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Cytosine Methylation Studies in Patients with Diabetic Kidney Disease

  • Pathogenesis of Type 2 Diabetes and Insulin Resistance (M-E Patti, Section Editor)
  • Published:
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Abstract

Purpose of the Review

Kidney disease is the major cause of morbidity and mortality in patients with diabetes. Poor glycemic control shows the strongest correlation with diabetic kidney disease (DKD) development. A period of poor glycemia increases kidney disease risk even after an extended period of improved glucose control—a phenomenon called metabolic memory. Changes in the epigenome have been proposed to mediate the metabolic memory effect, as epigenome editing enzymes are regulated by substrates of intermediate metabolism and changes in the epigenome can be maintained after cell division.

Recent Findings

Epigenome-wide association studies (EWAS) have reported differentially methylated cytosines in blood and kidney samples of DKD subjects when compared with controls. Differentially methylated cytosines were enriched on regulatory regions and some correlated with gene expression. Methylation changes predicted the speed of kidney function decline. Site-specific methylome editing tools now can be used to interrogate the functional role of differentially methylated regions.

Summary

Methylome changes can be detected in blood and kidneys of patients with DKD. Methylation changes can predict future kidney function changes. Future studies shall determine their role in disease development.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Gluck C, Ko YA, Susztak K. Precision medicine approaches to diabetic kidney disease: tissue as an issue. Curr Diabetes Rep. 2017;17:30.

    Article  Google Scholar 

  2. Reidy K, Kang HM, Hostetter T, Susztak K. Molecular mechanisms of diabetic kidney disease. J Clin Invest. 2014;124:2333–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Breyer MD, Susztak K. The next generation of therapeutics for chronic kidney disease. Nat Rev Drug Discov. 2016;15:568–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Pattaro C, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Qiu C, Huang S, Park J, Park YS, Ko YA, Seasock MJ, et al. Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease. Nat Med. 2018;24:1721–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. D. E. R. Group, et al. Intensive diabetes therapy and glomerular filtration rate in type 1 diabetes. N Engl J Med. 2011;365:2366–76.

    Article  CAS  Google Scholar 

  7. Barker DJ, et al. Type 2 (non-insulin-dependent) diabetes mellitus, hypertension and hyperlipidaemia (syndrome X): relation to reduced fetal growth. Diabetologia. 1993;36:62–7.

    Article  CAS  PubMed  Google Scholar 

  8. Jimenez-Chillaron JC, Isganaitis E, Charalambous M, Gesta S, Pentinat-Pelegrin T, Faucette RR, et al. Intergenerational transmission of glucose intolerance and obesity by in utero undernutrition in mice. Diabetes. 2009;58:460–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Oge A, Isganaitis E, Jimenez-Chillaron J, Reamer C, Faucette R, Barry K, et al. In utero undernutrition reduces diabetes incidence in non-obese diabetic mice. Diabetologia. 2007;50:1099–108.

    Article  CAS  PubMed  Google Scholar 

  10. Radford EJ, et al. In utero effects. In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science. 2014;345:1255903.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Seki Y, Suzuki M, Guo X, Glenn AS, Vuguin PM, Fiallo A, et al. In utero exposure to a high-fat diet programs hepatic hypermethylation and gene dysregulation and development of metabolic syndrome in male mice. Endocrinology. 2017;158:2860–72.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. • Chen Z, et al. Epigenomic profiling reveals an association between persistence of DNA methylation and metabolic memory in the DCCT/EDIC type 1 diabetes cohort. Proc Natl Acad Sci U S A. 2016;113:E3002–11. This study analyzed methylome changes in blood samples from the well-characterized DCCT cohort and identified methylation changes correlating with metabolic memory.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Cooper ME, El-Osta A. Epigenetics: mechanisms and implications for diabetic complications. Circ Res. 2010;107:1403–13.

    Article  CAS  PubMed  Google Scholar 

  14. Cooper ME, el-Osta A, Allen TJ, Watson AMD, Thomas MC, Jandeleit-Dahm KAM. Metabolic karma-the atherogenic legacy of diabetes: the 2017 Edwin Bierman Award Lecture. Diabetes. 2018;67:785–90.

    Article  CAS  PubMed  Google Scholar 

  15. Ko YA, Susztak K. Epigenomics: the science of no-longer-junk DNA. Why study it in chronic kidney disease? Semin Nephrol. 2013;33:354–62.

    Article  CAS  PubMed  Google Scholar 

  16. Pagliaroli L, Vető B, Arányi T, Barta C. From genetics to epigenetics: new perspectives in Tourette syndrome research. Front Neurosci. 2016;10:277.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kato M, Natarajan R. Epigenetics and epigenomics in diabetic kidney disease and metabolic memory. Nat Rev Nephrol. 2019;15:327–45.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Deaton AM, Bird A. CpG islands and the regulation of transcription. Genes Dev. 2011;25:1010–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Arányi T, et al. The tissue-specific methylation of the human tyrosine hydroxylase gene reveals new regulatory elements in the first exon. J Neurochem. 2005;94:129–39.

    Article  PubMed  CAS  Google Scholar 

  20. Mohtat D, Susztak K. Fine tuning gene expression: the epigenome. Semin Nephrol. 2010;30:468–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Li SY, Park J, Guan Y, Chung K, Shrestha R, Palmer MB, et al. DNMT1 in Six2 progenitor cells is essential for transposable element silencing and kidney development. J Am Soc Nephrol. 2019;30:594–609.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Rosen ED, Kaestner KH, Natarajan R, Patti ME, Sallari R, Sander M, et al. Epigenetics and epigenomics: implications for diabetes and obesity. Diabetes. 2018;67:1923–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Jenuwein T, Allis CD. Translating the histone code. Science. 2001;293:1074–80.

    CAS  PubMed  Google Scholar 

  24. Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr Protocols Mol Biol. 2015;109:21 29 21–9.

    Article  Google Scholar 

  25. Susztak K. Understanding the epigenetic syntax for the genetic alphabet in the kidney. J Am Soc Nephrol. 2014;25:10–7.

    Article  CAS  PubMed  Google Scholar 

  26. Brind'Amour J, et al. An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations. Nat Commun. 2015;6:6033.

    Article  CAS  PubMed  Google Scholar 

  27. Hyun BR, McElwee JL, Soloway PD. Single molecule and single cell epigenomics. Methods. 2015;72:41–50.

    Article  CAS  PubMed  Google Scholar 

  28. Tang F, Barbacioru C, Nordman E, Li B, Xu N, Bashkirov VI, et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc. 2010;5:516–35.

    Article  CAS  PubMed  Google Scholar 

  29. Clark SJ, Harrison J, Paul CL, Frommer M. High sensitivity mapping of methylated cytosines. Nucleic Acids Res. 1994;22:2990–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Feng L, Lou J. DNA methylation analysis. Methods Mol Biol. 2019;1894:181–227.

    Article  CAS  PubMed  Google Scholar 

  31. Schutsky EK, DeNizio JE, Hu P, Liu MY, Nabel CS, Fabyanic EB, et al. Nondestructive, base-resolution sequencing of 5-hydroxymethylcytosine using a DNA deaminase. Nat Biotechnol. 2018;36:1083–90.

    Article  CAS  Google Scholar 

  32. Vető B, Szabó P, Bacquet C, Apró A, Hathy E, Kiss J, et al. Inhibition of DNA methyltransferase leads to increased genomic 5-hydroxymethylcytosine levels in hematopoietic cells. FEBS Open Bio. 2018;8:584–92.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, le JM, et al. High density DNA methylation array with single CpG site resolution. Genomics. 2011;98:288–95.

    Article  CAS  PubMed  Google Scholar 

  34. Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, et al. Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol. 2016;17:208.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Bell CG, Teschendorff AE, Rakyan VK, Maxwell AP, Beck S, Savage DA. Genome-wide DNA methylation analysis for diabetic nephropathy in type 1 diabetes mellitus. BMC Med Genet. 2010;3:33.

    Google Scholar 

  36. Swan EJ, Maxwell AP, McKnight AJ. Distinct methylation patterns in genes that affect mitochondrial function are associated with kidney disease in blood-derived DNA from individuals with type 1 diabetes. Diabet Med. 2015;32:1110–5.

    Article  CAS  PubMed  Google Scholar 

  37. Qiu C, Hanson RL, Fufaa G, Kobes S, Gluck C, Huang J, et al. Cytosine methylation predicts renal function decline in American Indians. Kidney Int. 2018;93:1417–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chu AY, Tin A, Schlosser P, Ko YA, Qiu C, Yao C, et al. Epigenome-wide association studies identify DNA methylation associated with kidney function. Nat Commun. 2017;8:1286.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Ko YA, Mohtat D, Suzuki M, Park A, Izquierdo M, Han S, et al. Cytosine methylation changes in enhancer regions of core pro-fibrotic genes characterize kidney fibrosis development. Genome Biol. 2013;14:R108.

    Article  PubMed  PubMed Central  Google Scholar 

  40. •• Gluck C, et al. Kidney cytosine methylation changes improve renal function decline estimation in patients with diabetic kidney disease. Nat Commun. 2019;10:2461. The authors identified DNA methylation changes in kidneys of patients with DKD. Methylation levels predicted kidney function deline.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. •• J. Park et al., Functional methylome analysis of human diabetic kidney disease. JCI Insight, (2019). The authors reported whole genome base-resolution methylation changes in microdissected human DKD kidney samples. The authors demonstrated that dCAS9-based methylation editing of the TNF alpha locus leads to changes in gene expression.

  42. Park J, Shrestha R, Qiu C, Kondo A, Huang S, Werth M, et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science. 2018;360:758–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Vojta A, Dobrinić P, Tadić V, Bočkor L, Korać P, Julg B, et al. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 2016;44:5615–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Inoue K, Gan G, Ciarleglio M, Zhang Y, Tian X, Pedigo CE, et al. Podocyte histone deacetylase activity regulates murine and human glomerular diseases. J Clin Invest. 2019;129:1295–313.

    Article  PubMed  PubMed Central  Google Scholar 

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Correspondence to Katalin Susztak.

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Conflict of Interest

Tamas Aranyi declares that he has no conflict of interest.

Katalin Susztak reports grant support from GSK, Regeneron, Boehringer Ingelheim, Merck, Bayer, Eli Lilly and Company, and Gilead; and consulting for Chemocentryx, Janssen, and Maze Bio. However, the work is not related to any of the studies supported by industry.

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This article is part of the Topical Collection on Pathogenesis of Type 2 Diabetes and Insulin Resistance

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Aranyi, T., Susztak, K. Cytosine Methylation Studies in Patients with Diabetic Kidney Disease. Curr Diab Rep 19, 91 (2019). https://doi.org/10.1007/s11892-019-1214-6

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