BackgroundKawasaki disease (KD) is a prevalent pediatric disease worldwide and can cause coronary artery aneurysm as a severe complication. Typically, DNA methylation is thought to repress the expression of nearby genes. However, the cases in which DNA methylation promotes gene expression have been reported. In addition, globally, to what extent DNA methylation affects gene expression and how it contributes to the pathogenesis of KD are not yet well understood.MethodsTo address these important biological questions, we enrolled subjects, collected DNA and RNA samples from the subjects’ total white blood cells, and performed DNA methylation (M450K) and gene expression (HTA 2.0) microarray assays.ResultsBy analyzing the variation ratios of CpG beta values (methylation percentage) and gene expression intensities, we first concluded that the CpG markers close (− 1500 bp to + 500 bp) to the transcription start sites had higher variation ratios, reflecting significant regulation capacities. Next, we observed that, globally speaking, gene expression was modestly negatively correlated (correlation rho ≈ − 0.2) with the DNA methylation status of both upstream and downstream CpG markers in the promoter region. Third, we found that specific CpG markers were hypo-methylated in disease samples compared with healthy samples and hyper-methylated in convalescent samples compared with disease samples, promoting and repressing S100A genes’ expressions, respectively. Finally, using an in vitro cell model, we demonstrated that S100A family proteins enhanced leukocyte transendothelial migration in KD.ConclusionsThis is the first study to integrate genome-wide DNA methylation with gene expression assays in KD and showed that the S100A family plays important roles in the pathogenesis of KD.Electronic supplementary materialThe online version of this article (10.1186/s13148-018-0557-1) contains supplementary material, which is available to authorized users.

Motivation: Drosophila melanogaster is a major model organism for investigating the function and interconnection of animal genes in the earliest stages of embryogenesis. Today, images capturing Drosophila gene expression patterns are being produced at a higher throughput than ever before. The analysis of spatial patterns of gene expression is most biologically meaningful when images from a similar time point during development are compared. Thus, the critical first step is to determine the developmental stage of an embryo. This information is also needed to observe and analyze expression changes over developmental time. Currently, developmental stages (time) of embryos in images capturing spatial expression pattern are annotated manually, which is time- and labor-intensive. Embryos are often designated into stage ranges, making the information on developmental time course. This makes downstream analyses inefficient and biological interpretations of similarities and differences in spatial expression patterns challenging, particularly when using automated tools for analyzing expression patterns of large number of images.Results: Here, we present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In an analysis of 3724 images, the new approach shows high accuracy in predicting the developmental stage correctly (79%). In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores for all images containing expression patterns of the same gene enable a direct way to view expression changes over developmental time for any gene. We show that the genomewide-expression-maps generated using images from embryos in refined stages illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.Availability and implementation: The software package is available for download at: http://www.public.asu.edu/∼jye02/Software/Fly-Project/.Contact: jieping.ye@asu.eduSupplementary information: Supplementary data are available at Bioinformatics online.