An Integrative Multi-scale Analysis of the Dynamic DNA Methylation Landscape in Aging
Two well-known features of aging are the gradual decline of the body’s ability to regenerate tissues, as well as an increased incidence of diseases like cancer and Alzheimers. One of the most recent exciting findings which may underlie the aging process is a gradual modification of DNA, called epigenetic drift, which is effected by the covalent addition and removal of methyl groups, which in turn can deregulate the activity of nearby genes. However, this study presents the most convincing evidence to date that epigenetic drift acts to stabilize the activity levels of nearby genes. This study shows that instead, epigenetic drift may act primarly to disrupt DNA binding patterns of proteins which regulate the activity of many genes, and moreover identifies specific regulatory proteins with key roles in cancer and Alzheimers. The study also performs the most comprehensive analysis of epigenetic drift at different spatial scales, demonstrating that epigenetic drift on the largest length scales is highly reminiscent of those seen in cancer. In summary, this work substantially supports the view that epigenetic drift may contribute to the age-associated increased risk of diseases like cancer and Alzheimers, by disrupting master regulators of genomewide gene activity.
Vyšlo v časopise:
An Integrative Multi-scale Analysis of the Dynamic DNA Methylation Landscape in Aging. PLoS Genet 11(2): e32767. doi:10.1371/journal.pgen.1004996
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pgen.1004996
Souhrn
Two well-known features of aging are the gradual decline of the body’s ability to regenerate tissues, as well as an increased incidence of diseases like cancer and Alzheimers. One of the most recent exciting findings which may underlie the aging process is a gradual modification of DNA, called epigenetic drift, which is effected by the covalent addition and removal of methyl groups, which in turn can deregulate the activity of nearby genes. However, this study presents the most convincing evidence to date that epigenetic drift acts to stabilize the activity levels of nearby genes. This study shows that instead, epigenetic drift may act primarly to disrupt DNA binding patterns of proteins which regulate the activity of many genes, and moreover identifies specific regulatory proteins with key roles in cancer and Alzheimers. The study also performs the most comprehensive analysis of epigenetic drift at different spatial scales, demonstrating that epigenetic drift on the largest length scales is highly reminiscent of those seen in cancer. In summary, this work substantially supports the view that epigenetic drift may contribute to the age-associated increased risk of diseases like cancer and Alzheimers, by disrupting master regulators of genomewide gene activity.
Zdroje
1. Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, et al. (2010) Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res 20: 332–340. doi: 10.1101/gr.096826.109 20107151
2. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, et al. (2010) Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res 20: 440–446. doi: 10.1101/gr.103606.109 20219944
3. Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, et al. (2010) Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res 20: 434–439. doi: 10.1101/gr.103101.109 20219945
4. Heyn H, Li N, Ferreira HJ, Moran S, Pisano DG, et al. (2012) Distinct DNA methylomes of newborns and centenarians. Proceedings of the National Academy of Sciences.
5. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, et al. (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 49: 359–67. doi: 10.1016/j.molcel.2012.10.016 23177740
6. Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14: R115. doi: 10.1186/gb-2013-14-10-r115 24138928
7. Beerman I, Bock C, Garrison BS, Smith ZD, Gu H, et al. (2013) Proliferation-dependent alterations of the DNA methylation landscape underlie hematopoietic stem cell aging. Cell Stem Cell 12: 413–25. doi: 10.1016/j.stem.2013.01.017 23415915
8. Teschendorff AE, Jones A, Fiegl H, Sargent A, Zhuang JJ, et al. (2012) Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation. Genome Med 4: 24. doi: 10.1186/gm323 22453031
9. Teschendorff AE, West J, Beck S (2013) Age-associated epigenetic drift: implications, and a case of epigenetic thrift? Hum Mol Genet 22: R7–R15. doi: 10.1093/hmg/ddt375 23918660
10. West J, Widschwendter M, Teschendorff AE (2013) Distinctive topology of age-associated epigenetic drift in the human interactome. Proc Natl Acad Sci U S A 110: 14138–43. doi: 10.1073/pnas.1307242110 23940324
11. Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, et al. (2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol 31: 142–7. doi: 10.1038/nbt.2487 23334450
12. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Gayther SA, et al. (2009) An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS One 4: e8274. doi: 10.1371/journal.pone.0008274 20019873
13. Langevin SM, Houseman EA, Accomando WP, Koestler DC, Christensen BC, et al. (2014) Leukocyte-adjusted epigenome-wide association studies of blood from solid tumor patients. Epigenetics 9: 884–95. doi: 10.4161/epi.28575 24671036
14. Jaffe AE, Irizarry RA (2014) Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol 15: R31. doi: 10.1186/gb-2014-15-2-r31 24495553
15. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, et al. (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13: 86. doi: 10.1186/1471-2105-13-86 22568884
16. Houseman EA, Molitor J, Marsit CJ (2014) Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30: 1431–9. doi: 10.1093/bioinformatics/btu029 24451622
17. Berman BP, Weisenberger DJ, Aman JF, Hinoue T, Ramjan Z, et al. (2012) Regions of focal DNA hypermethylation and long-range hypomethylation in colorectal cancer coincide with nuclear lamina-associated domains. Nat Genet 44: 40–6. doi: 10.1038/ng.969
18. Timp W, Bravo HC, McDonald OG, Goggins M, Umbricht C, et al. (2014) Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med 6: 61. doi: 10.1186/s13073-014-0061-y 25191524
19. Ziller MJ, Gu H, Muller F, Donaghey J, Tsai LT, et al. (2013) Charting a dynamic DNA methylation landscape of the human genome. Nature 500: 477–81. doi: 10.1038/nature12433 23925113
20. Beck S (2010) Taking the measure of the methylome. Nat Biotechnol 28: 1026–8. doi: 10.1038/nbt1010-1026 20944589
21. Hovestadt V, Jones DT, Picelli S, Wang W, Kool M, et al. (2014) Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing. Nature 510: 537–41. doi: 10.1038/nature13268 24847876
22. Essaghir A, Toffalini F, Knoops L, Kallin A, van Helden J, et al. (2010) Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data. Nucleic Acids Res 38: e120. doi: 10.1093/nar/gkq149 20215436
23. Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, et al. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57–74. doi: 10.1038/nature11247
24. Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, et al. (2012) Architecture of the human regulatory network derived from encode data. Nature 489: 91–100. doi: 10.1038/nature11245 22955619
25. Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al. (2012) The accessible chromatin landscape of the human genome. Nature 489: 75–82. doi: 10.1038/nature11232 22955617
26. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, et al. (2014) Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics 30: 1363–9. doi: 10.1093/bioinformatics/btu049 24478339
27. Jaffe AE, Feinberg AP, Irizarry RA, Leek JT (2012) Significance analysis and statistical dissection of variably methylated regions. Biostatistics 13: 166–78. doi: 10.1093/biostatistics/kxr013 21685414
28. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, et al. (2012) Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol 13: R97. doi: 10.1186/gb-2012-13-10-r97 23034122
29. Jones A, Teschendorff AE, Li Q, Hayward JD, Kannan A, et al. (2013) Role of DNA methylation and epigenetic silencing of hand2 in endometrial cancer development. PLoS Med 10: e1001551. doi: 10.1371/journal.pmed.1001551 24265601
30. Network CGA (2012) Comprehensive molecular portraits of human breast tumours. Nature 490: 61–70. doi: 10.1038/nature11412
31. Nejman D, Straussman R, Steinfeld I, Ruvolo M, Roberts D, et al. (2014) Molecular rules governing de novo methylation in cancer. Cancer Res 74: 1475–83. doi: 10.1158/0008-5472.CAN-13-3042 24453003
32. Steegenga WT, Boekschoten MV, Lute C, Hooiveld GJ, de Groot PJ, et al. (2014) Genome-wide age-related changes in DNA methylation and gene expression in human pbmcs. Age (Dordr) 36: 9648. doi: 10.1007/s11357-014-9648-x
33. de Jong S, Neeleman M, Luykx JJ, ten Berg MJ, Strengman E, et al. (2014) Seasonal changes in gene expression represent cell-type composition in whole blood. Hum Mol Genet 23: 2721–8. doi: 10.1093/hmg/ddt665 24399446
34. Jiao Y, Widschwendter M, Teschendorff AE (2014) A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics 30: 2360–6. doi: 10.1093/bioinformatics/btu316 24794928
35. Votavova H, M MMD, Fejglova K, Vasikova A, Krejcik Z, et al. (2011) Transcriptome alterations in maternal and fetal cells induced by tobacco smoke. Placenta 32: 763–770. doi: 10.1016/j.placenta.2011.06.022 21803418
36. Beineke P, Fitch K, Tao H, Elashoff MR, Rosenberg S, et al. (2012) A whole blood gene expression-based signature for smoking status. BMC Med Genomics 5: 58. doi: 10.1186/1755-8794-5-58 23210427
37. Nazor KL, Altun G, Lynch C, Tran H, Harness JV, et al. (2012) Recurrent variations in DNA methylation in human pluripotent stem cells and their differentiated derivatives. Cell Stem Cell 10: 620–34. doi: 10.1016/j.stem.2012.02.013 22560082
38. Lee CS, Friedman JR, Fulmer JT, Kaestner KH (2005) The initiation of liver development is dependent on foxa transcription factors. Nature 435: 944–7. doi: 10.1038/nature03649 15959514
39. Cereghini S (1996) Liver-enriched transcription factors and hepatocyte differentiation. FASEB J 10: 267–82. 8641560
40. Lu T, Aron L, Zullo J, Pan Y, Kim H, et al. (2014) Rest and stress resistance in ageing and alzheimer’s disease. Nature 507: 448–54. doi: 10.1038/nature13163 24670762
41. Day K, Waite LL, Thalacker-Mercer A, West A, Bamman MM, et al. (2013) Differential DNA methylation with age displays both common and dynamic features. Genome Biol 14: R102. doi: 10.1186/gb-2013-14-9-r102 24034465
42. Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, et al. (2012) Age-associated DNA methylation in pediatric populations. Genome Res 22: 623–32. doi: 10.1101/gr.125187.111 22300631
43. Martino D, Loke YJ, Gordon L, Ollikainen M, Cruickshank MN, et al. (2013) Longitudinal, genome-scale analysis of DNA methylation in twins from birth to 18 months of age reveals rapid epigenetic change in early life and pair-specific effects of discordance. Genome Biol 14: R42. doi: 10.1186/gb-2013-14-5-r42 23697701
44. Glass D, Vinuela A, Davies MN, Ramasamy A, Parts L, et al. (2013) Gene expression changes with age in skin, adipose tissue, blood and brain. Genome Biol 14: R75. doi: 10.1186/gb-2013-14-7-r75 23889843
45. Huehne R, Thalheim T, Suehnel J (2014) Agefactdb–the jenage ageing factor database–towards data integration in ageing research. Nucleic Acids Res 42: D892–6. doi: 10.1093/nar/gkt1073
46. Walter D, Matter A, Fahrenkrog B (2014) Loss of histone h3 methylation at lysine 4 triggers apoptosis in saccharomyces cerevisiae. PLoS Genet 10: e1004095. doi: 10.1371/journal.pgen.1004095 24497836
47. Lui JC, Chen W, Cheung CS, Baron J (2014) Broad shifts in gene expression during early postnatal life are associated with shifts in histone methylation patterns. PLoS One 9: e86957. doi: 10.1371/journal.pone.0086957 24489814
48. Tacutu R, Craig T, Budovsky A, Wuttke D, Lehmann G, et al. (2013) Human ageing genomic resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res 41: D1027–33. doi: 10.1093/nar/gks1155 23193293
49. Ayer DE (1999) Histone deacetylases: transcriptional repression with siners and nurds. Trends Cell Biol 9: 193–8. doi: 10.1016/S0962-8924(99)01536-6 10322454
50. Nascimento EM, Cox CL, MacArthur S, Hussain S, Trotter M, et al. (2011) The opposing transcriptional functions of sin3a and c-myc are required to maintain tissue homeostasis. Nat Cell Biol 13: 1395–405. doi: 10.1038/ncb2385 22101514
51. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, et al. (2013) A beta-mixture quantile normalization method for correcting probe design bias in illumina infinium 450 k DNA methylation data. Bioinformatics 29: 189–96. doi: 10.1093/bioinformatics/bts680 23175756
52. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, et al. (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17: 520–525. doi: 10.1093/bioinformatics/17.6.520 11395428
53. Teschendorff AE, Zhuang J, Widschwendter M (2011) Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics 27: 1496–1505. doi: 10.1093/bioinformatics/btr171 21471010
Štítky
Genetika Reprodukčná medicínaČlánok vyšiel v časopise
PLOS Genetics
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