Super-enhancer switching drives a burst in gene expression at the mitosis-to-meiosis transition

Abstract

Owing to bursts in the expression of thousands of germline-specific genes, the testis has the most diverse and complex transcriptome of all organs. By analyzing the male germline of mice, we demonstrate that the genome-wide reorganization of super-enhancers (SEs) drives bursts in germline gene expression after the mitosis-to-meiosis transition. SE reorganization is regulated by two molecular events: the establishment of meiosis-specific SEs via A-MYB (MYBL1), a key transcription factor for germline genes, and the resolution of SEs in mitotically proliferating cells via SCML2, a germline-specific Polycomb protein required for spermatogenesis-specific gene expression. Before entry into meiosis, meiotic SEs are preprogrammed in mitotic spermatogonia to ensure the unidirectional differentiation of spermatogenesis. We identify key regulatory factors for both mitotic and meiotic enhancers, revealing a molecular logic for the concurrent activation of mitotic enhancers and suppression of meiotic enhancers in the somatic and/or mitotic proliferation phases.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The landscape of active enhancers during spermatogenesis.
Fig. 2: The identification of SEs during spermatogenesis.
Fig. 3: A-MYB-binding sites occupy a central location within the H3K27ac peaks of meiotic SEs.
Fig. 4: A-MYB establishes meiotic SEs for the targeted activation of germline genes.
Fig. 5: SCML2 is required for the resolution of mitotic SEs during meiosis.
Fig. 6: The distinct regulation of meiotic SEs on autosomes versus the sex chromosomes.
Fig. 7: The identification of key regulatory factors for mitotic and meiotic enhancers, and SEs.

Data availability

Crosslinking H3K27ac ChIP-seq data reported in this study have been deposited at the Gene Expression Omnibus (GEO) under accession code GSE130652. H3K27ac native ChIP-seq data in WT and A-myb mutant PSs reported in this study are described in the accompanying study by Sakashita et al.46 and are deposited under accession code GSE142173. All other next-generation sequencing datasets used in this study are publicly available. RNA-seq data from THY1+ spermatogonia, PSs and RSs were downloaded from the GEO (accession no. GSE55060)4. ATAC-seq data from KIT+ spermatogonia and PSs were downloaded from the GEO (accession no. GSE102954)10. ChIP-seq data for A-MYB and RNA-seq data from A-myb mutant and control testes were downloaded from the GEO (accession no. GSE44690)33. ChIP-seq data for H3K4me3 and H3K4me2 and RNA-seq data from KIT+ spermatogonia were downloaded from the GEO (GSE89502)37. Although generated for and analyzed in this study, our H3K27ac ChIP-seq data for wild-type PSs and RSs were initially introduced in another study that analyzed active enhancers on the sex chromosomes35; ChIP-seq data for H3K27ac in wild-type PSs and RSs were downloaded from the GEO (GSE107398)35. ChIP-seq data for H3K27ac from embryonic stem cells were downloaded from the GEO (GSE29184)76. ChIP-seq data for H3K27ac from sperm were downloaded from the GEO (accession no. GSE79230)77. Source data are provided with this paper.

Code availability

Source code for all software and tools used in this study, with documentation, examples and additional information, is available at the following URLs: https://github.com/alexdobin/STAR (STAR RNA-seq aligner), http://younglab.wi.mit.edu/super_enhancer_code.html (ROSE), http://crispor.tefor.net (CRISPOR), https://pypi.org/project/MACS2 (MACS2), https://bedtools.readthedocs.io/en/latest/content/installation.html (BEDTools), https://bioconductor.org/packages/release/bioc/html/DESeq2.html (DESeq2), https://david.ncifcrf.gov/summary.jsp (DAVID), https://www.bioinformatics.babraham.ac.uk/projects/seqmonk (SeqMonk), https://github.com/shenlab-sinai/ngsplot (ngsplot), https://github.com/tidyverse/ggplot2 (ggplot2), http://homer.ucsd.edu/homer (HOMER), http://great.stanford.edu/public/html (GREAT), https://imagej.net/Fiji/Downloads (Fiji – ImageJ) and https://github.com/WeirauchLab/RELI (RELI). Information for the BioWardrobe Experiment Management Platform, which is commercial software, is available at https://biowardrobe.com and https://github.com/Barski-lab/biowardrobe.

References

  1. 1.

    Shima, J. E., McLean, D. J., McCarrey, J. R. & Griswold, M. D. The murine testicular transcriptome: characterizing gene expression in the testis during the progression of spermatogenesis. Biol. Reprod. 71, 319–330 (2004).

    CAS  Google Scholar 

  2. 2.

    Namekawa, S. H. et al. Postmeiotic sex chromatin in the male germline of mice. Curr. Biol. 16, 660–667 (2006).

    CAS  Google Scholar 

  3. 3.

    Soumillon, M. et al. Cellular source and mechanisms of high transcriptome complexity in the mammalian testis. Cell Rep. 3, 2179–2190 (2013).

    CAS  Google Scholar 

  4. 4.

    Hasegawa, K. et al. SCML2 establishes the male germline epigenome through regulation of histone H2A ubiquitination. Dev. Cell 32, 574–588 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Sin, H. S., Kartashov, A. V., Hasegawa, K., Barski, A. & Namekawa, S. H. Poised chromatin and bivalent domains facilitate the mitosis-to-meiosis transition in the male germline. BMC Biol. 13, 53 (2015).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Ramskold, D., Wang, E. T., Burge, C. B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Brawand, D. et al. The evolution of gene expression levels in mammalian organs. Nature 478, 343–348 (2011).

    CAS  Google Scholar 

  8. 8.

    Kimmins, S. & Sassone-Corsi, P. Chromatin remodelling and epigenetic features of germ cells. Nature 434, 583–589 (2005).

    CAS  Google Scholar 

  9. 9.

    Hammoud, S. S. et al. Chromatin and transcription transitions of mammalian adult germline stem cells and spermatogenesis. Cell Stem Cell 15, 239–253 (2014).

    CAS  Google Scholar 

  10. 10.

    Maezawa, S., Yukawa, M., Alavattam, K. G., Barski, A. & Namekawa, S. H. Dynamic reorganization of open chromatin underlies diverse transcriptomes during spermatogenesis. Nucleic Acids Res. 46, 593–608 (2018).

    CAS  Google Scholar 

  11. 11.

    Wang, Y. et al. Reprogramming of meiotic chromatin architecture during spermatogenesis. Mol. Cell 73, 547–561 (2019).

    CAS  Google Scholar 

  12. 12.

    Alavattam, K. G. et al. Attenuated chromatin compartmentalization in meiosis and its maturation in sperm development. Nat. Struct. Mol. Biol. 26, 175–184 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Patel, L. et al. Dynamic reorganization of the genome shapes the recombination landscape in meiotic prophase. Nat. Struct. Mol. Biol. 26, 164–174 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Bulger, M. & Groudine, M. Functional and mechanistic diversity of distal transcription enhancers. Cell 144, 327–339 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Ong, C. T. & Corces, V. G. Enhancer function: new insights into the regulation of tissue-specific gene expression. Nat. Rev. Genet. 12, 283–293 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Calo, E. & Wysocka, J. Modification of enhancer chromatin: what, how and why? Mol. Cell 49, 825–837 (2013).

    CAS  Google Scholar 

  17. 17.

    Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Parker, S. C. et al. Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants. Proc. Natl Acad. Sci. USA 110, 17921–17926 (2013).

    CAS  Google Scholar 

  19. 19.

    Loven, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell 155, 934–947 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods 12, 963–965 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Adam, R. C. et al. Pioneer factors govern super-enhancer dynamics in stem cell plasticity and lineage choice. Nature 521, 366–370 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Gaucher, J. et al. Bromodomain-dependent stage-specific male genome programming by Brdt. EMBO J. 31, 3809–3820 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Chuma, S. et al. Tdrd1/Mtr-1, a tudor-related gene, is essential for male germ-cell differentiation and nuage/germinal granule formation in mice. Proc. Natl Acad. Sci. USA 103, 15894–15899 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Deng, W. & Lin, H. miwi, a murine homolog of piwi, encodes a cytoplasmic protein essential for spermatogenesis. Dev. Cell 2, 819–830 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Kneitz, B. et al. MutS homolog 4 localization to meiotic chromosomes is required for chromosome pairing during meiosis in male and female mice. Genes Dev. 14, 1085–1097 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Spitz, F. & Furlong, E. E. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Oatley, J. M., Kaucher, A. V., Avarbock, M. R. & Brinster, R. L. Regulation of mouse spermatogonial stem cell differentiation by STAT3 signaling. Biol. Reprod. 83, 427–433 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Matson, C. K. et al. The mammalian doublesex homolog DMRT1 is a transcriptional gatekeeper that controls the mitosis versus meiosis decision in male germ cells. Dev. Cell 19, 612–624 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Bolcun-Filas, E. et al. A-MYB (MYBL1) transcription factor is a master regulator of male meiosis. Development 138, 3319–3330 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Foulkes, N. S., Mellstrom, B., Benusiglio, E. & Sassone-Corsi, P. Developmental switch of CREM function during spermatogenesis: from antagonist to activator. Nature 355, 80–84 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Li, X. Z. et al. An ancient transcription factor initiates the burst of piRNA production during early meiosis in mouse testes. Mol. Cell 50, 67–81 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Zhou, L. et al. BTBD18 regulates a subset of piRNA-generating loci through transcription elongation in mice. Dev. Cell 40, 453–466 (2017).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Adams, S. R. et al. RNF8 and SCML2 cooperate to regulate ubiquitination and H3K27 acetylation for escape gene activation on the sex chromosomes. PLoS Genet. 14, e1007233 (2018).

    PubMed  PubMed Central  Google Scholar 

  36. 36.

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

    Google Scholar 

  37. 37.

    Maezawa, S. et al. Polycomb protein SCML2 facilitates H3K27me3 to establish bivalent domains in the male germline. Proc. Natl Acad. Sci. USA 115, 4957–4962 (2018).

    CAS  Google Scholar 

  38. 38.

    Ichijima, Y., Sin, H. S. & Namekawa, S. H. Sex chromosome inactivation in germ cells: emerging roles of DNA damage response pathways. Cell Mol. Life Sci. 69, 2559–2572 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Turner, J. M. Meiotic silencing in mammals. Annu. Rev. Genet. 49, 395–412 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Ichijima, Y. et al. MDC1 directs chromosome-wide silencing of the sex chromosomes in male germ cells. Genes Dev. 25, 959–971 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Sin, H. S. et al. RNF8 regulates active epigenetic modifications and escape gene activation from inactive sex chromosomes in post-meiotic spermatids. Genes Dev. 26, 2737–2748 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Harley, J. B. et al. Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity. Nat. Genet. 50, 699–707 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Zhu, Z. et al. Dynamics of the transcriptome during human spermatogenesis: predicting the potential key genes regulating male gametes generation. Sci. Rep. 6, 19069 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Ryser, S. et al. Gene expression profiling of rat spermatogonia and Sertoli cells reveals signaling pathways from stem cells to niche and testicular cancer cells to surrounding stroma. BMC Genomics 12, 29 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Luoh, S. W. et al. Zfx mutation results in small animal size and reduced germ cell number in male and female mice. Development 124, 2275–2284 (1997).

    CAS  Google Scholar 

  46. 46.

    Sakashita, A. et al. Endogenous retroviruses drive species-specific germline transcriptomes in mammals. Nat. Struct. Mol. Biol. https://doi.org/10.1038/s41594-020-0487-4 (2020).

  47. 47.

    Matzuk, M. M. et al. Small-molecule inhibition of BRDT for male contraception. Cell 150, 673–684 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Pattabiraman, S. et al. Mouse BRWD1 is critical for spermatid postmeiotic transcription and female meiotic chromosome stability. J. Cell Biol. 208, 53–69 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Bryant, J. M. et al. Characterization of BRD4 during mammalian postmeiotic sperm development. Mol. Cell Biol. 35, 1433–1448 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Defeo-Jones, D. et al. Cloning of cDNAs for cellular proteins that bind to the retinoblastoma gene product. Nature 352, 251–254 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Scanlan, M. J., Simpson, A. J. & Old, L. J. The cancer/testis genes: review, standardization and commentary. Cancer Immun. 4, 1 (2004).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Sohni, A. et al. The neonatal and adult human testis defined at the single-cell level. Cell Rep. 26, 1501–1517 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Guo, J. et al. The adult human testis transcriptional cell atlas. Cell Res. 28, 1141–1157 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Green, C. D. et al. A comprehensive roadmap of murine spermatogenesis defined by single-cell RNA-Seq. Dev. Cell 46, 651–667 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Hermann, B. P. et al. The mammalian spermatogenesis single-cell transcriptome, from spermatogonial stem cells to spermatids. Cell Rep. 25, 1650–1667 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Chen, Y. et al. Single-cell RNA-seq uncovers dynamic processes and critical regulators in mouse spermatogenesis. Cell Res. 28, 879–896 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Wang, M. et al. Single-cell RNA sequencing analysis reveals sequential cell fate transition during human spermatogenesis. Cell Stem Cell 23, 599–614 (2018).

    CAS  Google Scholar 

  58. 58.

    Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).

    CAS  Google Scholar 

  59. 59.

    Ozata, D. M. et al. Evolutionarily conserved pachytene piRNA loci are highly divergent among modern humans. Nat. Ecol. Evol. 4, 156–168 (2020).

    Google Scholar 

  60. 60.

    Li, E., Bestor, T. H. & Jaenisch, R. Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 69, 915–926 (1992).

    CAS  Google Scholar 

  61. 61.

    Bellve, A. R. Purification, culture and fractionation of spermatogenic cells. Methods Enzymol. 225, 84–113 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Alavattam, K. G., Abe, H., Sakashita, A. & Namekawa, S. H. Chromosome spread analyses of meiotic sex chromosome inactivation. Methods Mol. Biol. 1861, 113–129 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Concordet, J. P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Kartashov, A. V. & Barski, A. BioWardrobe: an integrated platform for analysis of epigenomics and transcriptomics data. Genome Biol. 16, 158 (2015).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Shao, Z., Zhang, Y., Yuan, G. C., Orkin, S. H. & Waxman, D. J. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets. Genome Biol. 13, R16 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Saldanha, A. J. Java Treeview—extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004).

    CAS  PubMed  Google Scholar 

  70. 70.

    Machanick, P. & Bailey, T. L. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Karolchik, D. et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32, D493–D496 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Meyer, L. R. et al. The UCSC Genome Browser database: extensions and updates 2013. Nucleic Acids Res. 41, D64–D69 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Jung, Y. H. et al. Chromatin states in mouse sperm correlate with embryonic and adult regulatory landscapes. Cell Rep. 18, 1366–1382 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Namekawa and Maezawa laboratories for discussions and helpful comments regarding the manuscript. We also thank the CCHMC Research Flow Cytometry Core for sharing MACS equipment, X. Li at the University of Rochester Medical Center for sharing A-myb mutant mice and M.A. Handel at the Jackson Laboratory for sharing the H1T antibody. We acknowledge the following funding sources: a research project grant by the Azabu University Research Services Division, Ministry of Education, Culture, Sports, Science and Technology (MEXT)-Supported Program for the Private University Research Branding Project (2016–2019); a Grant-in-Aid for Research Activity Start-up (19K21196); the Takeda Science Foundation (2019); and the Uehara Memorial Foundation Research Incentive Grant (2018) to S.M.; a Lalor Foundation Postdoctoral Fellowship and JSPS Overseas Research Fellowships to A.S.; an Albert J. Ryan Fellowship to K.G.A.; CCHMC Endowed Scholar and CpG grant awards to M.T.W.; National Institute of Health (NIH) DP2 GM119134 grant to A.B.; and NIH R01 GM122776 and GM098605 grants to S.H.N.

Author information

Affiliations

Authors

Contributions

S.M., A.S. and S.H.N. designed the study. S.M. performed crosslinking ChIP-seq experiments, and A.S. performed native ChIP-seq experiments. A.S. analyzed A-myb mutant mice with the help of K.T. A.S. and K.T. performed CRISPRa experiments. I.N. performed experiments with S.M. S.M., A.S., M.Y., X.C., K.G.A., M.T.W., A.B. and S.H.N. designed and interpreted the computational analyses. S.M., A.S., K.G.A. and S.H.N. wrote the manuscript with critical feedback from all other authors. S.M. and A.S. contributed equally to this work. S.H.N. supervised the project.

Corresponding authors

Correspondence to So Maezawa or Satoshi H. Namekawa.

Ethics declarations

Competing interests

A.B. is a cofounder of Datirium, LLC.

Additional information

Editor recognition statement Beth Moorefield was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Biological replicates for H3K27ac ChIP-seq data.

a, Scatter plots show the reproducibility of H3K27ac ChIP-seq enrichment at individual peaks between biological replicates. Each peak was identified using MACS (P < 1×10−5). H3K27ac ChIP-seq enrichment levels are shown in log2 RPKM values. The color scale indicates H3K27ac ChIP-seq peak density. Pearson correlation values (R) are shown. While generated for and analyzed in this study, our H3K27ac ChIP-seq data for wild-type PS and RS were initially introduced in another study that analyzed active enhancers on the male sex chromosomes (these data are adapted from Adams et al., PLOS Genet 2018)35. b, Average tag density and heatmaps for H3K27ac ChIP-seq at proximal peak regions in KIT+ and PS. Proximal peaks identified in Fig.1d were used for this analysis.

Extended Data Fig. 2 Identification of candidate TFs whose binding sites are enriched in meiotic SEs.

Identification of candidate TFs whose binding sites are enriched in meiotic SEs. HOMER analyses identify 72 TFs that have binding motifs in meiotic SEs. Among the 72 TFs, 9 TFs are highly expressed in PS and RS ( ≥ 4-fold in comparison to their expression levels in spermatogonia).

Extended Data Fig. 3 Spermatogenic phenotypes of A-myb mutant mice.

a, Testis and epididymis sections from wild-type (WT) littermate control (left panels) and A-myb mutants (right panels) at 8 weeks of age. The sections were stained with hematoxylin and eosin. Scale bars: 200 μm. b, Testis sections from WT and A-myb mutant mice immunostained with antibodies raised against H1T. Scale bars: 200 µm. Numbers of H1T+ cells per seminiferous tubule as mean and whiskers, which indicate 25% (bottom) and 75% (top) of the data points from two independent littermate pairs (right panel). ***P < 0.001, unpaired t test. c, Chromosome spreads of wild-type and A-myb mutant PS immunostained with antibodies raised against SYCP3 and H3K27ac. Scale bars: 10 µm. Late pachytene spermatocytes were not detected (N.D.) in A-myb mutant samples. d, Scatter plot depicts relationship between A-MYB enrichments at promoters (TSS ± 1 kb) and dysregulation of meiotic SE-adjacent genes that were also differentially expressed in A-myb mutant testis (211 genes identified in Fig. 4d). Red line represents a regression line. R: Pearson correlation coefficient.

Extended Data Fig. 4 Comparison of H3K27ac ChIP-seq enrichment between wild-type and Scml2-KO cells.

a, MAnorm analysis for H3K27ac peaks in THY1+ and KIT+ spermatogonia between wild-type and Scml2-KO. b, Track views of H3K27ac ChIP-seq enrichment on representative mitotic SEs in spermatogenesis. c, Average tag densities and heatmaps for H3K27ac ChIP-seq enrichment at genomic bivalent domains in PS and in RS.

Extended Data Fig. 5 SCML2 binds to and regulates the resolution of mitotic SEs.

a, Track views of SCML2 enrichment in GS cells and H3K27ac and H3K27me3 ChIP-seq enrichment at a representative mitotic SE in spermatogenesis. Light blue bars represent SCML2-binding sites with the peaks of H3K27ac in spermatogonia. After the mitosis-to-meiosis transition, SCML2 establishes H3K27me3 at these sites. b, MAnorm analysis for SCML2 peaks in GS and THY1+-H3K27ac peaks. c, Pie charts represent the genomic-entity distributions of SCML2-H3K27ac-common distal peaks detected by MAnorm. Meiotic SEs are shown apart from all other enhancers. d, Average tag densities and heatmaps for H3K27ac and H3K27me3 ChIP-seq signal at mitotic SEs that intersect with SCML2 peaks (n = 84). H3K27ac enrichment values: RPM.

Extended Data Fig. 6 ChIP-seq enrichment at various genomic loci.

ad, Box-and-whisker plots show the distributions of enrichment for ChIP-seq enrichment for the indicated genomic loci. Central bars represent medians, the boxes encompass 50% of the data points, and the whiskers indicate 90% of the data points.

Extended Data Fig. 7 Enrichment of H3K4me2, H3K4me3, and H3K27me3 at the promoters of genes adjacent to SEs.

Box-and-whisker plots show the distributions of ChIP-seq enrichment at TSSs ±2 kb for genes adjacent to SEs in spermatogenesis. Central bars represent medians, the boxes encompass 50% of the data points, and the whiskers indicate 90% of the data points.

Supplementary information

Supplementary Information

Supplementary Table 1. Summary of HOMER motif analyses for H3K27ac-enriched regions in KIT+ and PS.

Reporting Summary

Supplementary Data 1

A list of 11,433 distal H3K27ac ChIP-seq peaks that were present in at least one stage of spermatogenesis (for these analyses, we permitted only distal peaks with a normalized enrichment value of ≥4; see Methods).

Supplementary Data 2

A list of SEs identified in this study and their genomic locations.

Supplementary Data 3

A list of 101 genes that were categorized for ‘spermatogenesis’ and their gene expression profiles in each stage of spermatogenesis (RNA-seq RPKM values are shown).

Supplementary Data 4

A list of 2,623 late spermatogenesis genes that are not highly expressed in spermatogonia but are highly expressed in PS and/or RS by a ≥4-fold change as compared to spermatogonia. Their gene expression profiles in each stage of spermatogenesis (RNA-seq RPKM values) are shown.

Supplementary Data 5

TF motif enrichment analysis was performed using a modified version of the HOMER software package that uses a log base 2 scoring system and motifs contained in the Cis-BP database. There are 5,473 motifs contained in version 1.94d of the database. Each row in the data spreadsheet represents one motif. The −log of the P value for the given motif in the given dataset is provided, along with the maximum and standard deviation of these values across the four experiments.

Supplementary Data 6

RELI ChIP-seq intersection significance analysis was performed as previously described (see Methods). For Mitotic enhancer dataset, RELI was used to rank each ChIP-seq dataset based on intersection of genomic coordinates. The negative log of the RELI corrected P value for the given datasets and their gene expression profiles in each stage of spermatogenesis (RNA-seq RPKM values) are shown.

Supplementary Data 7

RELI ChIP-seq intersection significance analysis was performed as previously described (see Methods). For Mitotic super-enhancer dataset, RELI was used to rank each ChIP-seq dataset based on intersection of genomic coordinates. The negative log of the RELI corrected P value for the given datasets and their gene expression profiles in each stage of spermatogenesis (RNA-seq RPKM values) are shown.

Supplementary Data 8

RELI ChIP-seq intersection significance analysis was performed as previously described (see Methods). For Meiotic enhancer dataset, RELI was used to rank each ChIP-seq dataset based on intersection of genomic coordinates. The negative log of the RELI corrected P value for the given datasets and their gene expression profiles in each stage of spermatogenesis (RNA-seq RPKM values) are shown.

Supplementary Data 9

RELI ChIP-seq intersection significance analysis was performed as previously described (see Methods). For Meiotic super-enhancer dataset, RELI was used to rank each ChIP-seq dataset based on intersection of genomic coordinates. The negative log of the RELI corrected P value for the given datasets and their gene expression profiles in each stage of spermatogenesis (RNA-seq RPKM values) are shown.

Source data

Source Data Fig. 1

Statistical source data

Source Data Fig. 2

Statistical source data

Source Data Fig. 3

Statistical source data

Source Data Fig. 4

Statistical source data

Source Data Fig. 5

Statistical source data

Source Data Fig. 6

Statistical source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Maezawa, S., Sakashita, A., Yukawa, M. et al. Super-enhancer switching drives a burst in gene expression at the mitosis-to-meiosis transition. Nat Struct Mol Biol 27, 978–988 (2020). https://doi.org/10.1038/s41594-020-0488-3

Download citation

Search

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