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Charting a dynamic DNA methylation landscape of the human genome

Nature volume 500, pages 477–481 (2013)Cite this article

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Abstract

DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development1,2. Most cell types, except germ cells and pre-implantation embryos3,4,5, display relatively stable DNA methylation patterns, with 70–80% of all CpGs being methylated6. Despite recent advances, we still have a limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in-depth analysis of 42 whole-genome bisulphite sequencing data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, most of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription-factor-binding sites, which allow identification of key lineage-specific regulators. In addition, differentially methylated regions (DMRs) often contain single nucleotide polymorphisms associated with cell-type-related diseases as determined by genome-wide association studies. The results also highlight the general inefficiency of whole-genome bisulphite sequencing, as 70–80% of the sequencing reads across these data sets provided little or no relevant information about CpG methylation. To demonstrate further the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results suggest that only a fraction does so as part of coordinated regulatory programs. Therefore, our selected DMRs can serve as a starting point to guide new, more effective reduced representation approaches to capture the most informative fraction of CpGs, as well as further pinpoint putative regulatory elements.

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Figure 1: Identification and characteristics of DMRs in the human genome.
Figure 2: Dynamic CpG methylation regions frequently co-localize with TFBSs.
Figure 3: DMRs exhibit increased SNP frequency and show non-random GWAS SNP enrichment.
Figure 4: Effective classification and sample deconvolution using only the DMR set.

Accession codes

Accessions

Gene Expression Omnibus

Data deposits

WGBS data are deposited at the Gene Expression Omnibus (see Supplementary Table 1 for the specific accession numbers). Supplementary Table 2 is available under GEO accession number GSE46644.

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Acknowledgements

We would like to thank K. Clement, P. Samavarchi-Tehrani, Z. Smith, M. Chan and R. Karnik for discussions and feedback. We would also like to thank F. Kelley, T. Durham, C. Epstein, N. Shoresh, G. Lauwers and the Massachusetts General Hospital tissue repository for assisting in sample and data management. E.D.R. is supported by the National Institutes of Health (NIH) Roadmap Epigenomics Project (ES017690). D.A.B. is supported by NIH grants P30AG10161, R01AG17917, R01AG15819 and R01AG36042. A.M. is supported by the Pew Charitable Trusts and is a New York Stem Cell Foundation, Robertson Investigator. This work was funded by NIH grants (U01ES017155 and P01GM099117) and The New York Stem Cell Foundation.

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  1. Fabian Müller

    Present address: Present address: Max Planck Institute for Informatics, 66123 Saarbrücken, Germany.,

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, 02142, Massachusetts, USA

    Michael J. Ziller, Hongcang Gu, Julie Donaghey, Philip L. De Jager, Evan D. Rosen, Bradley E. Bernstein, Andreas Gnirke & Alexander Meissner

  2. Harvard Stem Cell Institute, Cambridge, 02138, Massachusetts, USA

    Michael J. Ziller, Julie Donaghey & Alexander Meissner

  3. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, 02138, Massachusetts, USA

    Michael J. Ziller, Fabian Müller, Julie Donaghey & Alexander Meissner

  4. Division of Endocrinology, Beth Israel Deaconess Medical Center, Boston, 02215, Massachusetts, USA

    Linus T.-Y. Tsai & Evan D. Rosen

  5. Applied Bioinformatics, Center for Bioinformatics and Quantitative Biology Center, University of Tübingen, 72074 Tübingen, Germany,

    Oliver Kohlbacher

  6. Departments of Neurology and Psychiatry, Program in Translational NeuroPsychiatric Genomics, Institute for the Neurosciences, Brigham and Women’s Hospital, 77 Avenue Louis Pasteur, NRB168, Boston, Massachusetts 02115, USA,

    Philip L. De Jager

  7. Rush Alzheimer’s Disease Center, Rush University Medical Center, 600 South Paulina Street, Chicago, Illinois 60612, USA,

    David A. Bennett

  8. Department of Pathology, Massachusetts General Hospital, 185 Cambridge Street, Boston, Massachusetts 02114, USA,

    Bradley E. Bernstein

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Contributions

M.J.Z. and A.M. conceived the study and interpreted the results. M.J.Z. designed the statistical framework, analysis strategy and analysed the data. H.G. performed in-house WGBS library production, F.M. contributed bioinformatics tools and J.D. performed cell culture experiments. L.T.-Y.T. and E.D.R. provided adipocyte nuclei for WGBS profiling, and P.L.D. and D.A.B. made adult brain and Alzheimer’s disease samples available. O.K. provided support on analysis strategy and statistical methods. B.E.B. and A.M. organized samples as part of the NIH Roadmap Epigenomics Project. H.G., A.G. and A.M. established the WGBS at the Broad Institute. A.M. supervised the project. M.Z. and A.M. wrote the paper with assistance from the other authors.

Corresponding author

Correspondence to Alexander Meissner.

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Competing interests

M.J.Z. and A.M. declare competing financial interests owing to the filing of a patent application on the selected regions.

Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-5, legends for Supplementary Tables 1-3 (see separate files for Supplementary Tables 1 and 3 and for Supplementary Table 2, see link in main paper), Supplementary Methods and additional references. (PDF 486 kb)

Supplementary Table 1

This file contains a summary of data sets, accession numbers and quality measures for all WGBS libraries use in this study. (XLSX 13 kb)

Supplementary Table 3

This file contains Motif enrichment results for cell type specific hypomethylated regions. (XLSX 240 kb)

Supplementary Data

This file contains the data associated with this paper. (ZIP 103 kb)

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Ziller, M., Gu, H., Müller, F. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013). https://doi.org/10.1038/nature12433

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