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Abstract: FR-PO433

Genome-Wide DNA Methylation Analysis for Diabetic Kidney Disease

Session Information

Category: Diabetic Kidney Disease

  • 601 Diabetic Kidney Disease: Basic

Authors

  • Smyth, Laura Jane, Queen's University Belfast, Belfast, United Kingdom
  • Kilner, Jill, Queen's University Belfast, Belfast, United Kingdom
  • Patterson, Chris C., Queen's University Belfast, Belfast, United Kingdom
  • Wooster, Christopher John, Queen's University Belfast, Belfast, United Kingdom
  • McKay, Gareth J., Queen's University Belfast, Belfast, United Kingdom
  • Maxwell, Alexander P., Queen's University Belfast, Belfast, United Kingdom
  • Mcknight, A.J., Queen's University Belfast, Belfast, United Kingdom

Group or Team Name

  • Epidemiology and Public Health Research Group
Background

Diabetic kidney disease (DKD) is a serious complication of diabetes, characterised by progressive development of proteinuria and loss of renal function. Increasing evidence suggests that epigenetic alterations, including DNA methylation, are involved in the development and progression of DKD. This investigation compared methylation profiles from individuals with type 1 diabetes (T1D) and DKD to individuals with T1D and no evidence of renal failure, to identify potential methylation-based biomarkers of DKD.

Methods

Using the Zymo EZ DNA methylation kit to bisulphite treat the DNA and the Infinium HD Methylation Assay, MethylationEPIC BeadChips from Illumina, the methylation status of >850,000 CpG sites, gene bodies, promoters and CpG islands have been determined for 106 individuals with T1D. Of these, 66 individuals had DKD and 40 controls had no evidence of renal disease. Cases and controls for this analysis were matched carefully for ethnicity, sex, age (≤1 year), and duration of diabetes. We also considered 192 individuals with Illumina’s HumanMethylation27K array data and 250 individuals with HumanMethylation450K array data for DKD. DNA obtained from each individual was treated consistently, with standard quality control applied.

Results

Methylation data was analysed using Genome Studio and Partek Genomics Suite v7.0. From the EPIC array, 891 CpG sites were identified as having significantly different levels of methylation in cases compared with controls, 9 of which had a significance level of p≤9.75x10-08. Among the genes identified, several including BCL2, CUX1, FKBP5, FBXO5, PRKAG2 and PSD3 have been linked with T1D. High concordance (R2=0.994) between duplicate samples (n=7) was observed. Across data from all arrays, top-ranked genes with differential methylation profiles included CUX1, FKBP5 and PRKAG2.

Conclusion

We have previously reported CUX1, FKBP5, and PRKAG2 genes associated with CKD, supported by changes in gene expression using RNA-Seq (Smyth et al., Epigenetics, 2014). This project supports meta-analysis of independent cohorts across arrays and demonstrates blood-derived methylation signatures may serve as minimally invasive biomarkers of DKD.