RNAseq-based transcriptomics of treatment-naïve multi-inflammatory syndrome in children (MIS-C) demonstrates predominant activation of matrisome, innate and humoral immune pathways.
Sibabratta PatnaikPrakashini MvKrushna Chandra MurmuSoumendu MahapatraA Raj Kumar PatroShubhini A SarafSanghamitra PatiPunit PrasadSakir AhmedPublished in: Rheumatology international (2023)
MIS-C is a rare, highly inflammatory state resembling incomplete Kawasaki disease, temporarily associated with COVID-19. The pathogenesis is not completely known. RNAseq was carried out on whole blood of six treatment-naïve MIS-C patients. This was compared against RNAseq transcriptomics data of five healthy controls (HC), four Kawasaki Disease (KD) and seven systemic Juvenile Idiopathic Arthritis (sJIA). Using PCA, MIS-C clustered separately from HC, KD and sJIA. Amongst the top 50 significant genes in the three comparisons with HC, KD, and sJIA, common genes were: TMCC2, ITGA2B, DMTN, GFI1B, PF4, QSER1, GRAP2, TUBB1. DSEA revealed that maximum number of hits for overexpressed pathways was for NABA matrisome activation when MIS-C was compared against HC. Cytokine stimulated cellular activation pathways, specifically IL-10 were downregulated. MIS-C had more activated pathways of neutrophil degranulation and acquired immune activation but less of coagulation system or heat-shock system involvement as compared to KD. As compared to sJIA, humoral immune response and complements were activated. Matrisome activation was higher, with increased cell-cell interaction and ECM signalling. This analysis revealed novel insights into the pathogenesis of MIS-C, including the potential role of matrisomes, humoral immune system and down-regulated interleukin-10 pathways.
Keyphrases
- immune response
- single cell
- juvenile idiopathic arthritis
- heat shock
- coronavirus disease
- oxidative stress
- sars cov
- ejection fraction
- genome wide
- end stage renal disease
- cell therapy
- toll like receptor
- machine learning
- young adults
- stem cells
- gene expression
- climate change
- systemic lupus erythematosus
- transcription factor
- patient reported outcomes
- bioinformatics analysis
- genome wide identification