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:

Reprogramming of H3K36me2 guides lineage-specific post-implantation de novo DNA methylation

Nature Cell Biology volume 27, pages 2128–2142 (2025) Cite this article

Subjects

Abstract

In mammals, DNA methylation is re-established after implantation following post-fertilization global erasure. Yet, the underlying mechanism remains elusive. Here we investigate H3K36me2 reprogramming in mouse early development and its role in post-implantation DNA methylation re-establishment. In oocytes, H3K36me2 accumulates in gene bodies upon transcription silencing and partially persists to the eight-cell stage. De novo H3K36me2 occurs at enhancers after zygotic genome activation, before spreading genome-wide after implantation, except on the inactive X chromosome. Mutation of the H3K36me2 methyltransferase NSD1 compromises global DNA methylation after implantation preferentially in extra-embryonic lineages and that at methylation-prone promoters, including those of germline-specific genes. However, DNA methylation establishment partially bypasses H3K36me2 through upregulated DNMT3B, a ‘leaky’ H3K36me2/3 reader. This contrasts with DNMT3A, which strictly requires H3K36me2/3 for DNA methylation through its PWWP domain. Finally, DNA methylation valleys escape de novo DNA methylation via PRC1/H2AK119ub1-mediated H3K36me2 exclusion. Thus, H3K36me2 reprogramming regulates lineage- and locus-specific post-implantation DNA methylation establishment.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Mapping H3K36me2 in mouse oocytes and early embryos.
The alternative text for this image may have been generated using AI.
Fig. 2: H3K36me2 is dynamically reprogrammed in mouse early development.
The alternative text for this image may have been generated using AI.
Fig. 3: H3K36me2 is de novo established at enhancers possibly mediated by histone acetylation.
The alternative text for this image may have been generated using AI.
Fig. 4: H3K36me2 correlates better with DNAme establishment in the extra-embryonic lineages than in embryonic lineages.
The alternative text for this image may have been generated using AI.
Fig. 5: H3K36me2 regulates post-implantation DNAme establishment in vivo.
The alternative text for this image may have been generated using AI.
Fig. 6: H3K36me2 regulates de novo DNAme establishment in vitro.
The alternative text for this image may have been generated using AI.
Fig. 7: DNMT3A, but not DNMT3B, strictly requires H3K36me2/3 for DNAme.
The alternative text for this image may have been generated using AI.
Fig. 8: PRC1 protects DMVs against H3K36me2/3 and DNAme.
The alternative text for this image may have been generated using AI.

Similar content being viewed by others

Data availability

All sequencing data generated in this study have been deposited to NCBI GEO under accession numbers GSE273314 (ChIP–seq), GSE273315 (DNAme) and GSE273316 (RNA-seq). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Suzuki, M. M. & Bird, A. DNA methylation landscapes: provocative insights from epigenomics. Nat. Rev. Genet. 9, 465–476 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Lee, H. J., Hore, T. A. & Reik, W. Reprogramming the methylome: erasing memory and creating diversity. Cell Stem Cell 14, 710–719 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Edwards, J. R., Yarychkivska, O., Boulard, M. & Bestor, T. H. DNA methylation and DNA methyltransferases. Epigenetics Chromatin 10, 23 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Greenberg, M. V. C. & Bourc’his, D. The diverse roles of DNA methylation in mammalian development and disease. Nat. Rev. Mol. Cell Biol. 20, 590–607 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Dan, J. & Chen, T. Genetic studies on mammalian DNA methyltransferases. Adv. Exp. Med Biol. 1389, 111–136 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kobayashi, H. et al. High-resolution DNA methylome analysis of primordial germ cells identifies gender-specific reprogramming in mice. Genome Res 23, 616–627 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kagiwada, S., Kurimoto, K., Hirota, T., Yamaji, M. & Saitou, M. Replication-coupled passive DNA demethylation for the erasure of genome imprints in mice. EMBO J. 32, 340–353 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Ohno, R. et al. A replication-dependent passive mechanism modulates DNA demethylation in mouse primordial germ cells. Development 140, 2892–2903 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Guibert, S., Forne, T. & Weber, M. Global profiling of DNA methylation erasure in mouse primordial germ cells. Genome Res 22, 633–641 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Smallwood, S. A. et al. Dynamic CpG island methylation landscape in oocytes and preimplantation embryos. Nat. Genet. 43, 811–814 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang, L. et al. Programming and inheritance of parental DNA methylomes in mammals. Cell 157, 979–991 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Xu, Q. et al. SETD2 regulates the maternal epigenome, genomic imprinting and embryonic development. Nat. Genet. 51, 844–856 (2019).

    Article  CAS  PubMed  Google Scholar 

  16. Yano, S. et al. Histone H3K36me2 and H3K36me3 form a chromatin platform essential for DNMT3A-dependent DNA methylation in mouse oocytes. Nat. Commun. 13, 4440 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Shirane, K., Miura, F., Ito, T. & Lorincz, M. C. NSD1-deposited H3K36me2 directs de novo methylation in the mouse male germline and counteracts Polycomb-associated silencing. Nat. Genet. 52, 1088–1098 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Li, J., Ahn, J. H. & Wang, G. G. Understanding histone H3 lysine 36 methylation and its deregulation in disease. Cell. Mol. Life Sci. 76, 2899–2916 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dukatz, M. et al. H3K36me2/3 binding and DNA binding of the DNA methyltransferase DNMT3A PWWP Domain Both Contribute To Its Chromatin Interaction. J. Mol. Biol. 431, 5063–5074 (2019).

    Article  CAS  PubMed  Google Scholar 

  20. Dhayalan, A. et al. The Dnmt3a PWWP domain reads histone 3 lysine 36 trimethylation and guides DNA methylation. J. Biol. Chem. 285, 26114–26120 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  22. Weinberg, D. N. et al. The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape. Nature 573, 281–286 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Xu, W. et al. DNMT3A reads and connects histone H3K36me2 to DNA methylation. Protein Cell 11, 150–154 (2020).

    Article  PubMed  Google Scholar 

  24. Smith, Z. D. et al. A unique regulatory phase of DNA methylation in the early mammalian embryo. Nature 484, 339–344 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Smith, Z. D. et al. Epigenetic restriction of extraembryonic lineages mirrors the somatic transition to cancer. Nature 549, 543–547 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhang, Y. et al. Dynamic epigenomic landscapes during early lineage specification in mouse embryos. Nat. Genet. 50, 96–105 (2018).

    Article  CAS  PubMed  Google Scholar 

  27. Xie, W. et al. Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153, 1134–1148 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  29. Bouniol-Baly, C. et al. Differential transcriptional activity associated with chromatin configuration in fully grown mouse germinal vesicle oocytes. Biol. Reprod. 60, 580–587 (1999).

    Article  CAS  PubMed  Google Scholar 

  30. Zuccotti, M., Piccinelli, A., Giorgi Rossi, P., Garagna, S. & Redi, C. A. Chromatin organization during mouse oocyte growth. Mol. Reprod. Dev. 41, 479–485 (1995).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  32. Jukam, D., Shariati, S. A. M. & Skotheim, J. M. Zygotic genome activation in vertebrates. Dev. Cell 42, 316–332 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Schulz, K. N. & Harrison, M. M. Mechanisms regulating zygotic genome activation. Nat. Rev. Genet. 20, 221–234 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yoh, S. M., Lucas, J. S. & Jones, K. A. The Iws1:Spt6:CTD complex controls cotranscriptional mRNA biosynthesis and HYPB/Setd2-mediated histone H3K36 methylation. Genes Dev. 22, 3422–3434 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Edmunds, J. W., Mahadevan, L. C. & Clayton, A. L. Dynamic histone H3 methylation during gene induction: HYPB/Setd2 mediates all H3K36 trimethylation. EMBO J. 27, 406–420 (2008).

    Article  CAS  PubMed  Google Scholar 

  36. Aoki, F., Worrad, D. M. & Schultz, R. M. Regulation of transcriptional activity during the first and second cell cycles in the preimplantation mouse embryo. Dev. Biol. 181, 296–307 (1997).

    Article  CAS  PubMed  Google Scholar 

  37. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Liu, B. et al. Mapping putative enhancers in mouse oocytes and early embryos reveals TCF3/12 as key folliculogenesis regulators. Nat. Cell Biol. 26, 962–974 (2024).

    Article  CAS  PubMed  Google Scholar 

  39. Sun, Z. et al. Chromatin regulation of transcriptional enhancers and cell fate by the Sotos syndrome gene NSD1. Mol. Cell 83, 2398–2416 e2312 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lasko, L. M. et al. Discovery of a selective catalytic p300/CBP inhibitor that targets lineage-specific tumours. Nature 550, 128–132 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yuan, G. et al. Histone H2A ubiquitination inhibits the enzymatic activity of H3 lysine 36 methyltransferases. J. Biol. Chem. 288, 30832–30842 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Oksuz, O. et al. Capturing the Onset of PRC2-Mediated Repressive Domain Formation. Mol. Cell 70, 1149–1162 e1145 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yu, J. R. et al. The H3K36me2 writer-reader dependency in H3K27M-DIPG. Sci. Adv. 7, eabg7444 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chen, Z., Djekidel, M. N. & Zhang, Y. Distinct dynamics and functions of H2AK119ub1 and H3K27me3 in mouse preimplantation embryos. Nat. Genet. 53, 551–563 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Inoue, A., Jiang, L., Lu, F., Suzuki, T. & Zhang, Y. Maternal H3K27me3 controls DNA methylation-independent imprinting. Nature 547, 419–424 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Loda, A., Collombet, S. & Heard, E. Gene regulation in time and space during X-chromosome inactivation. Nat. Rev. Mol. Cell Biol. 23, 231–249 (2022).

    Article  CAS  PubMed  Google Scholar 

  47. Kunath, T. et al. Imprinted X-inactivation in extra-embryonic endoderm cell lines from mouse blastocysts. Development 132, 1649–1661 (2005).

    Article  CAS  PubMed  Google Scholar 

  48. Du, Z. et al. Stepwise de novo establishment of inactive X chromosome architecture in early development. Nat. Genet. 56, 2185–2198 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yuan, W. et al. H3K36 methylation antagonizes PRC2-mediated H3K27 methylation. J. Biol. Chem. 286, 7983–7989 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Grosswendt, S. et al. Epigenetic regulator function through mouse gastrulation. Nature 584, 102–108 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Rayasam, G. V. et al. NSD1 is essential for early post-implantation development and has a catalytically active SET domain. EMBO J. 22, 3153–3163 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lewis, A. et al. Imprinting on distal chromosome 7 in the placenta involves repressive histone methylation independent of DNA methylation. Nat. Genet. 36, 1291–1295 (2004).

    Article  CAS  PubMed  Google Scholar 

  53. Borgel, J. et al. Targets and dynamics of promoter DNA methylation during early mouse development. Nat. Genet. 42, 1093–1100 (2010).

    Article  CAS  PubMed  Google Scholar 

  54. Auclair, G., Guibert, S., Bender, A. & Weber, M. Ontogeny of CpG island methylation and specificity of DNMT3 methyltransferases during embryonic development in the mouse. Genome Biol. 15, 545 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Karimi, M. M. et al. DNA methylation and SETDB1/H3K9me3 regulate predominantly distinct sets of genes, retroelements, and chimeric transcripts in mESCs. Cell Stem Cell 8, 676–687 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Dahlet, T. et al. Genome-wide analysis in the mouse embryo reveals the importance of DNA methylation for transcription integrity. Nat. Commun. 11, 3153 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bagci, H. & Fisher, A. G. DNA demethylation in pluripotency and reprogramming: the role of tet proteins and cell division. Cell Stem Cell 13, 265–269 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Sim, Y. J. et al. 2i maintains a naive ground state in ESCs through two distinct epigenetic mechanisms. Stem Cell Rep. 8, 1312–1328 (2017).

    Article  CAS  Google Scholar 

  59. Leitch, H. G. et al. Naive pluripotency is associated with global DNA hypomethylation. Nat. Struct. Mol. Biol. 20, 311–316 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Shirane, K. et al. Global landscape and regulatory principles of DNA methylation reprogramming for germ cell specification by mouse pluripotent stem cells. Dev. Cell 39, 87–103 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. Hayashi, K., Ohta, H., Kurimoto, K., Aramaki, S. & Saitou, M. Reconstitution of the mouse germ cell specification pathway in culture by pluripotent stem cells. Cell 146, 519–532 (2011).

    Article  CAS  PubMed  Google Scholar 

  62. Hayashi, K., de Sousa Lopes, S. M. C., Tang, F., Lao, K. & Surani, M. A. Dynamic equilibrium and heterogeneity of mouse pluripotent stem cells with distinct functional and epigenetic states. Cell Stem Cell 3, 391–401 (2008).

    Article  CAS  PubMed  Google Scholar 

  63. Shipman, G. A. et al. Systematic perturbations of SETD2, NSD1, NSD2, NSD3, and ASH1L reveal their distinct contributions to H3K36 methylation. Genome Biol. 25, 263 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Wang, Q. et al. Imprecise DNMT1 activity coupled with neighbor-guided correction enables robust yet flexible epigenetic inheritance. Nat. Genet. 52, 828–839 (2020).

    Article  CAS  PubMed  Google Scholar 

  65. Huo, D. et al. CpG island reconfiguration for the establishment and synchronization of polycomb functions upon exit from naive pluripotency. Mol. Cell 82, 1169–1185 e1167 (2022).

    Article  CAS  PubMed  Google Scholar 

  66. Blackledge, N. P. & Klose, R. J. The molecular principles of gene regulation by Polycomb repressive complexes. Nat. Rev. Mol. Cell Biol. 22, 815–833 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Li, Y. et al. Genome-wide analyses reveal a role of Polycomb in promoting hypomethylation of DNA methylation valleys. Genome Biol. 19, 18 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Zhang, H. et al. H3K27me3 shapes DNA methylome by inhibiting UHRF1-mediated H3 ubiquitination. Sci. China Life Sci. 65, 1685–1700 (2022).

    Article  CAS  PubMed  Google Scholar 

  69. Lu, J. et al. Structural basis for the allosteric regulation and dynamic assembly of DNMT3B. Nucleic Acids Res. 51, 12476–12491 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Guo, X. et al. Structural insight into autoinhibition and histone H3-induced activation of DNMT3A. Nature 517, 640–644 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Chen, T., Tsujimoto, N. & Li, E. The PWWP domain of Dnmt3a and Dnmt3b is required for directing DNA methylation to the major satellite repeats at pericentric heterochromatin. Mol. Cell. Biol. 24, 9048–9058 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Bogutz, A. B. et al. Evolution of imprinting via lineage-specific insertion of retroviral promoters. Nat. Commun. 10, 5674 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Kelsey, G. & Feil, R. New insights into establishment and maintenance of DNA methylation imprints in mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368, 20110336 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Lu, X. et al. Evolutionary epigenomic analyses in mammalian early embryos reveal species-specific innovations and conserved principles of imprinting. Sci. Adv. 7, eabi6178 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Inoue, A., Akiyama, T., Nagata, M. & Aoki, F. The perivitelline space-forming capacity of mouse oocytes is associated with meiotic competence. J. Reprod. Dev. 53, 1043–1052 (2007).

    Article  PubMed  Google Scholar 

  76. Solter, D. & Knowles, B. B. Immunosurgery of mouse blastocyst. Proc. Natl Acad. Sci. USA 72, 5099–5102 (1975).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Yang, H., Wang, H. & Jaenisch, R. Generating genetically modified mice using CRISPR/Cas-mediated genome engineering. Nat. Protoc. 9, 1956–1968 (2014).

    Article  CAS  PubMed  Google Scholar 

  78. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  79. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  82. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  PubMed  Google Scholar 

  84. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  86. Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Krueger, F. & Andrews, S. R. SNPsplit: Allele-specific splitting of alignments between genomes with known SNP genotypes. F1000Res 5, 1479 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. 27, 801–812 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Wang, C. et al. Reprogramming of H3K9me3-dependent heterochromatin during mammalian embryo development. Nat. Cell Biol. 20, 620–631 (2018)

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank G. Yuan from Sichuan University and R. Xu from the Institute of Biophysics, Chinese Academy of Sciences, for their suggestions and discussions on Nsd1-KO mouse generation; M. C. Lorincz from the University of British Columbia for critically reading the paper and sharing H3K36me2 antibody information; members of the Xie laboratory for their discussion and comments during paper preparation; and the Animal Research Center and Biocomputing Facility at Tsinghua University for their support. This work was supported by the National Key R&D Program of China (2023YFA1801804 to X.L. and 2021YFA1100102 to W.X.), the National Natural Science Foundation of China (31988101 and 31830047 to W.X.), the Tsinghua-Peking Center for Life Sciences (W.X.) and the Natural Science Foundation of Shanghai (23ZR1414300 to Y.Z.). W.X. is a recipient of an HHMI International Research Scholar award and is a New Cornerstone Investigator.

Author information

Author notes

  1. These authors contributed equally: Xukun Lu, Lijuan Wang.

Authors and Affiliations

  1. State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health, Shandong University, Jinan, China

    Xukun Lu

  2. National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, China

    Xukun Lu

  3. Key Laboratory of Reproductive Endocrinology (Shandong University), Ministry of Education, Jinan, China

    Xukun Lu

  4. Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, New Cornerstone Science Laboratory, School of Life Sciences, Tsinghua University, Beijing, China

    Xukun Lu, Lijuan Wang, Bofeng Liu, Xiaoyu Hu, Zhengmao Wang, Ling Liu, Guang Yu, Lijun Dong, Feng Kong, Qiang Fan & Wei Xie

  5. Tsinghua-Peking Center for Life Sciences, Beijing, China

    Xukun Lu, Lijuan Wang, Bofeng Liu, Xiaoyu Hu, Zhengmao Wang, Ling Liu, Guang Yu, Lijun Dong, Feng Kong, Qiang Fan & Wei Xie

  6. Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China

    Yu Zhang

  7. Shanghai Key Laboratory of Reproduction and Development, Shanghai, China

    Yu Zhang

Authors
  1. Xukun Lu
    View author publications

    Search author on:PubMed Google Scholar

  2. Lijuan Wang
    View author publications

    Search author on:PubMed Google Scholar

  3. Bofeng Liu
    View author publications

    Search author on:PubMed Google Scholar

  4. Xiaoyu Hu
    View author publications

    Search author on:PubMed Google Scholar

  5. Zhengmao Wang
    View author publications

    Search author on:PubMed Google Scholar

  6. Ling Liu
    View author publications

    Search author on:PubMed Google Scholar

  7. Guang Yu
    View author publications

    Search author on:PubMed Google Scholar

  8. Lijun Dong
    View author publications

    Search author on:PubMed Google Scholar

  9. Feng Kong
    View author publications

    Search author on:PubMed Google Scholar

  10. Qiang Fan
    View author publications

    Search author on:PubMed Google Scholar

  11. Yu Zhang
    View author publications

    Search author on:PubMed Google Scholar

  12. Wei Xie
    View author publications

    Search author on:PubMed Google Scholar

Contributions

X.L., Y.Z. and W.X. conceived and designed the project. X.L. and L.W. performed most of the experiments in embryos and cell lines. X.L. performed data analyses with the help of Y.Z., B.L. and G.Y. X.L. prepared the figures in consultation with Y.Z. and W.X. B.L. and X.H. established Nsd1-KO mES cells. Z.W. helped with Nsd1-KO mES cell derivation and western blot. L.L., L.D., F.K. and Q.F. helped with embryo collection. X.L. and W.X. interpreted the data and wrote the paper. X.L., Y.Z. and W.X. supervised this project. All authors discussed the results and contributed to the final paper.

Corresponding authors

Correspondence to Xukun Lu, Yu Zhang or Wei Xie.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work.

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 Dynamic H3K36me2 in mouse oocytes and early embryos.

a, Immunostaining showing H3K36me2 dynamics in mouse oocytes and early embryos. Dashed circles, chromatin in MII oocytes, and the maternal (♀) and paternal (♂) pronuclei in 1 C embryos (PN5 stage). NSN, non-surrounded nucleolus; SN, surrounded nucleolus; FGO, full-grown oocyte; MII, metaphase II; 1 C, 1-cell; 2 C, late 2-cell; 4 C, 4-cell; 8 C, 8-cell; Bl, blastocyst. PB, polar body. Scale bar, 50 μm. H3K36me2 is absent in sperm (arrows in ‘1 C’ panel). b, Jitter plot with mean ± SEM showing the relative H3K36me2 signals at each stage. M, maternal pronuclei; P, paternal pronuclei. The numbers of oocytes or embryos examined are 4 (NSN FGO), 8 (SN FGO), 8 (MII), 11 (PN5), 13 (2 C), 11 (4 C), 10 (8 C), and 10 (Bl). c, Box plots showing H3K36me2 (left) and H3K36me3 (right) in gene bodies of NSN FGOs, SN FGOs, and MII oocytes. Genes are ranked by their transcription levels in NSN FGOs. Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. d, Scatter plots comparing H3K36me2 and H3K36me3 (100-kb bin) in mouse NSN FGOs, SN FGOs, MII oocytes, and mESCs. The Pearson correlation coefficients are shown. Transcription (Trx) is active (+) in NSN FGOs and mESCs, inactive (-) in SN FGOs and MII oocytes. e, The UCSC genome browser views showing maternal (M) and paternal (P) H3K36me2 in mouse gametes and early embryos. Prospermatogonia (PSG) data are from a previous study17. f, Hierarchical clustering of mouse gametes and early embryos based on allelic H3K36me2 (5-kb bin). g, Box plot showing the differences of allelic H3K36me2 (5-kb bin) (paternal-maternal, P-M) normalized by total H3K36me2 (P + M) across the genome. Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. Source numerical data are available in source data.

Source Data

Extended Data Fig. 2 Allelic reprogramming of H3K36me2 in mouse early development.

a, Heatmaps showing RNA levels and allelic H3K36me2 (100-bp bin) in gene bodies and the flanking regions ( ± 1×gene body) of oocyte-specific, major ZGA, housekeeping (HK), and inactive genes at different developmental stages. b, A similar analysis to (a) for RNA and H3K36me3. c, Left-middle, heatmaps showing H3K36me2 and DNA methylation in oocyte-specific, major ZGA, HK, and inactive gene bodies and the flanking regions ( ± 1×gene body) in 1 C and 8 C embryos. Right, line charts showing DNA methylation levels89 in gene bodies and the flanking regions ( ± 1×gene body) in each group. d, The UCSC genome browser views comparing DNA methylation in 1 C and 8 C embryos. Note that DNA methylation is not increased in H3K36me2 de novo deposited regions (blue shades). Source numerical data are available in source data.

Source Data

Extended Data Fig. 3 H3K36me2 reprogramming in early embryos depends on zygotic transcription.

a, Left, schematic showing treatment of PN5 zygotes with α-amanitin (α-Ama) for 24 h. Right, heatmaps showing the RNA levels of oocyte-specific, major ZGA, HK, and inactive genes, and H3K36me2/3 near these genes ( ± 1 × gene body) upon treatment with α-amanitin. b, The UCSC genome browser views comparing the H3K36me2/3 in control and α-amanitin-treated late 2C embryos. Active gene bodies are shaded. c, Left, schematic showing treatment of PN5 zygotes with aphidicolin to block cell cycle. Right, heatmaps showing the RNA levels of oocyte-specific, major ZGA, HK, and inactive genes, and H3K36me2/3 near these genes ( ± 1 × gene body) upon treatment with aphidicolin for 44-46 h. d, The UCSC genome browser views comparing the H3K36me2/3 in control (8C) and aphidicolin-treated (2C) embryos. e, Bar chart showing the expression of H3K36me2 and H3K36me3 regulators in control (8C) and α-amanitin-treated (2C) embryos (n = 2 biological replicates). Source numerical data are available in source data.

Source Data

Extended Data Fig. 4 H3K36me2 is depleted from the inactive X chromosome and reappears at enhancers after X chromosome reactivation.

a, Left, schematic showing treatment of mouse embryos with A-485 (0.2 μM) from late 2C to 8C. Right, immunostaining showing H3K27ac and H3K36me2 in control and A-485-treated 8C embryos. Scale bar, 50 μm. b, Jitter plot showing H3K27ac and H3K36me2 in control (n = 6) and A-485-treated (n = 5) 8C embryos. Signals in two blastomeres of each embryo are averaged for statistics. The signal intensity of the control was set to 1.0. Bar = mean ± SD. P = 7e-5 (Ctrl vs. A-485 for H3K27ac) and P = 0.32 (Ctrl vs. A-485 for H3K36me2) (two-tailed Student’s t-test). c, Line charts comparing maternal and paternal H3K36me2 on autosomes (Autosome, left) and X chromosome (chrX, right). H3K36me2 is low on paternal chrX (arrows). d, The UCSC genome browser views showing allelic H3K36me2/3 on the active (Xa) and inactive (Xi) chrX in E6.5 ExE, WT and Xist-depleted (Xist-PA) XEN cells. Note the increase of H3K36me2 (black arrows) and H3K36me3 (green arrows) on the Xi (partially reactivated) after Xist deletion. e, Box plots comparing allelic H3K36me2 on autosomes and chrX in female E6.5 ExE, WT and Xist-PA XEN cells. Note H3K36me2 exclusion on WT paternal chrX but increase on Xist-PA paternal chrX (arrows). Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P = 1e-20 (two-sided paired Wilcoxon signed-rank test). f, Scatter plots comparing the changes of H3K36me2 (Xist-PA vs. WT) and those of H3K27ac, H2AK119ub1, or H3K27me3 (10-kb bin) upon Xist loss on the paternal chrX (Xp). The Pearson coefficients are shown. g, Heatmaps showing H3K27ac and H3K36me2 on the Xp in WT and Xist-PA XEN cells. h, Left, heatmaps showing H3K36me2 on Xp in WT and Xist-PA XEN cells. Right, cumulative distribution of H3K36me2 signals relative to the transcription start site (TSS) for derepressed, expressed, and silent genes in Xist-PA XEN cells. The x-axis shows the distance from gene TSSs to the nearest ectopic H3K36me2 peaks. The y-axis shows the cumulative fraction of genes within defined distances. Source numerical data are available in source data.

Source Data

Extended Data Fig. 5 Nsd1 deletion in mouse embryos.

a, Violin plots showing H3K36me2/3 enrichment in defined H3K36me3-enriched (K36me2LK36me3H), H3K36me2-enriched (K36me2HK36me3L), and H3K36me2/3-low (K36me2LK36me3L) regions in E6.5 Epi (left) and ExE (right). b, Top, schematic showing disruption of NSD1. Bottom, Sanger sequencing showing the deletion of 6 bps (GAATAT) in the DNA sequence encoding the SET domain. The TAT-encoded tyrosine (1972Y) is a conserved critical catalytic site. c, Western blot showing the levels of NSD1 and H3K36me2 in WT and Nsd1 KO mESCs derived from E3.5 Nsd1 KO blastocysts. β-actin and total H3 are loading controls. d, Immunofluorescent staining (left) and jitter plot (right) showing H3K36me2 levels in control and Nsd1 KO embryos from the 2C to blastocyst stages (n = 2 biological replicates). Scale bar, 50 μm. For the jitter plot, signals in two blastomeres of each embryo are averaged for statistics. The signal intensity of the control was set to 1.0. Bar = mean ± SD. P = 0.69, P = 0.06, P = 2e-6, P = 3e-7, and P = 8e-7 for Ctrl vs. Nsd1 KO 2C, 4C, 8C, morula, and blastocyst, respectively (two-tailed Student’s t-test). The numbers of embryos used are shown at the bottom. e, Volcano plots showing gene expression changes in E3.5 Nsd1 KO blastocysts. Differentially expressed genes (DEGs), adjusted P-value < 0.05, fold change of ≥ 2. f, Immunofluorescent staining (left) and jitter plot (right) showing H3K36me2 levels in E6.5 control (n = 4) and Nsd1 KO (n = 3) embryos (n = 2 biological replicates). Scale bar, 100 μm. P = 1e-4 (two-tailed Student’s t-test). OCT4 marks the epiblast. *non-specific signals. Source numerical data and unprocessed blots are available in source data.

Source Data

Extended Data Fig. 6 DNA methylation in Nsd1 KO mouse embryo.

a, Box plots showing the DNA methylation levels in the whole genome (All), H3K36me3-enriched (K36me2LK36me3H), H3K36me2-enriched (K36me2HK36me3L), and H3K36me2/3-low (K36me2LK36me3L) regions in control and Nsd1 KO embryonic (left, Epi for WT E6.5, ectoderm for WT E7.5, head fold for WT E8.5, Epi for KO E6.5-E8.5 embryos) and extra-embryonic (right, ExE) tissues from E6.5 to E8.5. Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P = 1e-23 for 5mC in H3K36me3-enriched regions between E7.5 Ctrl and Nsd1 KO Ect and P < 2e-308 for all other comparisons (two-sided paired Wilcoxon signed-rank test). b, The UCSC genome browser views showing DNA methylation in H3K36me3-enriched and H3K36me2-enriched regions (defined in control embryos) in control and Nsd1 KO embryonic and extra-embryonic lineages from E6.5 to E8.5. c, Hexbin plots showing DNA methylation changes (color-coded) upon Nsd1 KO, H3K36me2, and H3K36me3 enrichment over 10-kb bins in E6.5, E7.5, and E8.5 embryonic (Em) and extra-embryonic (Ex) tissues. d, Bar plots showing the expression of DNA methylation regulators in E6.5 control and Nsd1 KO Epi and ExE (n = 5 biological replicates). P = 0.024, P = 0.16, P = 1e-5, P = 0.09, P = 0.22, P = 0.54, and P = 0.36 for Dnmt3a, Dnmt3b, Dnmt3l, Dnmt1, Tet1, Tet2, and Tet3, respectively, between E6.5 Ctrl and Nsd1 KO Epi; P = 0.71, P = 0.013, P = 0.14, P = 0.25, P = 0.32, P = 0.03, and P = 0.04 for Dnmt3a, Dnmt3b, Dnmt3l, Dnmt1, Tet1, Tet2, and Tet3, respectively, between E6.5 Ctrl and Nsd1 KO ExE (two-tailed Student’s t-test). Source numerical data are available in source data.

Source Data

Extended Data Fig. 7 DMS germline genes are derepressed in Nsd1 KO embryos.

a, Volcano plots showing gene expression changes in E6.5 Nsd1 KO Epi and ExE. Differentially expressed genes (DEGs), adjusted p-value < 0.05, fold change ≥ 2. GO terms for down-regulated and up-regulated genes are also shown. b, Scatter plots comparing DNA methylation changes at promoters (TSS ± 500 bp) and gene expression changes between E6.5 Nsd1 KO and control embryos in Epi or ExE. Black dots, DNA methylation-sensitive (DMS) germline genes. Representative DMS genes are labeled. c, Box plots showing the transcription (RNA) of CGI and non-CGI DMS germline genes in control and Nsd1 KO embryonic and extra-embryonic lineages, and H3K36me2 at CGI and non-CGI promoters of DMS germline genes. All CGI and non-CGI genes are similarly analyzed as controls (‘All’). Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P = 3e-9 and P = 2e-7 for Ctrl vs. Nsd1 KO Em lineages for DMS CGI genes at E6.5 and E8.5, respectively; P < 2e-308 for Ctrl vs. Nsd1 KO Em lineages for all CGI genes both at E6.5 and E8.5; P = 2e-12 and P = 6e-8 for Ctrl vs. Nsd1 KO Em lineages for DMS non-CGI genes at E6.5 and E8.5, respectively; P = 4e-215 and P = 4e-4 for Ctrl vs. Nsd1 KO Em lineages for all non-CGI genes at E6.5 and E8.5, respectively; P = 6e-3 and P = 5e-4 for Ctrl vs. Nsd1 KO Ex lineages for DMS CGI genes at E6.5 and E8.5, respectively; P = 0.27 and P = 2e-100 for Ctrl vs. Nsd1 KO Ex lineages for all CGI genes at E6.5 and E8.5, respectively; P = 7e-7 and P = 3e-3 for Ctrl vs. Nsd1 KO Ex lineages for DMS non-CGI genes at E6.5 and E8.5, respectively; P = 8e-87 and P = 1e-34 for Ctrl vs. Nsd1 KO Ex lineages for all non-CGI genes at E6.5 and E8.5, respectively; P = 1e-23, P = 7e-7, P = 6e-11, and P = 1e-3 for DMS vs. all CGI genes in E6.5 Epi, DMS vs. all non-CGI genes in E6.5 Epi, DMS vs. all CGI genes in E6.5 ExE, and DMS vs. all non-CGI genes in E6.5 ExE, respectively (two-sided paired Wilcoxon signed-rank test for RNA, and two-sided Wilcoxon rank-sum test for K36me2). Source numerical data are available in source data.

Source Data

Extended Data Fig. 8 H3K36me2/3 and DNA methylation in Nsd1 KO cells.

a, Schematic showing disruption of Nsd1 in mESCs and a pair of gRNAs targeting the catalytic domain (AWS + SET+post-SET). b, The UCSC genome browser views showing the enrichment (spike-in normalized read counts) of H3K36me2 and H3K36me3 in WT and Nsd1 KO S/L mESCs. c, Western blot showing the levels of NSD1 and H3K36me2 in WT and Nsd1 KO S/L mESCs (n = 3 biological replicates). α-tubulin and total H3 are used as loading controls. d, Left, heatmaps showing H3K36me2 and H3K36me3 (5-kb bin) in identified H3K36me3-enriched (K36me2LK36me3H), H3K36me2-enriched (K36me2HK36me3L), and H3K36me2/3-low (K36me2LK36me3L) regions in WT 2i mESCs, S/L mESCs, and EpiLCs; Middle, heatmaps showing H3K36me2 (spike-in), H3K36me3 (non-spike-in), and DNA methylation in these regions in WT and Nsd1 KO 2i mESCs, S/L mESCs, and EpiLCs. DNA methylation in each group of regions is quantified with box plots on the right. Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P = 3e-18 for WT vs. Nsd1 KO S/L mESCs in H3K36me3-enriched regions and P < 2e-308 for all other comparisons (two-sided paired Wilcoxon signed-rank test). e, Bar chart showing the expression fold changes (Nsd1 KO /WT) of Dnmt3a, Dnmt3b, Dnmt1, and Dnmt3l in 2i mESCs, S/L mESCs, and EpiLCs (n = 2 biological replicates). f, Box plots showing DNA methylation at CGI and non-CGI promoters of the DMS germline genes in control and Nsd1 KO D3 EpiLCs. All CGI and non-CGI genes are similarly analyzed as controls (‘All’). Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P = 6e-12 (DMS CGI promoters between Ctrl vs. Nsd1 KO EpiLCs), P < 2e-308 (all CGI promoters between Ctrl vs. Nsd1 KO EpiLCs), P = 1e-15 (DMS non-CGI promoters between Ctrl vs. Nsd1 KO EpiLCs), and P < 2e-308 (all non-CGI promoters between Ctrl vs. Nsd1 KO EpiLCs) (two-sided paired Wilcoxon signed-rank test). Source numerical data and unprocessed blots are available in source data.

Source Data

Extended Data Fig. 9 DNMT3A, but not DNMT3B, strictly requires H3K36me2/3 for DNA methylation.

a, Bar plot showing the expression changes of DNA methylation regulators in Dnmt3a/3b-overexpressing Nsd1 KO S/L mESCs. The black arrows indicate the increase of Dnmt3a/3b after overexpression. b, Scatter plots comparing H3K36me2 or H3K36me3 over 10-kb bins between WT and SKO S/L mESCs. c, Left, the UCSC genome browser views showing H3K36me2 and H3K36me3 in WT, SKO, and SKO; Nsd1 KO S/L mESCs. Right, line plots showing H3K36me2 (red) and H3K36me3 (blue) in active gene bodies in WT, SKO, and SKO; Nsd1 KO S/L mESCs. d, Box plots showing the DNA methylation levels in SKO (left) and SKO; Nsd1 KO (right) S/L mESCs with the overexpression of GFP, DNMT3A, DNMT3AΔPWWP, DNMT3B, and DNMT3BΔPWWP. Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P < 2e-308 for all comparisons (two-sided paired Wilcoxon signed-rank test). e, Hexbin plots showing relative DNA methylation levels upon WT and mutant DNMT3A/3B overexpression, H3K36me2, and H3K36me3 enrichment over 100-kb bins in SKO S/L mESCs. The DNA methylation levels in H3K36me2-enriched (#1 dashed boxes), H3K36me3-enriched (#2 dashed boxes), and H3K36me2/3-low (#3 dashed boxes) regions are color-coded in each sample. f, A similar analysis to (e) for SKO;Nsd1 KO S/L mESCs. Source numerical data are available in source data.

Source Data

Extended Data Fig. 10 PRC1 protects DMVs against aberrant H3K36me2 and DNA methylation.

a, Schematic showing CRISPR/Cas9 mediated Ring1a deletion and Ring1b fusion with FKBP12F36V-GFP in S/L mESCs. b, Western blot showing RING1B and H2AK119ub1 levels in WT and PRC1deg mESCs treated with dTAG for the indicated time. Asterisk, non-specific band. c, Box plots showing the global DNA methylation levels, and DNA methylation levels at promoters, CpG islands (CGIs), exons, introns, intergenic regions, and DMVs in WT, PRC1deg, PRC1deg;Nsd1KO mESCs. Note the selective increase of DNA methylation in DMVs upon PRC1 deletion (arrow). Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P = 4e-65, P = 1e-115, and P = 3e-12 for WT vs. PRC1deg, PRC1deg vs. PRC1deg;Nsd1KO, and WT vs. PRC1deg;Nsd1KO, respectively, for CGI; P = 2e-95, P = 3e-147, and P = 3e-90 for the same three comparisons for DMV;P < 2e-308 for all other comparisons (two-sided paired Wilcoxon signed-rank test). d, Box plots comparing the DNA methylation levels in CGIs, intergenic, and gene bodies that reside in DMVs in WT, PRC1deg, and PRC1deg;Nsd1KO mESCs. Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P = 4e-68, P = 3e-69, and P = 0.065 for WT vs. PRC1deg, PRC1deg vs. PRC1deg;Nsd1KO, and WT vs. PRC1deg;Nsd1KO, respectively, for CGI; P = 4e-61, P = 7e-164, and P = 2e-133 for the same three comparions for intergenic regions; P = 6e-139, P = 2e-146, and P = 9e-24 for the same three comparions for gene body (two-sided paired Wilcoxon signed-rank test). e, Box plots comparing gene body DNA methylation levels of up-regulated, down-regulated, and unchanged DMV genes in WT, PRC1deg, and PRC1deg;Nsd1KO mESCs. DNA methylation at representative genes and their expression are shown below. Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. P = 6e-8, P = 6e-12, and P = 3e-4 for WT vs. PRC1deg, PRC1deg vs. PRC1deg;Nsd1KO, and WT vs. PRC1deg;Nsd1KO, respectively, for up-regulated genes; P = 6e-3, P = 0.037, and P = 0.014 for the same three comparions for down-regulated genes; P = 3e-83, P = 2e-97, and P = 3e-22 for the same three comparions for unchanged genes (two-sided paired Wilcoxon signed-rank test). f, Box plots showing DNA methylation changes in DMVs upon PRC1 (Ring1b KO or PRC1deg) or PRC2 (Eed KO) removal in vivo (E6.5 Epi) and in vitro (mESCs). In vivo Ring1b and Eed KO data50 and in vitro Eed KO data67 are from published studies. Center line, median; box, 25th and 75th percentiles; whiskers, 1.5 × IQR. g, The UCSC genome browser views (left) and line charts (right) showing H2AK119ub1 and H3K27me3 in control, PRC1deg, and Eed KO mESCs. Source numerical data and unprocessed blots are available in source data.

Source Data

Supplementary information

Source data

Source Data Figs. 1–8 (download XLSX )

Statistical source data for Figs. 1–8.

Source Data Extended Data Figs. 1–10 (download XLSX )

Statistical source data for Extended Data Figs. 1–10.

Uncropped blots (download PDF )

Uncropped western blots.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, X., Wang, L., Liu, B. et al. Reprogramming of H3K36me2 guides lineage-specific post-implantation de novo DNA methylation. Nat Cell Biol 27, 2128–2142 (2025). https://doi.org/10.1038/s41556-025-01805-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41556-025-01805-8

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