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Acute BAF perturbation causes immediate changes in chromatin accessibility

Nature Genetics volume 53, pages 269–278 (2021)Cite this article

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Abstract

Cancer-associated, loss-of-function mutations in genes encoding subunits of the BRG1/BRM-associated factor (BAF) chromatin-remodeling complexes1,2,3,4,5,6,7,8 often cause drastic chromatin accessibility changes, especially in important regulatory regions9,10,11,12,13,14,15,16,17,18,19. However, it remains unknown how these changes are established over time (for example, immediate consequences or long-term adaptations), and whether they are causative for intracomplex synthetic lethalities, abrogating the formation or activity of BAF complexes9,20,21,22,23,24. In the present study, we use the dTAG system to induce acute degradation of BAF subunits and show that chromatin alterations are established faster than the duration of one cell cycle. Using a pharmacological inhibitor and a chemical degrader of the BAF complex ATPase subunits25,26, we show that maintaining genome accessibility requires constant ATP-dependent remodeling. Completely abolishing BAF complex function by acute degradation of a synthetic lethal subunit in a paralog-deficient background results in an almost complete loss of chromatin accessibility at BAF-controlled sites, especially also at superenhancers, providing a mechanism for intracomplex synthetic lethalities.

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Fig. 1: A dTAG knock-in HAP1 cell line for acute degradation of SMARCA4.
Fig. 2: SMARCA4 degradation leads to rapid genome-wide accessibility changes.
Fig. 3: BAF complex ATPase inhibition confirms fast kinetics of DNA accessibility changes and ATPase activity requirements.
Fig. 4: Synthetic lethal conditions cause accessibility loss at additional chromatin sites.
Fig. 5: Superenhancers lose accessibility in BAF synthetic lethal conditions.

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Data availability

NGS data are deposited in the National Center for Biotechnology Information’s Gene Expression Omnibus repository (accession no. GSE148175) and MS data in PRIDE (project accession no. PXD018381).

Code availability

All code is available via GitHub (https://github.com/Kubicek-Lab-at-CeMM/BAF-kinetics).

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Acknowledgements

We thank the Biomedical Sequencing Facility, the Proteomics and Metabolomics Facility and the Platform Austria for Chemical Biology (PLACEBO) at CeMM for their support in generating and analyzing the NGS or proteomics data, respectively. We thank Haplogen and Horizon Discovery for HAP1 cell lines. Research in the Kubicek laboratory is supported by the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology, and Development, the Austrian Science Fund (FWF) F4701 and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC-CoG-772437). C.B. is supported by an ERC Starting Grant (European Union’s Horizon 2020 research and innovation program, grant agreement no. 679146). S.G. is supported by the Peter and Traudl Engelhorn Foundation.

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  1. These authors contributed equally: Sandra Schick, Sarah Grosche, Katharina Eva Kohl.

Authors and Affiliations

  1. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Sandra Schick, Sarah Grosche, Katharina Eva Kohl, Danica Drpic, Martin G. Jaeger, Nara C. Marella, Hana Imrichova, Jung-Ming G. Lin, Gerald Hofstätter, Michael Schuster, André F. Rendeiro, Anna Koren, Christoph Bock, André C. Müller, Georg E. Winter & Stefan Kubicek

  2. Christian Doppler Laboratory for Chemical Epigenetics and Antiinfectives, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Sandra Schick, Katharina Eva Kohl & Stefan Kubicek

  3. Institute of Molecular Biology, Mainz, Germany

    Sandra Schick

  4. Boehringer Ingelheim RCV GmbH & Co KG, Vienna, Austria

    Mark Petronczki

  5. Institute of Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria

    Christoph Bock

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Contributions

S.S., M.P. and S.K. planned the study and designed the experiments. K.K., S.S., D.D., M.J., G.H., J.G.L. and A.K. performed the experiments. M.J. and G.W. provided dTAG expertise, constructs and compounds. S.G., A.F.R., N.M., A.M., M.J., M.S., H.I., S.S. and S.K. analyzed the data. S.S. and S.K. wrote the manuscript with input from all coauthors. S.S., M.P., A.M., C.B., G.W. and S.K. supervised the work.

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Correspondence to Sandra Schick or Stefan Kubicek.

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Competing interests

M.P. is an employee of Boehringer Ingelheim RCV GmbH & Co KG. G.W. and S.K. are co-founders and shareholders of Proxygen GmbH. The other authors declare no competing interests.

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Peer review information Nature Genetics thanks Blaine Bartholomew, Julie Lessard and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Effects of SMARCA4 degradation on BAF complex members.

a, Western blot analysis of HAP1 ARID2KOSMARCA4dTAG cells after treatment for different times with 700 nM dTAG47, dTAG13, dTAG7 or DMSO as control (cropped images). b, Semi-quantitative analysis of SMARCA4 protein levels based on whole-cell extract western blot data normalized to α-Tubulin (shown in Fig. 1b) and based on nuclear-extract proteomics data (shown in Fig. 1d). Cropped western blot images of dTAG47 time-course in SMARCA4dTAG cells. c, Western blot analysis of HAP1 SMARCA4dTAG cells after treatment for different times with 300 nM dTAG47 or DMSO as control in various cellular fractions. Staining for SMARCA4, SMARCA2 and SMARCC1 as well as α-Tubulin and RCC1 as control. Cropped images, WCE=whole cell extract. Semi-quantitative analysis of SMARCA4 protein levels based on nucleoplasm and chromatin fraction western blot data normalized to RCC1. RCC1 run on a separate plot from the same experiment and was processed in parallel. d, Western blot analyses of BAF subunit members after SMARCA4 degradation (dTAG) induced with 300 nM dTAG47 for 24 h compared to control (DMSO) in different cellular compartments of HAP1 SMARCA4dTAG cells (cropped images). e, SMARCC1 immunoprecipitation in different cellular compartments of HAP1 SMARCA4dTAG cells treated with 300 nM dTAG47 or DMSO for 24 h. Western blot analysis for SMARCA4 (cropped images). f, HA immunoprecipitation in different cellular compartments of HAP1 ARID2KOSMARCA4dTAG cells treated with 300 nM dTAG47 or DMSO for 24 h. Western blot analysis for various BAF subunits and HDAC2 (cropped images). g, Heatmap showing mean log2 SMARCC1-normalized abundance values of mass spectrometry results of Fig. 1f. 0, protein abundance below quantification threshold.

Extended Data Fig. 2 Chromatin changes after degradation of SMARCA4.

a, Western blot analyses confirming the degradation of SMARCA4 upon dTAG47 treatment in HAP1 SMARCA4dTAG cells (cropped images). b, Principal component analysis (PCA) of the ATAC-seq time-course data. c, Volcano plots displaying the chromatin accessibility changes after SMARCA4 degradation compared to control for different treatment length. Significant changes (Padj < 0.01 and abs(log2(fold-change)) >1) are colored in red. d, Browser track examples for regions falling into the 5 different clusters.

Extended Data Fig. 3 Transcriptional changes upon dTAG47 treatment.

a, Heatmap of ATAC- and ChIP-seq signal measured as log2(fold-change) for genomic regions falling into clusters 1-5. b, Volcano plot of nascent transcriptional changes after 3 h dTAG47 treatment in WT SMARCA4dTAG cells (PRO-seq). Significant changes (Padj < 0.01 and abs(log2(fold-change)) >1) are colored in red. Two-sided Wald test was performed, False discovery rate (FDR) correction as implemented in DESeq2. c, PCA plot of variance stabilizing transformation normalized counts from the RNA-seq experiment in WT SMARCA4dTAG cells. d, Heatmap of ATAC- and RNA-seq signal measured as log2(fold-change) for genomic regions in the five clusters. e, Volcano plots of gene expression changes after dTAG treatment in SMARCA4dTAG cells compared to DMSO treatment and in SMARCA4KO cells compared to WT cells as measured by RNA-seq. Significant changes (Padj < 0.01 and abs(log2(fold-change)) >1) are colored in red. Two-sided Wald test was performed, False discovery rate (FDR) correction as implemented in DESeq2.

Extended Data Fig. 4 Chromatin, GO term and transcription factor motif analysis in clusters 1-5.

a, (left) Enrichment of different chromatin features and factor binding in the 5 clusters compared to all consensus regions. (right) Enrichment of HAP1-specific features on genomic regions per cluster. Enrichment was calculated against regions present in the 5 clusters. Color code corresponds to -log10(P-value). Dot size corresponds to the effect size measured as odds ratio (or). b, Gene ontology (GO) term enrichment for the different clusters. Only features reaching a significance threshold of P < 0.05 at a q-value of < 0.2 are depicted. c, Motif enrichment results measured as -log10(P value) per cluster. Top 3 motifs per cluster are shown. d, ATAC read density (RPGC normalized) at the different motifs detected in (c) shown in aggregation plots (middle) and in heatmaps (bottom) across the time-course of SMARCA4 degradation in SMARCA4dTAG cells and in WT and SMARCA4KO cells. The top 2000 motif sites in the ATAC consensus peaks were analyzed.

Extended Data Fig. 5 ATPase inhibitor BRM014 treatment leads to fast accessibility changes.

a, Western blot analysis of HAP1 WT cytoplasm, nucleoplasm and chromatin fractions after BRM014 treatment. Cropped images; WCE, whole cell extract. b, SMARCC1 immunoprecipitation in nucleoplasm and chromatin fraction of HAP1 cells after BRM014 treatment. Cropped western blot images. c, Volcano plots displaying the chromatin accessibility changes in HAP1 cells after BRM014 treatment compared to DMSO control for different treatment length. Significant changes (Padj <0.01 and abs(log2(fold-change)) >1) are colored in red. d, Boxplots of accessibility changes measured as log2(fold-change) after BRM014 treatment in WT cells for the clustered genomic regions in Fig. 2a (n = 2 independent experiments). First and third quartiles are denoted by lower and upper hinges, center is median. The upper/lower whisker extendes to the largest/ smallest value no further than 1.5* inter-quartile range. Data points beyond are plotted individually.

Extended Data Fig. 6 Motif analyses of BRM014 and ACBI1 time-course ATAC-seq data.

a, Motif enrichment results measured as -log10(P value) per cluster. Only -log10(P values) reaching a significance level of 20 in any cluster are shown. b, ATAC read density (RPGC normalized) at different motifs shown in aggregation plots (middle) and in heatmaps (bottom) across the time-course of BRM014 and ACBI1 treatment in WT cells. The top 2000 motif sites in the ATAC consensus peaks were analyzed.

Extended Data Fig. 7 Nascent transcription after BRM014 treatment.

a, Volcano plots displaying the PRO-seq nascent transcription changes in HAP1 cells after BRM014 treatment compared to DMSO control for different treatment length. Significant changes (Padj <0.01 and abs(log2(fold-change)) >1) are colored in red. b, Scatterplot of log2(fold-change) in ATAC-seq and PRO-seq signal after BRM014 treatment at different time-points compared to DMSO control experiments stratified by the five clusters from Fig. 3a. c, Aggregate coverage plots of ATAC-seq (top) and PRO-seq (bottom) signal after BRM014 treatment for different time-points in clusters I – V. Signal is centered and averaged (mean ± s.e.m.) over the genomic regions. Plus and minus strands are shown for PRO-Seq. d, Line plots of median log2(fold-change) of BRM014 treatment versus DMSO control with standard errors (s.e.) in ATAC-seq and PRO-seq experiments at different time-points for the five clusters from Fig. 3a (n = 2 independent experiments). Data is presented as median ± s.e.m. (number of loci: I 102, II 473, III 749, IV 456, V 721). e, Aggregate coverage plots of ATAC-seq (top) and PRO-seq (bottom) signal after BRM014 treatment at different time-points for BAF-bound active enhancer and superenhancer regions. Signal is centered and averaged (mean ± s.e.m.) over the genomic regions. Plus and minus strands are shown for PRO-Seq.

Extended Data Fig. 8 Dual SMARCA2 and SMARCA4 degradation by PROTAC leads to accessibility changes correlating with changes observed after inhibition of both ATPases.

a, Western blot analysis of HAP1 cell cytoplasm, nucleoplasm and chromatin fraction after ACBI1 treatment (cropped images). b, Volcano plots displaying the chromatin accessibility changes in HAP1 cells after ACBI1 treatment compared to DMSO control for 6 h and 72 h treatment. Significant changes (Padj <0.01 and abs(log2(fold-change)) >1) are colored in red. (c, left) Scatter plot of log2(fold-change) of chromatin accessibility after 24 h dTAG47 treatment of SMARCA4dTAG versus DMSO control (y-axis) against log2(fold-change) of chromatin accessibility after 24 h ACBI1 treatment of WT cells versus DMSO control (x-axis). Pearson correlation coefficient is depicted. (c, right) Scatter plot of log2(fold-change) of chromatin accessibility after 24 h BRM014 treatment versus DMSO control (y-axis) and log2(fold-change) of chromatin accessibility after 24 h ACBI1 treatment of WT cells versus DMSO control (x-axis). Pearson correlation coefficient is depicted. d, Heatmap of accessibility changes measured as log2(fold-change) after BRM014 or ACBI1 treatment versus DMSO control for the 5 clusters from Fig. 3a.

Extended Data Fig. 9 Chromatin and gene expression changes in synthetic lethal conditions.

a, Heatmap of Z-scores of ATAC-, ChIP- and RNA-seq counts for the genomic regions differentially accessible in the synthetic lethal conditions after dTAG47 treatment (cluster 6 – 11 from Fig. 4b). Quantile normalized counts are depicted for ATAC- and ChIP-seq experiments. Variance stabilizing transformation normalized counts are depicted for RNA-seq experiments. b, Chromatin accessibility signal (measured by ATAC-seq) and enrichment of different factors or histone modifications (measured by ChIP-seq in HAP1 cells) are displayed for all differential sites from the dTAG time-courses sorted by H3K27ac signal. Aggregate coverage plot on the top depicts the mean accessibility or factor enrichment of the regions per sample. c, Violin plots showing log2(counts) for ARID1A, H3K27ac and BRD4 binding to regions of the 11 different clusters under wild-type condition (n = 1). First and third quartiles are denoted by lower and upper hinges, center is median. The upper/lower whisker extendes to the largest/ smallest value no further than 1.5* inter-quartile range. Data points beyond are plotted individually. d, Enrichment of HAP1-specific features that overlap with BAF-bound regions on genomic regions per cluster. Enrichment was calculated against all consensus regions. Color code corresponds to the -log10(P value). Dot size corresponds to the effect size measured as odds ratio (or). Two-sided Fisher’s exact test was performed. False discovery rate (FDR) correction was performed as implemented in LOLA software. e, Volcano plots displaying the PRO-seq nascent transcription changes in SMARCA4KOSMARCA2dTAG cells after 3 h dTAG47 treatment compared to DMSO control. No significant changes (Padj <0.01 and abs(log2(fold-change)) >1). f, Enrichment of enhancer types overlapping BAF-bound genomic regions per cluster. Enrichment was calculated against all consensus regions. Color code corresponds to the -log10(P value). Dot size corresponds to the effect size measured as odds ratio (or). Two-sided Fisher’s exact test was performed. False discovery rate (FDR) correction was performed as implemented in LOLA software.

Extended Data Fig. 10 Chromatin and gene-expression alterations after loss of chromatin accessibility at superenhancers.

a, Boxplots showing log2(fold-change) of all differential regions annotated as superenhancers (n = 444) over time after dTAG47 treatment in the different HAP1 dTAG cell lines. First and third quartiles are denoted by lower and upper hinges, center is median. The upper/lower whisker extendes to the largest/ smallest value no further than 1.5* inter-quartile range. Data points beyond are plotted individually. b, Heatmap of log2(fold-change) of ATAC-seq and H3K27ac ChIP-seq signal in SMARCA4dTAG and SMARCA4KOSMARCA2dTAG cells after dTAG47 treatment versus DMSO control for the genomic regions differentially accessible in any cell line after dTAG47 treatment (cluster 1 – 11). c, Violin plots of log2(fold-change) from RNA-seq experiments for all regions, active enhancer and superenhancer regions showing a decrease in accessibility (ATAC-seq) and either a decrease (log2(fold-change) < 1) in H3K27 acetylation ChIP-signal at 24 h (red) or no response (log2(fold-change) >-1 and < 1) in H3K27 acetylation ChIP-signal at 24 h (blue) in SMARCA4dTAG, SMARCA4KOSMARCA2dTAG and SMARCC1KOSMARCC2dTAG cells after dTAG47 treatment (n = 2 independent experiments). First and third quartiles are denoted by lower and upper hinges, center is median. The upper/lower whisker extendes to the largest/ smallest value no further than 1.5* inter-quartile range. Data points beyond are plotted individually. Differential genes were defined as Padj < 0.01 und log2(fold-change) > 1.

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Supplementary Figs. 1–4, Methods and full scans of all western blots

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Schick, S., Grosche, S., Kohl, K.E. et al. Acute BAF perturbation causes immediate changes in chromatin accessibility. Nat Genet 53, 269–278 (2021). https://doi.org/10.1038/s41588-021-00777-3

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