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Diagnosis of Multisystem Inflammatory Syndrome in Children by a Whole-Blood Transcriptional Signature

Lookup NU author(s): Professor Marieke Emonts-le ClercqORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2023 The Author(s). Published by Oxford University Press on behalf of The Journal of the Pediatric Infectious Diseases Society.Background: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. Methods: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). Results: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. Conclusions: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.


Publication metadata

Author(s): Jackson HR, Miglietta L, Habgood-Coote D, D'Souza G, Shah P, Nichols S, Vito O, Powell O, Davidson MS, Shimizu C, Agyeman PKA, Beudeker CR, Brengel-Pesce K, Carrol ED, Carter MJ, De T, Eleftheriou I, Emonts M, Epalza C, Georgiou P, De Groot R, Fidler K, Fink C, Van Keulen D, Kuijpers T, Moll H, Papatheodorou I, Paulus S, Pokorn M, Pollard AJ, Rivero-Calle I, Rojo P, Secka F, Schlapbach LJ, Tremoulet AH, Tsolia M, Usuf E, Van Der Flier M, Von Both U, Vermont C, Yeung S, Zavadska D, Zenz W, Coin LJM, Cunnington A, Burns JC, Wright V, Martinon-Torres F, Herberg JA, Rodriguez-Manzano J, Kaforou M, Levin M, EUCLIDS Consortium, PERFORM Consortium, DIAMONDS Consortium

Publication type: Article

Publication status: Published

Journal: Journal of the Pediatric Infectious Diseases Society

Year: 2023

Volume: 12

Issue: 6

Pages: 322-331

Print publication date: 01/06/2023

Online publication date: 31/05/2023

Acceptance date: 30/05/2023

Date deposited: 05/09/2023

ISSN (print): 2048-7193

ISSN (electronic): 2048-7207

Publisher: Oxford University Press

URL: https://doi.org/10.1093/jpids/piad035

DOI: 10.1093/jpids/piad035

PubMed id: 37255317


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Funding

Funder referenceFunder name
668303
206508/Z/17/Z
279185
848196
DIAMONDS
EUCLIDS
European Union Horizon 2020 Program
Imperial Biomedical Research Centre (BRC)
Medical Research Foundation
MRF-160-0008-ELP-KAFO-C0801
National Institute for Health Research (NIHR)
PERFORM
Wellcome Trust

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