Immunologic and predictive features of MIS-C - the novel SARS coronavirus (SARS-COV-2) causes the severe pneumonia-like coronavirus disease (COVID-19)

SARS-COV-2 infected over 170 million individuals and has claimed over 3.5 million lives worldwide to date. If otherwise healthy, children were thought to be largely spared from SARS-COV-2 disease. However, in areas of high SARS-COV-2 infection rates, some children started presenting to pediatric critical care units 4-6 weeks following SARS-COV- 2 infection with kawasaki-like disease. Clinically, we now know that this is a distinct disease, which was recently termed - multisystem inflammatory syndrome in children (MIS-C). While the characteristic clinical features of MIS-C are becoming clear, the pathophysiology remains unknown. Here we propose to evaluate three independent cohorts of MIS-C during acute and convalescent phases of disease at clinical, genetic and immunologic levels using the latest technology. We will not only perform systemic immunological mapping of MIS-C as compared to controls, but also utilize machine learning algorithms to delineate how best to predict, diagnose and outcome stratify MIS-C. We anticipate discovering immunologic and genetic features which can aid US in assessing risks of MIS-C development, diagnosis and prognosis. In summary, our systematic analysis and computational modeling of the clinical and immune features of MIS-C will not only help illuminate the pathogenesis of this syndrome, but will also provide US with actionable biomarkers for disease risk, diagnosis and progression.