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Quantifying the stochastic component of epigenetic aging

Tong, Huige, Dwaraka, Varun B., Chen, Qingwen, Luo, Qi, Lasky-Su, Jessica A., Smith, Ryan, Teschendorff, Andrew E.·2024·Nature Aging
DOI 10.1038/s43587-024-00600-8PMC PMC11186785
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Abstract

DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock’s predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66–75% of the accuracy underpinning Horvath’s clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang’s clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath’s age acceleration in males and PhenoAge’s age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.

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Research works printed but not linked: 62

  1. Vijg, J. From DNA damage to mutations: all roads lead to aging. Ageing Res. Rev. 68, 101316 (2021). · DOI
  2. Laurie, C. C. et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat. Genet. 44, 642–650 (2012). · DOI · Paper page
  3. Jacobs, K. B. et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nat. Genet. 44, 651–658 (2012). · DOI · Paper page
  4. Ahuja, N. & Issa, J. P. Aging, methylation and cancer. Histol. Histopathol. 15, 835–842 (2000).
  5. Ahuja, N., Li, Q., Mohan, A. L., Baylin, S. B. & Issa, J. P. Aging and DNA methylation in colorectal mucosa and cancer. Cancer Res. 58, 5489–5494 (1998).
  6. Issa, J. P., Ahuja, N., Toyota, M., Bronner, M. P. & Brentnall, T. A. Accelerated age-related CpG island methylation in ulcerative colitis. Cancer Res. 61, 3573–3577 (2001).
  7. Christensen, B. C. et al. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 5, e1000602 (2009). · DOI
  8. de Magalhaes, J. P. Ageing as a software design flaw. Genome Biol. 24, 51 (2023). · DOI
  9. Teschendorff, A. E. et al. An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS ONE 4, e8274 (2009). · DOI
  10. Teschendorff, A. E. et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 20, 440–446 (2010). · DOI · Paper page
  11. Rakyan, V. K. et al. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome Res. 20, 434–439 (2010). · DOI
  12. Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell 49, 359–367 (2013). · DOI · Paper page
  13. Horvath, S. & Raj, K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat. Rev. Genet. 19, 371–384 (2018). · DOI · Paper page
  14. Marioni, R. E. et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 16, 25 (2015). · DOI · Paper page
  15. Levine, M. E. et al. DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative. Aging 7, 690–700 (2015). · DOI
  16. Levine, M. E. et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging 10, 573–591 (2018). · DOI · Paper page
  17. Seale, K., Horvath, S., Teschendorff, A., Eynon, N. & Voisin, S. Making sense of the ageing methylome. Nat. Rev. Genet. 23, 585–605 (2022). · DOI · Paper page
  18. West, J., Beck, S., Wang, X. & Teschendorff, A. E. An integrative network algorithm identifies age-associated differential methylation interactome hotspots targeting stem-cell differentiation pathways. Sci. Rep. 3, 1630 (2013). · DOI
  19. West, J., Widschwendter, M. & Teschendorff, A. E. Distinctive topology of age-associated epigenetic drift in the human interactome. Proc. Natl Acad. Sci. USA 110, 14138–14143 (2013). · DOI
  20. Nejman, D. et al. Molecular rules governing de novo methylation in cancer. Cancer Res. 74, 1475–1483 (2014). · DOI
  21. Maegawa, S. et al. Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res. 20, 332–340 (2010). · DOI · Paper page
  22. Slieker, R. C. et al. Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms. Genome Biol. 17, 191 (2016). · DOI
  23. Tarkhov, A. E. et al. Nature of epigenetic aging from a single-cell perspective. Preprint at https://www.biorxiv.org/content/10.1101/2022.09.26.509592v2 (2022). · DOI
  24. Minteer, C. J. et al. More than bad luck: cancer and aging are linked to replication-driven changes to the epigenome. Sci. Adv. 9, eadf4163 (2023). · DOI
  25. Vandiver, A. R. et al. Age and sun exposure-related widespread genomic blocks of hypomethylation in nonmalignant skin. Genome Biol. 16, 80 (2015). · DOI
  26. Poganik, J. R. et al. Biological age is increased by stress and restored upon recovery. Cell Metab. 35, 807–820 e5 (2023). · DOI · Paper page
  27. Reynolds, L. M. et al. Tobacco exposure-related alterations in DNA methylation and gene expression in human monocytes: the Multi-Ethnic Study of Atherosclerosis (MESA). Epigenetics 12, 1092–1100 (2017). · DOI
  28. Teschendorff, A. E. A comparison of epigenetic mitotic-like clocks for cancer risk prediction. Genome Med. 12, 56 (2020). · DOI
  29. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010). · DOI · Paper page
  30. Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414 e24 (2016). · DOI · Paper page
  31. Luo, Q. et al. A meta-analysis of immune-cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes. Genome Med. 15, 59 (2023). · DOI
  32. Cao, X. et al. Accelerated biological aging in COVID-19 patients. Nat. Commun. 13, 2135 (2022). · DOI
  33. Barturen, G. et al. Whole blood DNA methylation analysis reveals respiratory environmental traits involved in COVID-19 severity following SARS-CoV-2 infection. Nat. Commun. 13, 4597 (2022). · DOI
  34. Kim, J. Y., Tavare, S. & Shibata, D. Counting human somatic cell replications: methylation mirrors endometrial stem cell divisions. Proc. Natl Acad. Sci. USA 102, 17739–17744 (2005). · DOI
  35. Siegmund, K. D., Marjoram, P., Woo, Y. J., Tavare, S. & Shibata, D. Inferring clonal expansion and cancer stem cell dynamics from DNA methylation patterns in colorectal cancers. Proc. Natl Acad. Sci. USA 106, 4828–4833 (2009). · DOI
  36. Tomasetti, C. & Vogelstein, B. Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347, 78–81 (2015). · DOI · Paper page
  37. Jonkman, T. H. et al. Functional genomics analysis identifies T and NK cell activation as a driver of epigenetic clock progression. Genome Biol. 23, 24 (2022). · DOI
  38. Joehanes, R. et al. Epigenetic signatures of cigarette smoking. Circ. Cardiovasc. Genet. 9, 436–447 (2016). · DOI
  39. You, C. et al. A cell-type deconvolution meta-analysis of whole blood EWAS reveals lineage-specific smoking-associated DNA methylation changes. Nat. Commun. 11, 4779 (2020). · DOI
  40. Teschendorff, A. E. et al. Correlation of smoking-associated DNA methylation changes in buccal cells with DNA methylation changes in epithelial cancer. JAMA Oncol. 1, 476–485 (2015). · DOI
  41. van Dongen, J. et al. Effects of smoking on genome-wide DNA methylation profiles: a study of discordant and concordant monozygotic twin pairs. eLife 12, e83286 (2023). · DOI
  42. Endicott, J. L., Nolte, P. A., Shen, H. & Laird, P. W. Cell division drives DNA methylation loss in late-replicating domains in primary human cells. Nat. Commun. 13, 6659 (2022). · DOI
  43. Gao, Y. et al. The integrative epigenomic-transcriptomic landscape of ER-positive breast cancer. Clin. Epigenetics 7, 126 (2015). · DOI
  44. Chen, Y., Breeze, C. E., Zhen, S., Beck, S. & Teschendorff, A. E. Tissue-independent and tissue-specific patterns of DNA methylation alteration in cancer. Epigenetics Chromatin 9, 10 (2016). · DOI
  45. Zhu, L. et al. Multi-organ mapping of cancer risk. Cell 166, 1132–1146 e7 (2016). · DOI
  46. Levine, M.E., Higgins-Chen, A., Thrush, K., Minteer, C. & Niimi, P. Clock work: deconstructing epigenetic clock signals in aging, disease and reprogramming. Preprint at bioRxiv https://doi.org/10.1101/2022.02.13.480245 (2023). · DOI
  47. Martin-Herranz, D. E. et al. Screening for genes that accelerate the epigenetic aging clock in humans reveals a role for the H3K36 methyltransferase NSD1. Genome Biol. 20, 146 (2019). · DOI
  48. Reynolds, L. M. et al. Age-related variations in the methylome associated with gene expression in human monocytes and T cells. Nat. Commun. 5, 5366 (2014). · DOI
  49. Tserel, L. et al. Age-related profiling of DNA methylation in CD8+ T cells reveals changes in immune response and transcriptional regulator genes. Sci. Rep. 5, 13107 (2015). · DOI
  50. Paul, D. S. et al. Increased DNA methylation variability in type 1 diabetes across three immune effector cell types. Nat. Commun. 7, 13555 (2016). · DOI
  51. Norris, J.R. Markov Chains (Cambridge Univ. Press, 1997). · DOI
  52. Teschendorff, A. E. Avoiding common pitfalls in machine learning omic data science. Nat. Mater. 18, 422–427 (2019). · DOI · Paper page
  53. Pelegi-Siso, D., de Prado, P., Ronkainen, J., Bustamante, M. & Gonzalez, J. R. methylclock: a Bioconductor package to estimate DNA methylation age. Bioinformatics 37, 1759–1760 (2021). · DOI
  54. Teschendorff, A. E., Breeze, C. E., Zheng, S. C. & Beck, S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinform. 18, 105 (2017). · DOI
  55. Zheng, S.C. et al. EpiDISH web server: epigenetic dissection of intra-sample-heterogeneity with online GUI. Bioinformatics 36, 1950–1951 (2019). · DOI
  56. Johnson, K. C. et al. DNA methylation in ductal carcinoma in situ related with future development of invasive breast cancer. Clin. Epigenetics 7, 75 (2015). · DOI
  57. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520–525 (2001). · DOI · Paper page
  58. Huang, K. K. et al. Genomic and epigenomic profiling of high-risk intestinal metaplasia reveals molecular determinants of progression to gastric cancer. Cancer Cell 33, 137–150 e5 (2018). · DOI
  59. Luebeck, E. G. et al. Identification of a key role of widespread epigenetic drift in Barrett’s esophagus and esophageal adenocarcinoma. Clin. Epigenetics 9, 113 (2017). · DOI
  60. Maity, A. K. et al. Novel epigenetic network biomarkers for early detection of esophageal cancer. Clin. Epigenetics 14, 23 (2022). · DOI
  61. Bormann, F. et al. Cell-of-origin DNA methylation signatures are maintained during colorectal carcinogenesis. Cell Rep. 23, 3407–3418 (2018). · DOI
  62. Teschendorff, A. E. et al. DNA methylation outliers in normal breast tissue identify field defects that are enriched in cancer. Nat. Commun. 7, 10478 (2016). · DOI

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Metadata

Authors
Tong, H., Dwaraka, V. B., Chen, Q., Luo, Q., Lasky-Su, J. A., Smith, R., & Teschendorff, A. E.
ORCID
Venue
Nature Aging
Publisher
Springer Science and Business Media LLC
Published
2024-05-09
Volume
4
Issue
6
Pages
886-901
ISSN
2662-8465
DOI
10.1038/s43587-024-00600-8
PMC
PMC11186785
Cited by
83
References
14
Language
en
Licence
Funders
National Natural Science Foundation of China (32370699, 32170652, 31970632)

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