DNA methylation entropy as a measure of stem cell replication and aging.
Aging
Cell division
DNA methylation
Epigenetic clock
Stem cell
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
16 02 2023
16 02 2023
Historique:
received:
29
03
2022
accepted:
05
02
2023
entrez:
16
2
2023
pubmed:
17
2
2023
medline:
22
2
2023
Statut:
epublish
Résumé
Epigenetic marks are encoded by DNA methylation and accumulate errors as organisms age. This drift correlates with lifespan, but the biology of how this occurs is still unexplained. We analyze DNA methylation with age in mouse intestinal stem cells and compare them to nonstem cells. Age-related changes in DNA methylation are identical in stem and nonstem cells, affect most prominently CpG islands and correlate weakly with gene expression. Age-related DNA methylation entropy, measured by the Jensen-Shannon Distribution, affects up to 25% of the detectable CpG sites and is a better measure of aging than individual CpG methylation. We analyze this entropy as a function of age in seven other tissues (heart, kidney, skeletal muscle, lung, liver, spleen, and blood) and it correlates strikingly with tissue-specific stem cell division rates. Thus, DNA methylation drift and increased entropy with age are primarily caused by and are sensors for, stem cell replication in adult tissues. These data have implications for the mechanisms of tissue-specific functional declines with aging and for the development of DNA-methylation-based biological clocks.
Sections du résumé
BACKGROUND
Epigenetic marks are encoded by DNA methylation and accumulate errors as organisms age. This drift correlates with lifespan, but the biology of how this occurs is still unexplained. We analyze DNA methylation with age in mouse intestinal stem cells and compare them to nonstem cells.
RESULTS
Age-related changes in DNA methylation are identical in stem and nonstem cells, affect most prominently CpG islands and correlate weakly with gene expression. Age-related DNA methylation entropy, measured by the Jensen-Shannon Distribution, affects up to 25% of the detectable CpG sites and is a better measure of aging than individual CpG methylation. We analyze this entropy as a function of age in seven other tissues (heart, kidney, skeletal muscle, lung, liver, spleen, and blood) and it correlates strikingly with tissue-specific stem cell division rates. Thus, DNA methylation drift and increased entropy with age are primarily caused by and are sensors for, stem cell replication in adult tissues.
CONCLUSIONS
These data have implications for the mechanisms of tissue-specific functional declines with aging and for the development of DNA-methylation-based biological clocks.
Identifiants
pubmed: 36797759
doi: 10.1186/s13059-023-02866-4
pii: 10.1186/s13059-023-02866-4
pmc: PMC9933260
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
27Subventions
Organisme : NCI NIH HHS
ID : P50 CA254897
Pays : United States
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2023. The Author(s).
Références
López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The Hallmarks of Aging. Cell. 2013;153(6):1194–217.
doi: 10.1016/j.cell.2013.05.039
pubmed: 23746838
pmcid: 3836174
Issa J-P. Aging, DNA methylation and cancer. Crit Rev Oncol Hematol. 1999;32(1):31–43.
doi: 10.1016/S1040-8428(99)00019-0
pubmed: 10586353
Fuke C, Shimabukuro M, Petronis A, Sugimoto J, Oda T, Miura K, et al. Age related changes in 5-methylcytosine content in human peripheral leukocytes and placentas: an HPLC-based study. Ann Hum Genet. 2004;68(Pt 3):196–204.
doi: 10.1046/j.1529-8817.2004.00081.x
pubmed: 15180700
Issa J-PJ, Ottaviano Y, Celano P, Hamilton SR, Davidson NE, Baylin SB. Methylation of the oestrogen receptor CpG island links ageing and neoplasia in human colon. Nature Genetics. 1994;7(4):536–40.
doi: 10.1038/ng0894-536
pubmed: 7951326
Bjornsson HT. Intra-individual Change Over Time in DNA Methylation With Familial Clustering. JAMA. 2008;299(24):2877.
doi: 10.1001/jama.299.24.2877
pubmed: 18577732
pmcid: 2581898
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
pubmed: 19150625
Zhang Z, Deng C, Lu Q, Richardson B. Age-dependent DNA methylation changes in the ITGAL (CD11a) promoter. Mech Ageing Dev. 2002;123(9):1257–68.
doi: 10.1016/S0047-6374(02)00014-3
pubmed: 12020947
Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, et al. Widespread and tissue specific age-related DNA methylation changes in mice. Genome Res. 2010;20(3):332–40.
doi: 10.1101/gr.096826.109
pubmed: 20107151
pmcid: 2840983
Hernandez DG, Nalls MA, Gibbs JR, Arepalli S, Van Der Brug M, Chong S, et al. Distinct DNA methylation changes highly correlated with chronological age in the human brain. Hum Mol Genet. 2011;20(6):1164–72.
doi: 10.1093/hmg/ddq561
pubmed: 21216877
pmcid: 3043665
Pal S, Tyler JK. Epigenetics and aging. Science. Advances. 2016;2(7):e1600584.
Issa J-P. Aging and epigenetic drift: a vicious cycle. J Clin Investig. 2014;124(1):24–9.
doi: 10.1172/JCI69735
pubmed: 24382386
pmcid: 3871228
Maegawa S, Lu Y, Tahara T, Lee JT, Madzo J, Liang S, et al. Caloric restriction delays age-related methylation drift. Nature Communications. 2017;8(1):539.
doi: 10.1038/s41467-017-00607-3
pubmed: 28912502
pmcid: 5599616
Pirazzini C, Giuliani C, Bacalini MG, Boattini A, Capri M, Fontanesi E, et al. Space/Population and Time/Age in DNA methylation variability in humans: a study on IGF2/H19 locus in different Italian populations and in mono- and di-zygotic twins of different age. Aging. 2012;4(7):509–20.
doi: 10.18632/aging.100476
pubmed: 22879348
pmcid: 3433936
Christiansen L, Lenart A, Tan Q, Vaupel JW, Aviv A, Mcgue M, et al. DNA methylation age is associated with mortality in a longitudinal Danish twin study. Aging Cell. 2016;15(1):149–54.
Roos L, Spector TD, Bell CG. Using epigenomic studies in monozygotic twins to improve our understanding of cancer. Epigenomics. 2014;6(3):299–309.
Shibata D. Inferring human stem cell behaviour from epigenetic drift. J Pathol. 2009;217(2):199–205.
doi: 10.2217/epi.14.13
pubmed: 25111484
Aran D, Camarda R, Odegaard J, Paik H, Oskotsky B, Krings G, et al. Comprehensive analysis of normal adjacent to tumor transcriptomes. Nature Commun. 2017;8(1):1077.
doi: 10.1002/path.2461
pubmed: 19031430
pmcid: 4156515
Russi, Calice, Ruggieri, Laurino, Rocca, Amendola, et al. Gastric Normal Adjacent Mucosa Versus Healthy and Cancer Tissues: Distinctive Transcriptomic Profiles and Biological Features. Cancers. 2019;11(9):1248.
doi: 10.1038/s41467-017-01027-z
Panjarian S, Madzo J, Keith K, Slater CM, Sapienza C, Jelinek J, et al. Accelerated aging in normal breast tissue of women with breast cancer. Breast Cancer Res. 2021;23(1):58.
Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 2019;20(1):249.
doi: 10.1186/s13058-021-01434-7
pubmed: 34022936
pmcid: 8140515
Gross AM, Kreisberg JF, Ideker T. Analysis of Matched Tumor and Normal Profiles Reveals Common Transcriptional and Epigenetic Signals Shared across Cancer Types. PLoS ONE. 2015;10(11):e0142618.
doi: 10.1186/s13059-019-1824-y
pubmed: 31767039
pmcid: 6876109
Li S, Garrett-Bakelman F, Perl AE, Luger SM, Zhang C, To BL. Dynamic evolution of clonal epialleles revealed by methclone. Genome Biol. 2014;15(9):472.
doi: 10.1371/journal.pone.0142618
pubmed: 26555223
pmcid: 4640835
Jenkinson G, Abante J, Feinberg AP, Goutsias J. An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data. BMC Bioinformatics. 2018;19(1):87.
doi: 10.1186/s13059-014-0472-5
pubmed: 25260792
pmcid: 4242486
E HJ, C GA. Digestion and absorption in the gastrointestinal tract: Saunders/Elsevier; 2011.
doi: 10.1186/s12859-018-2086-5
pubmed: 29514626
pmcid: 5842653
Sun X, Han Y, Zhou L, Chen E, Lu B, Liu Y, et al. A comprehensive evaluation of alignment software for reduced representation bisulfite sequencing data. Bioinformatics. 2018;34(16):2715–23.
Landan G, Cohen NM, Mukamel Z, Bar A, Molchadsky A, Brosh R, et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat Genet. 2012;44(11):1207–14.
doi: 10.1093/bioinformatics/bty174
pubmed: 29579198
Jenkinson G, Pujadas E, Goutsias J, Feinberg AP. Potential energy landscapes identify the information-theoretic nature of the epigenome. Nat Genet. 2017;49(5):719–29.
doi: 10.1038/ng.2442
pubmed: 23064413
Gehart H, Clevers H. Tales from the crypt: new insights into intestinal stem cells. Nat Rev Gastroenterol Hepatol. 2019;16(1):19–34.
doi: 10.1038/ng.3811
pubmed: 28346445
pmcid: 5565269
Bell CG, Beck S. Advances in the identification and analysis of allele-specific expression. Genome Medicine. 2009;1(5):56.
doi: 10.1038/s41575-018-0081-y
pubmed: 30429586
Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates. Mol Cell. 2013;49(2):359–67.
doi: 10.1186/gm56
pubmed: 19490587
pmcid: 2689448
Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.
doi: 10.1016/j.molcel.2012.10.016
pubmed: 23177740
Thompson MJ, Vonholdt B, Horvath S, Pellegrini M. An epigenetic aging clock for dogs and wolves. Aging. 2017;9(3):1055–68.
doi: 10.1186/gb-2013-14-10-r115
pubmed: 24138928
pmcid: 4015143
Marioni RE, Suderman M, Chen BH, Horvath S, Bandinelli S, Morris T, et al. Tracking the Epigenetic Clock Across the Human Life Course: A Meta-analysis of Longitudinal Cohort Data. The Journals of Gerontology: Series A. 2019;74(1):57–61.
doi: 10.18632/aging.101211
pubmed: 28373601
pmcid: 5391218
Weidner C, Lin Q, Koch C, Eisele L, Beier F, Ziegler P, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15(2):R24.
doi: 10.1093/gerona/gly060
W Z, T H, B B, O M, W I, SM L, et al. DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse. Cell Genomics. 2022;2(7).
doi: 10.1186/gb-2014-15-2-r24
pubmed: 24490752
pmcid: 4053864
Wang H-Q, Tuominen LK, Tsai C-J. SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures. Bioinformatics. 2011;27(2):225–31.
Vaidya H, Jeong HS, Keith K, Maegawa S, Calendo G, Madzo J, et al. DNA methylation entropy as a measure of stem cell replication and aging. NCBI. Gene Expression Omnibus. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213723 . 2022.
doi: 10.1093/bioinformatics/btq650
pubmed: 21098430