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

Nature Structural & Molecular Biology volume 27, pages 978–988 (2020)Cite this article

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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.

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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.

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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.

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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

Author notes

  1. Akihiko Sakashita

    Present address: Department of Molecular Biology, Keio University School of Medicine, Tokyo, Japan

  2. Satoshi H. Namekawa

    Present address: Department of Microbiology and Molecular Genetics, University of California, Davis, Davis, CA, USA

  3. These authors contributed equally: So Maezawa, Akihiko Sakashita.

Authors and Affiliations

  1. Division of Reproductive Sciences, Division of Developmental Biology, Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    So Maezawa, Akihiko Sakashita, Kazuki Takahashi, Kris G. Alavattam & Satoshi H. Namekawa

  2. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    So Maezawa, Akihiko Sakashita, Masashi Yukawa, Kazuki Takahashi, Kris G. Alavattam, Matthew T. Weirauch, Artem Barski & Satoshi H. Namekawa

  3. Department of Animal Science and Biotechnology, School of Veterinary Medicine, Azabu University, Sagamihara, Kanagawa, Japan

    So Maezawa & Ippo Nakata

  4. Faculty of Science and Technology, Department of Applied Biological Science, Tokyo University of Science, Noda, Chiba, Japan

    So Maezawa

  5. Division of Allergy and Immunology, Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Masashi Yukawa & Artem Barski

  6. Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Xiaoting Chen & Matthew T. Weirauch

  7. Divisions of Biomedical Informatics and Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Matthew T. Weirauch

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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.

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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.

a–d, 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.

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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

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