International Journal of Molecular SciencesBioinformaticsPeer-reviewed journal article
Kawasaki Disease patient stratification and pathway analysis based on host transcriptomic and proteomic profiles
Heather JacksoniD, Stephanie MenikouiD, Shea HamiltoniD, Andrew James McArdleiD, Chisato ShimizuiD, Rachel GalassiniiD, Honglei HuangiD, Jihoon KimiD, Adriana H. TremouletiD, Adam M. ThorneiD, Roman Fischer, Marien Isaäk de JongeiD, Taco Willem KuijpersiD, Victoria J. WrightiD, Jane C. BurnsiD, Climent Casals‐PascualiD, Jethro Adam HerbergiD, Michael LeviniD, Myrsini KaforouiD
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 infection 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.
- organism: human
- assay: bulk RNA-seq
- assay: proteomics
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- Citations
- 19
- FWCI
- 3.05
- Field percentile
- top 8%
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