Usage Information
Abstract
Why multisystem inflammatory syndrome in children (MIS-C) develops after SARS-CoV-2 infection in a subset of children is unknown. We hypothesized that aberrant virus–specific T cell responses contribute to MIS-C pathogenesis. We quantified SARS-CoV-2–reactive T cells, serologic responses against major viral proteins, and cytokine responses from plasma and peripheral blood mononuclear cells in children with convalescent COVID-19, in children with acute MIS-C, and in healthy controls. Children with MIS-C had significantly lower virus-specific CD4+ and CD8+ T cell responses to major SARS-CoV-2 antigens compared with children convalescing from COVID-19. Furthermore, T cell responses in participants with MIS-C were similar to or lower than those in healthy controls. Serologic responses against spike receptor binding domain (RBD), full-length spike, and nucleocapsid were similar among convalescent COVID-19 and MIS-C, suggesting functional B cell responses. Cytokine profiling demonstrated predominant Th1 polarization of CD4+ T cells from children with convalescent COVID-19 and MIS-C, although cytokine production was reduced in MIS-C. Our findings support a role for constrained induction of anti–SARS-CoV-2–specific T cells in the pathogenesis of MIS-C.
Authors
Vidisha Singh, Veronica Obregon-Perko, Stacey A. Lapp, Anna Marie Horner, Alyssa Brooks, Lisa Macoy, Laila Hussaini, Austin Lu, Theda Gibson, Guido Silvestri, Alba Grifoni, Daniela Weiskopf, Alessandro Sette, Evan J. Anderson, Christina A. Rostad, Ann Chahroudi
Usage data is cumulative from December 2024 through December 2025.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 386 | 34 |
| 114 | 7 | |
| Figure | 128 | 0 |
| Table | 66 | 0 |
| Supplemental data | 44 | 3 |
| Citation downloads | 86 | 0 |
| Totals | 824 | 44 |
| Total Views | 868 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
