/14 concentration in platelet releasate. (F) Quantification of IL8 and IL6 release from microvascular ECs in response to treatment with platelet releasate isolated from COVID-19 patients or controls. CD36 silencing and EC transfection with CD36 small interfering RNA (siRNA) (or ctrl siRNA) for 72 hours before addition of platelet releasate. Data are expressed relative to each corresponding vehicle control + platelet releasate and are means ± SEM; *P < 0.05, as determined by t test; n = 6 to 7 subjects per group.

Fig. 5.. Circulating MRP8/14 correlate with adverse clinical outcomes in hospitalized COVID-19 patients.

(A) Summary of COVID-19 and control patient plasma samples collected at hospital admission. (B and C) Plasma MRP8/14 in controls (n = 21) versus COVID-19 patients (n = 291) and in COVID-19 patients stratified by adverse clinical event (n = 174) or no event (n = 117). (D) Plasma MRP8/14 in COVID-19 patients stratified by thrombosis (thrombosis, n = 54; no thrombosis, n = 237). (E) Adjusted ORs with corresponding 95% CIs for the outcomes of thrombosis, thrombosis or death, thrombosis or critically ill or death, or critically ill or death based on admission plasma MRP8/14 in COVID-19 patients. Adjusted ORs from logistic regression analysis for log-transformed biomarker levels adjusted for age, sex, race/ethnicity, BMI, diabetes, COPD/asthma, and history of coronary artery disease or cancer. (F) Admission MRP8/14 levels and days to discharge. (G) Length of stay as stratified by quartiles based on admission MRP8/14 levels, and linear regression analysis between baseline plasma MRP8/14 concentration and length of hospital stay. Longitudinal MRP8/14 measures (H) in those without a clinical event and (I) in those who subsequently died. Wilcoxon signed-rank test was performed to measure the difference of MRP8/14 levels between days 7 and 14 versus baseline. *P < 0.05, **P < 0.005.

Fig. 6.. P2Y₁₂ inhibition reduces platelet S100A8 and S100A9 expression and platelet-mediated EC activation.

A cohort of patients received (A) aspirin daily (n = 63) or (B) ticagrelor (90 mg) twice daily for 4 weeks (n = 49). Blood was collected and platelet RNA was extracted before the commencement of the study and following 4 weeks of antiplatelet therapy. Plots depict normalized expression counts of S100A8 and S100A9 at baseline and following 4 weeks of antiplatelet therapy. ****P < 0.0001 as determined by paired t test. (C) Forest plot of log fold change (LogFC) between baseline and follow-up platelet S100A8 and S100A9 expression. Data are means and 95% CI. (D) Expression of IL6, IL8, and CCL20 following exposure of ECs to platelet releasate generated in the presence of aspirin (1 mM), AZD1283 (1 μM, P2Y₁₂ inhibitor), or eptifibatide (18 μM, glycoprotein IIb/IIIa inhibitor) for 6 hours. Data expressed relative to platelet releasate–treated cells in the absence of platelet inhibitors. n = 6 unique releasate donors. Data are means ± SEM, expressed relative to the platelet releasate–treated ECs; *P < 0.05, **P < 0.01, ****P < 0.0001 by paired t test.

Fig. 7.. Proposed mechanism.

Schematic overview of the proposed mechanism of platelet-induced endotheliopathy.