Abstract

Histone 3 K4 trimethylation (depositing H3K4me3 marks) is typically associated with active promoters yet paradoxically occurs at untranscribed domains. Research to delineate the mechanisms of targeting H3K4 methyltransferases is ongoing. The oocyte provides an attractive system to investigate these mechanisms, because extensive H3K4me3 acquisition occurs in nondividing cells. We developed low-input chromatin immunoprecipitation to interrogate H3K4me3, H3K27ac and H3K27me3 marks throughout oogenesis. In nongrowing oocytes, H3K4me3 was restricted to active promoters, but as oogenesis progressed, H3K4me3 accumulated in a transcription-independent manner and was targeted to intergenic regions, putative enhancers and silent H3K27me3-marked promoters. Ablation of the H3K4 methyltransferase gene Mll2 resulted in loss of transcription-independent H3K4 trimethylation but had limited effects on transcription-coupled H3K4 trimethylation or gene expression. Deletion of Dnmt3a and Dnmt3b showed that DNA methylation protects regions from acquiring H3K4me3. Our findings reveal two independent mechanisms of targeting H3K4me3 to genomic elements, with MLL2 recruited to unmethylated CpG-rich regions independently of transcription.

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References

  1. 1.

    Shen, Y. et al. A map of the cis-regulatory sequences in the mouse genome. Nature 488, 116–120 (2012).

  2. 2.

    Zhou, L. Q. & Dean, J. Reprogramming the genome to totipotency in mouse embryos. Trends Cell Biol. 25, 82–91 (2015).

  3. 3.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  4. 4.

    Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

  5. 5.

    Guenther, M. G., Levine, S. S., Boyer, L. A., Jaenisch, R. & Young, R. A. A chromatin landmark and transcription initiation at most promoters in human cells. Cell 130, 77–88 (2007).

  6. 6.

    Ruthenburg, A. J., Allis, C. D. & Wysocka, J. Methylation of lysine 4 on histone H3: intricacy of writing and reading a single epigenetic mark. Mol. Cell 25, 15–30 (2007).

  7. 7.

    Howe, F. S., Fischl, H., Murray, S. C. & Mellor, J. Is H3K4me3 instructive for transcription activation? BioEssays 39, 1–12 (2017).

  8. 8.

    Bledau, A. S. et al. The H3K4 methyltransferase Setd1a is first required at the epiblast stage, whereas Setd1b becomes essential after gastrulation. Development 141, 1022–1035 (2014).

  9. 9.

    Ernst, P. et al. Definitive hematopoiesis requires the mixed-lineage leukemia gene. Dev. Cell 6, 437–443 (2004).

  10. 10.

    Lee, J. E. et al. H3K4 mono- and di-methyltransferase MLL4 is required for enhancer activation during cell differentiation. eLife 2, e01503 (2013).

  11. 11.

    Yagi, H. et al. Growth disturbance in fetal liver hematopoiesis of Mll-mutant mice. Blood 92, 108–117 (1998).

  12. 12.

    Glaser, S. et al. Multiple epigenetic maintenance factors implicated by the loss of Mll2 in mouse development. Development 133, 1423–1432 (2006).

  13. 13.

    Clouaire, T. et al. Cfp1 integrates both CpG content and gene activity for accurate H3K4me3 deposition in embryonic stem cells. Genes Dev. 26, 1714–1728 (2012).

  14. 14.

    Margaritis, T. et al. Two distinct repressive mechanisms for histone 3 lysine 4 methylation through promoting 3′-end antisense transcription. PLoS Genet. 8, e1002952 (2012).

  15. 15.

    Mikkelsen, T. S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007).

  16. 16.

    Bernstein, B. E. et al. A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 125, 315–326 (2006).

  17. 17.

    Thomson, J. P. et al. CpG islands influence chromatin structure via the CpG-binding protein Cfp1. Nature 464, 1082–1086 (2010).

  18. 18.

    Wachter, E. et al. Synthetic CpG islands reveal DNA sequence determinants of chromatin structure. eLife 3, e03397 (2014).

  19. 19.

    Reddington, J. P., Pennings, S. & Meehan, R. R. Non-canonical functions of the DNA methylome in gene regulation. Biochem. J. 451, 13–23 (2013).

  20. 20.

    Skalnik, D. G. The epigenetic regulator Cfp1. Biomol. Concepts 1, 325–334 (2010).

  21. 21.

    Ooi, S. K. et al. DNMT3L connects unmethylated lysine 4 of histone H3 to de novo methylation of DNA. Nature 448, 714–717 (2007).

  22. 22.

    Seisenberger, S. et al. The dynamics of genome-wide DNA methylation reprogramming in mouse primordial germ cells. Mol. Cell 48, 849–862 (2012).

  23. 23.

    Veselovska, L. et al. Deep sequencing and de novo assembly of the mouse oocyte transcriptome define the contribution of transcription to the DNA methylation landscape. Genome Biol. 16, 209 (2015).

  24. 24.

    Kobayashi, H. et al. Contribution of intragenic DNA methylation in mouse gametic DNA methylomes to establish oocyte-specific heritable marks. PLoS Genet. 8, e1002440 (2012).

  25. 25.

    Dahl, J. A. et al. Broad histone H3K4me3 domains in mouse oocytes modulate maternal-to-zygotic transition. Nature 537, 548–552 (2016).

  26. 26.

    Zhang, B. et al. Allelic reprogramming of the histone modification H3K4me3 in early mammalian development. Nature 537, 553–557 (2016).

  27. 27.

    Zheng, H. et al. Resetting epigenetic memory by reprogramming of histone modifications in mammals. Mol. Cell 63, 1066–1079 (2016).

  28. 28.

    Brind’Amour, J. et al. An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations. Nat. Commun. 6, 6033 (2015).

  29. 29.

    Taudt, A., Nguyen, M., Heinig, M., Johannes, F. & Colome-Tatche, M. chromstaR: tracking combinatorial chromatin state dynamics in space and time. Preprint at https://www.biorxiv.org/content/early/2016/02/04/038612 (2016).

  30. 30.

    Andreu-Vieyra, C. V. et al. MLL2 is required in oocytes for bulk histone 3 lysine 4 trimethylation and transcriptional silencing. PLoS Biol. 8, e1000453 (2010).

  31. 31.

    Denissov, S. et al. Mll2 is required for H3K4 trimethylation on bivalent promoters in embryonic stem cells, whereas Mll1 is redundant. Development 141, 526–537 (2014).

  32. 32.

    Hu, D. et al. Not all H3K4 methylations are created equal: Mll2/COMPASS dependency in primordial germ cell specification. Mol. Cell 65, 460–475.e6 (2017).

  33. 33.

    Lan, Z. J., Xu, X. & Cooney, A. J. Differential oocyte-specific expression of Cre recombinase activity in GDF-9-iCre, Zp3cre, and Msx2Cre transgenic mice. Biol. Reprod. 71, 1469–1474 (2004).

  34. 34.

    Kaneda, M. et al. Essential role for de novo DNA methyltransferase Dnmt3a in paternal and maternal imprinting. Nature 429, 900–903 (2004).

  35. 35.

    Mendenhall, E. M. et al. GC-rich sequence elements recruit PRC2 in mammalian ES cells. PLoS Genet. 6, e1001244 (2010).

  36. 36.

    Nashun, B. et al. Continuous histone replacement by Hira is essential for normal transcriptional regulation and de novo DNA methylation during mouse oogenesis. Mol. Cell 60, 611–625 (2015).

  37. 37.

    Lawinger, P., Rastelli, L., Zhao, Z. & Majumder, S. Lack of enhancer function in mammals is unique to oocytes and fertilized eggs. J. Biol. Chem. 274, 8002–8011 (1999).

  38. 38.

    Majumder, S., Zhao, Z., Kaneko, K. & DePamphilis, M. L. Developmental acquisition of enhancer function requires a unique coactivator activity. EMBO J. 16, 1721–1731 (1997).

  39. 39.

    Pinskaya, M., Gourvennec, S. & Morillon, A. H3 lysine 4 di- and tri-methylation deposited by cryptic transcription attenuates promoter activation. EMBO J. 28, 1697–1707 (2009).

  40. 40.

    Woo, H., Dam, Ha,S., Lee, S. B., Buratowski, S. & Kim, T. Modulation of gene expression dynamics by co-transcriptional histone methylations. Exp. Mol. Med. 49, e326 (2017).

  41. 41.

    Jones, P. A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484–492 (2012).

  42. 42.

    Yoder, J. A., Soman, N. S., Verdine, G. L. & Bestor, T. H. DNA (cytosine-5)-methyltransferases in mouse cells and tissues. Studies with a mechanism-based probe. J. Mol. Biol. 270, 385–395 (1997).

  43. 43.

    Neri, F. et al. Intragenic DNA methylation prevents spurious transcription initiation. Nature 543, 72–77 (2017).

  44. 44.

    Marks, H. et al. The transcriptional and epigenomic foundations of ground state pluripotency. Cell 149, 590–604 (2012).

  45. 45.

    Shirane, K. et al. Mouse oocyte methylomes at base resolution reveal genome-wide accumulation of non-CpG methylation and role of DNA methyltransferases. PLoS Genet. 9, e1003439 (2013).

  46. 46.

    Sachs, M. et al. Bivalent chromatin marks developmental regulatory genes in the mouse embryonic germline in vivo. Cell Rep. 3, 1777–1784 (2013).

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Acknowledgements

We thank K. Tabbada and C. Impey of the Babraham Next Generation Sequencing Facility, Babraham Institute, Cambridge, and A. Dahl and S. Reinhardt of the Deep Sequencing Group SFB 655, BIOTEC, Dresden, for their contribution to data generation. We thank H. Demond for help with sample collection; D. Spenserberger for the cultured ESCs; C. Novo and J. Ahringer for providing manuscript feedback; A. Segonds-Pichon for input into the statistical approach; and F. Krueger for contribution to sequencing QC and mapping at the Babraham Institute, Cambridge. C.W.H. and G.K. were supported by the UK Medical Research Council and Biotechnology and Biological Sciences Research Council (MR/K011332/1 and BB/J004499/1); A.F.S. and A.K. were supported by the Deutsche Forschungsgemeinschaft (KR2154/3-1 to A.K. and STE903/4-1 to A.F.S.).

Author information

Affiliations

  1. Epigenetics Programme, Babraham Institute, Cambridge, UK

    • Courtney W. Hanna
    • , Jiahao Huang
    • , Wendy Dean
    •  & Gavin Kelsey
  2. Centre for Trophoblast Research, University of Cambridge, Cambridge, UK

    • Courtney W. Hanna
    •  & Gavin Kelsey
  3. Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany

    • Aaron Taudt
    •  & Maria Colomé-Tatché
  4. European Research Institute for the Biology of Ageing, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    • Aaron Taudt
    •  & Maria Colomé-Tatché
  5. University of South Bohemia, Ceske Budejovice, Czech Republic

    • Lenka Gahurova
  6. Institute of Animal Physiology and Genetics, ASCR, Libechov, Czech Republic

    • Lenka Gahurova
  7. Biotechnology Center TU Dresden, Tatzberg, Germany

    • Andrea Kranz
    •  & A. Francis Stewart
  8. Bioinformatics Group, Babraham Institute, Cambridge, UK

    • Simon Andrews
  9. TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany

    • Maria Colomé-Tatché

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Contributions

C.W.H., W.D., A.F.S. and G.K. contributed to study design; C.W.H., J.H. and A.K. performed experiments; A.F.S. provided the Mll2 mouse model; G.K. supported the project development; C.W.H., A.T., M.C.-T., L.G. and S.A. analyzed data and generated figures; C.W.H., S.A., W.D., A.F.S. and G.K. contributed to data interpretation; C.W.H. wrote the manuscript with input from W.D., M.C.-T., A.F.S. and G.K.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to A. Francis Stewart or Gavin Kelsey.

Integrated supplementary information

  1. Supplementary Figure 1 Optimization of ULI-nChIP–seq.

    A) A snapshot of the SeqMonk browser that shows a chromosome view of enrichment for H3K4me3 in ESCs using the ULI-nChIP method compared to ENCODE data. Probes were 2kb running windows with 500bp step, quantitated as RPKM. B) A snapshot of the SeqMonk browser that shows a chromosome view of enrichment for titrated antibody for H3K27me3 and H3K27ac in GV oocytes. Probes were 5kb running windows with 500bp step, quantitated as RPKM. C) Cumulative distribution plot for titrated antibody for H3K27me3 and H3K27ac in GV oocytes. Probes were 5kb running windows with 500bp step, quantitated as RPKM. D) A snapshot from the SeqMonk browser showing enrichment for biological replicates of H3K4me3, H3K27me3 and H3K27ac in pools of 250 GV oocytes from 25-day old C57BL/6Babr mice. Probes were 500bp running windows with 100bp step, quantitated as RPKM. Chromatin states were called using chromstaR combinatorial peak calling approach with 1kb bin size (FDR<0.0001).

  2. Supplementary Figure 2 Optimization of H3K4me3 ULI-nChIP–seq in NGOs.

    A) Cumulative distribution plot for H3K4me3 ChIP-seq in d5 NGOs, d10 GOs, d15 GV and d25 GV oocytes compared to pooled input controls (left). Cumulative distribution plot for publically available H3K4me3 ChIP-seq in d7, d10 and d14 growing oocytes, 8-week GV oocytes1 compared to pooled input controls from this study (right). Probes were 5kb running windows, quantitated as RPKM. B) A snapshot of the SeqMonk browser that shows a chromosome view of enrichment for titrated antibody for H3K4me3 in d5 NGOs compared to published d7 GOs1. Probes were 500bp running windows with 100bp step, quantitated as RPKM. 1. Zhang, B. et al. Allelic reprogramming of the histone modification H3K4me3 in early mammalian development. Nature 537, 553-557 (2016).

  3. Supplementary Figure 3 Characterizing bivalency in oogenesis.

    A) Venn diagram shows the number of bivalent domains that are common between d25 GV oocytes, d5 NGOs and E11.5 PGCs. B) Pie charts show the proportion of bivalent peaks that overlap with CGI promoters, non-CGI promoter, orphan CGIs and distal regions called in PGCs, GV oocytes or that were common between PGCs and GV oocytes (p<0.0001, Chi-Square). C) SeqMonk screenshot shows enrichment for H3K4me3 and H3K27me3 in d25 GV oocytes. Chromatin states were called using chromstaR combinatorial peak calling approach with 1kb bin size (FDR<0.0001). Bivalent CGI promoters are highlighted in the grey shaded box. D) The top 30 results from gene ontology analysis for common PGC and GV oocyte bivalent domains, based on the closest TSS (within 5kb). E) Histogram of the distribution of GV oocyte transcript expression observed among genes with an unmethylated CGI (N=16,663) or non-CGI (N=44,324) promoter based on overlap with GV oocyte combinatorial states. Source data for panel d, e are available online.

  4. Supplementary Figure 4 Loss of transcription-independent H3K4 trimethylation in Mll2 GV oocytes.

    A) Hierarchical clustering of biological replicates for H3K4me3 ChIP-seq in d5, d10, d15, d25, Mll2 WT and Mll2 KO oocytes, based on quantitation of 5kb running windows (RPKM). Amount of antibody used for each ChIP-seq replicate is denoted in brackets. B) Bar chart shows the number of H3K4me3 peaks in the Mll2 KO and WT that overlap CGI promoters, non-CGI promoters, orphan CGIs, and distal genomic regions (p<0.0001). C) Aligned read plots show the density of reads for H3K4me3 across GV bivalent domains compared to active promoters, defined by H3K27ac enrichment, throughout oogenesis and in Mll2 KO and WT oocytes. D) Hierarchical clustering of biological replicates and pooled replicate sets for Mll2 KO and WT oocyte RNA-seq at annotated oocyte transcripts1. E) The heatmap shows the normalised RPKM for the significantly differentially expressed genes between Mll2 KO and WT oocytes. F) Correlation between gene expression in Mll2 KO and WT oocytes of genes associated with a promoters that lost an H3K4me3 peak in the Mll2 KO (p<2.2E-16). Gene annotation comprised of the oocyte transcripts1 and canonical genes, as the majority of associated genes are not in the oocyte transcriptome due to low expression levels. Source data for panel a, d are available online. 1. Veselovska, L. et al. Deep sequencing and de novo assembly of the mouse oocyte transcriptome define the contribution of transcription to the DNA methylation landscape. Genome Biol. 16, 209-015-0769-z (2015).

  5. Supplementary Figure 5 Profiling differential DNA methylation in Mll2-KO oocytes.

    A) Hierarchical clustering of biological replicates for DNA methylation in Mll2 KO and WT GV oocytes, based on quantitation of 100-CpG running windows using probes with a minimum of 1 read covering 20 CpGs. B) The heatmap shows the percentage methylation for each 100-CpG probe that was differentially methylated between biological replicates of Mll2 KO and WT oocytes. C) Pie charts show the proportion of hypermethylated and hypomethylated 100-CpG probes that overlap promoters, gene bodies, and distal regions (p<0.0001, Chi-Square). D) Gene expression for transcripts overlapping hypermethylated and hypomethylated 100-CpG probes is shown for Mll2 KO and WT oocytes. Mean expression levels were compared using a Mann-Whitney U-test. E) Relative read distribution for H3K4me3 in Mll2 KO and WT oocytes compared to pooled input controls, across Mll2 KO hypermethylated (N=1828) and hypomethylated (N=3503) DMRs, comprising merged adjacent 100-CpG probes within 500bp. F) SeqMonk screenshot shows DNA methylation across a hypomethylated DMR (merged adjacent 100-CpG windows, within 500bp distance) between Mll2 KO and WT oocytes. Mean DNA methylation across biological replicates is shown, with error bars depicting the standard deviation. H3K4me3 enrichment is shown below for Mll2 KO and WT oocytes using running windows of 500bp with 100bp step and normalised RPKM. Grey shaded boxes depict the location of DMRs. Source data for panel a, d are available online.

  6. Supplementary Figure 6 Profiling DNA methylation and H3K4 trimethylation in Dnmt3a/b-DKO oocytes.

    A) Global CpG methylation and percent methylation at domains normally fully methylated in GV oocytes1 is shown for biological replicates of Dnmt3a/b DKO and WT oocytes. Methylation data was quantitated in 10% inputs of samples used for H3K4me3 ChIP-seq experiments. Methylation for GV methylated domains was quantitated if the probe had a minimum of 1 read depth for 20 CpGs. B) Hierarchical clustering of biological replicates for H3K4me3 ChIP-seq in Dnmt3a/b DKO and WT oocytes compared to pooled input controls, based on quantitation of 5kb running windows (RPKM). Source data for panel a, b are available online. 1. Veselovska, L. et al. Deep sequencing and de novo assembly of the mouse oocyte transcriptome define the contribution of transcription to the DNA methylation landscape. Genome Biol. 16, 209-015-0769-z (2015).

  7. Supplementary Figure 7 Association between histone modifications and sequence composition in GV oocytes.

    A) The scatterplots show the enrichment for H3K4me3 and H3K27me3 in d25 GV oocytes in 5kb running windows within the unmethylated regions of the oocyte genome. The blue and red boxes represent the subset of probes used to evaluate sequence composition between: (top) domains with high H3K4me3 and H3K27me3 (bivalent domains; blue) and those domains that are enriched for neither (red), and (bottom) domains enriched only for H3K4me3 (blue) or H3K27me3 (red). The corresponding heatmaps show the relative enrichment for dinucleotide composition, and how well it clusters the selected chromatin state domains. B) The boxplots show the distribution of H3K4me3 enrichment in Dnmt3a/b DKO oocytes by increasing CpG enrichment among 5kb probes within nascent chromatin domains (left), defined as probes that fell within GV methylated domains, and randomly throughout the genome (right).

Supplementary information

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    Source Data, Supplementary Fig. 5a,d

  7. Source Data, Figure S6

    Source Data, Supplementary Fig. 6a,b

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https://doi.org/10.1038/s41594-017-0013-5

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