Differential host responses in coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C) remain poorly characterized. Here we use next-generation sequencing to longitudinally analyze blood samples from pediatric patients with acute COVID-19 (n=70) or MIS-C (n=141) across three hospitals. Profiling of plasma cell-free nucleic acids uncovers distinct signatures of cell injury and death between these two disease states, with increased heterogeneity and multi-organ involvement in MIS-C encompassing diverse cell types such as endothelial and neuronal Schwann cells. Whole blood RNA profiling reveals upregulation of similar pro-inflammatory signaling pathways in COVID-19 and MIS-C, but also MIS-C specific downregulation of T cell-associated pathways. Profiling of plasma cell-free RNA and whole blood RNA in paired samples yields different yet complementary signatures for each disease state. Our work provides a systems-level, multi-analyte view of immune responses and tissue damage in COVID-19 and MIS-C and informs the future development of new disease biomarkers.

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Background: As the transmission of endemic respiratory pathogens returns to prepandemic levels, understanding the epidemiology of respiratory coinfections in children with SARS-CoV-2 is of increasing importance. Methods: We performed a retrospective analysis of all pediatric patients 0–21 years of age who had a multiplexed BioFire Respiratory Panel 2.1 test performed at Children’s Healthcare of Atlanta, Georgia, from January 1 to December 31, 2021. We determined the proportion of patients with and without SARS-CoV-2 who had respiratory coinfections and performed Poisson regression to determine the likelihood of coinfection and its association with patient age. Results: Of 19,199 respiratory panel tests performed, 1466 (7.64%) were positive for SARS-CoV-2, of which 348 (23.74%) also had coinfection with another pathogen. The most common coinfection was rhino/enterovirus (n = 230, 15.69%), followed by adenovirus (n = 62, 4.23%), and RSV (n = 45, 3.507%). Coinfections with SARS-CoV-2 were most commonly observed in the era of Delta (B.1.617.2) predominance (190, 54.60%), which coincided with periods of peak rhino/enterovirus and RSV transmission. Although coinfections were common among all respiratory pathogens, they were significantly less common with SARS-CoV-2 than other pathogens, with exception of influenza A and B. Children <2 years of age had the highest frequency of coinfection and of detection of any pathogen, including SARS-CoV-2. Among children with SARS-CoV-2, for every 1-year increase in age, the rate of coinfections decreased by 8% (95% CI, 6–9). Conclusions: Respiratory coinfections were common in children with SARS-CoV-2. Factors associated with the specific pathogen, host, and time period influenced the likelihood of coinfection.

MIS-C is a severe hyperinflammatory condition with involvement of multiple organs that occurs in children who had COVID-19 infection. Accurate diagnostic tests are needed to guide management and appropriate treatment and to inform clinical trials of experimental drugs and vaccines, yet the diagnosis of MIS-C is highly challenging due to overlapping clinical features with other acute syndromes in hospitalized patients. Here we developed a gene expression-based classifier for MIS-C by RNA-Seq transcriptome profiling and machine learning based analyses of 195 whole blood RNA and 76 plasma cell-free RNA samples from 191 subjects, including 95 MIS-C patients, 66 COVID-19 infected patients with moderately severe to severe disease, and 30 uninfected controls. We divided the group into a training set (70%) and test set (30%). After selection of the top 300 differentially expressed genes in the training set, we simultaneously trained 13 classification models to distinguish patients with MIS-C and COVID-19 from controls using five-fold cross-validation and grid search hyperparameter tuning. The final optimal classifier models had 100% diagnostic accuracy for MIS-C (versus non-MIS-C) and 85% accuracy for severe COVID-19 (versus mild/asymptomatic COVID-19). Orthogonal validation of a random subset of 11 genes from the final models using quantitative RT-PCR confirmed the differential expression and ability to discriminate MIS-C and COVID-19 from controls. These results underscore the utility of a gene expression classifier for diagnosis of MIS-C and severe COVID-19 as specific and objective biomarkers for these conditions.