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Genome-wide nucleosome specificity and function of chromatin remodellers in ES cells

Nature volume 530, pages 113–116 (2016)Cite this article

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Abstract

ATP-dependent chromatin remodellers allow access to DNA for transcription factors and the general transcription machinery, but whether mammalian chromatin remodellers1,2,3 target specific nucleosomes to regulate transcription is unclear. Here we present genome-wide remodeller–nucleosome interaction profiles for the chromatin remodellers Chd1, Chd2, Chd4, Chd6, Chd8, Chd9, Brg1 and Ep400 in mouse embryonic stem (ES) cells. These remodellers bind one or both full nucleosomes that flank micrococcal nuclease (MNase)-defined nucleosome-free promoter regions (NFRs), where they separate divergent transcription. Surprisingly, large CpG-rich NFRs that extend downstream of annotated transcriptional start sites are nevertheless bound by non-nucleosomal or subnucleosomal histone variants (H3.3 and H2A.Z) and marked by H3K4me3 and H3K27ac modifications. RNA polymerase II therefore navigates hundreds of base pairs of altered chromatin in the sense direction before encountering an MNase-resistant nucleosome at the 3′ end of the NFR. Transcriptome analysis after remodeller depletion reveals reciprocal mechanisms of transcriptional regulation by remodellers. Whereas at active genes individual remodellers have either positive or negative roles via altering nucleosome stability, at polycomb-enriched bivalent genes the same remodellers act in an opposite manner. These findings indicate that remodellers target specific nucleosomes at the edge of NFRs, where they regulate ES cell transcriptional programs.

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Figure 1: Correlated occupancies across remodeller-bound nucleosomal regions.
Figure 2: Patterns of remodeller–nucleosome interactions and chromatin features around promoter NFRs.
Figure 3: Remodellers differentially regulate active versus bivalent genes.
Figure 4: Model of how remodellers might regulate distinct classes of genes in ES cells.

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Gene Expression Omnibus

Data deposits

Data have been deposited in Gene Expression Omnibus (GEO) under accession numbers GSE64825 (ChIP-seq, ATAC-seq, MNase-seq and ChIP-exo data) and GSE64819 (microarray).

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Acknowledgements

We thank J. C. Andrau, S. Ravens, L. Tora and M. de Chaldée for critical reading, J. B. Charbonnier’s team for sharing material, A. Krebs, T. Ye, I. Davidson, R. Guerois and F. Ochsenbein for discussions, and A. Martel for computational assistance. This work was supported by the CEA, and by grants from the Association pour la Recherche sur le Cancer (grant 3164), the Agence Nationale de la Recherche (ANR-05-BLAN-0396), the Fondation pour la Recherche Médicale (FRM), the National Institutes of Health (grant HG004160, to B.F.P.) and from Southern Medical University (grant B1000465, to K.Y.).

Author information

Author notes

  1. Fayçal Boussouar

    Present address: Present address:INSERM, U823; Université Grenoble Alpes; Institut Albert Bonniot Grenoble, 38700 Grenoble, France,

  2. Maud de Dieuleveult, Kuangyu Yen, Isabelle Hmitou and Arnaud Depaux: These authors contributed equally to this work.

  3. Kuangyu Yen, B. Franklin Pugh and Matthieu Gérard: These authors jointly supervised this work.

Authors and Affiliations

  1. Institute for Integrative Biology of the Cell (I2BC), IBITECS, CEA, CNRS, Université Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette, 91198, France

    Maud de Dieuleveult, Isabelle Hmitou, Arnaud Depaux, Fayçal Boussouar, Daria Bou Dargham, Sylvie Jounier, Hélène Humbertclaude, Lucie Carrière, Soizick Berlivet, Michel Werner, Jean-Christophe Aude & Matthieu Gérard

  2. Department of Developmental Biology, Key Laboratory of Zebrafish Modeling and Drug Screening for Human Diseases of Guangdong Higher Education Institutes, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, China

    Kuangyu Yen

  3. Department of Biochemistry and Molecular Biology, Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, 16802, Pennsylvania, USA

    Kuangyu Yen, Nina P. Farrell, Bongsoo Park & B. Franklin Pugh

  4. Centre National de Génotypage, Institut de Génomique, CEA, 91057, Evry, France

    Florence Ribierre, Céline Baulard, Jean-François Deleuze, Robert Olaso & Sophie Chantalat

  5. IGBMC (Institut de Génétique et de Biologie Moléculaire et Cellulaire), INSERM, U964, CNRS, UMR7104, Université de Strasbourg, Illkirch, 67404, France

    Céline Keime

  6. Centre Nacional D’Anàlisi Genòmica, Barcelona, 08028, Spain

    Marta Gut & Ivo Gut

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Contributions

M.D.D., I.H., A.D., F.B., D.B.D., S.J. and H.H. built cell lines and performed ChIP-seq and shRNA transfections. I.H., C.B. and R.O. conducted transcriptome experiments and analysis. F.R. and S.C. performed FAIRE-seq, ATAC-seq and RT-qPCR experiments. M.Ge. and S.C. carried out MNase-seq experiments. S.B. provided the pHyper shRNA vector. K.Y., M.Ge., L.C., C.K., B.P. and J.-C.A. conducted bioinformatics analysis. N.P.F. performed ChIP-exo and DNA sequencing at PSU. I.G., M.Gu. and J.-F.D. supervised DNA sequencing at CNAG and CNG. M.Ge., K.Y., S.C., M.W. and B.F.P. supervised the project. M.Ge., K.Y. and B.F.P. co-wrote the manuscript.

Corresponding authors

Correspondence to Kuangyu Yen, B. Franklin Pugh or Matthieu Gérard.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Experimental strategy for genome-wide remodeller–nucleosome interactions and transcriptome analysis in ES cells.

Using homologous recombination in ES cells, a sequence encoding a combination of Flag and haemagglutinin (HA) epitopes was introduced at the 3′ end of the coding sequence of the genes encoding the catalytic subunit of each remodeller. After in vivo crosslinking, chromatin was prepared and fragmented to mononucleosomes by MNase. Remodeller-bound mononucleosomes were isolated using a double-immunoaffinity procedure. Immunopurification efficiency was assessed by western blotting. Deep sequencing of the DNA from purified nucleosomes allowed the mapping of remodeller-bound nucleosomes across the mouse genome. The same tagged ES cell lines were used for shRNA-mediated depletion of remodellers and transcriptome analysis.

Extended Data Figure 2 Remodeller binding profile at a representative locus.

Counts indicate reads per 10 million. Promoters and enhancers are highlighted by blue and orange squares, respectively.

Extended Data Figure 3 Relationship between remodeller enrichment at promoters and RNA expression level.

Average binding profile of remodellers at promoters, divided in four quartiles based on RNA expression level of the corresponding genes. All promoters are transcribed from left to right. Promoter binding intensity of Chd1, Chd2, Chd9 and Ep400 at H3K4me3 promoters was correlated with RNA expression (see Methods). Consequently, binding of these remodellers to bivalent promoters, which are transcribed at lower levels, showed a significant reduction compared to H3K4me3 promoters. By contrast, Chd4, Chd6 and Brg1 enrichment at promoters showed little correlation with the transcription level of the corresponding genes, and was only slightly lower at bivalent, compared to H3K4me3 promoters.

Extended Data Figure 4 Comparison of MNase ChIP-seq and sonication ChIP-seq for Chd4.

The left panel shows the reference nucleosome map of 14,623 RefSeq genes, rank-ordered from smallest to largest NFR length, as in Fig. 2. The two panels on the right compare the distribution patterns obtained for Chd4 either by MNase ChIP-seq, with chromatin prepared from Chd4-tagged ES cells, or by ChIP-seq with sonicated chromatin (data set accession number: GSM687284).

Extended Data Figure 5 Nucleosome targeting by remodellers at H3K4me3-only and bivalent promoters.

Remodeller-bound nucleosomal tags were aligned to the promoters of 6,481 active (H3K4me3 promoters) or 3,411 bivalent genes, rank-ordered from narrow to wide NFR. Corresponding reference nucleosomes, remodeller occupancy and the other indicated features are shown as in Fig. 2.

Extended Data Figure 6 Western blot analysis of remodeller depletion by shRNA for transcriptome analysis.

ES cells tagged for each remodeller were transfected with the corresponding shRNA vector, or a control plasmid. After puromycin selection, ES cells were collected for RNA preparation and western blot analysis. Three independent experiments (indicated as 1, 2 and 3) were performed for each remodeller. Remodeller depletion was assessed using antibodies against Flag or HA epitopes. Loading control: Gapdh. For gel source data, see Supplementary Fig. 1.

Extended Data Figure 7 Validation of remodeller depletion effects on transcription by RT–qPCR.

Remodellers and histone marks enrichment profiles are shown as indicated on the left of each panel. A control ChIP profile, obtained with untagged ES cells, is shown for comparison. Scores indicate reads per 10 million. Shown on the right of each panel are the results of RT–qPCR analysis that quantify RNA expression levels of the corresponding genes upon remodeller depletion in ES cells. Two distinct shRNA vectors (shRNA 1 and shRNA 2, see Methods) were used for each remodeller. Scores on the y axis indicate the relative expression of the indicated genes compared to reference genes. Values are mean and s.d. of three independent transfection experiments.

Extended Data Figure 8 Effect of remodeller depletion on chromatin accessibility at promoters.

Consequence of remodeller depletion by shRNA vectors on ATAC-seq average profiles at all H3K4me3-only (top) and bivalent (bottom) promoters. Two replicate experiments are shown on each graph for both remodeller knockdown and controls.

Extended Data Figure 9 Analysis of Pol II distribution at promoters in remodeller-depleted ES cells.

a, Average Pol II distribution (ChIP-exo) profile in control ES cells (black) or after indicated remodeller knockdown (colour) at H3K4me3-only (left) and bivalent (right) genes. Left and right panels within a set represent the set of genes that are most upregulated (red) or downregulated (green) after remodeller knockdown. Pol II occupancy is indicated within a window spanning 500 and 2,000 bp on the upstream and downstream side of the TSS, respectively. All promoters are transcribed from left to right. b, Bargraphs showing Pol II occupancy change after remodeller knockdown relative to control, measured by ChIP-exo, at genes either downregulated (green) or upregulated (red) after the depletion of the indicated remodeller. c, Pol II distribution of remodeller knockdown at a representative locus. Counts indicate reads per 10 million. Pol II loading is markedly reduced at the Tyms narrow NFR, H3K4me3 promoter by either Ep400 and Chd4 depletion, suggesting that these two remodellers contribute to Pol II recruitment.

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de Dieuleveult, M., Yen, K., Hmitou, I. et al. Genome-wide nucleosome specificity and function of chromatin remodellers in ES cells. Nature 530, 113–116 (2016). https://doi.org/10.1038/nature16505

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