DNA methylation signature in type 1 diabetes

Abstract

Type 1 diabetes is an autoimmune disease due to the interaction of genetic and non-genetic factors, leading to an immune response against insulin secreting islet cells. Concordance rates for type 1 diabetes in monozygotic twins vary widely and no single environmental factor has been shown to cause the disease. Therefore, epigenetics has been suggested to play a role in diabetes aetiology. Preliminary results identified DNA methylation changes in CD14+ monocytes from childhood-onset type 1 diabetes which antedated the disease. Following on from this work, this present study was carried out to investigate whole-genome DNA methylation profiles in CD14+CD16- monocytes, CD4+ T cells, CD19+ B cells and buccal cells from 24 monozygotic twin pairs discordant for type 1 diabetes. DNA methylation was profiled using Illumina Infinium HumanMethylation450K BeadChip and analysed using the ChAMP pipeline. Bisulfite sequencing was also carried out on CD4+ cells from four monozygotic twin pairs also discordant for type 1 diabetes. Through bioinformatics analyses, 258 cell-type specific differentially-methylated positions were identified from the 450K BeadChip and 125 differentially-methylated regions from bisulfite sequencing. DNA methylation was also shown to be stable, as similar methylation differences found in the preliminary study, were again detected in the same twin pairs sampled years later. As DNA methylation is a stable marker, it could be used as a biomarker. β-cell death in diabetes releases DNA with unmethylated CpG sites in the insulin promoter region into the blood circulation. To detect these differences, an assay was also developed testing serum samples from monozygotic twin pairs. The data presented provided comprehensive DNA methylation profiles in type 1 diabetes from this discovery cohort. The methylation signature found will then be validated in diabetic, pre-diabetic and control singletons. This in turn will provide data for later functional analyses to identify genes associated with type 1 diabetes risk.