KDM4A regulates the maternal-to-zygotic transition by protecting broad H3K4me3 domains from H3K9me3 invasion in oocytes

Abstract

The importance of germline-inherited post-translational histone modifications on priming early mammalian development is just emerging1,2,3,4. Histone H3 lysine 9 (H3K9) trimethylation is associated with heterochromatin and gene repression during cell-fate change5, whereas histone H3 lysine 4 (H3K4) trimethylation marks active gene promoters6. Mature oocytes are transcriptionally quiescent and possess remarkably broad domains of H3K4me3 (bdH3K4me3)1,2. It is unknown which factors contribute to the maintenance of the bdH3K4me3 landscape. Lysine-specific demethylase 4A (KDM4A) demethylates H3K9me3 at promoters marked by H3K4me3 in actively transcribing somatic cells7. Here, we report that KDM4A-mediated H3K9me3 demethylation at bdH3K4me3 in oocytes is crucial for normal pre-implantation development and zygotic genome activation after fertilization. The loss of KDM4A in oocytes causes aberrant H3K9me3 spreading over bdH3K4me3, resulting in insufficient transcriptional activation of genes, endogenous retroviral elements and chimeric transcripts initiated from long terminal repeats during zygotic genome activation. The catalytic activity of KDM4A is essential for normal epigenetic reprogramming and pre-implantation development. Hence, KDM4A plays a crucial role in preserving the maternal epigenome integrity required for proper zygotic genome activation and transfer of developmental control to the embryo.

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Fig. 1: KDM4A plays a critical role in maintaining the genomic stability of early pre-implantation embryos.
Fig. 2: KDM4A preserves maternal epigenome integrity and prevents widespread co-occurrence of H3K4me3–H3K9me3 in oocytes.
Fig. 3: H3K4me3–H3K9me3 co-occurrence in oocytes leads to defective activation of ZGA genes.
Fig. 4: Aberrant H3K9me3 negatively impacts the expression of a subset of high-copy LTRs and LTR-initiated transcripts.
Fig. 5: The catalytic activity of KDM4A is required for pre-implantation development.

Data availability

All sequencing data related to this study have been deposited at the Gene Expression Omnibus (GEO) under the accession number GSE129735. RNA-Seq data for human single oocytes have been submitted to the European Genome Phenome Archive (EGA) under the reference EGAS00001004220. Published H3K4me3 and H3K9me3 µChIP-Seq data re-analysed in this study are available at GEO using the accession numbers GSE72784 and GSM2588560, respectively. A dedicated EaSeq session related to this study is available at http://easeq.net/Sessions/Sankar.eas. Data relating to human single oocyte mRNA-Seq are available under the material transfer agreement and General Data Protection Regulation in accordance with Danish law from the corresponding author along with any other data on reasonable request.

Code availability

Scripts used for processing and analysis of genome-wide data are available under https://doi.org/10.5281/zenodo.3701229.

References

  1. 1.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Hanna, C. W. et al. MLL2 conveys transcription-independent H3K4 trimethylation in oocytes. Nat. Struct. Mol. Biol. 25, 73–82 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Becker, J. S., Nicetto, D. & Zaret, K. S. H3K9me3-dependent heterochromatin: barrier to cell fate changes. Trends Genet. 32, 29–41 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Pedersen, M. T. et al. Continual removal of H3K9 promoter methylation by Jmjd2 demethylases is vital for ESC self-renewal and early development. EMBO J. 35, 1550–1564 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Eckersley-Maslin, M. A., Alda-Catalinas, C. & Reik, W. Dynamics of the epigenetic landscape during the maternal-to-zygotic transition. Nat. Rev. Mol. Cell Biol. 19, 436–450 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Huang, Y., Fang, J., Bedford, M. T., Zhang, Y. & Xu, R. M. Recognition of histone H3 lysine-4 methylation by the double tudor domain of JMJD2A. Science 312, 748–751 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Pedersen, M. T. et al. The demethylase JMJD2C localizes to H3K4me3-positive transcription start sites and is dispensable for embryonic development. Mol. Cell. Biol. 34, 1031–1045 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Sankar, A. et al. Maternal expression of the histone demethylase Kdm4a is crucial for pre-implantation development. Development 144, 3264–3277 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Chung, Y. G. et al. Histone demethylase expression enhances human somatic cell nuclear transfer efficiency and promotes derivation of pluripotent stem cells. Cell Stem Cell 17, 758–766 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Matoba, S. et al. Embryonic development following somatic cell nuclear transfer impeded by persisting histone methylation. Cell 159, 884–895 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Cheloufi, S. et al. The histone chaperone CAF-1 safeguards somatic cell identity. Nature 528, 218–224 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Chen, J. et al. H3K9 methylation is a barrier during somatic cell reprogramming into iPSCs. Nat. Genet. 45, 34–42 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Agger, K. et al. Jmjd2/Kdm4 demethylases are required for expression of Il3ra and survival of acute myeloid leukemia cells. Genes Dev. 30, 1278–1288 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Kooistra, S. M. & Helin, K. Molecular mechanisms and potential functions of histone demethylases. Nat. Rev. Mol. Cell Biol. 13, 297–311 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Rugg-Gunn, P. J., Cox, B. J., Ralston, A. & Rossant, J. Distinct histone modifications in stem cell lines and tissue lineages from the early mouse embryo. Proc. Natl Acad. Sci. USA 107, 10783–10790 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Matsumura, Y. et al. H3K4/H3K9me3 bivalent chromatin domains targeted by lineage-specific DNA methylation pauses adipocyte differentiation. Mol. Cell 60, 584–596 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Macfarlan, T. S. et al. Embryonic stem cell potency fluctuates with endogenous retrovirus activity. Nature 487, 57–63 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Park, S. J., Shirahige, K., Ohsugi, M. & Nakai, K. DBTMEE: a database of transcriptome in mouse early embryos. Nucleic Acids Res. 43, D771–D776 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Huang, C. J., Chen, C. Y., Chen, H. H., Tsai, S. F. & Choo, K. B. TDPOZ, a family of bipartite animal and plant proteins that contain the TRAF (TD) and POZ/BTB domains. Gene 324, 117–127 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Ma, P. & Schultz, R. M. Histone deacetylase 1 (HDAC1) regulates histone acetylation, development, and gene expression in preimplantation mouse embryos. Dev. Biol. 319, 110–120 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Pan, H. & Schultz, R. M. Sox2 modulates reprogramming of gene expression in two-cell mouse embryos. Biol. Reprod. 85, 409–416 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Hamdane, N. et al. Conditional inactivation of Upstream Binding Factor reveals its epigenetic functions and the existence of a somatic nucleolar precursor body. PLoS Genet. 10, e1004505 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Yamada, M. et al. Involvement of a novel preimplantation-specific gene encoding the high mobility group box protein Hmgpi in early embryonic development. Hum. Mol. Genet. 19, 480–493 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Prendergast, L. et al. The CENP-T/-W complex is a binding partner of the histone chaperone FACT. Genes Dev. 30, 1313–1326 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Singer, J. D., Gurian-West, M., Clurman, B. & Roberts, J. M. Cullin-3 targets cyclin E for ubiquitination and controls S phase in mammalian cells. Genes Dev. 13, 2375–2387 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Moghe, S. et al. The CUL3-KLHL18 ligase regulates mitotic entry and ubiquitylates Aurora-A. Biol. Open 1, 82–91 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Schulz, V. P. & Zakian, V. A. The saccharomyces PIF1 DNA helicase inhibits telomere elongation and de novo telomere formation. Cell 76, 145–155 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Russell, P. & Nurse, P. Negative regulation of mitosis by wee1 +, a gene encoding a protein kinase homolog. Cell 49, 559–567 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Toledo, L. I. et al. ATR prohibits replication catastrophe by preventing global exhaustion of RPA. Cell 155, 1088–1103 (2013).

    Article  CAS  Google Scholar 

  33. 33.

    Peaston, A. E. et al. Retrotransposons regulate host genes in mouse oocytes and preimplantation embryos. Dev. Cell 7, 597–606 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Franke, V. et al. Long terminal repeats power evolution of genes and gene expression programs in mammalian oocytes and zygotes. Genome Res. 27, 1384–1394 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Brind’Amour, J. et al. LTR retrotransposons transcribed in oocytes drive species-specific and heritable changes in DNA methylation. Nat. Commun. 9, 3331 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Rodriguez-Terrones, D. & Torres-Padilla, M. E. Nimble and ready to mingle: transposon outbursts of early development. Trends Genet. 34, 806–820 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Gaydos, L. J., Wang, W. & Strome, S. Gene repression. H3K27me and PRC2 transmit a memory of repression across generations and during development. Science 345, 1515–1518 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Alabert, C. et al. Two distinct modes for propagation of histone PTMs across the cell cycle. Genes Dev. 29, 585–590 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Black, J. C. et al. Conserved antagonism between JMJD2A/KDM4A and HP1γ during cell cycle progression. Mol. Cell 40, 736–748 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Sankar A. Collection and handling of mouse oocytes/embryos. Protoc. Exch. https://doi.org/10.21203/rs.2.21552/v1 (2020).

  42. 42.

    Sankar A. & Gonzalez J. M. In vitro fertilization of mouse oocytes. Protoc. Exch. https://doi.org/10.21203/rs.2.21562/v1 (2020).

  43. 43.

    Sankar A. In vitro transcription and micro-injection of Kdm4a mRNA into mouse oocytes. Protoc. Exch. https://doi.org/10.21203/rs.2.21613/v1 (2020).

  44. 44.

    Sankar A. Immunofluorescence of mouse zygotes and preimplantation embryos. Protoc. Exch. https://doi.org/10.21203/rs.2.21543/v1 (2020).

  45. 45.

    Sankar A. Metaphase spread of mouse oocytes. Protoc. Exch. https://doi.org/10.21203/rs.2.18674/v1 (2020).

  46. 46.

    Dahl, J. A. & Klungland, A. Micro chromatin immunoprecipitation (muChIP) from early mammalian embryos. Methods Mol. Biol. 1222, 227–245 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Dahl, J. A. & Collas, P. A rapid micro chromatin immunoprecipitation assay (microChIP). Nat. Protoc. 3, 1032–1045 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Sankar A. & Lerdrup M. ChIP-Seq data processing, normalization and visualization. Protoc. Exch. https://doi.org/10.21203/rs.2.21645/v1 (2020).

  49. 49.

    Sankar A. and Johansen J. V. Single oocyte/embryo RNASeq data processing. Protoc. Exch. https://doi.org/10.21203/rs.2.21804/v1 (2020).

  50. 50.

    Lerdrup, M., Johansen, J. V., Agrawal-Singh, S. & Hansen, K. An interactive environment for agile analysis and visualization of ChIP-sequencing data. Nat. Struct. Mol. Biol. 23, 349–357 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    R Core Team. R: A language and environment for statistical computing (2018).

  52. 52.

    Eklund, A. beeswarm: the bee swarm plot, an alternative to stripchart. Version 0.2.0 (CRAN, 2016).

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Acknowledgements

We thank the members of the Dahl, Helin and Hoffmann laboratories for discussions. We thank Y. Antoku of the BRIC Microscopy core facility for technical help, and V. Shukla and J. Hussain for their help with mouse genotyping. The work in the Klungland laboratory is funded by the South-Eastern Norway Regional Health Authority. The work in the Dahl laboratory is funded by the South-Eastern Norway Regional Health Authority (Early Career grant no. 2016058), the Norwegian Cancer Society and the Research Council of Norway (Young Research Talent Grant). The work in the Helin laboratory was supported by the Danish National Research Foundation (grant no. DNRF82), the Independent Research Foundation (grant no. DFF 7016-00067), a centre grant from the NNF to the NNF Center for Stem Cell Biology (grant no. NNF17CC0027852) and the Memorial Sloan Kettering Cancer Center Support Grant NIH (grant no. P30 CA008748). The Hoffmann laboratory was supported by a NNF Young Investigator Grant (grant no. NNF15OC0016662), ERC Consolidator Grant (grant no. 724718-ReCAP), a project grant from the DFF-FSS (grant no. 1111831001) and DNRF Center Grant (grant no. 6110-00344B).

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Authors

Contributions

A.S. conceived the project. A.S., M.L., K.Helin and E.R.H. designed the experiments and supervised the project. A.S., A.M., J.M.G. and R.Blanshard carried out the experiments. A.S., M.L., J.V.J. and R.Borup analysed and interpreted the data, and performed the bioinformatics and statistical analyses. C.Y.A. provided the human oocyte samples. J.A.D. designed all μChIP-Seq experiments and supervised A.M. who performed them. C.Y.A., K.Hansen, A.K. and J.A.D. discussed the data, and provided critical input and supervision. A.S., M.L., J.A.D., K.Helin and E.R.H. wrote the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Aditya Sankar or John Arne Dahl or Kristian Helin or Eva R. Hoffmann.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Extended characterization of the role of KDM4A in oocytes and preimplantation embryos.

a, Structure of KDM4A/B/C/D with corresponding catalytic JmjC domain(s) and H3K4me3 binding double Tudor domain(s) shown. b, FPKM table of KDM4A/B/C/D expression in P7, P10, P14, 8 weeks and MII oocytes as well as in post-fertilization zygote and 2-cell embryos from published sources. c, Representative images of metaphase chromosome spreads from two independent Kdm4a+/+ and Kdm4a−/− MII oocytes with DNA counterstained using DAPI. Spreads were performed on ten MII oocytes derived from three independent donor females (Scale bar: 10 µm). d, Flattened Z-stack images of three independent Kdm4a+/− control and Kdm4a−/−(MZ) preimplantation embryos from three independent mice per genotype. All embryos were imaged in a single session. Micronuclei are highlighted with red arrows (Scale bar: 10 µm). Statistical source data are provided in source data Extended Data fig. 1. Source data

Extended Data Fig. 2 Increased maternal H3K9me3 in KDM4A MZ mutant zygotes.

a, Immunofluorescence imaging of H3K9me3 in maternal pronuclei of from Kdm4a+/− and Kdm4a−/−(MZ) zygotes. Representative additional mid-section Z-stack images from the same batch of zygotes as in Fig. 2a. DNA is counterstained using DAPI. All images are of a maternal pronuclei mid-section shown for comparison (Scale bar: 10 µm).

Extended Data Fig. 3 Comparing changes in H3K36me3 and H3K9me3 to H3K4me3 in KDM4A knockout oocytes and ZGA specific genes.

a, Heatmap of published H3K4me3 data at region-sets: bdH3K4me3 and UCSC genes with non-redundant transcription start-sites. Region-sets were ordered according to enrichment of H3K9me3 (FPKM) in our wildtype sample. b, Heat maps (left panel) and line graphs (right panel) of averaged H3K36me3 µChIP-Seq signal in Kdm4a+/+ (blue) and Kdm4a−/− (red) MII oocytes at bdH3K4me3 region set. c, Heat maps (left panel) and line graphs (right panel) of averaged H3K36me3 µChIP-seq signal in Kdm4a+/+ (blue) and Kdm4a−/− (red) MII oocytes at UCSC genes as defined in (a). d, 2D-histograms comparing H3K9me3 gain in Kdm4a−/− MII oocytes (Y-axis) to that of published H3K4me3 (top) or the corresponding input (bottom) sample. The log10 counts in each bin are color-coded according to the left side color scale, values on X- and Y-axes are in FPKM, and r-values represent Pearson correlation coefficients. e, 2D-histograms comparing H3K9me3 changes (Y-axis) to that of H3K4me3 (top) or the corresponding input (bottom) sample in Kdm4a−/− MII oocytes. The log10 counts in each bin are color-coded according to the left side color scale, values on X- and Y-axes are in FPKM, and r-values represent Pearson correlation coefficients. f, Published genes with specific expression in oocytes or 2-cell embryos (left panel), with associated changes in H3K4me3 and H3K9me3 changes in such genes within Kdm4a-/- MII oocytes (middle panel). The ChIP signal is expressed as shown in a ratio with strength in FPKM. g, Beeswarm plot (right panel) quantifying H3K9me3 abundance in Kdm4a−/− MII oocytes at gene bodies and TSS of genes with differential expression in oocyte or 2-cell embryos according to Macfarlan et al., 2012. n represents genes enriched for oocytes or 2-cell embryos. Significance was calculated using a two-sided Wilcoxon-Mann-Whitney test. Bold and narrow lines indicate the median and interquartile ranges, respectively. Statistical source data are provided in source data Extended Data fig. 3. Source data

Extended Data Fig. 4 H3K9me3 µChIP-Seq signal using standard FPKM normalization.

a, 2D-histogram comparing H3K9me3 signal in wild type MII oocytes replicate 1 (X-axis) to that of previously published MII oocyte H3K9me3 (Y-axis) quantified within 24, 402 non-redundant gene bodies (blue,) and 12, 133 bdH3K4me3 (orange,) [AU: please define the), where n represents the number] of each type of loci. Values are FPKM normalized, and represent average signal at each locus independent of its size. Pearson’s correlation coefficients(r) are shown for each subset of loci. b, Heat maps of published H3K9me3 µChIP-seq signal in Kdm4a+/+ MII oocytes at bdH3K4me3 (left) and entire genes (right), ordered and visualized as in Fig. 2b,c. c, Illustration of changes in read distribution for H3K9me3 towards covering bdH3K4me3 areas previously devoid of the mark, resulting in signal dispersion and lowering read densities at canonically enriched areas such as gene bodies. d, Line graphs of averaged H3K9me3 µChIP-Seq signal in Kdm4a+/+ (blue) and Kdm4a−/− (red) MII oocytes at bdH3K4me3 (left) and surrounding loci as well as non-redundant UCSC genes (right), using two different normalization strategies: standard FPKM (upper row) and FPKM followed by scaling of Kdm4a/ signal to Kdm4a+/+ samples based on 5000 gene bodies with the highest H3K9me3 signal in each sample. e, Heat maps of averaged H3K9me3 µChIP-Seq signal in Kdm4a+/+ (blue) and Kdm4a−/− (red) MII oocytes at bdH3K4me3 (left) and surrounding loci as well as non-redundant UCSC genes (right) ordered and visualized as in Fig. 2, using two different normalization strategies as described above in (d). Statistical source data are provided in source data Extended Data fig. 4. Source data

Extended Data Fig. 5 Differential gene expression analysis of KDM4A knockout oocytes and preimplantation embryos.

a, Principal component analysis (PCA) plot visualizing individual transcriptomes by developmental stage and genotype. n = 16 biological replicates per genotype. b, Volcano plots of log2- fold changes and p-values from DeSeq2 analysis on all samples from (a). Significantly deregulated genes are defined as having FDR < 0.05 and log2 fold change > =1 are in red. DeSeq2 uses a two-sided Wald test with p-values adjusted using Benjamini-Hochberg procedure, where n represents biological replicates. c, Significantly deregulated genes from (b) numbers plotted in a four way Venn diagram. d, Overlap of genes downregulated from (b) in MZ 2-cell embryos with MSigDB Gene ontology collection where FDR q-values are the false discovery rate analog of hypergeometric p-value after correction according to Benjamini-Hochberg procedure. e, DeSeq2 FPKM values (with adjusted p-value <0.05) plotted as fold change relative to mean expression levels in the control samples in each 2-cell embryo for ribosome biogenesis category genes in (d) that are significantly deregulated, where n represents biological replicates. Violin plot elements show data distribution with median and quartiles. Lowest p-value range for the DeSeq2 two-sided Wald test is shown. Exact gene specific p-values are available in the associated source data. f, DeSeq2 FPKM values (with adjusted p-value <0.05) plotted as in (e) for candidate genes involved in ZGA, DNA repair, mitosis and transcription regulation, where n represents biological replicates and violin plots display data distribution with median and quartiles. Lowest p-value range for the DeSeq2 two-sided Wald test is shown. Exact gene specific p-values are shown in the associated source data. Statistical source data are provided in source data Extended Data fig. 5. Source data

Extended Data Fig. 6 Integration of changes in H3K9me3 to that of gene expression during ZGA in KDM4A MZ mutant embryos.

a, Heat maps (left panel) and line graphs (right panel) of averaged H3K9me3 µChIP-Seq signal in Kdm4a+/− (blue) and Kdm4a−/−(MZ) (red) 2-cell embryos at bdH3K4me3 region set as in Fig. 2b. b, Heat maps (left panel) and line graphs (right panel) of averaged H3K9me3 µChIP-seq signal in Kdm4a+/− (blue) and Kdm4a−/−(MZ) (red) 2-cell embryos at non-redundant UCSC genes region set as in Fig. 2c. c, Tracks of µChIP-Seq signal for H3K4me3 and H3K9me3 in oocytes, and of H3K9me3 signal in 2-cell embryos at the Obox6/Crxos and Ubtf loci. Red arrowheads depict transcript directionality and and bdH3K4me3 are highlighted in green. FPKM values from single-embryo RNA-seq expression analysis of selected genes. Genes correspond to those highlighted in the right part of the panel. p-values derived using DeSeq2 and two-sided Wald test and adjusted p-value < 0.05. n represents number of embryos examined, and violin plots show data distribution with median and quartiles (right panel). d, 2-D histogram plotting H3K9me3 abundance at gene bodies (GB) along X-axis, with TSS along Y-axis, for DeSeq2 derived differentially expressed genes in Kdm4a−/−(MZ) 2-cell embryos. n represents number of genes, color indicates expression trend, and intensity indicates ChIP signal strength (left). The green frame indicates the subpopulation used for the scatter plot in the right side of the panel. The r-value indicates the Spearman correlation coefficient within a small subpopulation of genes with similar differences in TSS (0.95 <log2fd <1.05), where changes in gene body H3K9me3 correlates with expression of these genes (green bounding box). e, Beeswarm plot quantifying trends in gene expression change for total differentially expressed genes according to each quadrant as defined in panel (d). The lines represent medians and quartiles and n represents the number of total differentially expressed genes. f, Bar diagram depicting the number of up (orange) or down-regulated (blue) genes in Kdm4a−/−(MZ) 2-cell embryos subdivided within each quadrant as in (d, e). Statistical source data are provided in source data Extended Data fig. 6. Source data

Extended Data Fig. 7 KDM4A loss results in reduced activation of LTR subgroups.

a, Sampling scheme for generation of stranded total RNASeq samples. b, Bar diagram showing changes in expression of selected groups of LTR-containing ERVs expressed in oocytes and 2-cell embryos. Data are from Franke et al., 2017 and developmental categories are from Fig. 4c. c, Volcano plots showing the relationship between log2 fold difference in expression between Kdm4a+/− and Kdm4a−/−(MZ) 2-cell embryos (x-axis) as well as –log10 p-values (y-axis) at selected groups of repeats. n represents six biological replicates per genotype. Each plot shows individual types of repeat-masker entries within the repeat group and the size of each data point is adjusted to its total coverage in the genome. Repeats with atleast six non-zero read count samples and a minimum threshold of 200 reads were fitted to a negative binomial generalized log-linear model and statistic tested using a two-sided gene-wise likelihood ratio test and Benjamini-Hochberg adjusted for multiple testing.

Extended Data Fig. 8 LTR-initiated transcripts are downregulated in the absence of KDM4A.

a, Composed panel showing LITs clustered according to maternal or zygotic enrichment along expression differences in Kdm4a−/− MII oocytes and Kdm4a−/−(MZ) mutant 2-cell embryos. n represents sixteen biological replicates. Heatmap (left panel) shows the expression in each replicate normalized to the average expression across all replicates per cell-type. Beeswarm plot (middle panel) shows average log2 expression change of LITs in 2-cell embryo against MII oocyte. Bold and narrow lines indicate median and interquartile ranges, respectively. Bubble plot (right panel) shows cluster-wise abundance of certain LTR-containing ERV types annotated in the LITs relative to the randomly expected abundance (color), with size of the bubble representing –log10 of adjusted p-values. Statistical significance was calculated using Chi-square tests and Benjamini-Hochberg corrected for multiple testing. b, Normalized mRNA-Seq read coverage in Kdm4a+/+ and Kdm4a−/− MII oocytes along regular gene bodies (left panel) and known annotated LTR-initiated transcripts (LITs) (right panel). X-axis reflects the read position along feature and Y-axis values are normalized to the average of all replicates. c, Normalized mRNA-Seq read coverage in Kdm4a+/− and Kdm4a−/−(MZ) 2-cell embryos along regular gene bodies (left panel) and known annotated LTR-initiated transcripts (LITs) (right panel). X-axis reflects the read position along feature and Y-axis values are normalized to the average of all replicates. d, Composed panel showing LITs clustered with expression changes as in panel (a). n represents sixteen 16 biological replicates per genotype per cell-type. Beeswarm plots show changes in LIT expression in Kdm4a−/− MII oocytes or Kdm4a−/−(MZ) 2-cell embryos. Bold and narrow lines indicate median and interquartile ranges, respectively. Heatmap (right panel) show associated changes in H3K9me3 µChIP-seq signals in Kdm4a−/− MII oocytes in a manner described previously. Statistical source data are provided in source data Extended Data fig. 8. Source data

Extended Data Fig. 9 Testing efficacy of Kdm4a mRNA on maternal H3K9me3 removal in oocytes.

a, Representative immunofluorescence images against H3K9me3 and DNA (DAPI) of control zygotes microinjected with Kdm4a wildtype or H188A mutant mRNA to visualize efficiency of maternal H3K9me3 removal by KDM4A protein. Non-injected mouse zygotes were used as control for H3K9me3 staining. The experiment was performed once on oocytes pooled from three female mice to ensure quality control of freshly prepared mRNA intended for preparing multiple single thaw frozen aliquots for use in rescue experiment (Scale bar: 10 µm).

Supplementary information

Reporting Summary

Supplementary Table 1

Single mouse (DeSeq2) and human MII oocytes FPKM data.

Supplementary Table 2

UCSC genome browser coordinates for regionsets used in µChIP-seq analysis.

Supplementary Table 3

Reproduced Supplementary Table 1 from MacFarlane et al. (2012) with differentially expressed genes in two-cell embryo compared with the oocyte, where UCSC genome browser coordinates were added in addition for such genes in this study.

Supplementary Table 4

DeSeq2-based differentially expressed genes (FPKM) in KDM4A-knockout oocytes, two-, four- and eight-cell mouse embryos. DESeq2 fits the count data to a negative binomial GLM (generalized linear model) and performs a two-sided Wald test on the GLM coefficients (‘nbinomWaldTest’). The P values are then adjusted for multiple comparisons using the Benjamini–Hochberg procedure. n represents sixteen biological replicates per genotype per developmental stage.

Supplementary Table 5

Differentially expressed repeat(s) in KDM4A-knockout two-cell embryos according to Repenrich. Repeats with >200 reads and at least six samples >0 were fitted to a negative binomial generalized log-linear model and statistically tested using a two-sided gene-wise likelihood ratio test Benjamini–Hochberg adjusted for multiple testing. n represents six biological replicates per genotype.

Supplementary Table 6

Repeat coordinates used for quantifying changes in µChIP-seq signal between KDM4A-knockout oocytes and two-cell embryos.

Supplementary Table 7

Genomic coordinates of LITs as defined in Brind’Amour et al. (2018) in tabular form reproduced from Supplementary Data 1 of the published manuscript.

Supplementary Table 8

Changes in read counts at/across LITs in KDM4A-knockout oocytes and embryos.

Source data

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Source Data Extended Data Fig. 1

Statistical source data for Extended Data Fig. 1

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Statistical source data for Extended Data Fig. 6

Source Data Extended Data Fig. 8

Statistical source data for Extended Data Fig. 8

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Sankar, A., Lerdrup, M., Manaf, A. et al. KDM4A regulates the maternal-to-zygotic transition by protecting broad H3K4me3 domains from H3K9me3 invasion in oocytes. Nat Cell Biol 22, 380–388 (2020). https://doi.org/10.1038/s41556-020-0494-z

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