Letter | Published:

Epigenetic restriction of extraembryonic lineages mirrors the somatic transition to cancer

Nature volume 549, pages 543547 (28 September 2017) | Download Citation

Abstract

In mammals, the canonical somatic DNA methylation landscape is established upon specification of the embryo proper and subsequently disrupted within many cancer types1,2,3,4. However, the underlying mechanisms that direct this genome-scale transformation remain elusive, with no clear model for its systematic acquisition or potential developmental utility5,6. Here, we analysed global remethylation from the mouse preimplantation embryo into the early epiblast and extraembryonic ectoderm. We show that these two states acquire highly divergent genomic distributions with substantial disruption of bimodal, CpG density-dependent methylation in the placental progenitor7,8. The extraembryonic epigenome includes specific de novo methylation at hundreds of embryonically protected CpG island promoters, particularly those that are associated with key developmental regulators and are orthologously methylated across most human cancer types9. Our data suggest that the evolutionary innovation of extraembryonic tissues may have required co-option of DNA methylation-based suppression as an alternative to regulation by Polycomb-group proteins, which coordinate embryonic germ-layer formation in response to extraembryonic cues10. Moreover, we establish that this decision is made deterministically, downstream of promiscuously used—and frequently oncogenic—signalling pathways, via a novel combination of epigenetic cofactors. Methylation of developmental gene promoters during tumorigenesis may therefore reflect the misappropriation of an innate trajectory and the spontaneous reacquisition of a latent, developmentally encoded epigenetic landscape.

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Acknowledgements

We thank members of the Meissner and Michor laboratories for discussions and advice, in particular R. Karnik for help with data processing and alignment, as well as B. E. Bernstein and R. P. Koche for their expertise. F.M. and J.S. gratefully acknowledge support from the Dana-Farber Cancer Institute Physical Sciences-Oncology Center (NIH U54CA193461). The work was funded by the New York Stem Cell Foundation, the Broad-ISF Partnership for Cell Circuit Research, the Starr Foundation, NIH grants (1P50HG006193, P01GM099117 and R01DA036898) and the Max Planck Society. A.M. is a New York Stem Cell Foundation Robertson Investigator.

Author information

Author notes

    • Alexander Meissner

    Present address: Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin 14195, Germany.

    • Zachary D. Smith
    •  & Jiantao Shi

    These authors contributed equally to this work.

    • Franziska Michor
    •  & Alexander Meissner

    These authors jointly supervised this work.

Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    • Zachary D. Smith
    • , Hongcang Gu
    • , Julie Donaghey
    • , Kendell Clement
    • , Davide Cacchiarelli
    • , Andreas Gnirke
    • , Franziska Michor
    •  & Alexander Meissner
  2. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA

    • Zachary D. Smith
    • , Julie Donaghey
    • , Kendell Clement
    • , Davide Cacchiarelli
    • , Franziska Michor
    •  & Alexander Meissner
  3. Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA

    • Zachary D. Smith
  4. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    • Jiantao Shi
    •  & Franziska Michor
  5. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA

    • Jiantao Shi
    •  & Franziska Michor
  6. Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USA

    • Kendell Clement

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Contributions

Z.D.S., J.S., F.M. and A.M. designed and conceived the study and prepared the manuscript. Z.D.S. performed all experiments and assisted in data analysis as performed by J.S. J.D. made the ATAC–seq libraries, D.C. made RNA-seq libraries, and H.G. made the dual RRBS and RNA-seq libraries with supervision from A.G. and alignment by K.C. F.M. and A.M. jointly supervised the work.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Franziska Michor or Alexander Meissner.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    De novo methylation of CGIs during extraembryonic development. Methylation status of CGIs in Epiblast and Extraembryonic Ectoderm (ExE), including designation of differential methylation status in ExE as described in the Methods (hyper, hypermethylated; hypo, hypomethylated; NC, no change; ND, insufficient measurements). Assignment to nearest gene and distance to the TSS are included.

  2. 2.

    Supplementary Table 2

    Promoter methylation and associated transcriptional dynamics during implantation are influenced by CGI methylation status. Methylation values for gene promoters (classified as the region +/– 1 kb of an annotated TSS), Log2 normalized TPM (Transcripts per Million) across late preimplantation and early postimplantation samples. Promoter methylation is reported if at least 5 CpGs are covered ≥5x. The 'Symbol' column identifies all annotated genes for a given promoter and the reported expression value is either the TPM of the associated gene or the mean TPM if multiple genes begin at the same TSS. 'CpGs' indicates the number of CpGs that exist within the promoter boundary.

  3. 3.

    Supplementary Table 3

    CGI methylation status for ICM outgrowths under defined conditions. CGI methylation status as measured by RRBS for ICM explanted under conditions of modulated FGF and WNT signaling. CGIs are assigned to their nearest TSS and those existing within +/– 2 kb were given the additional assignment of TSS-associated. DMR status indicates differential methylation between Epiblast and ExE from WGBS data, and PRC2 regulatory status is taken from Ref 67. We observe three discrete scenarios where CGIs are preferentially methylated: within the ExE, in the external portion of FGF+CHIR stimulated ICM outgrowths, and in FGF stimulated outgrowths. A CGI whose methylation status deviates by ≥0.1 from epiblast is scored as 'dynamic' and used to generate the heatmap in Fig. 2f.

  4. 4.

    Supplementary Table 4

    Promoter methylation status and transcriptional dynamics for ICM outgrowths under defined conditions.Promoter methylation and associated gene expression data of ICM outgrowth conditions as measured by dual RRBS and RNA-seq. Promoter methylation is reported if at least 5 CpGs are covered ≥5x. The 'Symbol' column identifies all annotated genes for a given promoter and the reported expression value is either the TPM of the associated gene or the mean TPM if multiple genes begin at the same TSS.

  5. 5.

    Supplementary Table 5

    CGIs methylation status for epigenetic regulator deficient E6.5 embryos. CGI methylation status as measured by RRBS for samples isolated from CRISPR/Cas9 injected embryos. CGIs are assigned to their nearest TSS and those existing within +/– 2 kb were given the additional assignment of TSS-associated. DMR status indicates differential methylation between Epiblast and ExE from WGBS data, and PRC2 regulatory status is taken from Ref 67. A CGI whose methylation status deviates by ≥0.1 from its wild type tissue is scored as 'dynamic' and is highlighted in Fig. 3d.

  6. 6.

    Supplementary Table 6

    Promoter methylation status and transcriptional dynamics for epigenetic regulator deficient E6.5 embryos. Promoter methylation and associated gene expression data of CRISPR/Cas9 targeted embryos as measured by dual RRBS and RNA-seq. Promoter methylation is reported if at least 5 CpGs are covered ≥5x. The 'Symbol' column identifies all annotated genes for a given promoter and the reported expression value is either the TPM of the associated gene or the mean TPM if multiple genes begin at the same TSS. In general, ExE Hyper CGIs are preferentially induced in both the Epiblast and ExE fraction of Eed targeted E6.5 embryos and de novo methylation of these regions in ExE is specifically blocked.

  7. 7.

    Supplementary Table 7

    Methylation status of ExE hypermethylated CGIs within human tissues, cancers, and cell lines. Mean and median methylation status of the 489 orthologously mapped CGIs that are called as ExE-hypermethylated in mouse across 107 ENCODE and Roadmap Initiative samples. Note, the lymphoblastoid cell line GM12878 is not characterized as cancer cell line within Encode but was generated using the Epstein-Barr Virus and scored as such in this study. Information includes designation as cancer versus normal as well as other assignments included in Extended Data Figure 9.

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https://doi.org/10.1038/nature23891

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