| Literature DB >> 34073389 |
Heather Jackson1, Stephanie Menikou1, Shea Hamilton1, Andrew McArdle1, Chisato Shimizu2, Rachel Galassini1, Honglei Huang3, Jihoon Kim4, Adriana Tremoulet2, Adam Thorne5, Roman Fischer6, Marien I de Jonge7, Taco Kuijpers8, Victoria Wright1, Jane C Burns2, Climent Casals-Pascual9, Jethro Herberg1, Mike Levin1, Myrsini Kaforou1.
Abstract
The aetiology of Kawasaki disease (KD), an acute inflammatory disorder of childhood, remains unknown despite various triggers of KD having been proposed. Host 'omic profiles offer insights into the host response to infection and inflammation, with the interrogation of multiple 'omic levels in parallel providing a more comprehensive picture. We used differential abundance analysis, pathway analysis, clustering, and classification techniques to explore whether the host response in KD is more similar to the response to bacterial or viral infections at the transcriptomic and proteomic levels through comparison of 'omic profiles from children with KD to those with bacterial and viral infections. Pathways activated in patients with KD included those involved in anti-viral and anti-bacterial responses. Unsupervised clustering showed that the majority of KD patients clustered with bacterial patients on both 'omic levels, whilst application of diagnostic signatures specific for bacterial and viral infections revealed that many transcriptomic KD samples had low probabilities of having bacterial or viral infections, suggesting that KD may be triggered by a different process not typical of either common bacterial or viral infections. Clustering based on the transcriptomic and proteomic responses during KD revealed three clusters of KD patients on both 'omic levels, suggesting heterogeneity within the inflammatory response during KD. The observed heterogeneity may reflect differences in the host response to a common trigger, or variation dependent on different triggers of the condition.Entities:
Keywords: Kawasaki disease; classification; clustering; host ‘omics; infectious diseases; paediatrics; pathway analysis; proteomics; systems biology; transcriptomics
Year: 2021 PMID: 34073389 DOI: 10.3390/ijms22115655
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923