Broad histone H3K4me3 domains in mouse oocytes modulate maternal-to-zygotic transition (original) (raw)

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All data from this study have been deposited in the Gene Expression Omnibus database under the accession number GSE72784.

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Acknowledgements

We thank the members of the Ren laboratory for support and critical suggestions throughout the course of this work. We are thankful to L. Ellevog, G. Flor Lien, T. Catterall, S. Khan, I. Johnson, the Norwegian Transgenic Center, the Animal Facility OUS and Genoway for help with embryo collection, animal care and morpholino injections. We are grateful to A. Local for the gift of recombinant histone octameres and to R. Ahmed, M. Indahl and E. Skarpen for assistance with staining and imaging of embryos. We are also thankful to K. Shirane (Kyushu University) for sharing data. I. Jung would like to give special thanks to Y. Lee. This work was funded by the Ludwig Institute for Cancer Research, U54HG006997 (to B.R.), American Heart Association Postdoctoral Fellowship (to I. Jung), the Oslo University Hospital Early Career Award (to J.A.D.), the Norwegian Cancer Society (to A.K., J.A.D.), the Anders Jahre Foundation (to J.A.D.) and the Norwegian Research council (to A.K.).

Author information

Author notes

  1. John Arne Dahl and Inkyung Jung: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Microbiology, Oslo University Hospital, Rikshospitalet, NO-0027, Oslo, Norway
    John Arne Dahl, Håvard Aanes, Adeel Manaf, Rajikala Suganthan, Magnar Bjørås & Arne Klungland
  2. Ludwig Institute for Cancer Research, La Jolla, 92093, California, USA
    Inkyung Jung, Guoqiang Li, Samantha Kuan, Bin Li, Ah Young Lee, Sebastian Preissl & Bing Ren
  3. Department of Gynecology, Section for Reproductive Medicine, Oslo University Hospital, Rikshospitalet, NO-0027, Oslo, Norway
    Gareth D. Greggains & Peter Fedorcsak
  4. The Biotech Research and Innovation Centre and Centre for Epigenetics, University of Copenhagen, Copenhagen, DK-2200, Denmark
    Mads Lerdrup & Klaus Hansen
  5. Norwegian Transgenic Centre, Institute of Basic Medical Sciences, University of Oslo, Oslo, NO-0317, Norway
    Ingunn Jermstad & Knut Tomas Dalen
  6. Department of Tumor Biology and Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, NO-0424, Norway
    Mads Haugland Haugen
  7. Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, NO-7491, Norway
    Magnar Bjørås
  8. Department of Nutrition, Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, NO-0027, Norway
    Knut Tomas Dalen
  9. Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, California, 92093, USA
    Bing Ren
  10. UCSD Moores Cancer Center, University of California, San Diego, La Jolla, 92093, California, USA
    Bing Ren
  11. Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, NO-0317, Norway
    Arne Klungland

Authors

  1. John Arne Dahl
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  2. Inkyung Jung
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  3. Håvard Aanes
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  4. Gareth D. Greggains
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  5. Adeel Manaf
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  6. Mads Lerdrup
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  7. Guoqiang Li
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  8. Samantha Kuan
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  9. Bin Li
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  10. Ah Young Lee
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  11. Sebastian Preissl
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  12. Ingunn Jermstad
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  13. Mads Haugland Haugen
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  14. Rajikala Suganthan
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  15. Magnar Bjørås
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  16. Klaus Hansen
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  17. Knut Tomas Dalen
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  18. Peter Fedorcsak
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  19. Bing Ren
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  20. Arne Klungland
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Contributions

J.A.D., I. Jung, B.R., and A.K. conceived the study. J.A.D. led the experiments with assistance from I. Jung, G.D.G., A.M., G.L., S.P., A.Y.L., I.J., M.H.H. and R.S. J.A.D. developed μChIP–seq, performed μChIP–seq with oocytes and embryos. J.A.D., A.M., I.J. and R.S. collected and prepared embryos, growing and mature oocytes. K.T.D. and M.B. supervised the mouse work. M.H.H., A.M. and J.A.D. performed western blot. G.D.G. performed and J.A.D. and P.F. supervised knockdown experiments and time laps imaging. G.D.G. performed I.F. with assistance from A.M. I. Jung performed RNA-seq with assistance from S.P. I. Jung and G.L. performed WGBS. I. Jung led the data analysis with assistance from H.A. and M.L. K.H. supervised data analysis by M.L. S.K. and B.L. operated sequencing instruments and data processing. J.A.D. and I. Jung prepared the manuscript with assistance from H.A., M.L., B.R., and A.K. All authors read and commented on the manuscript.

Corresponding authors

Correspondence toJohn Arne Dahl, Bing Ren or Arne Klungland.

Ethics declarations

Competing interests

Inven2 (on behalf of J.A.D. and A.K.) have registered a patent application entitled ‘ChIP–seq assays’.

Additional information

Reviewer Information Nature thanks R. Schultz and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Proof of principle for μChIP–seq in human NCCIT cells and mouse ES cell.

a, Genome browser snapshot for H3K4me3 μChIP–seq with different numbers of human NCCIT cells. The number of cells used is shown at the beginning of each genome browser track. Input ChIP–seq and RefSeq gene annotation are shown in the two last rows. b, The percentage of overlapping H3K4me3 peaks between the top-10,000 H3K4me3 peaks detected from multiple μChIP–seq experiments with different numbers of human NCCIT cells. c, A genome browser snapshot for H3K27ac and H3K4me3 μChIP–seq in mouse E14 ES cells. ENCODE ChIP–seq is also shown. Cell number in μChIP–seq experiments ranged from 100,000 to 500 cells. d, The percentage of overlapping H3K4me3 peaks between top 10,000 H3K4me3 peaks when comparing multiple μChIP–seq experiments performed with different numbers of mouse ES cells. Applying μChIP–seq to 1,000 cells allowed detection of 90% of ENCODE peaks and 94% of peaks from large scale ChIP–seq from the same ES-cell culture batch. e, The percentage of overlapping H3K27ac peaks between multiple experiments using different numbers of mouse ES cells. We considered the top 10,000 H3K27ac peaks in each sample. The sample named ENCODE are H3K27ac peaks from the ENCODE dataset. f, A scatter plot comparing RPKM values obtained from 1,000-cell μChIP–seq and ENCODE ChIP–seq results using an H3K4me3 antibody in mouse ES cells. Each dot represents an H3K4me3 peak identified using the H3K4me3 μChIP–seq results (n = 26,397). Pearson correlation coefficient is also shown. g, Receiver operating characteristic (ROC) curve plots for H3K4me3 peaks identified from multiple μChIP–seq experiments. True H3K4me3 peaks were defined using ENCODE H3K4me3 ChIP–seq result and false H3K4me3 peaks were created by randomly selecting genomic regions. For the 1,000-cell μChIP–seq result we also calculated ROC scores for two additional subsets of true H3K4me3 peaks; the top 30,000 and 40,000 H3K4me3 ENCODE peaks. h, Box plots of GC content comparing 500-cell H3K4me3 ChIP–seq detected and undetected ENCODE H3K4me3 peaks (Kolmogorov–Smirnov test P = 0.81). i, Box plots of ENCODE H3K4me3 signal comparing 500-cell H3K4me3 ChIP–seq detected and undetected H3K4me3 peaks (Kolmogorov–Smirnov test P < 2.2−16). Whiskers correspond to the highest and lowest points within the 1.5× interquartile range (h, i).

Extended Data Figure 2 Reproducibility of μChIP–seq experiments.

a, Genome browser snapshots of H3K4me3 and H3K27ac μChIP–seq results in MII oocytes, 2- and 8-cell embryos and ES cells. For each histone-modification mark, we generated two biological replicates. b, Genome browser snapshots of H3K4me3 μChIP–seq results from P12 and P15 oocytes. We generated two biological replicates. c, d, Bar plots show the Pearson correlation coefficient between two biological replicates for H3K4me3 (c) and H3K27ac (d). Pearson correlation coefficients were calculated using RPKM values for 1-kb-binned regions of the whole genome.

Extended Data Figure 3 Relationship between promoter H3K4me3 and expression

a, Box plots for H3K4me3 RPKM values at promoter regions after grouping promoters into 10 equally sized groups according to gene expression levels. b, Box plots for gene expression levels for each group are shown. Whiskers correspond to the highest and lowest points within the 1.5× interquartile range (a, b). c, Fraction of the mouse genome covered by H3K4me3 peaks at different developmental stages and in various adult tissues26. H3K4me3 peaks were defined using default MACS2 peak call settings except for oocyte. Oocyte H3K4me3 peaks were defined using MACS2 broad peak call settings. d, Scatter plot of H3K4me3 signals in 1kb bins between oocyte and 2-cell-stage embryos. Genomic regions marked with a strong H3K4m3 signal in the oocyte show reduced signal in 2-cell-stage embryos. e, Fraction of the mouse genome covered by H3K27ac peaks using default MACS2 peak call settings for different developmental stages.

Extended Data Figure 4 H3K4me3 and DNA staining in oocytes, zygotes and cleavage-stage embryos.

a, Panel from Fig. 1d with consistently adjusted brightness and contrast to allow visual observation of H3K4me3 signal following rapid loss at the late 2-cell stage. The signal of the strongly staining stages appears saturated due to this. b, c, Quantification of H3K4me3 and DNA staining by integrated H3K4me3-specific and DAPI-specific fluorescence intensity, respectively, over pronuclei and nuclei was determined using an epifluorescence microscope and the ImageJ software. The nuclear DNA content of ‘early’ and ‘late’ 2-cell embryos reveals that DNA synthesis is almost completed at the ‘early’ time point. Therefore suggesting an active mechanism of H3K4me3 removal rather than passive dilution over DNA replication (n = 18, 18, 20, 41, 18, 74 for respective time points; error bars show s.e.m.).

Extended Data Figure 5 Broad H3K4me3 domain calling.

a, Genome browser snapshot of H3K4me3 μChIP–seq and RNA-seq signals. The H3K4me3 μChIP–seq signal in oocytes shows enrichment over broader regions compared to sperm, 2- and 8-cell embryos and mouse ES cells (mESCs). The broad H3K4me3 domain call is shown as a green bar below the oocyte H3K4me3 track. The TSS of Foxa1 (ZGA gene) is located in a broad H3K4me3 domain. The Foxa1 gene becomes expressed in 2- and 8-cell embryos. b, Decreasing fraction of H3K4me3 domains in oocytes when increasing the distance threshold to merge adjacent H3K4me3 peaks. At 5 kb (asterisk), this fraction becomes stable, which is used as the threshold to call broad H3K4me3 domains in oocytes. c, Histogram showing fraction of H3K4me3 domains of various breadth for TSS-containing (n = 15,608) and non-TSS-containing (n = 47,934) domains. TSS-containing H3K4me3 domains tend to be broader (median, 12 kb) than non-TSS-containing domains (median, 3.7 kb) (P value < 2.2−16, Kolmogorov–Smirnov test). d, An anti-correlated relationship between genomic regions covered by H3K4me3 (x axis) and median-input-normalized H3K4me3 ChIP–seq RPKM values at 5,000 top-ranked promoters (y axis). The samples more widely covered by H3K4me3 tend to show relatively lower H3K4me3 RPKM values. e, Line plots are shown for median input normalized H3K4me3 RPKM values (y axis) with corresponding number of top-ranked promoters (x axis). When the genomic coverage of H3K4me3 is similar between samples (liver, cerebellum, and heart) the median of input normalized H3K4me3 RPKM values are highly consistent between samples at a given number of top-ranked promoters. f, Robustness of H3K4me3 RPKM adjustment factor. y axis indicates H3K4me3 RPKM adjustment factor for each cell type. Mouse ES cell data are used as a reference (that is, adjustment factor = 1). x axis indicates number of top-ranked promoters that was used to calculate H3K4me3 RPKM adjustment factor. The adjustment factors are very robust regardless of number of top-ranked promoters considered. g–i, Genome browser tracks for several loci show H3K4me3 μChIP–seq for oocytes, 2-cell and 8-cell embryos and mouse ES cells. y axis indicates auto scale for H3K4me3 raw reads (g), fixed scale for H3K4me3 RPKM (h), and fixed scale for H3K4me3 signal after applying the adjustment factor (i).

Extended Data Figure 6 Establishment of broad H3K4me3 domains in oocytes and removal in the early embryo.

a, b, Heat maps of H3K4me3 raw read counts normalized by total read numbers in indicated stages for non-TSS (a) and TSS-containing H3K4me3 domains (b) ordered according to increasing size. Horizontal positions reflect spatial distribution relative to the domain centre, and densities correspond to H3K4me3 signal intensity. c, Genome browser snapshot of H3K4me3 μChIP–seq from P12, P15 and MII oocytes. d, Superimposed tracks of H3K4me3 signal in P12, P15 and MII oocytes at the boundaries of non-TSS-containing H3K4me3 domains. The y axes show H3K4me3 signal, colouring reflects the total count of domains with a given signal intensity, and horizontal positions reflect spatial distribution relative to the domain border.

Extended Data Figure 7 DNA methylation level within and outside broad H3K4me3 domains.

a, Percentage of broad domains displaying hypomethylated (<0.25), hypermethylated (>0.75) and intermediate (>0.25 and <0.75) levels within TSS-containing domains, non-TSS-containing domains, and outside broad domains. b, Histograms of average DNA methylation levels within TSS-containing (n = 15,135) and non-TSS-containing domains (n = 35,567), and outside of domains (n = 61,674). Domains containing at least 5 covered CpGs were included. c, Bar plots displaying the fraction of ZGA genes5 and genes not expressed that reside in broad H3K4me3 domains. (***P < 10−3, Fisher’s exact test). d, e, Heat maps of H3K4me3 and DNA methylation signal for indicated oocyte stages for non-TSS-containing H3K4me3 domains ordered according to increasing sizes. Horizontal positions reflect spatial distribution relative to the domain centre, and densities correspond to H3K4me3 signal intensity (d) or CpG methylation level (e). NGO, non-growing oocyte; GVO, germinal vesicle oocytes. f, Bar plots displaying the number of genes expressed from the maternal allele for genes that reside in broad H3K4me3 domains. The maternally expressed genes are significantly enriched within broad H3K4me3 domains compared to what would be expected by chance (***P < 10−3, Fisher’s exact test). g, _k-_means clustered heat maps of H3K4me3 and DNA methylation signal for P12, P15 and MII oocytes at H3K4me3 domain boundaries. Boundaries of domains larger than 5 kbp were clustered according to H3K4me3 and DNA methylation signals at the 2-kbp regions flanking the boundaries, and densities correspond to H3K4me3 signal or CpG methylation level. C1–C10, Cluster 1–10. h, i, Box plots for distance from domain boundary to the nearest CpG island (h) and bar plots for fraction of TSS-containing broad domains (i) in each cluster. Whiskers correspond to the highest and lowest points within the 1.5× interquartile range. j–l, Line plots are shown for average DNA methylation profiles at both 5′ and 3′ end of ZGA gene TSS-containing broad domains (n = 2,313) (j) and non-TSS-containing broad domains (n = 12,691) (k) and other TSS-containing domains (remaining TSS-containing domains after excluding ZGA gene TSS-containing domains, n = 6,390) (l). Domains larger than 10 kb were included. y axis indicates DNA methylation levels and x axis indicates distance relative to the domain boundaries. Black, green, blue, and orange colour indicates sperm, oocyte, P15, and P12, respectively (P < 2.2−16, Kolmogorov–Smirnov test of sperm DNA methylation within broad domains between ZGA gene- and non-TSS/other TSS-containing domains).

Extended Data Figure 8 Identification of stage-restricted _c_REs.

a, Box plots of the shortest distance between 2-/8-cell stage restricted _c_REs (n = 33,609) and between randomly selected cREs (n = 33,609). 2-/8-cell common _c_REs are significantly closer to each other than other _c_REs (P < 2.2−16, Kolmogorov–Smirnov test). b, H3K27ac levels at putative _c_REs in the vicinity of non-ZGA genes (n = 13,334). c, Enriched motifs found in different groups of stage restricted _c_REs. d, Box plots of gene expression values (FPKM) for each developmental stage for genes in the vicinity of stage restricted _c_REs (n = 1,612 for oocyte, n = 4,166 for 2-cell, n = 2,005 for 8-cell, n = 6,236 for 2/8-cell common, and n = 1,357 for mouse ES cells). Whiskers correspond to the highest and lowest points within the 1.5× interquartile range (a, b, d). e, Accumulated fraction curve of _c_REs according to distance between _c_REs and nearest broad H3K4me3 domains. Putative _c_REs in the vicinity of ZGA genes are positioned closer to broad H3K4me3 domains (P < 2.2−16, Kolmogorov–Smirnov test).

Extended Data Figure 9 Histone H3 lysine 4 methyltransferases and demethylases in relation to establishment and removal of broad H3K4me3 domains.

a, Gene expression levels for multiple H3K4 methyltransferases and demethylases in the oocyte, 2- and 8-cell-stage embryos and mouse ES cells. b, Genome browser snapshot of the KMT2B dependent Cdkn1a gene. The Cdkn1a gene is not expressed in oocytes, but highly expressed in 2- and 8-cell-stage embryos. c, Average GC content in oocyte H3K4me3 covered regions maintained or demethylated in the 8-cell-stage embryo. Regions where H3K4me3 is specifically maintained show higher GC content (P < 2.2−16, Kolmogorov–Smirnov test) d, Simple western immunoblots show Kdm5a and Kdm5b morpholino knock-down of KDM5A and KDM5B proteins in 2-cell embryos. β-actin is shown as loading control. For gel source data, see Supplementary Fig. 1. e, Quantification of H3K4me3 immunofluorescence signal in late 2-cell stage embryos after injection with morpholinos targeting Kdm5a and Kdm5b (n = 37), or 5-base mismatch-control morpholinos (n = 23). (H3K4me3 signal intensity, arbitrary units. P = 2.2−4, Kolmogorov–Smirnov test.). Error bars show the s.d.

Extended Data Figure 10 RNA-seq analysis of KDM5A and KDM5B depleted 2-cell embryos.

a, Scatter plots show FPKM values of GENCODE annotated genes (each dot, n = 32,287) between two biological replicates for four different conditions. Pearson correlation coefficient values are also shown. b, Box plots show the effect of α-amanitin treatment on transcriptional inhibition (Kolmogorov–Smirnov test P < 2.2−16). c, Scatter plots show FPKM values of GENCODE annotated genes (each dot, n = 32,287) between Kdm5a and Kdm5b morpholino (Kdm5a+b MO)-injected embryos (x axis) and α-amanitin-treated embryos (y axis). We present log2-transformed average FPKM values from two biological replicates. Red lines indicate twofold up- or downregulation. d, Scatter plots show FPKM values of GENCODE annotated genes between Kdm5a+b MO injected embryos (x axis) and control MO injected embryos (y axis). We present log2-transformed average FPKM values from two biological replicates. Red lines indicate 1.5-fold up- or downregulation. Grey, all genes; blue, downregulated ZGA genes. e, Box plots are shown for FPKM values of maternally expressed genes in oocyte, Kdm5a and Kdm5b morpholino replicates, and control morpholino replicates. NS indicates non-significant P value from one-sided paired _t_-test. In 2-cell embryos maternally expressed genes are significantly down regulated in both Kdm5a and Kdm5b morpholino and control morpholino injected embryos (P values <2.2−16, Kolmogorov–Smirnov test). Whiskers correspond to the highest and lowest points within the 1.5× interquartile range (b, e). f, Rate of development to various embryonic stages for Kdm5a and Kdm5b morpholino injected and control morpholino injected embryos resulting from natural mating (control MO, n = 15; Kdm5a+b MO, n = 26). g, Number of embryos at the 8-cell and 4-cell or abnormal stage in Kdm5a+b MO (n = 46) and control MO (n = 42) (Fisher’s exact test P = 0.028, odds ratio = 3.5), for in vitro fertilized embryos for the experiment shown in Fig. 5c.

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Dahl, J., Jung, I., Aanes, H. et al. Broad histone H3K4me3 domains in mouse oocytes modulate maternal-to-zygotic transition.Nature 537, 548–552 (2016). https://doi.org/10.1038/nature19360

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