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Whole-genome fingerprint of the DNA methylome during human B cell differentiation

Nature Genetics volume 47, pages 746–756 (2015)Cite this article

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Abstract

We analyzed the DNA methylome of ten subpopulations spanning the entire B cell differentiation program by whole-genome bisulfite sequencing and high-density microarrays. We observed that non-CpG methylation disappeared upon B cell commitment, whereas CpG methylation changed extensively during B cell maturation, showing an accumulative pattern and affecting around 30% of all measured CpG sites. Early differentiation stages mainly displayed enhancer demethylation, which was associated with upregulation of key B cell transcription factors and affected multiple genes involved in B cell biology. Late differentiation stages, in contrast, showed extensive demethylation of heterochromatin and methylation gain at Polycomb-repressed areas, and genes with apparent functional impact in B cells were not affected. This signature, which has previously been linked to aging and cancer, was particularly widespread in mature cells with an extended lifespan. Comparing B cell neoplasms with their normal counterparts, we determined that they frequently acquire methylation changes in regions already undergoing dynamic methylation during normal B cell differentiation.

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Figure 1: Analysis of the DNA methylome of different B cell subpopulations by WGBS and microarray.
Figure 2: Non-CpG methylation detected during B cell differentiation.
Figure 3: Dynamic DNA methylation during B cell differentiation.
Figure 4: Association between DNA methylation and gene expression in different chromatin states.
Figure 5: DNA methylation changes during B cell differentiation in the context of cancer and aging.
Figure 6: DNA methylation changes in various B cell neoplasms in comparison to their normal counterparts.

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Acknowledgements

We thank C. López-Otín for critical reading of this manuscript, M. Dabad Castellà for his assistance with the WGBS data analysis and M.A. Peinado (Institute of Predictive and Personalized Medicine of Cancer, Barcelona) for providing RNA from HCT116 DKO cells. This work was funded by the European Union's Seventh Framework Programme through the Blueprint Consortium (grant agreement 282510) and the Spanish Ministry of Economy and Competitivity (MINECO; project SAF2009-08663). Methylation microarrays were outsourced to the Spanish Centro Nacional de Genotipado (CEGEN-ISCIII). We are indebted to the Genomics core facility of the Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS) for technical help. This work was partially developed at the Centro Esther Koplowitz (CEK; Barcelona, Spain). M.K. is supported by Agéncia de Gestió d'Ajuts Universitaris i de Recerca (AGAUR; Generalitat de Catalunya), E.C. is an Academia Researcher of the Institució Catalana de Recerca i Estudis Avançats and J.I.M.-S. is a Ramón y Cajal researcher of MINECO.

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Authors and Affiliations

  1. Department of Anatomic Pathology, Institut d'Investigacions Biomédiques August Pi i Sunyer (IDIBAPS), Pharmacology and Microbiology, University of Barcelona, Barcelona, Spain

    Marta Kulis, Ana C Queirós, Giancarlo Castellano, Renée Beekman, Guillem Clot, Néria Verdaguer-Dot, Martí Duran-Ferrer, Nuria Russiñol, Roser Vilarrasa-Blasi, Elías Campo & José I Martín-Subero

  2. Centro Nacional de Análisis Genómico (CNAG), Parc Científic de Barcelona, Barcelona, Spain

    Angelika Merkel, Simon Heath, Ronald P Schuyler, Emanuele Raineri, Anna Esteve, Martí Duran-Ferrer, Lidia Agueda, Julie Blanc, Marta Gut & Ivo G Gut

  3. Structural Biology and Biocomputing Program, Spanish National Cancer Research Centre (CNIO), Spanish National Bioinformatics Institute, Madrid, Spain

    Simone Ecker, Vera Pancaldi, Daniel Rico & Alfonso Valencia

  4. European Molecular Biology Laboratory–European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK

    David Richardson, Laura Clarke, Avik Datta & Paul Flicek

  5. Area de Oncología, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, Pamplona, Spain

    Marien Pascual, Xabier Agirre & Felipe Prosper

  6. Servicio de Hematología, Clínica Universidad de Navarra, Pamplona, Spain

    Felipe Prosper & Bruno Paiva

  7. Flow Cytometry Core, Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra, Pamplona, Spain

    Diego Alignani & Bruno Paiva

  8. Université de Rennes 1, INSERM U917, Hematology Laboratory, University Hospital of Rennes, Rennes, France

    Gersende Caron & Thierry Fest

  9. Blood Systems Research Institute, San Francisco, California, USA

    Marcus O Muench & Marina E Fomin

  10. Department of Laboratory Medicine, University of California, San Francisco, San Francisco, California, USA

    Marcus O Muench & Marina E Fomin

  11. Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea

    Seung-Tae Lee

  12. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA

    Joseph L Wiemels

  13. Molecular Biology, Nijmegen Centre for Molecular Life Sciences (NCMLS), Faculties of Science and Medicine, Radboud University, Nijmegen, the Netherlands

    Hendrik G Stunnenberg

  14. Institute of Human Genetics, Christian Albrechts University, Kiel, Germany

    Reiner Siebert

  15. Institute of Cell Biology (Cancer Research), Medical School, University of Duisburg-Essen, Essen, Germany

    Ralf Küppers

Authors
  1. Marta Kulis
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  2. Angelika Merkel
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  3. Simon Heath
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  40. Ivo G Gut
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  41. Elías Campo
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  42. José I Martín-Subero
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Contributions

M.K., A.C.Q., N.R., M.P., X.A., F.P., D.A., B.P., G. Caron, T.F., M.O.M., M.E.F., S.-T.L. and J.L.W. provided samples from healthy donors and/or purified B cell subpopulations. M.K., A.C.Q., G. Castellano, R.B. and G. Clot analyzed DNA methylation and gene expression arrays. L.A., J.B. and M.G. performed WGBS library preparation and sequencing. A.M., S.H., R.P.S., E.R., A.E. and M.D.-F. processed and analyzed WGBS data. M.K., N.V.-D. and R.V.-B. performed validation experiments. M.K., G. Castellano. S.E., V.P., D. Rico and A.V. functionally characterized dynamically methylated genes. D. Richardson, L.C., A.D. and P.F. were in charge of data management. I.G.G. and H.G.S. coordinated sequencing efforts and performed primary data analysis. H.G.S., R.S., R.K. and E.C. participated in the study design and data interpretation. J.I.M.-S. conceived the study. J.I.M.-S. led the experiments and wrote the manuscript with predominant assistance from M.K. and R.B.

Corresponding author

Correspondence to José I Martín-Subero.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–26 and Supplementary Tables 1–3. (PDF 2463 kb)

Supplementary Data Set 1

Annotation of CpGs belonging to each of the methylation modules detected by microarrays. (XLSX 7664 kb)

Supplementary Data Set 2

Enrichment analysis of transcription factor binding sites in the 20 DNA methylation modules. (XLSX 144 kb)

Supplementary Data Set 3

Enrichment analysis of transcription factor binding sites using differentially methylated CpGs identified by WGBS. (XLSX 61 kb)

Supplementary Data Set 4

Gene Ontology analysis of dynamic CpGs belonging to 20 main modules. (XLSX 46 kb)

Supplementary Data Set 5

Differential methylation analysis of B cell neoplasms and their normal cell counterparts. (XLSX 7982 kb)

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Kulis, M., Merkel, A., Heath, S. et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat Genet 47, 746–756 (2015). https://doi.org/10.1038/ng.3291

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