# Immunologic and Predictive Features of MIS-C

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $610,099

## Abstract

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.

## Key facts

- **NIH application ID:** 10423273
- **Project number:** 1R01HD108467-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Dusan Bogunovic
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $610,099
- **Award type:** 1
- **Project period:** 2022-07-18 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10423273

## Citation

> US National Institutes of Health, RePORTER application 10423273, Immunologic and Predictive Features of MIS-C (1R01HD108467-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10423273. Licensed CC0.

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