DNA methylation loss in late-replicating domains is linked to mitotic cell division

Wanding Zhou, Huy Q. Dinh, Zachary Ramjan, Daniel J. Weisenberger, Charles M. Nicolet, Hui Shen*, Peter W. Laird, Benjamin P. Berman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

176 Scopus citations


DNA methylation loss occurs frequently in cancer genomes, primarily within lamina-associated, late-replicating regions termed partially methylated domains (PMDs). We profiled 39 diverse primary tumors and 8 matched adjacent tissues using whole-genome bisulfite sequencing (WGBS) and analyzed them alongside 343 additional human and 206 mouse WGBS datasets. We identified a local CpG sequence context associated with preferential hypomethylation in PMDs. Analysis of CpGs in this context ('solo-WCGWs') identified previously undetected PMD hypomethylation in almost all healthy tissue types. PMD hypomethylation increased with age, beginning during fetal development, and appeared to track the accumulation of cell divisions. In cancer, PMD hypomethylation depth correlated with somatic mutation density and cell cycle gene expression, consistent with its reflection of mitotic history and suggesting its application as a mitotic clock. We propose that late replication leads to lifelong progressive methylation loss, which acts as a biomarker for cellular aging and which may contribute to oncogenesis.

Original languageAmerican English
Pages (from-to)591-602
Number of pages12
JournalNature Genetics
Issue number4
StatePublished - 1 Apr 2018
Externally publishedYes

Bibliographical note

Funding Information:
We thank T. Hinoue and H. Noushmehr for help selecting TCGA WGBS samples based on analysis of other TCGA data types and H. Goodridge and D.-C. Lin for useful discussions and comments. We thank The Cancer Genome Atlas Research program office, especially K. Shaw, for helping to get the WGBS project up and running. We also thank the members of the TCGA Research Network along with the dozens of other research groups that generated the published datasets that were used here to gain new insights. This project was supported by the Van Andel Research Institute, the Cedars-Sinai Center for Bioinformatics and Functional Genomics and the Samuel Oschin Comprehensive Cancer Institute, and the University of Southern California USC Epigenome Center. The work was funded by the following grants: National Institutes of Health/National Cancer Institute grants U24 CA143882 (P.W.L., B.P.B., H.Q.D., and H.S.); R01 CA170550 (P.W.L.); U01 CA184826 (B.P.B.); U24 CA210969 (P.W.L., B.P.B., and H.S.), Ovarian Cancer Research Fund Grant 373933 (H.S.), and National Institutes of Health/National Human Genome Research Institute grant R01 HG006705 (B.P.B.).

Publisher Copyright:
© 2018 The Author(s).


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