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Remodelling bivalent chromatin is essential for mouse peri-implantation embryogenesis

Nature Cell Biology (2025)Cite this article

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Abstract

Bivalency regulates developmental genes during lineage commitment. However, mechanisms governing bivalent domain establishment, maintenance and resolution in early embryogenesis remain unclear. Here we comprehensively trace bivalent chromatin remodelling throughout mouse peri-implantation development, revealing bifurcated establishment modes that partition epiblast and primitive endoderm regulatory programmes. We identify transiently maintained bivalent domains (TB domains) enriched in the epiblast, where gradual resolution fine-tunes pluripotency progression. Through targeted screening in embryos, we uncover 22 TB domain regulators, including the essential factor ZBTB17. Genetic ablation or degradation of ZBTB17 causes peri-implantation arrest. Mechanistically, ZBTB17 collaborates with KDM6A/B to resolve bivalency by removing H3K27me3 and priming the activation of key pluripotency genes. Remarkably, TB domain dynamics are evolutionarily shared in human pluripotent transitions, with ZBTB17 involvement despite species differences. Our work establishes a framework for bivalent chromatin regulation in early mammalian development and elucidates how its resolution precisely controls lineage commitment.

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Fig. 1: Genome-wide profiling of histone modifications and transcriptomes in mouse peri-implantation embryos.
Fig. 2: Dynamics of bivalent chromatin in EPI development.
Fig. 3: Candidate factor evaluation and screening for bivalent chromatin regulation in peri-implantation embryos.
Fig. 4: ZBTB17 is essential for peri-implantation development in mice embryos.
Fig. 5: ZBTB17 degradation impairs the resolution of TB-marked genes during ES cell pluripotency transition.
Fig. 6: Conserved regulatory role of TB domains from mouse to human.

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Data availability

All sequencing data generated in this study are available via GEO under accession number GSE274495. Source data are provided with this paper.

Code availability

This study did not generate original code or algorithms. All software tools used are publicly available online. Unless otherwise specified in Methods, all data analyses were performed using default pipelines and parameters. Any additional information required to reanalyse the data reported in this article is available upon reasonable request.

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Acknowledgements

We thank the members of the Gao laboratory for discussions and comments. This work was primarily supported by the Ministry of Science and Technology of China (grant nos. 2022YFC2702200, S.G.; 2024YFA1803600, C.X.; 2021YFA1100300, C.J., 2021YFA1102900, W.L.; and 2020YFA0113200, Y.G.) and the National Natural Science Foundation of China (grant nos. 32488101, S.G.; 32330030, S.G.; 32470907, Y. Li; 32200665, Y. Li; 32270858, C.J.; 32270909, W.L.; and 32300684, D.B.). This work was also supported by the major project in the basic research field of Shanghai Science and Technology Innovation Action Plan (grant no. 22JC1402300, C.J.), China Postdoctoral Science Foundation (grant nos. 280869, Y. Li; 2202M722419, Y. Li; and 2023M732660, D.B.), Shanghai Pilot Program for Basic Research (W.L.), Shanghai Post-doctoral Excellence Program (2023616, Yan Zhang), Postdoctoral Fellowship Program of CPSF (GZB20230523, D.B.), the Fundamental Research Funds for the Central Universities (grant no. 22120250374, S.G.), Shenzhen Medical Research Fund (grant no. B2402013, S.G.) and Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai (S.G.).

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Author notes

  1. These authors contributed equally: Yanhe Li, Jincan He, Yingdong Liu.

Authors and Affiliations

  1. Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Yanhe Li, Yingdong Liu, Shanyao Liu, Yalin Zhang, Ziwen Xu, Zhuoao Zhang, Yan Zhang, Dandan Bai, Xinyi Lei, Xiaochen Kou, Yanhong Zhao, Jiqing Yin, Yawei Gao, Miaoxin Chen, Hong Wang, Shaorong Gao & Wenqiang Liu

  2. Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Orthopaedic Department of Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Jincan He, Guang Yang, Jia Zhao & Cizhong Jiang

  3. Translational Medical Center for Stem Cell Therapy, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China

    Yi Hui & Xiaoqing Zhang

  4. Institutes of Biomedical Sciences, Fudan University, Shanghai, China

    Yan Xiong

  5. Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Tingting Xu, Xiaoqing Zhang & Congling Xu

  6. Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China

    Jing Du & Zheng Guo

Authors
  1. Yanhe Li
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Contributions

S.G. supervised the entire project. S.G., W.L., C.J., Y. Li and J.H. conceived of and designed the experiments. Y. Li, W.L. and Y. Liu performed most of the experiments. J.H. and C.J. performed the computational analysis. Y. Li and J.H. designed and performed the data analysis. Y.H., S.L., Yalin Zhang, Y.X., T.X., Z.X, Z.Z., Yan Zhang, G.Y., J.Z., D.B., X.L., X.K., Y. Zhao, J.D., Z.G., J.Y., X.Z., C.X., Y.G., M.C. and H.W. assisted with the experiments. Y. Li, J.H., C.J., W.L. and S.G. wrote the paper. All authors approved the final paper.

Corresponding authors

Correspondence to Cizhong Jiang, Shaorong Gao or Wenqiang Liu.

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The authors declare no competing interests.

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Nature Cell Biology thanks Mélanie Eckersley-Maslin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Sampling strategy and transcriptome analysis of distinct lineages in mouse peri-implantation embryos.

a. Summary table of in vitro and in vivo datasets spanning pre- to post-implantation stages. Public datasets are indicated with their respective GEO accession numbers; all other data were generated in this study. b. Heatmap showing the expression of lineage-specific markers across peri-implantation embryo lineages. c. PCA of gene expression profiles among the EPI and PE lineages. d. PCA of gene expression profiles among the extra-embryonic (EXE and TGC) lineages. Source numerical data are available in source data.

Source data

Extended Data Fig. 2 Global histone modification and RNA Pol II enrichment profiles in early mouse embryonic lineages.

a. Pearson correlation coefficients between replicates for H3K4me3 and H3K27me3 ChIP-seq across embryonic lineages from E2.5 to E5.5. Pre-implantation samples (underlined) are from GEO accession GSE73952. b. IGV genome browser snapshots showing H3K4me3 and H3K27me3 at the Hoxa gene cluster in ICM/EPI (E3.5, E4.5, E4.75, E5.0, E5.25, E5.5), PrE (E4.5) and VE (E5.5). c. Heatmaps of Pearson correlation coefficients between gene expression and histone modifications or RNA Pol II enrichment at gene promoters. RNA Pol II data are not available for E2.5, E4.75, and E5.25 EPI. d. IGV genome browser snapshots showing RNA Pol II occupancy at housekeeping genes in ICM (E3.5), EPI (E4.5, E5.0, E5.5), PrE (E4.5), and VE (E5.5). Source numerical data are available in source data.

Extended Data Fig. 3 Dynamics of H3K4me3, H3K27me3, and RNA Pol II resetting during mouse peri-implantation embryonic development.

a. Genome-wide occupancy dynamics of H3K4me3 and H3K27me3 during EPI and PE lineage segregation. b. Distribution of H3K4me3, H3K27me3 and bivalent domains across genomic regions in EPI and PE lineages during implantation. c. PCA of H3K4me3 (left) and H3K27me3 (right) profiles across EPI and PE lineages during peri-implantation development. d. The numbers of genes gain and lose H3K4me3, H3K27me3, or bivalent marks at different stages, calculated relative to the preceding stage. e. Dynamic changes in RNA Pol II signals around the TSSs of lineage-specific genes in ICM, EPI, and PrE/VE. Source numerical data are available in source data.

Extended Data Fig. 4 Close association between bivalent domains and pluripotency during peri-implantation embryonic development.

a. Heatmap of gene expression for EPI pluripotency and PE modules from E3.5 to E9.5. Underlined post-implantation stage samples are from Xiang et al, Zhang et al and Wang et al. b. GSVA scores of EPI pluripotency modules across different EPI pluripotency cell lines, with each cell line comprising 2-6 biological samples. The box represents the interquartile range (IQR), with the lower and upper bounds indicating the 25th and 75th percentiles respectively. The median is shown by the line inside the box. Whiskers extend to the minimum and maximum values within 1.5 times the IQR. c. GO analysis of EPI pluripotency and PE modules. P-values were determined by two-sided hypergeometric test. d. Bubble plot showing statistically significant overlaps between H3K4me3-, H3K27me3-, and bivalently marked gene sets at each stage and EPI modules. Color represents P-value determined by two-sided hypergeometric test without multiple testing correction, and bubble size indicates the number of overlapping genes. e. IGV genome browser snapshots of H3K4me3 and H3K27me3 distribution for representative genes in each EPI module. f. Bubble plot displaying statistically significant overlaps between H3K4me3-, H3K27me3-, and bivalently marked gene sets at each stage and the PE module. Color represents P-value determined by two-sided hypergeometric test without multiple testing correction, and bubble size indicates the number of overlapping genes. g. Dynamics of bivalent percentage of each module during peri-implantation development. Source numerical data are available in source data.

Extended Data Fig. 5 Critical role of bivalent domain resolution in pluripotency progression.

a. Comparison of KDM6A/B enrichment at the promoters of TB- and CB-marked genes in ESCs. b. Representative images showing embryo development at E5.0 following treatment with GSK-J4 or DMSO. Experiments were repeated three times using independent biological samples. Scale bar, 100 μm. Source numerical data are available in source data. c. PCA results of transcriptomes comparing wild-type EPI samples across developmental stages (E3.5 to E5.5), and EPI samples treated with GSK-J4 or DMSO at E4.5 and E5.0. d. Enrichment of H3K4me3 and H3K27me3 at the promoters of TB-marked genes in EPI isolated from embryos treated with DMSO or GSK-J4 at E4.5. e. IGV genome browser view showing H3K27me3 enrichment at TB-marked genes in EPI isolated from embryos treated with DMSO or GSK-J4 at E4.5. f. Immunofluorescence images of embryos treated with GSK-J4 or DMSO at E4.5, co-stained for H3K27me3, H3.1/2, and DAPI. Scale bar: 20 μm. g. Quantification of H3K27me3 signal intensity normalized to H3.1/2 levels in E4.5 EPI following DMSO or GSK-J4 treatment. Data are presented as mean ± s.d. n indicates the number of embryos analyzed per condition. P values were calculated using unpaired two-sided Student’s t-test. Experiments were repeated independently three times. Source numerical data are available in source data. h. GSEA scores showing changes in gene sets following GSK-J4 treatment in E5.0 EPI. GSEA scores represent –log (adjusted P value) multiplied by the normalized enrichment score (NES). The adjusted P-values were calculated using a one-sided permutation-based method with Benjamini-Hochberg correction. Source numerical data are available in source data.

Extended Data Fig. 6 Overview of screening and identification of potential regulators of bivalent domains.

a. Schematic framework for the assessment and screening of factors regulating bivalent domains. See methods for details. b. Quantification of blastocyst formation rates following candidate factor screening. Data are presented as mean ± s.d. Source numerical data are available in source data. c. Number of differentially expressed genes in E5.0 EPI following knockout of candidate factors. d. Bubble plots showing the changes of individual gene sets analyzed by GSEA in E5.0 EPI following knockout of individual candidate factors. Color represents NES; bubble Size indicates adjusted P values determined by a one-sided permutation-based method with Benjamini-Hochberg correction. e. Heatmap showing the expression of pluripotency marker genes in E5.0 EPI from WT, Otx2−/−, and Tfap2c−/− embryos. f. Scatter plots showing H3K27me3 signals at the promoters of H3K27me3-only genes in E5.0 EPI from WT, Otx2−/−, and Tfap2c−/− embryos. The blue line represents fitted linear regression line. P-values were determined by two-sided Wilcoxon test without multiple testing correction. Source numerical data are available in source data.

Extended Data Fig. 7 Impaired resolution of TB-marked genes in ZBTB17-depleted EPIs during peri-implantation development.

a. PCA of transcriptomes comparing wild-type EPI samples across developmental stages (E3.5 to E5.5) and Zbtb17−/− EPI at E4.5 and E5.0. b. Heatmaps (left) and box plots (right) of H3K27me3 enrichment at the promoters of TB-marked genes in E4.5 EPI from WT and Zbtb17−/−embryos. Box plots show normalized H3K27me3 CUT&Tag signal intensity. P values were calculated using two-sided Wilcoxon rank-sum test. c. Heatmaps showing the expression of pluripotency-associated genes in EPI from WT and Zbtb17−/−embryos at E4.5 and E5.0. d. IGV genome browser views showing H3K27me3 and H3K4me3 enrichment at pluripotency-associated genes in EPI from WT and Zbtb17−/− embryos at E4.5 and E5.0. e. Heatmaps of H3K4me3 and H3K27me3 enrichment at the promoters of TB-marked genes in the EPI of WT and Zbtb17−/−embryos across peri-implantation stages. f. Box plots quantifying H3K4me3 and H3K27me3 enrichment at the promoters of TB-marked genes in the EPI of WT and Zbtb17−/− embryos. P values were calculated using two-sided Wilcoxon rank-sum test. g. PCA of H3K27me3 profiles comparing WT EPI samples across developmental stages (E3.5 to E5.5) and Zbtb17−/− EPI at E5.0. h. Bubble plot showing statistically significant enrichment between up- and down-regulated genes in E5.0 EPI treated with dTAG-13 and bivalent or pluripotency-associated gene sets. Bubble color indicates P value determined by two-sided hypergeometric test without multiple testing correction; bubble size reflects the number of overlapping genes. Source numerical data are available in source data.

Extended Data Fig. 8 Genome-wide distribution of ZBTB17 and its interacting factors.

a. Heatmap showing expression of pluripotency genes in Zbtb17dTAG/dTAG mESCs treated with DMSO, dTAG13, or dTAG13 + EED226 during transition from 2i/LIF to FA medium at 0, 12, 24, and 48 h. b. Pie charts showing (left) the proportion of genes activated after 48 h of pluripotency transition among 844 validated TB-marked genes (confirmed by ESC reChIP), and (right) the expression changes of these activated genes following ZBTB17 degradation. c. ZBTB17-binding motif identified in ESC ZBTB17 ChIP-seq peaks using HOMER. d. Genomic distribution preference of ZBTB17 ChIP-seq peaks. e. Box plot showing changes in EED levels at TB-marked and H3K27me3-only gene promoters after ZBTB17 degradation for 48 h, grouped by ZBTB17 promoter-binding intensity (1 = lowest, 5 = highest). P-values were calculated using one-sided Jonckheere–Terpstra test to assess the continuous trend of changes between groups. f. Box plot showing changes in H3K27me3 and H3K4me3 levels at H3K27me3-only gene promoters after 48 h of ZBTB17 degradation, and KDM6A/B enrichment at the same regions in ESCs, grouped by ZBTB17 promoter-binding intensity (1 = lowest, 5 = highest). P-values were calculated using one-sided Jonckheere–Terpstra test to assess the continuous trend of changes between groups. g. Comparison of KDM6A and KDM6B enrichment at ZBTB17-bound versus unbound promoters. All box plots represent the interquartile range (IQR), with the lower and upper bounds indicating the 25th and 75th percentiles, respectively. The median is shown by the line inside the box. Whiskers extend to the minimum and maximum values within 1.5 times the IQR. Source numerical data are available in source data.

Extended Data Fig. 9 Dynamics of H3K4me3 and H3K27me3 resetting during the naïve-to-primed transition in human embryonic stem cells (hESCs).

a. Heatmaps showing the dynamics of H3K27me3 and H3K4me3 at the promoters of H3K27me3-only and H3K4me3-only genes identified in hESCs during the naive to primed transition. b. Expression levels of H3K4me3-only and H3K4me3-only genes during the transition from naïve to primed states in hESCs. The box represents the interquartile range (IQR), with the lower and upper bounds indicating the 25th and 75th percentiles respectively. The median is shown by the line inside the box. Whiskers extend to the minimum and maximum values within 1.5 times the IQR. c. Bubble plot showing statistically significant overlaps between hTB-, hCB-, H3K27me3-only, H3K4me3-only, and other-marked gene sets in hESCs and pluripotency-specific gene sets. Color represents P-value, and bubble size indicates the number of overlapping genes. Primed-specific genet sets were defined as the top 200 highly expressed genes in H9 primed hESC compared to cRH9 D0 hESC. Naïve-specific genet sets were defined as the top 200 highly expressed genes in cRH9 D0 hESC compared to H9 primed hESC. d. Enrichment of histone modifications at the promoters of hTB- and hCB-marked genes in hESCs. All datasets were obtained from the ENCODE project using H9 cell line cultured in mTeSER medium. e. Venn diagrams showing the overlap of orthologous genes between those marked as hTB and mTB. Among these shared TB-marked genes, an IGV genome browser view shows the dynamics of H3K4me3 and H3K27me3 during pluripotency progression at four hTB-marked genes. P-value was calculated by Monte Carlo simulation with 10,000 iterations followed by a z-test. f. Venn diagrams showing the overlap of orthologous genes between those marked as hTB and mCB. P-value was calculated by Monte Carlo simulation with 10,000 iterations followed by a z-test. Source numerical data are available in source data.

Extended Data Fig. 10 Bivalency changes following ZBTB17 depletion during the pluripotency transition of human ESCs.

a. Morphology of ZBTB17 knockout and control hEPSCs and primed hESCs. Experiments were repeated three times using independent biological samples. Scale bar, 20 μm. b. Bar plot showing Gene Ontology enrichment analysis of differentially expressed genes between ZBTB17 knockout and control primed hESCs. P-values were determined by two-sided hypergeometric test without multiple testing correction. c. Box plots showing gene expression levels across primed-specific, hTB-, and hCB-marked gene sets in ZBTB17 knockout and control primed hESCs. P-values were calculated using two-sided Wilcoxon test. d. Heatmap showing the expression of human primed-state marker genes in ZBTB17 knockout and control primed hESCs. e. Boxplots comparing changes in gene expression, H3K27me3, and H3K4me3 levels among overactivated, unchanged, and silenced hTB-marked genes upon ZBTB17 knockout. f. IGV browser views showing changes in H3K27me3 and H3K4me3 at silenced hTB-marked genes in ZBTB17 depleted primed hESCs. g. GO of repressed hTB genes following ZBTB17 depletion involved in differentiation and gastrulation. P-values were determined by two-sided hypergeometric test without multiple testing correction. All box plots represent the interquartile range (IQR), with the lower and upper bounds indicating the 25th and 75th percentiles, respectively. The median is shown by the line inside the box. Whiskers extend to the minimum and maximum values within 1.5 times the IQR. Source numerical data are available in source data.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and source data for the supplementary figures.

Reporting Summary

Supplementary Tables 1–8

Supplementary Table 1. Summary of alignment information of wild-type data. Supplementary Table 2. Summary of modification state of promoters in wild-type samples. Supplementary Table 3. Summary of alignment information from verification sample. Supplementary Table 4. Self-defined gene list. Supplementary Table 5. TR and CR screening sgRNA design list. Supplementary Table 6. Targeting efficiency assessment of TB genes. Supplementary Table 7. List of differentially expressed genes of transcription factors screening calculated by DESeq2. Supplementary Table 8. Critical commercial assays and antibodies.

Source data

Source Data Fig. 1

Unprocessed western blots.

Source Data Extended Data Fig. 1

Statistical source data.

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Li, Y., He, J., Liu, Y. et al. Remodelling bivalent chromatin is essential for mouse peri-implantation embryogenesis. Nat Cell Biol (2025). https://doi.org/10.1038/s41556-025-01776-w

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