Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

TETs compete with DNMT3 activity in pluripotent cells at thousands of methylated somatic enhancers

Nature Genetics volume 52, pages 819–827 (2020)Cite this article

Subjects

Abstract

Mammalian cells stably maintain high levels of DNA methylation despite expressing both positive (DNMT3A/B) and negative (TET1-3) regulators. Here, we analyzed the independent and combined effects of these regulators on the DNA methylation landscape using a panel of knockout human embryonic stem cell (ESC) lines. The greatest impact on global methylation levels was observed in DNMT3-deficient cells, including reproducible focal demethylation at thousands of normally methylated loci. Demethylation depends on TET expression and occurs only when both DNMT3s are absent. Dynamic loci are enriched for hydroxymethylcytosine and overlap with subsets of putative somatic enhancers that are methylated in ESCs and can be activated upon differentiation. We observe similar dynamics in mouse ESCs that were less frequent in epiblast stem cells (EpiSCs) and scarce in somatic tissues, suggesting a conserved pluripotency-linked mechanism. Taken together, our data reveal tightly regulated competition between DNMT3s and TETs at thousands of somatic regulatory sequences within pluripotent cells.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Focal demethylation by TET enzymes in the absence of DNMT3s.
Fig. 2: Re-expression of TET enzymes demethylates cDKO-DMRs in PKO cells.
Fig. 3: TETs demethylate LINEs and somatic enhancers following DNMT3 loss.
Fig. 4: Targeted demethylation in the absence of DNMT3 appears conserved in mice.
Fig. 5: TET enzymes also have widespread global activity.

Similar content being viewed by others

Data availability

Data have been deposited in the Gene Expression Omnibus under accession GSE126958. Other published datasets used in this study include: HUES64 DNMT3A single-knockout ESCs (GSM1545005), DNMT3B single-knockout ESCs (GSM1545006) and mouse WT ESCs (GSM2339908). Source data for Extended Data Figs. 1, 6 and 8 are provided.

References

  1. Jadhav, U. et al. Extensive recovery of embryonic enhancer and gene memory stored in hypomethylated enhancer DNA. Mol. Cell 74, 542–554.e545 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhou, W. et al. DNA methylation loss in late-replicating domains is linked to mitotic cell division. Nat. Genet. 50, 591–602 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Jin, Z. & Liu, Y. DNA methylation in human diseases. Genes Dis. 5, 1–8 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Klutstein, M., Nejman, D., Greenfield, R. & Cedar, H. DNA methylation in cancer and aging. Cancer Res. 76, 3446–3450 (2016).

    CAS  PubMed  Google Scholar 

  5. Gangisetty, O., Cabrera, M. A. & Murugan, S. Impact of epigenetics in aging and age related neurodegenerative diseases. Front. Biosci. (Landmark Ed.) 23, 1445–1464 (2018).

    CAS  Google Scholar 

  6. Ziller, M. J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Tahiliani, M. et al. Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324, 930–935 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Ito, S. et al. Role of Tet proteins in 5mC to 5hmC conversion, ES-cell self-renewal and inner cell mass specification. Nature 466, 1129–1133 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ito, S. et al. Tet proteins can convert 5-methylcytosine to 5-formylcytosine and 5-carboxylcytosine. Science 333, 1300–1303 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. He, Y. F. et al. Tet-mediated formation of 5-carboxylcytosine and its excision by TDG in mammalian DNA. Science 333, 1303–1307 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Maiti, A. & Drohat, A. C. Thymine DNA glycosylase can rapidly excise 5-formylcytosine and 5-carboxylcytosine: potential implications for active demethylation of CpG sites. J. Biol. Chem. 286, 35334–35338 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Smith, Z. D. & Meissner, A. DNA methylation: roles in mammalian development. Nat. Rev. Genet. 14, 204–220 (2013).

    CAS  PubMed  Google Scholar 

  13. Li, E., Bestor, T. H. & Jaenisch, R. Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 69, 915–926 (1992).

    CAS  PubMed  Google Scholar 

  14. Okano, M., Bell, D. W., Haber, D. A. & Li, E. DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99, 247–257 (1999).

    CAS  PubMed  Google Scholar 

  15. Gu, T. P. et al. The role of Tet3 DNA dioxygenase in epigenetic reprogramming by oocytes. Nature 477, 606–610 (2011).

    CAS  PubMed  Google Scholar 

  16. Dawlaty, M. M. et al. Combined deficiency of Tet1 and Tet2 causes epigenetic abnormalities but is compatible with postnatal development. Dev. Cell 24, 310–323 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Baubec, T. et al. Genomic profiling of DNA methyltransferases reveals a role for DNMT3B in genic methylation. Nature 520, 243–247 (2015).

    CAS  PubMed  Google Scholar 

  18. Liao, J. et al. Targeted disruption of DNMT1, DNMT3A and DNMT3B in human embryonic stem cells. Nat. Genet. 47, 469–478 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Manzo, M. et al. Isoform-specific localization of DNMT3A regulates DNA methylation fidelity at bivalent CpG islands. EMBO J. 36, 3421–3434 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Jeong, M. et al. Large conserved domains of low DNA methylation maintained by Dnmt3a. Nat. Genet. 46, 17–23 (2014).

    CAS  PubMed  Google Scholar 

  21. Verma, N. et al. TET proteins safeguard bivalent promoters from de novo methylation in human embryonic stem cells. Nat. Genet. 50, 83–95 (2018).

    CAS  PubMed  Google Scholar 

  22. Wu, H. et al. Dual functions of Tet1 in transcriptional regulation in mouse embryonic stem cells. Nature 473, 389–393 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Lu, F., Liu, Y., Jiang, L., Yamaguchi, S. & Zhang, Y. Role of Tet proteins in enhancer activity and telomere elongation. Genes Dev. 28, 2103–2119 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Wang, L. et al. TET2 coactivates gene expression through demethylation of enhancers. Sci. Adv. 4, eaau6986 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Hon, GaryC. et al. 5mC oxidation by Tet2 modulates enhancer activity and timing of transcriptome reprogramming during differentiation. Mol. Cell 56, 286–297 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Huang, Y. et al. Distinct roles of the methylcytosine oxidases Tet1 and Tet2 in mouse embryonic stem cells. Proc. Natl Acad. Sci. USA 111, 1361–1366 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Xu, Y. et al. Genome-wide regulation of 5hmC, 5mC, and gene expression by Tet1 hydroxylase in mouse embryonic stem Cells. Mol. Cell 42, 451–464 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Ko, M. et al. Modulation of TET2 expression and 5-methylcytosine oxidation by the CXXC domain protein IDAX. Nature 497, 122–126 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhang, W. et al. Isoform switch of TET1 regulates DNA demethylation and mouse development. Mol. Cell 64, 1062–1073 (2016).

    CAS  PubMed  Google Scholar 

  30. de la Rica, L. et al. TET-dependent regulation of retrotransposable elements in mouse embryonic stem cells. Genome Biol. 17, 234 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. Rinaldi, L. et al. Dnmt3a and Dnmt3b associate with enhancers to regulate human epidermal stem cell homeostasis. Cell Stem Cell 19, 491–501 (2016).

    CAS  PubMed  Google Scholar 

  32. Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    CAS  PubMed  Google Scholar 

  33. Alvarez-Dominguez, J. R. et al. Circadian entrainment triggers maturation of human in vitro islets. Cell Stem Cell 26, 108–122.e110 (2020).

    CAS  PubMed  Google Scholar 

  34. Tsankov, A. M. et al. Transcription factor binding dynamics during human ES cell differentiation. Nature 518, 344–349 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Guallar, D. et al. RNA-dependent chromatin targeting of TET2 for endogenous retrovirus control in pluripotent stem cells. Nat. Genet. 50, 443–451 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–220 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Cheng, Y. et al. Principles of regulatory information conservation between mouse and human. Nature 515, 371–375 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Huntriss, J. et al. Expression of mRNAs for DNA methyltransferases and methyl-CpG-binding proteins in the human female germ line, preimplantation embryos, and embryonic stem cells. Mol. Reprod. Dev. 67, 323–336 (2004).

    CAS  PubMed  Google Scholar 

  39. Gordon, C. A., Hartono, S. R. & Chédin, F. Inactive DNMT3B splice variants modulate de novo DNA methylation. PLoS ONE 8, e69486 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Ziller, M. J. et al. Dissecting the functional consequences of de novo DNA methylation dynamics in human motor neuron differentiation and physiology. Cell Stem Cell 22, 559–574.e559 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. López-Moyado, I. F. et al. Paradoxical association of TET loss of function with genome-wide DNA hypomethylation. Proc. Natl Acad. Sci. USA 116, 16933–16942 (2019).

    PubMed  PubMed Central  Google Scholar 

  42. Rulands, S. et al. Genome-scale oscillations in DNA methylation during exit from pluripotency. Cell Syst. 7, 63–76.e12 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Nakagawa, T. et al. CRL4(VprBP) E3 ligase promotes monoubiquitylation and chromatin binding of TET dioxygenases. Mol. Cell 57, 247–260 (2015).

    CAS  PubMed  Google Scholar 

  44. Yu, C. et al. CRL4 complex regulates mammalian oocyte survival and reprogramming by activation of TET proteins. Science 342, 1518–1521 (2013).

    CAS  PubMed  Google Scholar 

  45. Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl Acad. Sci. USA 107, 21931–21936 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Nguyen, M. L. T., Jones, S. A., Prier, J. E. & Russ, B. E. Transcriptional enhancers in the regulation of T cell differentiation. Front. Immunol. 6, 462 https://doi.org/10.3389/fimmu.2015.00462 (2015).

  47. Rada-Iglesias, A. et al. A unique chromatin signature uncovers early developmental enhancers in humans. Nature 470, 279–283 (2010).

    PubMed  PubMed Central  Google Scholar 

  48. Pauklin, S., Madrigal, P., Bertero, A. & Vallier, L. Initiation of stem cell differentiation involves cell cycle-dependent regulation of developmental genes by cyclin D. Genes Dev. 30, 421–433 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Yiangou, L. et al. Cell cycle regulators control mesoderm specification in human pluripotent stem cells. J. Biol. Chem. 294, 17903–17914 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Yin, Y. et al. Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356, eaaj2239 (2017).

  51. Walter, M., Teissandier, A., Pérez-Palacios, R. & Bourc’his, D. An epigenetic switch ensures transposon repression upon dynamic loss of DNA methylation in embryonic stem cells. eLife 5, e11418 (2016).

    PubMed  PubMed Central  Google Scholar 

  52. Kunarso, G. et al. Transposable elements have rewired the core regulatory network of human embryonic stem cells. Nat. Genet. 42, 631–634 (2010).

    CAS  PubMed  Google Scholar 

  53. Schmidt, D. et al. Waves of retrotransposon expansion remodel genome organization and CTCF binding in multiple mammalian lineages. Cell 148, 335–348 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Spencer, D. H. et al. CpG island hypermethylation mediated by DNMT3A is a consequence of AML progression. Cell 168, 801–816.e813 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Cirulli, E. T. et al. Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways. Science 347, 1436–1441 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Greber, B. et al. Conserved and divergent roles of FGF signaling in mouse epiblast stem cells and human embryonic stem cells. Cell Stem Cell 6, 215–226 (2010).

    CAS  PubMed  Google Scholar 

  57. Giesselmann, P., Hetzel, S., Müller, F.-J., Meissner, A. & Kretzmer, H. Nanopype: a modular and scalable nanopore data processing pipeline. Bioinformatics 35, 4536 (2019).

    PubMed  Google Scholar 

  58. Sedlazeck, F. J. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods 15, 461–468 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Simpson, J. T. et al. Detecting DNA cytosine methylation using nanopore sequencing. Nat. Methods 14, 407–410 (2017).

    CAS  PubMed  Google Scholar 

  60. Galonska, C. et al. Genome-wide tracking of dCas9-methyltransferase footprints. Nat. Commun. 9, 597 (2018).

    PubMed  PubMed Central  Google Scholar 

  61. Zhao, L. et al. The dynamics of DNA methylation fidelity during mouse embryonic stem cell self-renewal and differentiation. Genome Res. 24, 1296–1307 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Sun, M.-A., Velmurugan, K. R., Keimig, D. & Xie, H. HBS-tools for hairpin bisulfite sequencing data processing and analysis. Adv. Bioinformatics 2015, 760423–760423 (2015).

    PubMed  PubMed Central  Google Scholar 

  63. Illingworth, R. et al. A novel CpG island set identifies tissue-specific methylation at developmental gene loci. PLoS Biol. 6, e22 (2008).

    PubMed  PubMed Central  Google Scholar 

  64. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Meissner laboratory for their support, in particular H. Kretzmer and S. Grosswendt for critical reading of the manuscript as well as R. Karnik for initial processing of the mouse Dnmt3a knockout data. We also thank D. Huangfu and her team for providing the TKO and QKO HUES8 cells. We also acknowledge the Max Planck Institute for Molecular Genetics Sequencing Core. E.K. is a Les Turner ALS Research Center Investigator and a New York Stem Cell Foundation–Robertson Investigator, and part of this work was supported by NIH grant no. NINDS/NIA R01NS104219. A.G. and A.M. are supported by NIGMS grant no. P01GM099117 and A.M. is supported by the Max Planck Society.

Author information

Authors and Affiliations

  1. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Jocelyn Charlton, Eunmi J. Jung, Alexandra L. Mattei, Jing Liao, Zachary D. Smith & Alexander Meissner

  2. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jocelyn Charlton, Elena K. Stamenova, Andreas Gnirke, Zachary D. Smith & Alexander Meissner

  3. Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany

    Jocelyn Charlton, Alexandra L. Mattei, Nina Bailly, Pay Giesselmann, Björn Brändl, Franz-Josef Müller & Alexander Meissner

  4. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

    Alexandra L. Mattei

  5. Institute of Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany

    Nina Bailly & Alexander Meissner

  6. The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Eric J. Martin & Evangelos Kiskinis

  7. Department of Physiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Eric J. Martin & Evangelos Kiskinis

  8. Zentrum für Integrative Psychiatrie gGmbH, Universitätsklinikum Schleswig-Holstein, Kiel, Germany

    Franz-Josef Müller

Authors
  1. Jocelyn Charlton
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eunmi J. Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexandra L. Mattei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nina Bailly
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eric J. Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pay Giesselmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Björn Brändl
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Elena K. Stamenova
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Franz-Josef Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Evangelos Kiskinis
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Andreas Gnirke
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zachary D. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Alexander Meissner
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.C. and A.M. designed the study. J.C. performed data processing and bioinformatic analyses. E.J.J. generated the human ESC knockouts, cultured cells and collected samples. A.L.M. generated the mouse ESC and EpiSC knockouts and performed Nanopore sequencing together with B.B. J.L. generated the Dnmt3a knockout mice. E.J.M. and E.K. differentiated, cultured and collected human motor neurons. N.B. generated the TET rescue constructs. E.K.S. Z.D.S. and A.G. performed library preparation and sequencing. P.G. processed the Nanopore data and F.-J.M. cosupervised the Nanopore work. J.C., Z.D.S. and A.M. interpreted the data and wrote the manuscript.

Corresponding author

Correspondence to Alexander Meissner.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data

Extended Data Fig. 1 Characterization of pentuple knockout ESCs.

a, Sequencing data showing the mutations introduced into the DKO and PKO ESCs for DNMT3A. Green box = PAM sequence, grey box = sgRNA sequence. Codons are colored black or blue, grey text denotes non-coding DNA. The respective amino acid (aa) single letter name and sequence number is displayed below the DNA. b, Sequencing data for DNMT3B, as described in a. A different sgRNA was used to target DNMT3B in HUES8 ESCs compared to HUES64. c, Western blot for DNMT3A in HUES8 WT, HUES64 DKO clone A, HUES8 TKO, QKO and PKO cells. GAPDH is shown as the loading control. Full blots in Source Data Fig. 1. PKO cells were assessed for DNMT3A expression by Western blot six times with consistent results. d, HUES8 lines at 10X magnification showing that KO cell lines appear morphologically normal. These images are representative of a full 10 cm culture dish over weeks of passaging. e, Heatmap showing RNA sequencing data as log2 TPM expression values for a set of pluripotency and self-renewal associated genes (highly expressed), as well as a subset of differentiation associated genes (low expression). DKO-A was collected at passage 6 and PKO at passage 12. f, Heatmap showing RNA sequencing data as log2 TPM expression values DNMTs and TETs, grey boxes depict genes that are knocked out in each sample. g, Comparison of global methylation levels between HUES64 and HUES8 wildtype cells where intensity of blue shading indicates density of data points. The Pearson correlation coefficient (cor.) is displayed.

Source data

Extended Data Fig. 2 Characterization of DKO-DMRs and TET rescue in PKO cells.

a, Bar chart showing the proportion of CpGs with varying methylation loss in DKO cells compared to WT. CpGs that lost >60% methylation were generally located in closer proximity to one another compared to CpGs that lost 10–20% methylation.b, The proportion of cDKO-DMRs with respective mean methylation levels in single DNMT3A−/− (3AKO) or DNMT3B−/− (3BKO) knockout ESCs. c, Composite plot showing methylation difference between passage (P) 20 and 6 in PKO cells as distance from the cDKO-DMR center. There is a small background loss (−0.045) with a slightly greater focal decrease (−0.07), but methylation levels stay generally high. d, The methylation level in HUES64 DKO-A, HUES64 WT and HUES8 TKO cells including 2 kb from each boundary. For regions where the neighboring 2 kb is hypomethylated in WT cells (class 1), almost every region shows an increase in methylation following TET loss. e, Violin plots showing methylation at TKO-DMRs across different HUES8 lines. We used stringent parameters to define TKO-DMRs that aberrantly gain methylation upon loss of TET expression (also described later in Extended Data Fig. 9a). Methylation levels were rescued by re-expression of TET. Violin plots extend from the data minima to the maxima, white dot indicates median, thick bar shows the interquartile range and thin bar shows 1.5x interquartile range. f, Methylation levels across 1 kb tiles for PKO ESCs rescued with either TET1s or TET2. Intensity of blue shading indicates the relative density of data points. Pearson correlation coefficient (cor) is displayed. g, ChIP-seq (fragments per bp, per peak) enrichment (from ref. 21) for TET1 and DNMT3B over CpG islands (CGI), H1-specific enhancers and cDKO-DMRs. h, Representative genome browser tracks displaying methylation levels in WT and DKO cells as well as 5hmC levels in WT ESCs. Increased 5hmC is observed over the cDKO-DMR and at the border of the hypomethylated CGI, where TETs are known to localize.

Extended Data Fig. 3 LINE 5’UTRs are actively demethylated.

a, Methylation composite plots for WT and DKO-A cells over LINE L1 elements (oriented with 5’UTR to the left). L1Hs are the youngest and L1PA4 the oldest class displayed. 5’UTR methylation of younger elements appears more sensitive to DNMT3 loss. In particular, sensitivity appears associated with ZNF93 binding site loss within the 5’UTR. b, Smoothed scatter plots display the relationship between WT or DKO methylation level and CpG density within the 5’UTR, for all LINE L1 elements where intensity of blue shading indicates density of data points. Lower CpG density elements tend to retain methylation after DNMT3 loss while CpG dense 5’UTRs lose methylation. c, Histogram of read lengths generated from Nanopore sequencing for HUES64 WT cells. Top = all reads, bottom = reads < 100 kb. d, Example of CpG methylation across a group 4 L1Hs element (defined in Figure 3c) showing the focal decrease within the 5’UTR. Below, just the 5’UTR is displayed and CpGs with the most methylation loss following DNMT3 knockout are highlighted with a blue box. e, Each L1Hs 5’UTR was aligned to the consensus sequence. The CpG locations within this sequence are illustrated by vertical blue lines. Below, a summary for genetic variants at each position is shown for each group of L1Hs. Colored boxes display the proportion of bases that vary from the consensus sequence. For each group, nine example 5’UTR sequences are displayed for the region containing the six CpGs highlighted in d. Each element is represented as a grey bar with colored letters indicating variations from the consensus sequence (shown at the bottom of the panel). None of the groups exhibit a unique common variant. f, Density plots showing the distribution of 5’UTR GC content and CpG density for each group of L1Hs.

Extended Data Fig. 4 cDKO-DMRs overlap with tissue-specific enhancers.

a, TET1 enrichment (from ref. 21) for LINE 5’UTRs and active H1 enhancers. b, A region within the gene NCOR2 that has many cDKO-DMRs. These lose methylation in different somatic tissues as shown below. H3K27ac and H3K4me1 tracks display ENCODE data derived from H1 ESCs. Methylation tracks are from ref. 6. c, The percentage of overlap with putative somatic enhancers (defined in ref. 32) for class 1 and class 2 cDKO-DMRs, an equal number of same-sized randomly selected regions, H3K4me1 peaks in ESCs (H1) and 1 kb tiles with matched CpG density to cDKO-DMRs (3.1–3.3%). d, B cell enhancers (defined in ref. 32) separated by methylation levels in WT ESCs, DKO ESCs and B cells. e, Composites showing methylation levels in passage (P) 3 and 28 DKO ESCs for P3 and P28 DKO-DMRs. f, For 86 different previously defined putative enhancer sets32, the stacked bar plots display the proportion that are already hypomethylated in WT ESCs, lose methylation in DKO cells (WT – DKO difference > 0.2) or remain methylated in DKO cells. For this analysis, P28 DKO was used. g, Schematic of in vitro pancreatic islet cell differentiation (from ref. 33). h, The proportion of cDKO-DMRs or P28 DKO-DMRs that overlap with each set of cell type specific enhancers. Enhancers were previously defined33. i, For regions defined as showing dynamic methylation changes during differentiation to beta islet cells33, methylation levels are shown for these cell types as well as HUES64 WT and DKO-A ESCs.

Extended Data Fig. 5 Chromatin states and transcription factor interaction at cDKO-DMRs.

a, CpG density for randomly selected background regions, cDKO-DMRs and CGIs. b, For 1 kb tiles with CpG density matched to cDKO-DMRs (3.1–3.3%), the mean methylation in HUES64 WT and DKO-A is shown. Methylation loss is not dependent on CpG density. c, ATAC-seq signal enrichment (fragments per bp, per peak) for cDKO-DMRs. d, Enrichment for chromatin modifications (ENCODE, H1 ESCs; fragments per bp, per peak) as distance from cDKO-DMRs (fragments per bp, per peak). e, A representative locus showing enrichment of H3K4me1 and 5hmC at a cDKO-DMR. P = passage. f, Methylation levels for ENCODE-defined H3K4me1 peaks (≥3 CpGs, methylation ≥0.1 in WT ESCs, n = 71,665) for WT and DKO cells at P28. Intensity of blue shading indicates the relative density of data points. Many regions remain methylated indicating that H3K4me1 enrichment does not predict demethylation. g, For cDKO-DMRs that overlap with putative somatic enhancers, the proportion that are H3K4me1 and/or H3K27me3 enriched is displayed. Overlap with both marks indicates a poised chromatin state that is more common for already hypomethylated poised enhancers. h, ChIP-seq enrichment (ENCODE, H1 ESCs; fragments per bp, per peak) for different enhancer-associated factors at cDKO-DMRs and active ESC enhancers. i, HAND1 (GSM1505812) and GATA4 (GSM1505644) enrichment in HUES64 differentiated mesoderm (ref. 34) for cDKO-DMRs that show H3K27ac (GSM1505669) enrichment in mesoderm (indicating an active enhancer-like chromatin state). j, Browser tracks displaying regions that lose methylation while gaining H3K27ac and transcription factor (HAND1) binding upon differentiation to mesoderm. The same CpGs also lose methylation in DKO ESCs. ChIP-seq data is from ref. 34. k, Enrichment for TF binding (ENCODE, H1 ESCs) at cDKO-DMRs, open DNA (based on HUES64 ATAC-seq) and active ESC enhancers is displayed. The most enriched factors at cDKO-DMRs (CTBP2, E2F6 and GABPA) show only minimal enrichment that could be confounded by the more open chromatin at cDKO-DMRs. No factor showed unique enrichment that would support their role as candidate factors for TET/DNMT recruitment. l, The percentage of cDKO-DMRs that overlap with different TF binding sites, defined by ChIP-seq peak data (ref. 34). Overlap is consistently low. m, Expression levels for differentially expressed genes between WT and DKO with examples to the right. P = passage. Each P28 DKO-DMR was assigned to its nearest gene, and those with a DKO-DMR assigned are highlighted with the blue bar. n, The distribution of absolute distance to the nearest gene for P28 DKO-DMRs. ‘DEG-linked’ refers to DKO-DMRs that have been assigned to a differentially expressed gene by proximity. Statistics refer to a 2-sided T-test. For the box plots: median is shown in bold, box displays interquartile range, whiskers extend to 1.5x the interquartile range.

Extended Data Fig. 6 Generation of DNMT3A and 3B knockout mouse EpiSCs.

a, Sequencing data showing the Dnmt3a and Dnmt3b mutations introduced into mouse DKO EpiSCs. Two different sgRNAs were used for each gene. Green box = PAM sequence, grey box = sgRNA sequence. Codons are colored black or blue, grey text denotes non-coding DNA. The respective single letter amino acid (aa) and protein sequence number is displayed above the DNA. b, Western blots showing protein levels for DNMT1, DNMT3A and DNMT3B in WT and DKO EpiSCs. PCNA is used as a loading control. Full blots in Source Data Fig. 2. Western blots were performed twice with consistent results. c, Violin plots display methylation levels for cDKO-DMRs and EpiSC DKO-DMRs in WT and DKO cells for each species. The UCSC LiftOver tool was used to map DKO-DMRs between species. Violin plots extend from the data minima to the maxima with the white dot indicating median, thick bar showing the interquartile range and thin bar showing 1.5x interquartile range. d, Schematic depicting how orthologous genes shared between human and mouse were assigned to the nearest DKO-DMR (left). Each gene was given a number (in kb) denoting the distance to the nearest DKO-DMR either up- or down-stream. Box plots for genes that were binned based in their distance to the nearest DKO-DMR in EpiSCs (shown in legend key), displaying the distance to the nearest DKO-DMR in human ESCs (right). A matched trend between distances in mouse and human cells in evident. For the box plots: the median is shown in bold, the box displays interquartile range and whiskers extend to 1.5 times the interquartile range. e, Venn diagram showing the overlap of orthologous genes that have a DKO-DMR within the gene body (0 kb away, as shown in panel d) in mouse EpiSCs and human ESCs. This overlap was highly significant (hypergeometric test).

Source data

Extended Data Fig. 7 DNMT and TET expression in somatic tissues.

a, Heat map showing Log2 RPKM for DNMTs and TETs across human somatic tissues, based on data from the human protein atlas (ebi.ac.uk). b, Heat map showing Log2 transcript counts per cell for murine somatic tissues, taken from single cell mouse atlas data.

Extended Data Fig. 8 DKO-DMRs are associated with pluripotency.

a, Sequence mutations introduced into the DKO ESCs. Green box = PAM sequence, grey box = sgRNA. Codons are colored black or blue, grey text denotes non-coding DNA. Amino acid (aa) and protein sequence number are displayed above the DNA. b, Western blots for WT, DNMT3A−/− (3AKO), DNMT3B−/− (3BKO) and double knockout (DKO) mouse ESCs. GAPDH or H3 were used as loading controls. Full blots in Source Data Fig. 3. Western blots were performed three times with consistent results. c, Overlap between DKO-DMRs called in each Dnmt3a−/− tissue. d, The difference in methylation between WT and Dnmt3a−/− tissues, ESCs or EpiSCs. (e) Methylation levels for EpiSC DKO-DMRs in EpiSCs and ESCs. Violin plots extend from the data minima to the maxima, white dot indicates median, thick bar shows the interquartile range and thin bar shows 1.5x interquartile range. f, Methylation levels for tissue-specific DKO-DMRs in WT and Dnmt3a−/− E6.5 epiblast and 8 day old tissues (Dnmt3a−/− E6.5 epiblast data from GSE137337). Somatic DKO-DMRs were fully methylated in Dnmt3a−/− embryos following implantation due to DNMT3B expression, hence must have lost methylation at a later stage. Violin plots extend from the data minima to the maxima, white dot indicates median, thick bar shows the interquartile range and thin bar shows 1.5x interquartile range. g, The number of DKO-DMRs identified when using a reduced stringency of 0.25 differential methylation instead of 0.6 to compensate for mixed cell types. We identified more DKO-DMRs (1,186-2,182) but still many less than in ESCs, and this may include more false positives. h, The percentage of DKO-DMRs that fall into class 1 or 2 (described in Figure 1). i, The overlap of DKO-DMRs with genomic features. Categories are not exclusive. CGI = CpG island, TSS = transcription start site. j, Schematic showing the differentiation of human ESCs to motor neurons (MNs) via neuronal progenitor cells (NPCs). For WT and 3AKO ESCs expression of DNMT3A and DNMT3B is displayed across the time course. After day two, 3AKO cells are ‘DKO-like’ as they do not express either DNMT3. k, The percentage of cDKO-DMRs with mean methylation <0.2 in each sample during differentiation. Only 1.3% and 2.4% of cDKO-DMRs lost methylation for WT and 3AKO MNs respectively. l, Expression of TET1-3 in WT and 3AKO ESCs during differentiation. WGBS and RNA-seq data for k and i are from ref. 39.

Source data

Extended Data Fig. 9 Identification of DMRs across human ESCs.

a, Schematic illustrating how DMR calling was performed between each pair of samples. The number of DMRs that gain methylation (Incr.) in the cell type shown to the right is located above the arrow, and the number of DMRs that lose methylation (Decr.) is shown below the arrow. As we identified only few regions for other comparisons that met the same criteria as for DKO-DMRs (stringent DMRs; black) we also used less stringent criteria (Δ > 0.25) to define DMRs (blue). P = passage. b, CpG methylation of TKO-, QKO- and PKO-DMRs detected with lower stringency as defined in panel a. The n is lower than shown in panel a due to drop-out from requiring 10x coverage per region. c, Location of the DMRs shown in b. Features are not exclusive. Enh. = enhancer, CGI = CpG island, TSS = transcription start site, IG = intergenic. As expected and previously reported in ref. 21, the majority of DMRs that occur in the absence of TETs were CGI promoters or shores of developmental genes that may be more susceptible to hypermethylation due to lack of transcriptional activity. d, Low stringency TKO-DMRs are grouped by their methylation status in QKO and PKO cells. Of the TKO-DMRs, 23% lost the aberrantly gained methylation upon KO of DNMT3B (in QKO), 21% only upon further loss of DNMT3A (PKO) and 56% remained highly methylated despite loss of both DNMT3s, pointing to the importance of TET activity in maintaining these regions in a hypomethylated. e, The difference in methylation over passaging across each genomic feature. f, The location of DMRs identified between PKO at P6 and P20. Categories are not exclusive. g, Loess-smoothed regression for methylation levels across a representative early-replicating region, where methylation in DKO cells decreases the most, (left) and a representative late-replicating region, where PKO cells show the strongest methylation loss (right).

Extended Data Fig. 10 Broad TET activity results in genome-wide 5hmC enrichment and methylation heterogeneity.

a, Mean 5hmC level in HUES64 differentiated motor neurons (MN) over gene bodies grouped into categories according to their expression status, d = day. The median is shown in bold, the box displays interquartile range and whiskers extend to 1.5x the interquartile range. b, The difference (d60 – d16) in 5hmC vs. methylation for MNs at different genomic features. CGI = CpG island, TSS = transcription start site, IG = intergenic. c, Transcript expression for genes involved in TET-mediated demethylation and base excision repair pathways. d, For genomic features, the mean difference in methylation between ESCs is displayed. e, Cell counts for HUES8 WT and TKO cells over 5d culture. Center points show the mean and error bars display the standard deviation for three independent cell culture replicates. f, Schematic showing how stochastic turnover of cytosine modification can lead to intercellular heterogeneity. mC = methylcytosine (black circle), 5hmC = hydroxymethylcytosine (grey circle), C = unmethylated cytosine (white circle). g, Representative browser tracks showing the methylation status for CpGs detected using hairpin bisulfite sequencing (Hairpin-BS; top) and WGBS (bottom). Each CpG covered by hairpin-BS is colored according to dyad methylation state. h, Proportion of CpG dyads that were methylated, unmethylated or hemi-methylated in arrested HUES64 WT or PKO ESCs. i, For regions enriched with different chromatin marks (ENCODE, H1 ESCs), the proportion of CpG dyads with each methylation status is displayed. j, The percentage of CpG dyads that were hemi-methylated according to location within each genomic feature. TE = active typical enhancer. k, Single cell RRBS data showing the percentage of unmethylated CpGs per cell. The violin plot extends from the data minima to the maxima where each dot is a single cell. Of CpGs that are normally methylated in bulk WGBS (≥ 0.9), we observed that 6.6% ±3.37 showed zero methylation in individual cells. These may have been targeted by TETs and are likely an underestimate as 5hmC cannot be discriminated from 5mC with our RRBS based approach. l, The proportion of CpGs with zero, intermediate (not exactly zero or one) or one methylation status for arrested WT ESCs. In bulk populations, ESC arrest removes replication-associated heterogeneity and increases the proportion of CpGs that are reported as fully methylated. However, ∼25% remain intermediate. The density plot shows methylation loss for intermediate and fully methylated CpGs. Intermediate CpGs show a greater loss, suggesting they may be more frequently targeted by TETs. Hence TET activity may contribute to intermediate methylation levels in bulk populations.

Supplementary information

Source data

Source Data Extended Data Fig. 1

Unprocessed western blots.

Source Data Extended Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 8

Unprocessed western blots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Charlton, J., Jung, E.J., Mattei, A.L. et al. TETs compete with DNMT3 activity in pluripotent cells at thousands of methylated somatic enhancers. Nat Genet 52, 819–827 (2020). https://doi.org/10.1038/s41588-020-0639-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-020-0639-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing