Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Genome Biology

Volumn 17, Issue 1, 2016, Pages

  Slieker, Roderick C ^a   van Iterson, Maarten ^a   Luijk, René ^a   Beekman, Marian ^a   Zhernakova, Daria V ^b   Moed, Matthijs H ^a   Mei, Hailiang ^a   van Galen, Michiel ^a   Deelen, Patrick ^b   Bonder, Marc Jan ^b   Zhernakova, Alexandra ^b   Uitterlinden, André G ^c   Tigchelaar, Ettje F ^b   Stehouwer, Coen D A ^d   Schalkwijk, Casper G ^d   van der Kallen, Carla J H ^d   Hofman, Albert ^c   van Heemst, Diana ^a   de Geus, Eco J ^e   van Dongen, Jenny ^e   Deelen, Joris ^a   van den Berg, Leonard H ^f   van Meurs, Joyce ^c   Jansen, Rick ^g   't Hoen, Peter A C ^a   Franke, Lude ^b   Wijmenga, Cisca ^b   Veldink, Jan H ^f   Swertz, Morris A ^b   van Greevenbroek, Marleen M J ^d   van Duijn, Cornelia M ^c   Boomsma, Dorret I ^e   Slagboom, P Eline ^a   Heijmans, Bastiaan T ^a   more..

^a LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

^b UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^c ERASMUS MEDICAL CENTER   (Netherlands)

^d MAASTRICHT UNIVERSITY MEDICAL CENTER   (Netherlands)

^e VU UNIVERSITY AMSTERDAM   (Netherlands)

^f UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^g VU UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

450k; Ageing; DNA damage; DNA methylation; Variability

Indexed keywords

POLYCOMB REPRESSIVE COMPLEX 2;

ADULT; AGED; AGING; APOPTOSIS; ARTICLE; BLOOD CELL; CELL COMPOSITION; CELL METABOLISM; CPG ISLAND; DNA DAMAGE; DNA METHYLATION; DNA STRUCTURE; DOWN REGULATION; GENE EXPRESSION REGULATION; GENE FUNCTION; GENE IDENTIFICATION; GENETIC ASSOCIATION; HUMAN; HUMAN CELL; HUMAN GENOME; HUMAN TISSUE; METHYLOMIC VARIABILITY; MOLECULAR GENETICS; PRC2 GENE; SIGNAL TRANSDUCTION; TRANS ISOMER; UPREGULATION; VARIABLY METHYLATED POSITION;

EID: 84988511927     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-016-1053-6     Document Type: Article

References (77)

1. Kenyon CJ. The genetics of ageing. Nature. 2010;464:504-12.
2. Vijg J, Campisi J. Puzzles, promises and a cure for ageing. Nature. 2008;454:1065.
3. Kirkwood TB. Understanding the odd science of aging. Cell. 2005;120:437-47.
4. López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153:1194-217.
5. Benayoun BA, Pollina EA, Brunet A. Epigenetic regulation of ageing: linking environmental inputs to genomic stability. Nat Rev Mol Cell Biol. 2015;16:593-610.
6. Suzuki MM, Bird A. DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet. 2008;9:465-76.
7. Xu Z, Taylor JA. Genome-wide age-related DNA methylation changes in blood and other tissues relate to histone modification, expression and cancer. Carcinogenesis. 2014;35:356-64.
8. Rakyan VK, Down TA, Maslau S, Andrew T, Yang T-P, Beyan H, Whittaker P, McCann OT, Finer S, Valdes AM. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 2010;20:434-9.
9. Yuan T, Jiao Y, de Jong S, Ophoff RA, Beck S, Teschendorff AE. An integrative multi-scale analysis of the dynamic DNA methylation landscape in aging. PLoS Genet. 2015;11, e1004996.
10. Johansson Å, Enroth S, Gyllensten U. Continuous aging of the human DNA methylome throughout the human lifespan. PLoS One. 2013;8:e67378.
11. McClay JL, Aberg KA, Clark SL, Nerella S, Kumar G, Xie LY, Hudson AD, Harada A, Hultman CM, Magnusson PK. A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects. Hum Mol Genet. 2014;23:1175-85.
12. Steegenga WT, Boekschoten MV, Lute C, Hooiveld GJ, de Groot PJ, Morris TJ, Teschendorff AE, Butcher LM, Beck S, Müller M. Genome-wide age-related changes in DNA methylation and gene expression in human PBMCs. Age. 2014;36:1523-40.
13. Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, Di Blasio AM, Gentilini D, Vitale G, Collino S. Methylation of ELOVL2 gene as a new epigenetic marker of age. Aging Cell. 2012;11:1132-4.
14. Florath I, Butterbach K, Müller H, Bewerunge-Hudler M, Brenner H. Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Hum Mol Genet. 2014;23:1186-201.
15. Marttila S, Kananen L, Häyrynen S, Jylhävä J, Nevalainen T, Hervonen A, Jylhä M, Nykter M, Hurme M. Ageing-associated changes in the human DNA methylome: genomic locations and effects on gene expression. BMC Genomics. 2015;16:179.
16. Bell JT, Tsai P-C, Yang T-P, Pidsley R, Nisbet J, Glass D, Mangino M, Zhai G, Zhang F, Valdes A. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet. 2012;8:e1002629.
17. Reynolds LM, Taylor JR, Ding J, Lohman K, Johnson C, Siscovick D, Burke G, Post W, Shea S, Jacobs Jr DR, et al. Age-related variations in the methylome associated with gene expression in human monocytes and T cells. Nat Commun. 2014;5:1-8.
18. Heyn H, Li N, Ferreira HJ, Moran S, Pisano DG, Gomez A, Diez J, Sanchez-Mut JV, Setien F, Carmona FJ. Distinct DNA methylomes of newborns and centenarians. Proc Natl Acad Sci U S A. 2012;109:10522-7.
19. van Dongen J, Nivard MG, Willemsen G, Hottenga J-J, Helmer Q, Dolan CV, Ehli EA, Davies GE, van Iterson M, Breeze CE. Genetic and environmental influences interact with age and sex in shaping the human methylome. Nature Commun. 2016;7:1-13.
20. Hernandez DG, Nalls MA, Gibbs JR, Arepalli S, van der Brug M, Chong S, Moore M, Longo DL, Cookson MR, Traynor BJ. Distinct DNA methylation changes highly correlated with chronological age in the human brain. Hum Mol Genet. 2011;20:1164-72.
21. Fernández AF, Bayón GF, Urdinguio RG, Toraño EG, García MG, Carella A, Petrus-Reurer S, Ferrero C, Martinez-Camblor P, Cubillo I. H3K4me1 marks DNA regions hypomethylated during aging in human stem and differentiated cells. Genome Res. 2015;25:27-40.
22. Day K, Waite LL, Thalacker-Mercer A, West A, Bamman MM, Brooks JD, Myers RM, Absher D. Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biol. 2013;14:R102.
23. Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, Zhang N, Liang S, Donehower LA, Issa J-PJ. Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res. 2010;20:332-40.
24. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.
25. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49:359-67.
26. Deelen J, Beekman M, Capri M, Franceschi C, Slagboom PE. Identifying the genomic determinants of aging and longevity in human population studies: progress and challenges. Bioessays. 2013;35:386-96.
27. Talens RP, Christensen K, Putter H, Willemsen G, Christiansen L, Kremer D, Suchiman HED, Slagboom PE, Boomsma DI, Heijmans BT. Epigenetic variation during the adult lifespan: cross-sectional and longitudinal data on monozygotic twin pairs. Aging Cell. 2012;11:694-703.
28. Phipson B, Oshlack A. DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging. Genome Biol. 2014;15:465.
29. Shannon CE, Weaver W. The mathematical theory of communication. Champaign: University of Illinois Press; 1959.
30. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:1.
31. Jaffe AE, Irizarry RA. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 2014;15:R31.
32. Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518:317-30.
33. Slieker RC, Bos SD, Goeman JJ, Bovée J, Talens RP, van der Breggen R, Suchiman H, Lameijer E-W, Putter H, van den Akker EB. Identification and systematic annotation of tissue-specific differentially methylated regions using the Illumina 450k array. Epigenetics Chromatin. 2013;6:26.
34. Vandiver AR, Irizarry RA, Hansen KD, Garza LA, Runarsson A, Li X, Chien AL, Wang TS, Leung SG, Kang S. Age and sun exposure-related widespread genomic blocks of hypomethylation in nonmalignant skin. Genome Biol. 2015;16:80.
35. Berdasco M, Esteller M. Aberrant epigenetic landscape in cancer: how cellular identity goes awry. Dev Cell. 2010;19:698-711.
36. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010;20:440-6.
37. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, Reinmaa E, Sutphin GL, Zhernakova A, Schramm K. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015;6:1-14.
38. Tserel L, Kolde R, Limbach M, Tretyakov K, Kasela S, Kisand K, Saare M, Vilo J, Metspalu A, Milani L. Age-related profiling of DNA methylation in CD8+ T cells reveals changes in immune response and transcriptional regulator genes. Sci Rep. 2015;5:1-11.
39. Schellenberg A, Lin Q, Schüler H, Koch CM, Joussen S, Denecke B, Walenda G, Pallua N, Suschek CV, Zenke M. Replicative senescence of mesenchymal stem cells causes DNA-methylation changes which correlate with repressive histone marks. Aging (Albany NY). 2011;3:873.
40. O'Hagan HM, Wang W, Sen S, Shields CD, Lee SS, Zhang YW, Clements EG, Cai Y, Van Neste L, Easwaran H. Oxidative damage targets complexes containing DNA methyltransferases, SIRT1, and polycomb members to promoter CpG Islands. Cancer Cell. 2011;20:606-19.
41. Ding N, Bonham EM, Hannon BE, Amick TR, Baylin SB, O'Hagan HM. Mismatch repair proteins recruit DNA methyltransferase 1 to sites of oxidative DNA damage. J Mol Cell Biol. 2016;8:244-25.
42. Rube C, Fricke A, Widmann TA, Furst T, Madry H, Pfreundschuh M, Rube C. Accumulation of DNA damage in hematopoietic stem and progenitor cells during human aging. PLoS One. 2011;6, e17487.
43. Rossi DJ, Bryder D, Seita J, Nussenzweig A, Hoeijmakers J, Weissman IL. Deficiencies in DNA damage repair limit the function of haematopoietic stem cells with age. Nature. 2007;447:725-9.
44. Xie M, Lu C, Wang J, McLellan MD, Johnson KJ, Wendl MC, McMichael JF, Schmidt HK, Yellapantula V, Miller CA. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat Med. 2014;20:1472-8.
45. Zhernakova D, Deelen P, Vermaat M, van Iterson M, van Galen M, Arindrarto W, van t Hof P, Mei H, van Dijk F, Westra H-J, et al. Hypothesis-free identification of modulators of genetic risk factors. BioRxiv. 2015.
46. Bonder MJ, Luijk R, Zhernakova D, Moed M, Deelen P, Vermaat M, van Iterson M, van Dijk F, van Galen M, Bot J, et al. Disease variants alter transcription factor levels and methylation of their binding sites. BioRxiv. 2015.
47. van Greevenbroek MM, Jacobs M, van der Kallen CJ, Vermeulen VM, Jansen EH, Schalkwijk CG, Ferreira I, Feskens EJ, Stehouwer CD. The cross-sectional association between insulin resistance and circulating complement C3 is partly explained by plasma alanine aminotransferase, independent of central obesity and general inflammation (the CODAM study). Eur J Clin Invest. 2011;41:372-9.
48. Tigchelaar EF, Zhernakova A, Dekens JA, Hermes G, Baranska A, Mujagic Z, Swertz MA, Muñoz AM, Deelen P, Cénit MC. Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics. BMJ Open. 2015;5:e006772.
49. Westendorp RG, Van Heemst D, Rozing MP, Frölich M, Mooijaart SP, Blauw GJ, Beekman M, Heijmans BT, De Craen AJ, Slagboom PE. Nonagenarian siblings and their offspring display lower risk of mortality and morbidity than sporadic nonagenarians: The Leiden Longevity Study. J Am Geriatr Soc. 2009;57:1634-7.
50. Boomsma DI, Vink JM, Van Beijsterveldt TC, de Geus EJ, Beem AL, Mulder EJ, Derks EM, Riese H, Willemsen GA, Bartels M. Netherlands Twin Register: a focus on longitudinal research. Twin Res. 2002;5:401-6.
51. Hofman A, Brusselle GG, Murad SD, van Duijn CM, Franco OH, Goedegebure A, Ikram MA, Klaver CC, Nijsten TE, Peeters RP. The Rotterdam Study: 2016 objectives and design update. Eur J Epidemiol. 2015;30:661-708.
52. Huisman MH, de Jong SW, van Doormaal PT, Weinreich SS, Schelhaas HJ, van der Kooi AJ, de Visser M, Veldink JH, van den Berg LH. Population based epidemiology of amyotrophic lateral sclerosis using capture-recapture methodology. J Neurol Neurosurg Psychiatry. 2011;82:1165-70.
53. Westra H-J, Jansen RC, Fehrmann RS, te Meerman GJ, Van Heel D, Wijmenga C, Franke L. MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. Bioinformatics. 2011;27:2104-11.
54. van Iterson M, Tobi EW, Slieker RC, den Hollander W, Luijk R, Slagboom PE, Heijmans BT. MethylAid: visual and interactive quality control of large Illumina 450 k datasets. Bioinformatics. 2014;30:3435-7.
55. Fortin J-P, Labbe A, Lemire M, Zanke B, Hudson T, Fertig E, Greenwood C, Hansen K. Functional normalization of 450 k methylation array data improves replication in large cancer studies. Genome Biol. 2014;15:503.
56. Chen Y-A, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, Gallinger S, Hudson TJ, Weksberg R. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8:203-9.
57. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118-27.
58. Andrews S. FastQC: a quality control tool for high throughput sequence data. Reference Source; 2010.
59. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10-2.
60. Joshi N, Fass J. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files (version 1.33). 2011. https://github.com/najoshi/sickle.
61. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15-21.
62. Hansen KD, Irizarry RA, Zhijin W. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics. 2012;13:204-16.
63. Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15:1.
64. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363-69.
65. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 2013;41:D991-5.
66. Kolesnikov N, Hastings E, Keays M, Melnichuk O, Tang YA, Williams E, Dylag M, Kurbatova N, Brandizi M, Burdett T, et al. ArrayExpress update-simplifying data submissions. Nucleic Acids Res. 2015;43:D1113-6.
67. Grundberg E, Small KS, Hedman ÅK, Nica AC, Buil A, Keildson S, Bell JT, Yang T-P, Meduri E, Barrett A. Mapping cis-and trans-regulatory effects across multiple tissues in twins. Nat Genet. 2012;44:1084-9.
68. Qu Y, Lennartsson A, Gaidzik VI, Deneberg S, Karimi M, Bengtzén S, Höglund M, Bullinger L, Döhner K, Lehmann S. Differential methylation in CN-AML preferentially targets non-CGI regions and is dictated by DNMT3A mutational status and associated with predominant hypomethylation of HOX genes. Epigenetics. 2014;9:1108-19.
69. Breusch TS, Pagan AR. A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica. 1979;47:1287-94.
70. Luijk R, Goeman JJ, Slagboom EP, Heijmans BT, van Zwet EW. An alternative approach to multiple testing for methylation QTL mapping reduces the proportion of falsely identified CpGs. Bioinformatics. 2015;31:340-5.
71. ENCODE. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57-74.
72. Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011;6:e21800.
73. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498-504.
74. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16:284-7.
75. Wickham H. ggplot2: elegant graphics for data analysis. Berlin: Springer Science & Business Media; 2009.
76. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639-45.
77. Hahne F, Durinck S, Ivankek R, Mueller A, Lianoglou S. Gviz: plotting data and annotation information along genomic coordinates. R package version 1.2. 1. Bioconductor; 2012. https://bioconductor.org/packages/release/bioc/html/Gviz.html.