Differential methylation pattern in patients with coronary artery disease: pilot study

Abstract

Epidemiological studies have revealed that coronary artery disease (CAD) is highly heritable. However, genetic studies have not been able to fully elucidate its etiology. Accumulating evidences suggest that epigenetic alterations like DNA methylation may provide an alternative and additional explanation of its pathophysiology. DNA methylation regulates hypomethylation and hypermethylation of various genes which are involved in the development of CAD. Our aim was to identify differentially methylated regions (DMRs) in genome of CAD patients by using the microarray chip having a coverage of > 4,50,000 CpG sites (Illumina’s Infinium HumanMethylation450 BeadChip). In this pilot study, an epigenome-wide analysis of DNA methylation from whole blood was performed in six angiographically positive male cases, who were age and gender matched with six angiographically negative controls. All subjects were non-smokers, non-diabetic, non-alcoholic, with no previous history of cardiac ailment. Illumina’s GenomeStudio (v 2011.1) software was used to identify DMRs and pathway analysis, gene ontology was carried out using DAVID (Database for Annotation, Visualisation and Integrated Discovery). 429 DMRs were found to be significant of which 222 were hypomethylated and 207 were hypermethylated. Antigen processing and presentation was identified to be the most significant biological function with a statistical significance of p = 4.35 × 10− 5. HLA-DRB1, HLA-DQA1, HLA-DQB1 along with non-classical HLA molecules HLA-G, HLA-C are responsible for triggering the inflammatory pathway which have been identified in our study. To the best of our knowledge, this is the first study to identify a panel of DMRs using a high coverage microarray chip in India.

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References

  1. 1.

    Gupta R, Mohan I, Narula J (2016) Trends in coronary heart disease epidemiology in India. Ann Glob Health 82:307–315

    Article  PubMed  Google Scholar 

  2. 2.

    Yamada Y, Horibe H, Oguri M, Sakuma J, Takeuchi I, Yasukochi Y et al (2018) Identification of novel hyper- or hypomethylated CpG sites and genes associated with atherosclerotic plaque using an epigenome-wide association study. Int J Mol Med 41:2724–2732

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Wang JC, Bennett M (2012) Aging and atherosclerosis: mechanisms, functional consequences, and potential therapeutics for cellular senescence. Circ Res 111:245–259

    Article  CAS  PubMed  Google Scholar 

  4. 4.

    Zhang BK, Lai X, Jia SJ (2015) Epigenetics in atherosclerosis: a clinical perspective. Discov Med 19:73–80

    PubMed  Google Scholar 

  5. 5.

    Peden JF, Farrall M (2011) Thirty-five common variants for coronary artery disease: the fruits of much collaborative labour. Hum Mol Genet 20:198–205

    Article  CAS  Google Scholar 

  6. 6.

    Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR et al (2013) Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet 45:25–33

    Article  CAS  PubMed  Google Scholar 

  7. 7.

    Dubé JB, Hegele RA (2013) Genetics 100 for cardiologists: basics of genome-wide association studies. Can J Cardiol 29:10–17

    Article  PubMed  Google Scholar 

  8. 8.

    Marian AJ, Belmont J (2011) Strategic approaches to unraveling genetic causes of cardiovascular diseases. Circ Res 108:1252–1269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Yang J, Manolio TA, Pasquale LR, Boerwinkle E, Caporaso N, Cunningham JM et al (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43:519–525

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Baccarelli A, Rienstra M, Benjamin EJ (2010) Cardiovascular epigenetics: basic concepts and results from animal and human studies. Circ Cardiovasc Genet 3:567–573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    José M, Ordovás, Caren E, Smith (2010) Epigenetics and cardiovascular disease. Nat Rev Cardiol 7:510–519

    Article  CAS  Google Scholar 

  12. 12.

    Thom T (2006) Heart disease and stroke statistics–2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 113:85–151

    Google Scholar 

  13. 13.

    Muka T, Koromani F, Portilla E, O’Connor A, Bramer WM, Troup J et al (2016) The role of epigenetic modifications in cardiovascular disease: a systematic review. Int J Cardiol 212:174–183

    Article  PubMed  Google Scholar 

  14. 14.

    Stricker SH, Köferle A, Beck S (2016) From profiles to function in epigenomics. Nat Rev Genet 18:51–66

    Article  CAS  PubMed  Google Scholar 

  15. 15.

    Speybroeck L (2006) From epigenesis to epigenetics. Ann N Y Acad Sci 981:61–81

    Article  Google Scholar 

  16. 16.

    Arasaradnam RP, Commane DM, Bradburn D, Mathers JC (2008) A review of dietary factors and its influence on DNA methylation in colorectal carcinogenesis. Epigenetics 3:193–198

    Article  CAS  PubMed  Google Scholar 

  17. 17.

    Schleithoff C, Voelter-Mahlknecht S, Dahmke I, Mahlknecht U (2012) On the epigenetics of vascular regulation and disease. Clin Epigenetics 4:7

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Zaina S, Heyn H, Carmona FJ, Varol N, Sayols S, Condom E et al (2014) DNA methylation map of human atherosclerosis. Circ Cardiovasc Genet 7:692–700

    Article  CAS  PubMed  Google Scholar 

  19. 19.

    Irvin MR, Zhi D, Joehanes R, Mendelson M, Aslibekyan S, Claas SA et al (2014) Epigenome-wide association study of fasting blood lipids in the genetics of lipid-lowering drugs and diet network study. Circulation 130:565–572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aïssi D, Wahl S et al (2014) DNA methylation and body-mass index: a genome-wide analysis. Lancet 383:1990–1998

    Article  CAS  PubMed  Google Scholar 

  21. 21.

    Valencia-Morales M del P, Zaina S, Heyn H, Carmona FJ, Varol N, Sayols S et al (2015) The DNA methylation drift of the atherosclerotic aorta increases with lesion progression. BMC Med Genom 8:7

    Article  CAS  Google Scholar 

  22. 22.

    Laukkanen MO, Mannermaa S, Hiltunen MO, Aittomäki S, Airenne K, Jänne J et al (1999) Local hypomethylation in atherosclerosis found in rabbit ec-sod gene. Arterioscler Thromb Vasc Biol 19:2171–2178

    Article  CAS  PubMed  Google Scholar 

  23. 23.

    Zaina S (2014) Unraveling the DNA methylome of atherosclerosis. Curr Opin Lipidol 25:148–153

    Article  CAS  PubMed  Google Scholar 

  24. 24.

    Ross R (1993) The pathogenesis of atherosclerosis: a perspective for the 1990s. Nature 362:801–809

    Article  CAS  PubMed  Google Scholar 

  25. 25.

    Liu B, Xiong L, Tian C, Zhou Q, Zhong Y, Li A et al (2012) HLA-DRB1*12:02:01 plays a protective role against coronary artery disease in women of southern Han Chinese descent. Hum Immunol 73:122–126

    Article  CAS  PubMed  Google Scholar 

  26. 26.

    Ross R (1999) Atherosclerosis—an inflammatory disease. N Engl J Med 340:115–126

    Article  CAS  PubMed  Google Scholar 

  27. 27.

    Guay S, Voisin G, Brisson D, Munger J, Lamarche B, Gaudet D et al (2012) Epigenome-wide analysis in familial hypercholesterolemia identified new loci associated with high-density lipoprotein cholesterol concentration. Epigenomics 4:623–639

    Article  CAS  PubMed  Google Scholar 

  28. 28.

    Guay SP, Légaré C, Houde AA, Mathieu P, Bossé Y, Bouchard L (2014) Acetylsalicylic acid, aging and coronary artery disease are associated with ABCA1 DNA methylation in men. Clin Epigenetics 6:14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Kim M, Long TI, Arakawa K, Wang R, Yu MC, Laird PW (2010) DNA methylation as a biomarker for cardiovascular disease risk. PLoS ONE 5(3):e9692

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Sharma P, Kumar J, Garg G, Kumar A, Patowary A, Karthikeyan G et al (2008) Detection of altered global DNA methylation in coronary artery disease patients. DNA Cell Biol 27:357–365

    Article  CAS  PubMed  Google Scholar 

  31. 31.

    Sharma P, Garg G, Kumar A, Mohammad F, Kumar SR, Tanwar VS et al (2014) Genome wide DNA methylation profiling for epigenetic alteration in coronary artery disease patients. Gene 541:31–40

    Article  CAS  PubMed  Google Scholar 

  32. 32.

    Miller SA, Dykes DD, Polesky HF (1988) A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16:1215

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Sandoval J, Heyn HA, Moran S, Serra-Musach J, Pujana MA, Bibikova M et al (2011) Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6:692–702

    Article  CAS  PubMed  Google Scholar 

  34. 34.

    Marabita F, Almgren M, Lindholm ME, Ruhrmann S, Fagerström-Billai F, Jagodic M et al (2013) An evaluation of analysis pipelines for DNA methylation profiling using the illumina humanmethylation450 BeadChip platform. Epigenetics 8:333–346

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Ruga S, Karberg M, Jia XY, Eden MV (2009) Perfecting bisulfite treatment for DNA methylation detection. Peanuts A Biotechnical Newsletter 9:18–19

    Google Scholar 

  36. 36.

    Dudoit S, Shaffer JP, Boldrick JC (2003) Multiple hypothesis testing in microarray experiments. Stat Sci 18:71–103

    Article  Google Scholar 

  37. 37.

    Asomaning N, Archer KJ (2012) High-throughput DNA methylation datasets for evaluating false discovery rate methodologies. Comput Stat Data Anal 56:1748–1756

    Article  CAS  PubMed  Google Scholar 

  38. 38.

    Diz AP, Carvajal-rodríguez A, Skibinski DOF (2011) Multiple Hypothesis Testing in Proteomics: a strategy for experimental work. Mol Cell Proteom 10:1–10

    Article  CAS  Google Scholar 

  39. 39.

    Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L et al (2010) Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinform 11:587

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Huang DW, Sherman BT, Lempicki RA (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57

    Article  CAS  Google Scholar 

  41. 41.

    Libby P (2002) Inflammation in atherosclerosis. Nature 420:868–874

    Article  CAS  PubMed  Google Scholar 

  42. 42.

    Libby P (2013) Mechanisms of acute coronary syndromes and their implications for therapy. N Engl J Med 368:2004–2013

    Article  CAS  PubMed  Google Scholar 

  43. 43.

    Muka T, Imo D, Jaspers L, Colpani V, Chaker L, van der Lee SJ et al (2015) The global impact of non-communicable diseases on healthcare spending and national income: a systematic review. Eur J Epidemiol 30:251–277

    Article  PubMed  Google Scholar 

  44. 44.

    Friso S, Udali S, Guarini P, Pellegrini C, Pattini P, Moruzzi S et al (2013) Global DNA hypomethylation in peripheral blood mononuclear cells as a biomarker of cancer risk. Cancer Epidemiol Biomark Prev 22:348–355

    Article  CAS  Google Scholar 

  45. 45.

    Shen J, Wang S, Zhang YJ, Wu HC, Kibriya MG, Jasmine F et al (2013) Exploring genome-wide DNA methylation profiles altered in hepatocellular carcinoma using Infinium HumanMethylation 450 Beadchips. Epigenetics 8:34–43

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Terry MB, Delgado-Cruzata L, Vin-Raviv N, Wu HC, Santella RM (2011) DNA methylation in white blood cells: association with risk factors in epidemiologic studies. Epigenetics 6:828–837

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Davies RW, Wells GA, Stewart AFR, Erdmann J, Shah SH, Ferguson JF et al (2012) A genome-wide association study for coronary artery disease identifies a novel susceptibility locus in the major histocompatibility complex. Circ Cardiovasc Genet 5:217–225

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Lahoute C, Herbin O, Mallat Z, Tedgui A (2011) Adaptive immunity in atherosclerosis: mechanisms and future therapeutic targets. Nat Rev Cardiol 8:348–358

    Article  CAS  PubMed  Google Scholar 

  49. 49.

    McPherson R, Davies RW (2012) Inflammation and coronary artery disease: insights from genetic studies. Can J Cardiol 28:662–666

    Article  PubMed  Google Scholar 

  50. 50.

    Zidi I, Kharrat N, Abdelhedi R, Hassine AB, Laaribi AB, Yahia HB et al (2016) Nonclassical human leukocyte antigen (HLA-G, HLA-E, and HLA-F) in coronary artery disease. Hum Immunol 77:325–329

    Article  CAS  PubMed  Google Scholar 

  51. 51.

    Schunkert H, König IR, Kathiresan S, Reilly MP, Assimes TL, Holm H et al (2011) Large-scale association analyses identifies 13 new susceptibility loci for coronary artery disease. Nat Genet 43:333–338

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Guay SP, Brisson D, Mathieu P, Bossé Y, Gaudet D, Bouchard L (2015) A study in familial hypercholesterolemia suggests reduced methylomic plasticity in men with coronary artery disease. Epigenomics 7:17–34

    Article  CAS  PubMed  Google Scholar 

  53. 53.

    Hansson GK, Hermansson A (2011) The immune system in atherosclerosis. Nat Immunol 12:204–212

    Article  CAS  PubMed  Google Scholar 

  54. 54.

    Li D, Xie Z, Le Pape M, Dye T (2015) An evaluation of statistical methods for DNA methylation microarray data analysis. BMC Bioinform 16:1–20

    Google Scholar 

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Acknowledgements

We are grateful to National Health and Education Society (NHES) for funding the study.

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Correspondence to Tester F. Ashavaid.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Banerjee, S., Ponde, C.K., Rajani, R.M. et al. Differential methylation pattern in patients with coronary artery disease: pilot study. Mol Biol Rep 46, 541–550 (2019). https://doi.org/10.1007/s11033-018-4507-y

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Keywords

  • Coronary artery disease
  • Epigenetics
  • DNA methylation
  • Epigenome-wide association study
  • Microarray
  • Human leucocyte antigen (HLA) gene