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. 2022 Jun 25:50:101515.
doi: 10.1016/j.eclinm.2022.101515. eCollection 2022 Aug.

Epigenetic profiling linked to multisystem inflammatory syndrome in children (MIS-C): A multicenter, retrospective study

Affiliations

Epigenetic profiling linked to multisystem inflammatory syndrome in children (MIS-C): A multicenter, retrospective study

Veronica Davalos et al. EClinicalMedicine. .

Abstract

Background: Most children and adolescents infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain asymptomatic or develop a mild coronavirus disease 2019 (COVID-19) that usually does not require medical intervention. However, a small proportion of pediatric patients develop a severe clinical condition, multisystem inflammatory syndrome in children (MIS-C). The involvement of epigenetics in the control of the immune response and viral activity prompted us to carry out an epigenomic study to uncover target loci regulated by DNA methylation that could be altered upon the appearance of MIS-C.

Methods: Peripheral blood samples were recruited from 43 confirmed MIS-C patients. 69 non-COVID-19 pediatric samples and 15 COVID-19 pediatric samples without MIS-C were used as controls. The cases in the two groups were mixed and divided into discovery (MIS-C = 29 and non-MIS-C = 56) and validation (MIS-C = 14 and non-MIS-C = 28) cohorts, and balanced for age, gender and ethnic background. We interrogated 850,000 CpG sites of the human genome for DNA methylation variants.

Findings: The DNA methylation content of 33 CpG loci was linked with the presence of MIS-C. Of these sites, 18 (54.5%) were located in described genes. The top candidate gene was the immune T-cell mediator ZEB2; and others highly ranked candidates included the regulator of natural killer cell functional competence SH2D1B; VWA8, which contains a domain of the Von Willebrand factor A involved in the pediatric hemostasis disease; and human leukocyte antigen complex member HLA-DRB1; in addition to pro-inflammatory genes such as CUL2 and AIM2. The identified loci were used to construct a DNA methylation profile (EPIMISC) that was associated with MIS-C in both cohorts. The EPIMISC signature was also overrepresented in Kawasaki disease patients, a childhood pathology with a possible viral trigger, that shares many of the clinical features of MIS-C.

Interpretation: We have characterized DNA methylation loci that are associated with MIS-C diagnosis. The identified genes are likely contributors to the characteristic exaggerated host inflammatory response observed in these patients. The described epigenetic signature could also provide new targets for more specific therapies for the disorder.

Funding: Unstoppable campaign of Josep Carreras Leukaemia Foundation, Fundació La Marató de TV3, Cellex Foundation and CERCA Programme/Generalitat de Catalunya.

Keywords: COVID-19; DNA methylation; Epigenetics; Kawasaki disease; Multisystem inflammatory syndrome in children.

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Conflict of interest statement

Dr. Esteller declares grants from Ferrer International, personal fees from Quimatryx, outside the submitted work. Dr. Rivière reports personal fees from Grifols, CSL behring and Takeda, outside the submitted work. The other authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The volcano plot shows significant differences in the DNA methylation status of 850K CpG sites between MIS-C and non-MISC using the described experimental and bioinformatic pipeline. Y-axis shows the -log10P-value and X-axis shows the mean methylation difference according to beta value. A total of 33 CpGs with a delta beta >0.15 and FDR adjusted P-value <0.05 are shown in red. For those with an associated coding sequence, the gene name is also indicated. CpG-sites that exhibited a methylation beta value difference <0.15 and/or FDR adjusted P-value >0.05 are shown in grey. Dashed lines indicate cut-offs for significance.
Figure 2
Figure 2
Heatmap representing the entire cohort of MIS-C and non-MIS-C cases clustered by methylation beta values of the 33 CpGs defining the EPIMISC signature. Cluster analysis was performed using the Ward.D clustering method and assuming Manhattan distances.

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