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Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging

Kerepesi, Csaba, Zhang, Bohan, Lee, Sang-Goo, Trapp, Alexandre, Gladyshev, Vadim N.·2021·Science Advances

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Epigenetic clocks reveal a rejuvenation event during embryogenesis followed by aging.

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Fig. 1 A rejuvenation event during early embryogenesis revealed by aging clocks. (A) Overview of the model, which posits that germline cells age during development and adulthood and are rejuvenated in the offspring after conception. The model also suggests that there is a time point corresponding to the lowest biological age (ground zero). (B) Multi-tissue and blood rDNA clocks applied to five datasets spanning the first 8 days of mouse embryogenesis (Table 1, datasets 1 to 5). We rescaled epigenetic age of each dataset to the interval [0,1] for comparison (“relative rDNA age”). 0 represents the lowest epigenetic age, and 1 represents the highest epigenetic age of each dataset. Blue lines indicate the mean of each group; P values of two-sided t test comparing the means of the two groups (before and after E6) are displayed. (C) Application of four genome-wide epigenetic aging clocks to two available mouse RRBS datasets. (D) Epigenetic age of human ESCs and iPSCs as a function of passage number. The Horvath human multi-tissue clock was applied.
Fig. 1 A rejuvenation event during early embryogenesis revealed by aging clocks. (A) Overview of the model, which posits that germline cells age during development and adulthood and are rejuvenated in the offspring after conception. The model also suggests that there is a time point corresponding to the lowest biological age (ground zero). (B) Multi-tissue and blood rDNA clocks applied to five datasets spanning the first 8 days of mouse embryogenesis (Table 1, datasets 1 to 5). We rescaled epigenetic age of each dataset to the interval [0,1] for comparison (“relative rDNA age”). 0 represents the lowest epigenetic age, and 1 represents the highest epigenetic age of each dataset. Blue lines indicate the mean of each group; P values of two-sided t test comparing the means of the two groups (before and after E6) are displayed. (C) Application of four genome-wide epigenetic aging clocks to two available mouse RRBS datasets. (D) Epigenetic age of human ESCs and iPSCs as a function of passage number. The Horvath human multi-tissue clock was applied.CC BY-NC · PubMed Central
Fig. 2 Organismal aging begins in mid-embryonic development in mice and humans. (A) Epigenetic age (multi-tissue and blood rDNA clocks) analysis of the dataset that contains both early and late mouse embryo samples (E13.5 samples are based on primordial germline cells). (B) Application of genome-wide epigenetic clocks to later stages of mouse embryogenesis (r, Pearson correlation coefficient; P, P value of the correlation). (C) The same data as above but separated by tissue. An increasing trend is observed for almost all tissues, with few nonsignificant exceptions. (D) Epigenetic age dynamics of four independent prenatal human 450K methylation array datasets based on the Horvath human multi-tissue clock. (E) The same data as above but separated by tissue (five significant increases, nine nonsignificant increases, four nonsignificant decreases, and zero significant decreases).
Fig. 2 Organismal aging begins in mid-embryonic development in mice and humans. (A) Epigenetic age (multi-tissue and blood rDNA clocks) analysis of the dataset that contains both early and late mouse embryo samples (E13.5 samples are based on primordial germline cells). (B) Application of genome-wide epigenetic clocks to later stages of mouse embryogenesis (r, Pearson correlation coefficient; P, P value of the correlation). (C) The same data as above but separated by tissue. An increasing trend is observed for almost all tissues, with few nonsignificant exceptions. (D) Epigenetic age dynamics of four independent prenatal human 450K methylation array datasets based on the Horvath human multi-tissue clock. (E) The same data as above but separated by tissue (five significant increases, nine nonsignificant increases, four nonsignificant decreases, and zero significant decreases).CC BY-NC · PubMed Central
Fig. 3 Epigenetic age of mouse ESCs during early passaging under different culture conditions. (A) Epigenetic age (by rDNA clocks) of mouse ESCs after outgrowth (passage 0) and passage 5 under three different culture conditions (2i, both self-renewal supporting inhibitors used; PD, only one inhibitor; mES, no inhibitor). (B) Application of genome-wide mouse epigenetic clocks to the same data. (C) Principal components analysis (PCA) of RRBS methylation profiles of embryo (circles) and ESC (crosses) sample groups after passage 0 (left) and passage 5 (right). Convex hull around the ESC samples in the same group (if n > 2) is displayed to help distinguish ESC samples from embryo samples.
Fig. 3 Epigenetic age of mouse ESCs during early passaging under different culture conditions. (A) Epigenetic age (by rDNA clocks) of mouse ESCs after outgrowth (passage 0) and passage 5 under three different culture conditions (2i, both self-renewal supporting inhibitors used; PD, only one inhibitor; mES, no inhibitor). (B) Application of genome-wide mouse epigenetic clocks to the same data. (C) Principal components analysis (PCA) of RRBS methylation profiles of embryo (circles) and ESC (crosses) sample groups after passage 0 (left) and passage 5 (right). Convex hull around the ESC samples in the same group (if n > 2) is displayed to help distinguish ESC samples from embryo samples.CC BY-NC · PubMed Central
Fig. 4 Localization of the epigenetic age minimum (ground zero) during mouse embryonic development. (A) We concatenated results for the entire period of embryogenesis by using the genome-wide mouse epigenetic clocks indicated. (B) We concatenated results for the entire period of embryogenesis by using the indicated mouse rDNA epigenetic clocks. (C) Application of genome-wide mouse epigenetic clocks to dataset 20 that contains mid-embryonic stages from E3.5 to E11.5. (D) Application of rDNA mouse epigenetic clocks to dataset 20.
Fig. 4 Localization of the epigenetic age minimum (ground zero) during mouse embryonic development. (A) We concatenated results for the entire period of embryogenesis by using the genome-wide mouse epigenetic clocks indicated. (B) We concatenated results for the entire period of embryogenesis by using the indicated mouse rDNA epigenetic clocks. (C) Application of genome-wide mouse epigenetic clocks to dataset 20 that contains mid-embryonic stages from E3.5 to E11.5. (D) Application of rDNA mouse epigenetic clocks to dataset 20.CC BY-NC · PubMed Central
Fig. 5 Role of TET enzymes in the rejuvenation event. (A) Application of rDNAm clocks to TET triple KO (TET KO) and wild-type (WT) E6.5 epiblast samples (dataset 17). (B) Application of our recently developed single-cell clock [scAge; (49)] to TET KO and WT ESCs at days 2 and 5 of differentiation (dataset 18). Specific lineages and stages (Mapped lineage and Mapped E-day) were assigned by mapping the RNA expression profiles of the in vitro cells to an in vivo gastrulation atlas. Two-sided t tests were calculated (ns, P > 0.05; *, 1 × 10−2 < P ≤ 5 × 10−2; **, 1 × 10−3 < P ≤ 1 × 10−2; ***, 1 × 10−4 < P ≤ 1 × 10−3; ****P ≤ 1 × 10−4).
Fig. 5 Role of TET enzymes in the rejuvenation event. (A) Application of rDNAm clocks to TET triple KO (TET KO) and wild-type (WT) E6.5 epiblast samples (dataset 17). (B) Application of our recently developed single-cell clock [scAge; (49)] to TET KO and WT ESCs at days 2 and 5 of differentiation (dataset 18). Specific lineages and stages (Mapped lineage and Mapped E-day) were assigned by mapping the RNA expression profiles of the in vitro cells to an in vivo gastrulation atlas. Two-sided t tests were calculated (ns, P > 0.05; *, 1 × 10−2 < P ≤ 5 × 10−2; **, 1 × 10−3 < P ≤ 1 × 10−2; ***, 1 × 10−4 < P ≤ 1 × 10−3; ****P ≤ 1 × 10−4).CC BY-NC · PubMed Central
Fig. 6 Role of DNMTs in the rejuvenation event. (A) We applied rDNA clocks to single KO of DNMT1 (Dnmt1−/−), double KO of DNMT3A and DNMT3B (DKO), wild type (WT), and heterozygous control (3a−/+ and 3a−/+ 3b−/+) embryos at E8.5 (dataset 19). (B) Application of four genome-wide epigenetic aging clocks to the same dataset. Two-sided t tests were calculated (ns, P > 0.05; *, 1 × 10−2 < P ≤ 5 × 10−2; **, 1 × 10−3 < P ≤ 1 × 10−2; ***, 1 × 10−4 < P ≤ 1 × 10−3; ****P ≤ 1 × 10−4).
Fig. 6 Role of DNMTs in the rejuvenation event. (A) We applied rDNA clocks to single KO of DNMT1 (Dnmt1−/−), double KO of DNMT3A and DNMT3B (DKO), wild type (WT), and heterozygous control (3a−/+ and 3a−/+ 3b−/+) embryos at E8.5 (dataset 19). (B) Application of four genome-wide epigenetic aging clocks to the same dataset. Two-sided t tests were calculated (ns, P > 0.05; *, 1 × 10−2 < P ≤ 5 × 10−2; **, 1 × 10−3 < P ≤ 1 × 10−2; ***, 1 × 10−4 < P ≤ 1 × 10−3; ****P ≤ 1 × 10−4).CC BY-NC · PubMed Central

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Research works printed but not linked: 60

  1. 10.1159/000173977 · DOI
  2. 10.1007/s00018-015-1896-0 · DOI
  3. 10.1111/acel.12480 · DOI
  4. 10.1016/j.cell.2012.01.003 · DOI · Paper page
  5. 10.1016/j.fsigen.2015.05.014 · DOI
  6. 10.1371/journal.pgen.1004458 · DOI
  7. 10.1186/s12864-018-5153-4 · DOI
  8. 10.1016/j.molmed.2020.08.012 · DOI
  9. 10.2174/1389202918666170412112130 · DOI
  10. 10.1007/s10522-017-9683-y · DOI
  11. 10.1038/s41576-018-0004-3 · DOI · Paper page
  12. 10.1186/gb-2014-15-2-r24 · DOI · Paper page
  13. 10.18632/aging.101414 · DOI · Paper page
  14. 10.1016/j.molcel.2012.10.016 · DOI · Paper page
  15. 10.18632/aging.100908 · DOI
  16. 10.1186/s13059-015-0584-6 · DOI · Paper page
  17. 10.1093/ije/dyu277 · DOI
  18. 10.1186/s13148-016-0186-5 · DOI
  19. 10.18632/aging.100859 · DOI
  20. 10.1111/acel.12325 · DOI
  21. 10.18632/aging.101217 · DOI
  22. 10.1186/s13059-017-1203-5 · DOI · Paper page
  23. 10.1186/s13059-017-1186-2 · DOI
  24. 10.7554/eLife.37462 · DOI
  25. 10.1111/acel.12877 · DOI · Paper page
  26. MAMMALIAN METHYLATION CONSORTIUM A. T. Lu Z. Fei A. Haghani T. R. Robeck J. A. Zoller C. Z. Li J. Zhang J. Ablaeva D. M. Adams J. Almunia R. Ardehali A. Arneson C. S. Baker K. Belov P. Black D. T. Blumstein E. K. Bors C. E. Breeze R. T. Brooke J. L. Brown A. Caulton J. M. Cavin I. Chatzistamou H. Chen P. Chiavellini O.-W. Choi S. Clarke J. DeYoung C. Dold C. K. Emmons S. Emmrich C. G. Faulkes S. H. Ferguson C. J. Finno J.-M. Gaillard E. Garde V. N. Gladyshev V. Gorbunova R. G. Goya M. J. Grant E. N. Hales M. B. Hanson M. Haulena A. N. Hogan C. J. Hogg T. A. Hore A. J. Jasinska G. Jones E. Jourdain O. Kashpur H. Katcher E. Katsumata V. Kaza H. Kiaris M. S. Kobor P. Kordowitzki W. R. Koski B. Larison S.-G. Lee Y. C. Lee M. Lehmann J.-F. Lemaitre A. J. Levine C. Li X. Li D. T. Lin N. Macoretta D. Maddox C. O. Matkin J. A. Mattison J. Mergl J. J. Meudt K. Mozhui A. Naderi M. Nagy P. Narayan P. W. Nathanielsz N. B. Nguyen C. Niehrs A. G. Ophir E. A. Ostrander P. O. Ginn K. M. Parsons K. C. Paul M. Pellegrini G. M. Pinho J. Plassais N. A. Prado B. Rey B. R. Ritz J. Robbins M. Rodriguez J. Russell E. Rydkina L. L. Sailer A. B. Salmon A. Sanghavi K. M. Schachtschneider D. Schmitt T. Schmitt L. Schomacher L. B. Schook K. E. Sears A. Seluanov D. Shanmuganayagam A. Shindyapina K. Singh I. Sinha R. G. Snell E. Soltanmaohammadi M. L. Spangler L. Staggs K. J. Steinman V. J. Sugrue B. Szladovits M. Takasugi E. C. Teeling M. J. Thompson B. Van Bonn S. C. Vernes D. Villar H. V. Vinters M. C. Wallingford N. Wang R. K. Wayne G. S. Wilkinson C. K. Williams R. W. Williams X. W. Yang B. G. Young B. Zhang Z. Zhang P. Zhao Y. Zhao J. Zimmermann W. Zhou J. Ernst K. Raj S. Horvath Universal DNA methylation age across mammalian tissues. bioRxiv 2021.01.18.426733 [ Preprint ]. 19 January 2021. https://doi.org/10.1101/2021.01.18.426733. · DOI
  27. 10.1038/s41598-019-39919-3 · DOI · Paper page
  28. 10.1186/s13059-016-1068-z · DOI
  29. 10.1101/gr.180273.114 · DOI · Paper page
  30. 10.1101/gad.283838.116 · DOI
  31. 10.1089/scd.2012.0256 · DOI
  32. 10.1186/s13059-014-0545-5 · DOI
  33. 10.1038/s41586-019-1825-8 · DOI
  34. A. Trapp C. Kerepesi V. N. Gladyshev Profiling epigenetic age in single cells. bioRxiv 2021.03.13.435247 [ Preprint ]. 15 March 2021. https://doi.org/10.1101/2021.03.13.435247. · DOI
  35. 10.1016/S0092-8674(00)81656-6 · DOI · Paper page
  36. 10.1038/s41467-020-16919-w · DOI
  37. 10.1038/ncb1664 · DOI
  38. 10.1016/j.cell.2014.04.017 · DOI
  39. 10.1126/science.1245316 · DOI · Paper page
  40. B. Zhang, V. N. Gladyshev, How can aging be reversed? Exploring rejuvenation from a damage-based perspective. Adv. Genet. 1, e10025 (2020). · DOI
  41. 10.1111/j.1439-0418.2008.01356.x · DOI
  42. 10.1016/j.semcdb.2014.06.010 · DOI
  43. 10.1007/978-1-4939-7481-8_33 · DOI
  44. 10.1016/j.celrep.2019.11.091 · DOI
  45. 10.1186/s13148-017-0315-9 · DOI
  46. 10.1038/nature10960 · DOI
  47. 10.1038/s41556-018-0093-4 · DOI
  48. 10.1038/s41467-019-12664-x · DOI
  49. 10.1038/s41586-020-2119-x · DOI
  50. 10.1111/acel.12738 · DOI
  51. 10.1093/bioinformatics/btr167 · DOI
  52. 10.1038/nature13581 · DOI
  53. 10.1371/journal.pgen.1005583 · DOI
  54. 10.1016/j.stem.2012.02.013 · DOI
  55. 10.1186/s13072-016-0054-8 · DOI
  56. 10.1016/j.scr.2012.09.002 · DOI
  57. 10.1038/nmeth.2255 · DOI · Paper page
  58. L. Zimmerlin, T. S. Park, J. S. Huo, K. Verma, S. R. Pather, C. C. Talbot, J. Agarwal, D. Steppan, Y. W. Zhang, M. Considine, H. Guo, X. Zhong, C. Gutierrez, L. Cope, M. V. Canto-Soler, A. D. Friedman, S. B. Baylin, E. T. Zambidis, Tankyrase inhibition promotes a stable human naïve pluripotent state with improved functionality. Development 143, 4368–4380 (2016). · DOI
  59. 10.1016/j.scr.2013.11.010 · DOI
  60. 10.1073/pnas.1413299111 · DOI

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Metadata

Authors
Kerepesi, C., Zhang, B., Lee, S.-G., Trapp, A., & Gladyshev, V. N.
Affiliations
Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA.
Venue
Science Advances
Published
2021-06-25
Volume
7
Issue
26
ISSN
2375-2548
Cited by
130
References
18
Language
en
Funders
  • National Institutes of Health (AG064223)
  • National Institute on Aging (AG067782, AG065403)

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