Cell-free DNA reveals distinct pathology of multisystem inflammatory syndrome in children
Temesgen E. Andargie, … , Andrea L. Cox, Sean Agbor-Enoh
Temesgen E. Andargie, … , Andrea L. Cox, Sean Agbor-Enoh
Published August 31, 2023
Citation Information: J Clin Invest. 2023;133(21):e171729. https://doi.org/10.1172/JCI171729.
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Research Article Infectious disease Inflammation

Cell-free DNA reveals distinct pathology of multisystem inflammatory syndrome in children

Abstract

Multisystem inflammatory syndrome in children (MIS-C) is a rare but life-threatening hyperinflammatory condition induced by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes pediatric COVID-19 (pCOVID-19). The relationship of the systemic tissue injury to the pathophysiology of MIS-C is poorly defined. We leveraged the high sensitivity of epigenomics analyses of plasma cell-free DNA (cfDNA) and plasma cytokine measurements to identify the spectrum of tissue injury and glean mechanistic insights. Compared with pediatric healthy controls (pHCs) and patients with pCOVID-19, patients with MIS-C had higher levels of cfDNA primarily derived from innate immune cells, megakaryocyte-erythroid precursor cells, and nonhematopoietic tissues such as hepatocytes, cardiac myocytes, and kidney cells. Nonhematopoietic tissue cfDNA levels demonstrated significant interindividual variability, consistent with the heterogenous clinical presentation of MIS-C. In contrast, adaptive immune cell–derived cfDNA levels were comparable in MIS-C and pCOVID-19 patients. Indeed, the cfDNA of innate immune cells in patients with MIS-C correlated with the levels of innate immune inflammatory cytokines and nonhematopoietic tissue–derived cfDNA, suggesting a primarily innate immunity–mediated response to account for the multisystem pathology. These data provide insight into the pathogenesis of MIS-C and support the value of cfDNA as a sensitive biomarker to map tissue injury in MIS-C and likely other multiorgan inflammatory conditions.

Authors

Temesgen E. Andargie, Katerina Roznik, Neelam Redekar, Tom Hill, Weiqiang Zhou, Zainab Apalara, Hyesik Kong, Oren Gordon, Rohan Meda, Woojin Park, Trevor S. Johnston, Yi Wang, Sheila Brady, Hongkai Ji, Jack A. Yanovski, Moon K. Jang, Clarence M. Lee, Andrew H. Karaba, Andrea L. Cox, Sean Agbor-Enoh

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Figure 6

Integrated cfDNA and cytokine analysis distinguishes MIS-C from pCOVID-19.

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Integrated cfDNA and cytokine analysis distinguishes MIS-C from pCOVID-1...
(A and B) PCA of cfDNA and cytokine profile to differentiate MIS-C from pCOVID-19. (A) Graph of each sample with PC1 (Dim 1) on the x axis and PC2 (Dim 2) on the y axis. The large shapes (red triangle for MIS-C and blue circle for pCOVID-19) represent the average or center of their respective groups, with the ellipses representing 95% CIs of where the true average may lie. The percentages on the axes indicate the amount of variability in the data explained by that axis. (B) Cos2 plot of the representation of each variable for each dimension of the PCA. The darker and larger the circle, the more that variable is represented by the dimension it is listed under. The color gradient on the right-hand side of the panel correlates color with the approximate cos2 value. Pink text indicates key innate immune cfDNA and cytokine features. (C) Rank of important cfDNA and cytokine features to distinguish patients with MIS-C from those with pCOVID-19 using the random forest model. (D) Performance of combined cfDNA and cytokine features to distinguish MIS-C from pCOVID-19. ROC curves for the 10 different runs and the dashed line represent the average curve.