Differential Methylation of Telomere-Related Genes Is Associated with Kidney Disease in Individuals with Type 1 Diabetes

Author:

Hill Claire1ORCID,Duffy Seamus1,Kettyle Laura M.2,McGlynn Liane3,Sandholm Niina456ORCID,Salem Rany M.7,Thompson Alex8ORCID,Swan Elizabeth J.1,Kilner Jill1,Rossing Peter91011,Shiels Paul G.12,Lajer Maria10ORCID,Groop Per-Henrik45613,Maxwell Alexander Peter114ORCID,McKnight Amy Jayne1,

Affiliation:

1. Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK

2. Centre for Cancer Research and Cell Biology, Queen’s University of Belfast, Belfast BT9 7AE, UK

3. College of Medical, Veterinary & Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK

4. Folkhälsan Institute of Genetics, Folkhälsan Research Center, 00290 Helsinki, Finland

5. Division of Nephrology, Department of Medicine, Helsinki University Central Hospital, 00290 Helsinki, Finland

6. Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, 00290 Helsinki, Finland

7. Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA

8. School of Medicine, The Biodiscovery Institute, University of Nottingham, Nottingham NG7 2RD, UK

9. Nordsjaellands Hospital, Hilleroed, Denmark and Health, Aarhus University, 8000 Aarhus, Denmark

10. Steno Diabetes Center, 2730 Gentofte, Denmark

11. Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark

12. School of Molecular Biosciences, Davidson Building, University of Glasgow, Glasgow G12 8QQ, UK

13. Department of Diabetes, Central Clinical School, Monash University, Melbourne, VIC 3800, Australia

14. Regional Nephrology Unit, Belfast City Hospital, Belfast BT9 7AB, UK

Abstract

Diabetic kidney disease (DKD) represents a major global health problem. Accelerated ageing is a key feature of DKD and, therefore, characteristics of accelerated ageing may provide useful biomarkers or therapeutic targets. Harnessing multi-omics, features affecting telomere biology and any associated methylome dysregulation in DKD were explored. Genotype data for nuclear genome polymorphisms in telomere-related genes were extracted from genome-wide case–control association data (n = 823 DKD/903 controls; n = 247 end-stage kidney disease (ESKD)/1479 controls). Telomere length was established using quantitative polymerase chain reaction. Quantitative methylation values for 1091 CpG sites in telomere-related genes were extracted from epigenome-wide case–control association data (n = 150 DKD/100 controls). Telomere length was significantly shorter in older age groups (p = 7.6 × 10−6). Telomere length was also significantly reduced (p = 6.6 × 10−5) in DKD versus control individuals, with significance remaining after covariate adjustment (p = 0.028). DKD and ESKD were nominally associated with telomere-related genetic variation, with Mendelian randomisation highlighting no significant association between genetically predicted telomere length and kidney disease. A total of 496 CpG sites in 212 genes reached epigenome-wide significance (p ≤ 10−8) for DKD association, and 412 CpG sites in 193 genes for ESKD. Functional prediction revealed differentially methylated genes were enriched for Wnt signalling involvement. Harnessing previously published RNA-sequencing datasets, potential targets where epigenetic dysregulation may result in altered gene expression were revealed, useful as potential diagnostic and therapeutic targets for intervention.

Funder

QUB International PhD fellowship

Northern Ireland Health and Social Care Research and Development Office

Medical Research Council

a Science Foundation Ireland and the Department for the Economy Northern Ireland partnership award

Folkhälsan Research Foundation, the Wilhelm and Else Stockmann Foundation, the Academy of Finland

Novo Nordisk Foundation

Sigrid Juselius Foundation, the “Liv och Hälsa” Society, EVO governmental

Finnish Diabetes Research Foundation

Blood Cancer UK

Publisher

MDPI AG

Subject

Genetics (clinical),Genetics

Reference138 articles.

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3. The UK Renal Registry (2020). UK Renal Registry 22nd Annual Report—Data to 31/12/2018, The UK Renal Registry.

4. (2020). United States Renal Data System, USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States.

5. Genetic and Epigenetic Factors Influencing Chronic Kidney Disease;Smyth;Am. J. Physiol.—Ren. Physiol.,2014

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