An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease

• Published in: Nature communications Vol. 13; no. 1; p. 2687
• Main Authors: Ghosh, Pradipta, Katkar, Gajanan D., Shimizu, Chisato, Kim, Jihoon, Khandelwal, Soni, Tremoulet, Adriana H., Kanegaye, John T., Bocchini, Joseph, Das, Soumita, Burns, Jane C., Sahoo, Debashis
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16.05.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 13/51; 38/91; 631/553/1833; 692/699/255; Artificial Intelligence; Child; Children; Computational Biology - methods; Computer applications; Coronaviruses; COVID-19; COVID-19 - complications; COVID-19 - genetics; COVID-19 - immunology; Cytokine storm; Cytokines; Disorders; Eosinopenia; Gene Expression Profiling; Gene sequencing; Heart; Humanities and Social Sciences; Humans; Immune response; Immune system; Immunity - physiology; Immunopathogenesis; Infections; Interleukin 1; Interleukin 15; Kawasaki disease; Laboratories; Mucocutaneous lymph node syndrome; Mucocutaneous Lymph Node Syndrome - genetics; Mucocutaneous Lymph Node Syndrome - immunology; multidisciplinary; Multisystem inflammatory syndrome in children; Pandemics; Parameters; Pediatrics; Phenotypes; SARS-CoV-2; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Signatures; Systemic Inflammatory Response Syndrome - genetics; Systemic Inflammatory Response Syndrome - immunology; Thrombocytopenia; Viral diseases

Multisystem inflammatory syndrome in children (MIS-C) is an illness that emerged amidst the COVID-19 pandemic but shares many clinical features with the pre-pandemic syndrome of Kawasaki disease (KD). Here we compare the two syndromes using a computational toolbox of two gene signatures that were de...