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Acute BAF perturbation causes immediate changes in chromatin accessibility

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.

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.

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).

References

  1. 1.

    Hodges, C., Kirkland, J. G. & Crabtree, G. R. The many roles of BAF (mSWI/SNF) and PBAF complexes in cancer. Cold Spring Harb. Perspect. Med 6, a026930 (2016).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Kadoch, C. & Crabtree, G. R. Mammalian SWI/SNF chromatin remodeling complexes and cancer: mechanistic insights gained from human genomics. Sci. Adv. 1, e1500447 (2015).

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Kadoch, C. et al. Proteomic and bioinformatic analysis of mammalian SWI/SNF complexes identifies extensive roles in human malignancy. Nat. Genet. 45, 592–601 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Mittal, P. & Roberts, C. W. M. The SWI/SNF complex in cancer—biology, biomarkers and therapy. Nat. Rev. Clin. Oncol. 17, 435–448 (2020).

    CAS  Google Scholar 

  5. 5.

    Shain, A. H. & Pollack, J. R. The spectrum of SWI/SNF mutations, ubiquitous in human cancers. PLoS ONE 8, e55119 (2013).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Hargreaves, D. C. & Crabtree, G. R. ATP-dependent chromatin remodeling: genetics, genomics and mechanisms. Cell Res. 21, 396–420 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Pulice, J. L. & Kadoch, C. Composition and function of mammalian swi/snf chromatin remodeling complexes in human disease. Cold Spring Harb. Symp. Quant. Biol. 81, 53–60 (2016).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385 e18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Schick, S. et al. Systematic characterization of BAF mutations provides insights into intracomplex synthetic lethalities in human cancers. Nat. Genet. 51, 1399–1410 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kelso, T. W. R. et al. Chromatin accessibility underlies synthetic lethality of SWI/SNF subunits in ARID1A-mutant cancers. eLife 6, e30506 (2017).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Barisic, D., Stadler, M. B., Iurlaro, M. & Schubeler, D. Mammalian ISWI and SWI/SNF selectively mediate binding of distinct transcription factors. Nature 569, 136–140 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Hodges, H. C. et al. Dominant-negative SMARCA4 mutants alter the accessibility landscape of tissue-unrestricted enhancers. Nat. Struct. Mol. Biol. 25, 61–72 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Menon, D. U., Shibata, Y., Mu, W. & Magnuson, T. Mammalian SWI/SNF collaborates with a polycomb-associated protein to regulate male germline transcription in the mouse. Development 146, dev174094 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Xu, G. et al. ARID1A determines luminal identity and therapeutic response in estrogen-receptor-positive breast cancer. Nat. Genet. 52, 198–207 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Mathur, R. et al. ARID1A loss impairs enhancer-mediated gene regulation and drives colon cancer in mice. Nat. Genet. 49, 296–302 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Alver, B. H. et al. The SWI/SNF chromatin remodelling complex is required for maintenance of lineage specific enhancers. Nat. Commun. 8, 14648 (2017).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Wang, X. et al. SMARCB1-mediated SWI/SNF complex function is essential for enhancer regulation. Nat. Genet. 49, 289–295 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Nakayama, R. T. et al. SMARCB1 is required for widespread BAF complex-mediated activation of enhancers and bivalent promoters. Nat. Genet. 49, 1613–1623 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    King, H. W. & Klose, R. J. The pioneer factor OCT4 requires the chromatin remodeller BRG1 to support gene regulatory element function in mouse embryonic stem cells. eLife 6, e22631 (2017).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Hoffman, G. R. et al. Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers. Proc. Natl Acad. Sci. USA 111, 3128–3133 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Wilson, B. G. et al. Residual complexes containing SMARCA2 (BRM) underlie the oncogenic drive of SMARCA4 (BRG1) mutation. Mol. Cell. Biol. 34, 1136–1144 (2014).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Oike, T. et al. A synthetic lethality-based strategy to treat cancers harboring a genetic deficiency in the chromatin remodeling factor BRG1. Cancer Res. 73, 5508–5518 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Mashtalir, N. et al. Modular organization and assembly of SWI/SNF family chromatin remodeling complexes. Cell 175, 1272–1288 e20 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Narayanan, R. et al. Loss of BAF (mSWI/SNF) complexes causes global transcriptional and chromatin state changes in forebrain development. Cell Rep. 13, 1842–1854 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Papillon, J. P. N. et al. Discovery of orally active inhibitors of brahma homolog (BRM)/SMARCA2 ATPase activity for the treatment of brahma related gene 1 (BRG1)/SMARCA4-mutant cancers. J. Med. Chem. 61, 10155–10172 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Farnaby, W. et al. BAF complex vulnerabilities in cancer demonstrated via structure-based PROTAC design. Nat. Chem. Biol. 15, 672–680 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Mashtalir, N. et al. A structural model of the endogenous human BAF complex informs disease mechanisms. Cell 183, 802–817 e24 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Han, Y., Reyes, A. A., Malik, S. & He, Y. Cryo-EM structure of SWI/SNF complex bound to a nucleosome. Nature 579, 452–455 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    He, S. et al. Structure of nucleosome-bound human BAF complex. Science 367, 875–881 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Wagner, F. R. et al. Structure of SWI/SNF chromatin remodeller RSC bound to a nucleosome. Nature 579, 448–451 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Ye, Y. et al. Structure of the RSC complex bound to the nucleosome. Science 366, 838–843 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Owen-Hughes, T., Utley, R. T., Cote, J., Peterson, C. L. & Workman, J. L. Persistent site-specific remodeling of a nucleosome array by transient action of the SWI/SNF complex. Science 273, 513–516 (1996).

    CAS  Google Scholar 

  33. 33.

    Phelan, M. L., Sif, S., Narlikar, G. J. & Kingston, R. E. Reconstitution of a core chromatin remodeling complex from SWI/SNF subunits. Mol. Cell 3, 247–253 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Wang, W. et al. Purification and biochemical heterogeneity of the mammalian SWI–SNF complex. EMBO J. 15, 5370–5382 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Wang, W. et al. Diversity and specialization of mammalian SWI/SNF complexes. Genes Dev. 10, 2117–2130 (1996).

    CAS  Google Scholar 

  36. 36.

    Biggar, S. R. & Crabtree, G. R. Continuous and widespread roles for the Swi–Snf complex in transcription. EMBO J. 18, 2254–2264 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Kubik, S. et al. Opposing chromatin remodelers control transcription initiation frequency and start site selection. Nat. Struct. Mol. Biol. 26, 744–754 (2019).

    CAS  Google Scholar 

  38. 38.

    Kadoch, C. et al. Dynamics of BAF–polycomb complex opposition on heterochromatin in normal and oncogenic states. Nat. Genet. 49, 213–222 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Miller, E. L. et al. TOP2 synergizes with BAF chromatin remodeling for both resolution and formation of facultative heterochromatin. Nat. Struct. Mol. Biol. 24, 344–352 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Nabet, B. et al. The dTAG system for immediate and target-specific protein degradation. Nat. Chem. Biol. 14, 431–441 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Rago, F. et al. Degron mediated BRM/SMARCA2 depletion uncovers novel combination partners for treatment of BRG1/SMARCA4-mutant cancers. Biochem. Biophys. Res. Commun. 508, 109–116 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21 29 1–21 29 9 (2015).

    Google Scholar 

  43. 43.

    Mahat, D. B. et al. Base-pair-resolution genome-wide mapping of active RNA polymerases using precision nuclear run-on (PRO-seq). Nat. Protoc. 11, 1455–1476 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    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 

  45. 45.

    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 

  46. 46.

    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 

  47. 47.

    Bao, X. et al. A novel ATAC-seq approach reveals lineage-specific reinforcement of the open chromatin landscape via cooperation between BAF and p63. Genome Biol. 16, 284 (2015).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Berns, K. et al. ARID1A mutation sensitizes most ovarian clear cell carcinomas to BET inhibitors. Oncogene 37, 4611–4625 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Brand, M. & Winter, G. E. Locus-specific knock-in of a degradable tag for target validation studies. Methods Mol. Biol. 1953, 105–119 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Erb, M. A. et al. Transcription control by the ENL YEATS domain in acute leukaemia. Nature 543, 270–274 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Weintraub, A. S. et al. YY1 is a structural regulator of enhancer-promoter loops. Cell 171, 1573–1588 e28 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Wisniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Jaeger, M. G. et al. Selective mediator dependence of cell-type-specifying transcription. Nat. Genet. 52, 719–727 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  55. 55.

    Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Sheffield, N. C. & Bock, C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32, 587–589 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    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 

  58. 58.

    Nguyen, N. T. T. et al. RSAT 2018: regulatory sequence analysis tools 20th anniversary. Nucleic Acids Res. 46, W209–W214 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Frith, M. C. Cluster-Buster: finding dense clusters of motifs in DNA sequences. Nucleic Acids Res. 31, 3666–3668 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS J. Integ. Biol. 16, 284–287 (2012).

    CAS  Google Scholar 

  62. 62.

    Robinson, J. T., Thorvaldsdottir, H., Wenger, A. M., Zehir, A. & Mesirov, J. P. Variant review with the integrative genomics viewer. Cancer Res. 77, e31–e34 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Dietlein, F. et al. Identification of cancer driver genes based on nucleotide context. Nat. Genet. 52, 208–218 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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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.

Author information

Affiliations

Authors

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.

Corresponding authors

Correspondence to Sandra Schick or Stefan Kubicek.

Ethics declarations

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.

Additional information

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.

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

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 (2021). https://doi.org/10.1038/s41588-021-00777-3

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