Cardiovascular Signatures of COVID-19 Predict Mortality and Identify Barrier Stabilizing Therapies

STRUCTURED ABSTRACT Background Endothelial cell (EC) activation, endotheliitis, vascular permeability, and thrombosis have been observed in patients with severe COVID-19, indicating that the vasculature is affected during the acute stages of SARS-CoV-2 infection. It remains unknown whether circulating vascular markers are sufficient to predict clinical outcomes, are unique to COVID-19, and if vascular permeability can be therapeutically targeted. Methods Evaluating the prevalence of circulating inflammatory, cardiac and EC activation markers, and the development of a microRNA atlas in 241 patients with suspected SARS-CoV-2 infection, allowed their prognostic value to be assessed by a Random Forest model machine learning approach. Subsequent ex vivo experiments assessed EC permeability responses to patient plasma and were used to uncover modulated gene regulatory networks from which rational therapeutic design was inferred. Findings Multiple inflammatory and EC activation biomarkers were associated with mortality in COVID-19 patients and in severity-matched SARS-CoV-2-negative patients, while dysregulation of specific microRNAs at presentation was specific for poor COVID-19-related outcomes and revealed disease-relevant pathways. Integrating the datasets using a machine learning approach further enhanced clinical risk prediction for in-hospital mortality. Exposure of ECs to COVID-19 patient plasma resulted in severity-specific gene expression responses and EC barrier dysfunction which was ameliorated using angiopoietin-1 mimetic or recombinant Slit2-N. Interpretation Integration of multi-omics data identified microRNA and vascular biomarkers prognostic of in-hospital mortality in COVID-19 patients and revealed that vascular stabilizing therapies should be explored as a treatment for endothelial dysfunction in COVID-19, and other severe diseases where endothelial dysfunction has a central role in pathogenesis. RESEARCH IN CONTEXT Evidence before this study While diagnostic testing has allowed for the rapid identification of COVID-19 cases, the lack of post-diagnosis risk assessment metrics, especially among the highest-risk subgroups, thereby undermined the cascade and allocation of care. To date, the integration of clinical data with broad omics technologies has opened up new avenues for efficiently delineating complex patient phenotypes and their associations with clinical outcomes, with circulating profiles of plasma microRNAs (miRNA), in particular, having been shown to be tightly associated with disease, and capable of providing not only detailed prognostic information but also mechanistic insight. Added value of this study Markers of endothelial dysfunction at presentation, while indicative of poor outcomes in COVID-19-positive patients, likely reflect systemic vascular dysfunction in critically ill patients and are not specific to SARS-CoV-2 infection. More so, the generation of a plasma microRNA atlas uncovers COVID-19-specific prognostic markers and multiple disease-specific pathways of interest, including endothelial barrier dysfunction. Furthermore, synthesis of electronic health record data with clinically relevant multi-omic datasets using a machine learning approach provides substantially better metrics by which mortality can be estimated in patients with severe COVID-19. Finally, targeted stabilization of the endothelial barrier with Q-Peptide and Slit2-N are novel therapeutic avenues that should be explored in COVID-19 patients. Implications of all the available evidence Together, our work provides biological insight into the role of the endothelium in SARS-CoV-2 infection, the importance of miRNA as disease- and pathway-specific biomarkers, and the exciting possibility that endothelial barrier stabilizing treatments might hold promise in COVID-19.

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