Gene-specific DNA methylation profiles and LINE-1 hypomethylation are associated with myocardial infarction risk

Springer Science and Business Media LLC - Tập 7 - Trang 1-12 - 2015
Simonetta Guarrera1,2, Giovanni Fiorito1,2, N. Charlotte Onland-Moret3, Alessia Russo1,2, Claudia Agnoli4, Alessandra Allione1,2, Cornelia Di Gaetano1,2, Amalia Mattiello5, Fulvio Ricceri6, Paolo Chiodini7, Silvia Polidoro1, Graziella Frasca8, Monique W. M. Verschuren3,9, Jolanda M. A. Boer9, Licia Iacoviello10, Yvonne T. van der Schouw3, Rosario Tumino8, Paolo Vineis1,11, Vittorio Krogh4, Salvatore Panico5, Carlotta Sacerdote6, Giuseppe Matullo1,2
1Human Genetics Foundation (HuGeF), Turin, Italy
2Medical Sciences Department, University of Turin, Turin, Italy
3Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands
4Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Tumori, Milan, Italy
5Department of Clinical and Experimental Medicine, Federico II University, Naples, Italy
6Cancer Epidemiology, CPO Piemonte, Turin, Italy
7Department of Public Health, Second University of Naples, Naples, Italy
8Cancer Registry and Histopathology Unit, “Civile—M.P. Arezzo” Hospital, ASP 7, Ragusa, Italy
9Centre for Nutrition, Prevention and Health Services, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
10Department of Epidemiology and Prevention, IRCCS Istituto Neurologico Mediterraneo NEUROMED, Pozzilli, Italy
11Epidemiology and Public Health, Imperial College London, London, UK

Tóm tắt

DNA methylation profiles are responsive to environmental stimuli and metabolic shifts. This makes DNA methylation a potential biomarker of environmental-related and lifestyle-driven diseases of adulthood. Therefore, we investigated if white blood cells’ (WBCs) DNA methylation profiles are associated with myocardial infarction (MI) occurrence. Whole-genome DNA methylation was investigated by microarray analysis in 292 MI cases and 292 matched controls from the large prospective Italian European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (EPICOR study). Significant signals (false discovery rate (FDR) adjusted P < 0.05) were replicated by mass spectrometry in 317 MI cases and 262 controls from the Dutch EPIC cohort (EPIC-NL). Long interspersed nuclear element-1 (LINE-1) methylation profiles were also evaluated in both groups. A differentially methylated region (DMR) within the zinc finger and BTB domain-containing protein 12 (ZBTB12) gene body and LINE-1 hypomethylation were identified in EPICOR MI cases and replicated in the EPIC-NL sample (ZBTB12-DMR meta-analysis: effect size ± se = −0.016 ± 0.003, 95 % CI = −0.021;−0.011, P = 7.54 × 10−10; LINE-1 methylation meta-analysis: effect size ± se = −0.161 ± 0.040, 95 % CI = −0.239;−0.082, P = 6.01 × 10−5). Moreover, cases with shorter time to disease had more pronounced ZBTB12-DMR hypomethylation (meta-analysis, men: effect size ± se = −0.0059 ± 0.0017, P TREND = 5.0 × 10−4; women: effect size ± se = −0.0053 ± 0.0019, P TREND = 6.5 × 10−3) and LINE-1 hypomethylation (meta-analysis, men: effect size ± se = −0.0010 ± 0.0003, P TREND = 1.6 × 10−3; women: effect size ± se = −0.0008 ± 0.0004, P TREND = 0.026) than MI cases with longer time to disease. In the EPIC-NL replication panel, DNA methylation profiles improved case-control discrimination and reclassification when compared with traditional MI risk factors only (net reclassification improvement (95 % CI) between 0.23 (0.02–0.43), P = 0.034, and 0.89 (0.64–1.14), P < 1 × 10−5). Our data suggest that specific methylation profiles can be detected in WBCs, in a preclinical condition, several years before the occurrence of MI, providing an independent signature of cardiovascular risk. We showed that prediction accuracy can be improved when DNA methylation is taken into account together with traditional MI risk factors, although further confirmation on a larger sample is warranted. Our findings support the potential use of DNA methylation patterns in peripheral blood white cells as promising early biomarkers of MI.

Tài liệu tham khảo

Karaderi T, Drong AW, Lindgren CM. Insights into the genetic susceptibility to type 2 diabetes from genome-wide association studies of obesity-related traits. Curr Diab Rep. 2015;15(10):83. doi:10.1007/s11892-015-0648-8. Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478(7367):103–9. doi:10.1038/nature10405. Lange LA, Willer CJ, Rich SS. Recent developments in genome and exome-wide analyses of plasma lipids. Curr Opin Lipidol. 2015;26(2):96–102. doi:10.1097/MOL.0000000000000159. Bell CG, Beck S. The epigenomic interface between genome and environment in common complex diseases. Brief Funct Genomics. 2010;9(5–6):477–85. doi:10.1093/bfgp/elq026. Kanherkar RR, Bhatia-Dey N, Csoka AB. Epigenetics across the human lifespan. Front Cell Dev Biol. 2014;2:49. doi:10.3389/fcell.2014.00049. Mathers JC, Strathdee G, Relton CL. Induction of epigenetic alterations by dietary and other environmental factors. Adv Genet. 2010;71:3–39. doi:10.1016/B978-0-12-380864-6.00001-8. Reddington JP, Pennings S, Meehan RR. Non-canonical functions of the DNA methylome in gene regulation. Biochem J. 2013;451(1):13–23. doi:10.1042/BJ20121585. Klose RJ, Bird AP. Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci. 2006;31(2):89–97. doi:10.1016/j.tibs.2005.12.008. Schulz WA, Steinhoff C, Florl AR. Methylation of endogenous human retroelements in health and disease. Curr Top Microbiol Immunol. 2006;310:211–50. Baccarelli A, Wright R, Bollati V, Litonjua A, Zanobetti A, Tarantini L, et al. Ischemic heart disease and stroke in relation to blood DNA methylation. Epidemiology. 2010;21(6):819–28. doi:10.1097/Ede.0b013e3181f20457. Cash HL, McGarvey ST, Houseman EA, Marsit CJ, Hawley NL, Lambert-Messerlian GM, et al. Cardiovascular disease risk factors and DNA methylation at the LINE-1 repeat region in peripheral blood from Samoan Islanders. Epigenetics. 2011;6(10):1257–64. doi:10.4161/epi.6.10.17728. Valinluck V, Tsai HH, Rogstad DK, Burdzy A, Bird A, Sowers LC. Oxidative damage to methyl-CpG sequences inhibits the binding of the methyl-CpG binding domain (MBD) of methyl-CpG binding protein 2 (MeCP2). Nucleic Acids Res. 2004;32(14):4100–8. doi:10.1093/nar/gkh739. Lund G, Zaina S. Atherosclerosis: an epigenetic balancing act that goes wrong. Curr Atheroscler Rep. 2011;13(3):208–14. doi:10.1007/s11883-011-0174-3. Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels JL, et al. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 2009;5(8), e1000602. doi:10.1371/journal.pgen.1000602. Bollati V, Schwartz J, Wright R, Litonjua A, Tarantini L, Suh H, et al. Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech Ageing Dev. 2009;130(4):234–9. doi:10.1016/j.mad.2008.12.003. Portela A, Esteller M. Epigenetic modifications and human disease. Nat Biotechnol. 2010;28(10):1057–68. doi:10.1038/nbt.1685. Klengel T, Pape J, Binder EB, Mehta D. The role of DNA methylation in stress-related psychiatric disorders. Neuropharmacology. 2014;80C:115–32. doi:10.1016/j.neuropharm.2014.01.013. Dang MN, Buzzetti R, Pozzilli P. Epigenetics in autoimmune diseases with focus on type 1 diabetes. Diabetes Metab Res Rev. 2013;29(1):8–18. doi:10.1002/dmrr.2375. Breton CV, Park C, Siegmund K, Gauderman WJ, Whitfield-Maxwell L, Hodis HN, et al. NOS1 methylation and carotid artery intima-media thickness in children. Circ Cardiovasc Genet. 2014;7(2):116–22. doi:10.1161/CIRCGENETICS.113.000320. Fiorito G, Guarrera S, Valle C, Ricceri F, Russo A, Grioni S, et al. B-vitamins intake, DNA-methylation of one carbon metabolism and homocysteine pathway genes and myocardial infarction risk: the EPICOR study. Nutr Metab Cardiovasc Dis. 2013. doi:10.1016/j.numecd.2013.10.026. Sharma P, Garg G, Kumar A, Mohammad F, Kumar SR, Tanwar VS, et al. Genome wide DNA methylation profiling for epigenetic alteration in coronary artery disease patients. Gene. 2014;541(1):31–40. doi:10.1016/j.gene.2014.02.034. Soriano-Tarraga C, Jimenez-Conde J, Giralt-Steinhauer E, Mola M, Ois A, Rodriguez-Campello A, et al. Global DNA methylation of ischemic stroke subtypes. PLoS One. 2014;9(4), e96543. doi:10.1371/journal.pone.0096543. Yamada Y, Nishida T, Horibe H, Oguri M, Kato K, Sawabe M. Identification of hypo- and hypermethylated genes related to atherosclerosis by a genome-wide analysis of DNA methylation. Int J Mol Med. 2014;33(5):1355–63. doi:10.3892/ijmm.2014.1692. Sofer T, Schifano ED, Hoppin JA, Hou L, Baccarelli AA. A-clustering: a novel method for the detection of co-regulated methylation regions, and regions associated with exposure. Bioinformatics. 2013;29(22):2884–91. doi:10.1093/bioinformatics/btt498. Lerner DJ, Kannel WB. Patterns of coronary heart disease morbidity and mortality in the sexes: a 26-year follow-up of the Framingham population. Am Heart J. 1986;111(2):383–90. Anand SS, Islam S, Rosengren A, Franzosi MG, Steyn K, Yusufali AH, et al. Risk factors for myocardial infarction in women and men: insights from the INTERHEART study. Eur Heart J. 2008;29(7):932–40. doi:10.1093/eurheartj/ehn018. Tziomalos K, Giampatzis V, Baltatzi M, Efthymiou E, Psianou K, Papastergiou N, et al. Sex-specific differences in cardiovascular risk factors and blood pressure control in hypertensive patients. J Clin Hypertens (Greenwich). 2014;16(4):309–12. doi:10.1111/jch.12289. Andreasson AN, Unden AL, Elofsson S, Brismar K. Leptin and adiponectin: distribution and associations with cardiovascular risk factors in men and women of the general population. Am J Hum Biol. 2012;24(5):595–601. doi:10.1002/ajhb.22279. Caviezel S, Dratva J, Schaffner E, Schindler C, Zemp Stutz E, de Groot E, et al. Sex-specific associations of cardiovascular risk factors with carotid stiffness—results from the SAPALDIA cohort study. Atherosclerosis. 2014;235(2):576–84. doi:10.1016/j.atherosclerosis.2014.05.963. El-Maarri O, Becker T, Junen J, Manzoor SS, Diaz-Lacava A, Schwaab R, et al. Gender specific differences in levels of DNA methylation at selected loci from human total blood: a tendency toward higher methylation levels in males. Hum Genet. 2007;122(5):505–14. doi:10.1007/s00439-007-0430-3. Inoshita M, Numata S, Tajima A, Kinoshita M, Umehara H, Yamamori H, et al. Sex differences of leukocytes DNA methylation adjusted for estimated cellular proportions. Biol Sex Differ. 2015;6:11. doi:10.1186/s13293-015-0029-7. Liu J, Morgan M, Hutchison K, Calhoun VD. A study of the influence of sex on genome wide methylation. PLoS One. 2010;5(4), e10028. doi:10.1371/journal.pone.0010028. Boks MP, Derks EM, Weisenberger DJ, Strengman E, Janson E, Sommer IE, et al. The relationship of DNA methylation with age, gender and genotype in twins and healthy controls. PLoS One. 2009;4(8), e6767. doi:10.1371/journal.pone.0006767. Brenet F, Moh M, Funk P, Feierstein E, Viale AJ, Socci ND, et al. DNA methylation of the first exon is tightly linked to transcriptional silencing. PLoS One. 2011;6(1), e14524. doi:10.1371/journal.pone.0014524. Clouaire T, Stancheva I. Methyl-CpG binding proteins: specialized transcriptional repressors or structural components of chromatin? Cell Mol Life Sci. 2008;65(10):1509–22. doi:10.1007/s00018-008-7324-y. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, et al. Human Protein Reference Database—2009 update. Nucleic Acids Res. 2009;37(Database issue):D767–72. doi:10.1093/nar/gkn892. Carmona G, Gottig S, Orlandi A, Scheele J, Bauerle T, Jugold M, et al. Role of the small GTPase Rap1 for integrin activity regulation in endothelial cells and angiogenesis. Blood. 2009;113(2):488–97. doi:10.1182/blood-2008-02-138438. Serban D, Leng J, Cheresh D. H-ras regulates angiogenesis and vascular permeability by activation of distinct downstream effectors. Circ Res. 2008;102(11):1350–8. doi:10.1161/CIRCRESAHA.107.169664. Ueno H, Yamamoto H, Ito S, Li JJ, Takeshita A. Adenovirus-mediated transfer of a dominant-negative H-ras suppresses neointimal formation in balloon-injured arteries in vivo. Arterioscler Thromb Vasc Biol. 1997;17(5):898–904. Bueno OF, Molkentin JD. Involvement of extracellular signal-regulated kinases 1/2 in cardiac hypertrophy and cell death. Circ Res. 2002;91(9):776–81. Hur K, Cejas P, Feliu J, Moreno-Rubio J, Burgos E, Boland CR, et al. Hypomethylation of long interspersed nuclear element-1 (LINE-1) leads to activation of proto-oncogenes in human colorectal cancer metastasis. Gut. 2014;63(4):635–46. doi:10.1136/gutjnl-2012-304219. Nakkuntod J, Avihingsanon Y, Mutirangura A, Hirankarn N. Hypomethylation of LINE-1 but not Alu in lymphocyte subsets of systemic lupus erythematosus patients. Clin Chim Acta. 2011;412(15–16):1457–61. doi:10.1016/j.cca.2011.04.002. Sidler C, Woycicki R, Kovalchuk I, Kovalchuk O. WI-38 senescence is associated with global and site-specific hypomethylation. Aging (Albany NY). 2014;6(7):564–74. Turunen MP, Aavik E, Yla-Herttuala S. Epigenetics and atherosclerosis. Bba-Gen Subjects. 2009;1790(9):886–91. doi:10.1016/j.bbagen.2009.02.008. Jintaridth P, Tungtrongchitr R, Preutthipan S, Mutirangura A. Hypomethylation of Alu elements in post-menopausal women with osteoporosis. PLoS One. 2013;8(8), e70386. doi:10.1371/journal.pone.0070386. Chang SY, Wang L, Guan YQ, Shangguan SF, Du QA, Wang Y, et al. Long interspersed nucleotide element-1 hypomethylation in folate-deficient mouse embryonic stem cells. J Cell Biochem. 2013;114(7):1549–58. doi:10.1002/Jcb.24496. Riboli E, Kaaks R, The EPIC Project: rationale and study design. European Prospective Investigation into Cancer and Nutrition. Int J Epidemiol. 1997;26 Suppl 1:S6–S14. Wang L, Wang F, Guan J, Le J, Wu L, Zou J, et al. Relation between hypomethylation of long interspersed nucleotide elements and risk of neural tube defects. Am J Clin Nutr. 2010;91(5):1359–67. doi:10.3945/ajcn.2009.28858. Bendinelli B, Masala G, Saieva C, Salvini S, Calonico C, Sacerdote C, et al. Fruit, vegetables, and olive oil and risk of coronary heart disease in Italian women: the EPICOR study. Am J Clin Nutr. 2011;93(2):275–83. doi:10.3945/ajcn.110.000521. Palli D, Berrino F, Vineis P, Tumino R, Panico S, Masala G, et al. A molecular epidemiology project on diet and cancer: the EPIC-Italy prospective study. Design and baseline characteristics of participants. Tumori. 2003;89(6):586–93. Beulens JW, Monninkhof EM, Verschuren WM, van der Schouw YT, Smit J, Ocke MC, et al. Cohort profile: the EPIC-NL study. Int J Epidemiol. 2010;39(5):1170–8. doi:10.1093/ije/dyp217. Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–9. doi:10.4161/epi.23470. Marabita F, Almgren M, Lindholm ME, Ruhrmann S, Fagerstrom-Billai F, Jagodic M, et al. An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform. Epigenetics. 2013;8(3):333–46. doi:10.4161/epi.24008. R-CoreTeam. R. A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014. http://www.R-project.org/. Liang KY, Zeger SL. Longitudinal data-analysis using generalized linear-models. Biometrika. 1986;73(1):13–22. doi:10.1093/biomet/73.1.13. Mendis S, Puska P, Norrving B, editors. Global atlas on cardiovascular disease prevention and control. Geneva: World Health Organization (in collaboration with the World Heart Federation and World Stroke Organization); 2011. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88. doi:0197-2456(86)90046-2. Houseman EA, Christensen BC, Yeh RF, Marsit CJ, Karagas MR, Wrensch M, et al. Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics. 2008;9:365. doi:10.1186/1471-2105-9-365. Delong ER, Delong DM, Clarkepearson DI. Comparing the areas under 2 or more correlated receiver operating characteristic curves—a nonparametric approach. Biometrics. 1988;44(3):837–45. doi:10.2307/2531595. Pencina MJ, D'Agostino Sr RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11–21. doi:10.1002/sim.4085. Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression, 3rd edition. Wiley Ser Probab St. 2013:1–500. doi:10.1002/9781118548387.
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