PT  - JOURNAL ARTICLE
AU  - LJ Smyth
AU  - J Kilner
AU  - V Nair
AU  - H Liu
AU  - E Brennan
AU  - K Kerr
AU  - N Sandholm
AU  - J Cole
AU  - E Dahlström
AU  - A Syreeni
AU  - RM Salem
AU  - RG Nelson
AU  - HC Looker
AU  - C Wooster
AU  - K Anderson
AU  - GJ McKay
AU  - F Kee
AU  - I Young
AU  - NICOLA Collaborative Team
AU  - Warren 3 and Genetics of Kidneys in Diabetes (GoKinD) Study Group
AU  - D Andrews
AU  - C Forsblom
AU  - JN Hirschhorn
AU  - C Godson
AU  - PH Groop
AU  - AP Maxwell
AU  - K Susztak
AU  - M Kretzler
AU  - JC Florez
AU  - AJ McKnight
AU  - on behalf of the GENIE consortium
TI  - Assessment of differentially methylated loci in individuals with end-stage kidney disease attributed to diabetic kidney disease
AID  - 10.1101/2020.07.30.228734
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.30.228734
4099  - http://biorxiv.org/content/early/2020/07/31/2020.07.30.228734.short
4100  - http://biorxiv.org/content/early/2020/07/31/2020.07.30.228734.full
AB  - A subset of individuals with type 1 diabetes mellitus (T1DM) are predisposed to developing diabetic kidney disease (DKD), which is the most common cause globally of end-stage kidney disease (ESKD). Emerging evidence suggests epigenetic changes in DNA methylation may have a causal role in both T1DM and DKD. The aim of this investigation was to assess differences in blood-derived DNA methylation patterns between individuals with T1DM-ESKD and individuals with long-duration T1DM but no evidence of kidney disease upon repeated testing. Blood-derived DNA from individuals (107 cases, 253 controls and 14 experimental controls) were bisulphite treated before DNA methylation patterns from both groups were generated and analysed using Illumina’s Infinium MethylationEPIC BeadChip arrays (n=862,927 sites). Differentially methylated CpG sites (dmCpGs) were identified (false discovery rate adjusted p≤×10−8 and fold change ±2) by comparing methylation levels between ESKD cases and T1DM controls at single site resolution. Gene annotation and functionality was investigated to enrich and rank methylated regions associated with ESKD in T1DM.Top-ranked genes within which several dmCpGs were located and supported by in silico functional data, and replication where possible, include; AFF3, ARID5B, CUX1, ELMO1, FKBP5, HDAC4, ITGAL, LY9, PIM1, RUNX3, SEPTIN9, and UPF3A. Top-ranked enrichment pathways included pathways in cancer, TGF-β signalling and Th17 cell differentiation.Epigenetic alterations provide a dynamic link between an individual’s genetic background and their environmental exposures. This robust evaluation of DNA methylation in carefully phenotyped individuals, has identified biomarkers associated with ESKD, revealing several genes and implicated key pathways associated with ESKD in individuals with T1DM.Competing Interest StatementThe authors have declared no competing interest.BACRBead Array Controls ReporterCKDchronic kidney diseaseDKDdiabetic kidney diseasedmCpGsdifferentially methylatedeGFRestimated glomerular filtration rateeQTLexpression quantitative trait lociESKDend-stage kidney diseaseEWASepigenome-wide association studyFCfold changeFDRfalse discovery rateGBMglomerular basement membrane widthGENIEGEnetics of Nephropathy an International EffortGOgene ontologyGoKinDGenetics of Kidneys in DiabetesGWASgenome wide association studiesHbA1chaemoglobin A1cHDACshistone deacetylasesKEGGKyoto Encyclopedia of Genes and GenomesNICOLANorthern Ireland Cohort for the Longitudinal Study of AgeingP_FENpercent of endothelial fenestration falling on the peripheral glomerular basement membranePGSPartek Genomics SuiteQCquality controlSFsupplementary figureSTsupplementary tableSVsurface volume of peripheral glomerular basement membrane per glomerulusT1DMtype 1 diabetes mellitusT2DMtype 2 diabetes mellitusVVINTcortical interstitial fractional volumeVVMESmesangial fractional volumeVVPCvolume fraction of podocyte cell per glomerulusWCCswhite cell counts