Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

BRWD1 orchestrates small pre-B cell chromatin topology by converting static to dynamic cohesin

Nature Immunology volume 25, pages 129–141 (2024)Cite this article

Subjects

Abstract

Lymphocyte development consists of sequential and mutually exclusive cell states of proliferative selection and antigen receptor gene recombination. Transitions between each state require large, coordinated changes in epigenetic landscapes and transcriptional programs. How this occurs remains unclear. Here we demonstrate that in small pre-B cells, the lineage and stage-specific epigenetic reader bromodomain and WD repeat-containing protein 1 (BRWD1) reorders three-dimensional chromatin topology to affect the transition between proliferative and gene recombination molecular programs. BRWD1 regulated the switch between poised and active enhancers interacting with promoters, and coordinated this switch with Igk locus contraction. BRWD1 did so by converting chromatin-bound static to dynamic cohesin competent to mediate long-range looping. ATP-depletion revealed cohesin conversion to be the main energetic mechanism dictating dynamic chromatin looping. Our findings provide a new mechanism of cohesin regulation and reveal how cohesin function can be dictated by lineage contextual mechanisms to facilitate specific cell fate transitions.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: BRWD1 enhances long-range genomic interactions including Igk locus contraction.
Fig. 2: Fraction of BRWD1 regulated genes associated with A/B compartment conversion.
Fig. 3: BRWD1-dependent changes in intra-TAD interactions associated with changes in gene expression.
Fig. 4: BRWD1 regulates transition from poised to active enhancers interacting with promoters.
Fig. 5: BRWD1 modulates the genome-wide binding of different cohesin complexes.
Fig. 6: BRWD1 converts static to dynamic cohesin complexes.
Fig. 7: At stripes, BRWD1 orchestrates conversion of static to dynamic cohesin complexes.
Fig. 8: BRWD1-mediated cohesin conversion is a major ATP-dependent mechanism regulating cohesin function.

Similar content being viewed by others

Data availability

BRWD1 ChIP-seq and ATAC-seq datasets were deposited in the GEO database with accession number GSE63302. HiC data for pro-B cells can be obtained from GEO accession code GSE40173. Data for HiC of WT small pre-B, Brwd1−/− small pre-B and WT Immature B cells, ChIP-seq for H3K4me1, H3K27Ac, CTCF, RAD21, SMC3, NIPBL and WAPL from WT and Brwd1−/− small pre-B cells, and ChIP-seq for RAD21, SMC3, NIPBL and WAPL from ATP-depleted WT small pre-B cells were deposited in the GEO database with accession number GSE221519. The subseries for HiC is GSE221517. The subseries for ChIP-seq is GSE221518. Source data are provided with this paper.

References

  1. Clark, M. R., Mandal, M., Ochiai, K. & Singh, H. Orchestrating B cell lymphopoiesis through interplay of IL-7 receptor and pre-B cell receptor signalling. Nat. Rev. Immunol. 14, 69–80 (2014).

    CAS  PubMed  Google Scholar 

  2. Mandal, M. et al. BRWD1 orchestrates epigenetic landscape of late B lymphopoiesis. Nat. Commun. 9, 3888–3902 (2018).

    PubMed  PubMed Central  Google Scholar 

  3. Mandal, M. et al. Epigenetic repression of the Igk locus by STAT5-mediated recruitment of the histone methyltransferase Ezh2. Nat. Immunol. 12, 1212–1220 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Lu, R., Medina, K. L., Lancki, D. W. & Singh, H. IRF-4,8 orchestrate the pre-B-to-B transition in lymphocyte development. Genes Dev. 17, 1703–1708 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Mandal, M. et al. Histone reader BRWD1 targets and restricts recombination to the Igk locus. Nat. Immunol. 16, 1094–1103 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Fulton, S. L. et al. Rescue of deficits by Brwd1 copy number restoration in the Ts65Dn mouse model of Down syndrome. Nat. Commun. 13, 6384 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Mandal, M. et al. CXCR4 signaling directs Igk recombination and the molecular mechanisms of late B lymphopoiesis. Nat. Immunol. 20, 1393–1403 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Wright, N. E., Mandal, M. & Clark, M. R. Molecular mechanisms insulating proliferation from genotoxic stress in B lymphocytes. Trends Immunol. 44, 668–677 (2023).

    CAS  PubMed  Google Scholar 

  9. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Teng, G. et al. RAG represents a widespread threat to the lymphocyte genome. Cell 162, 751–765 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Stadhouders, R. et al. Pre-B cell receptor signaling induces immunoglobulin κ locus accessibility by functional redistribution of enhancer-mediated chromatin interactions. PLoS Biol. 12, e1001791 (2014).

    PubMed  PubMed Central  Google Scholar 

  12. Zhang, Y. et al. Spatial organization of the mouse genome and its role in recurrent chromosomal translocations. Cell 148, 908–921 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Forcato, M. et al. Comparison of computational methods for Hi-C data analysis. Nat. Methods 14, 679–685 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 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 

  16. Bozek, M. & Gompel, N. Developmental transcriptional enhancers: a subtle interplay between accessibility and activity. Bioessays 42, e1900188 (2020).

    PubMed  Google Scholar 

  17. Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    CAS  PubMed  Google Scholar 

  18. Pott, S. & Lieb, J. D. What are super-enhancers? Nat. Genet. 47, 8–12 (2015).

    CAS  PubMed  Google Scholar 

  19. Seo, W. & Taniuchi, I. The roles of RUNX family proteins in development of immune cells. Mol. Cells 43, 107–113 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Miller, C. H. et al. Eomes identifies thymic precursors of self-specific memory-phenotype CD8+ T cells. Nat. Immunol. 21, 567–577 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Gounari, F. & Khazaie, K. TCF-1: a maverick in T cell development and function. Nat. Immunol. 23, 671–678 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Dekker, J. & Mirny, L. The 3D genome as moderator of chromosomal communication. Cell 164, 1110–1121 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Merkenschlager, M. & Nora, E. P. CTCF and cohesin in genome folding and transcriptional gene regulation. Annu Rev. Genomics Hum. Genet. 17, 17–43 (2016).

    CAS  PubMed  Google Scholar 

  24. Zhang, Y. et al. The fundamental role of chromatin loop extrusion in physiological V(D)J recombination. Nature 573, 600–604 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Hill, L. et al. Wapl repression by Pax5 promotes V gene recombination by Igh loop extrusion. Nature 584, 142–147 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Vian, L. et al. The energetics and physiological impact of cohesin extrusion. Cell 173, 1165–1178.e1120 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Dai, H. Q. et al. Loop extrusion mediates physiological Igh locus contraction for RAG scanning. Nature 590, 338–343 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Banigan, E. J. & Mirny, L. A. Loop extrusion: theory meets single-molecule experiments. Curr. Opin. Cell Biol. 64, 124–138 (2020).

    CAS  PubMed  Google Scholar 

  29. Davidson, I. F. et al. DNA loop extrusion by human cohesin. Science 366, 1338–1345 (2019).

    CAS  PubMed  Google Scholar 

  30. Haarhuis, J. H. I. et al. The cohesin release factor WAPL restricts chromatin loop extension. Cell 169, 693–707.e614 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Liu, N. Q. et al. WAPL maintains a cohesin loading cycle to preserve cell-type-specific distal gene regulation. Nat. Genet. 53, 100–109 (2021).

    CAS  PubMed  Google Scholar 

  32. Nora, E. P. et al. Molecular basis of CTCF binding polarity in genome folding. Nat. Commun. 11, 5612 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Davidson, I. F. & Peters, J. M. Genome folding through loop extrusion by SMC complexes. Nat. Rev. Mol. Cell Biol. 22, 445–464 (2021).

    CAS  PubMed  Google Scholar 

  34. Karki, S. et al. Regulated capture of Vκ gene topologically associating domains by transcription factories. Cell Rep. 24, 2443–2456 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Yoon, S., Chandra, A. & Vahedi, G. Stripenn detects architectural stripes from chromatin conformation data using computer vision. Nat. Commun. 13, 1602 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Kraft, K. et al. Serial genomic inversions induce tissue-specific architectural stripes, gene misexpression and congenital malformations. Nat. Cell Biol. 21, 305–310 (2019).

    CAS  PubMed  Google Scholar 

  37. Barrington, C. et al. Enhancer accessibility and CTCF occupancy underlie asymmetric TAD architecture and cell type specific genome topology. Nat. Commun. 10, 2908 (2019).

    PubMed  PubMed Central  Google Scholar 

  38. Guo, Y. et al. CRISPR inversion of CTCF sites alters genome topology and enhancer/promoter function. Cell 162, 900–910 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Kim, Y., Shi, Z., Zhang, H., Finkelstein, I. J. & Yu, H. Human cohesin compacts DNA by loop extrusion. Science 366, 1345–1349 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Olan, I. et al. Transcription-dependent cohesin repositioning rewires chromatin loops in cellular senescence. Nat. Commun. 11, 6049 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhu, Y., Denholtz, M., Lu, H. & Murre, C. Calcium signaling instructs NIPBL recruitment at active enhancers and promoters via distinct mechanisms to reconstruct genome compartmentalization. Genes Dev. 35, 65–81 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Fournier, M. et al. FOXA and master transcription factors recruit Mediator and Cohesin to the core transcriptional regulatory circuitry of cancer cells. Sci. Rep. 6, 34962 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Okoreeh, M. K. et al. Asymmetrical forward and reverse developmental trajectories determine molecular programs of B cell antigen receptor editing. Sci. Immunol. 7, eabm1664 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Philipps, D. L. et al. The dual bromodomain and WD repeat-containing mouse protein BRWD1 is required for normal spermiogenesis and the oocyte–embryo transition. Dev. Biol. 317, 72–82 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320.e324 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Kane, L. et al. Cohesin is required for long-range enhancer action at the Shh locus. Nat. Struct. Mol. Biol. 29, 891–897 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhang, Y., Zhang, X., Dai, H. Q., Hu, H. & Alt, F. W. The role of chromatin loop extrusion in antibody diversification. Nat. Rev. Immunol. 22, 550–566 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Beck, K., Peak, M. M., Ota, T., Nemazee, D. & Murre, C. Distinct roles for E12 and E47 in B cell specification and the sequential rearrangement of immunoglobulin light chain loci. J. Exp. Med. 206, 2271–2284 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Sakamoto, S. et al. E2A and CBP/p300 act in synergy to promote chromatin accessibility of the immunoglobulin κ locus. J. Immunol. 188, 5547–5560 (2012).

    CAS  PubMed  Google Scholar 

  50. Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Wolff, J. et al. Galaxy HiCExplorer: a web server for reproducible Hi-C data analysis, quality control and visualization. Nucleic Acids Res. 46, W11–w16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Zhang, H. et al. Fast alignment and preprocessing of chromatin profiles with Chromap. Nat. Commun. 12, 6566 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 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 

  55. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  Google Scholar 

  56. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Google Scholar 

  57. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  58. Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Stansfield, J. C., Cresswell, K. G., Vladimirov, V. I. & Dozmorov, M. G. HiCcompare: an R-package for joint normalization and comparison of HI-C datasets. BMC Bioinformatics 19, 279 (2018).

    PubMed  PubMed Central  Google Scholar 

  60. Phanstiel, D. H., Boyle, A. P., Araya, C. L. & Snyder, M. P. Sushi.R: flexible, quantitative and integrative genomic visualizations for publication-quality multi-panel figures. Bioinformatics 30, 2808–2810 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank M. Olson and D. Leclerc for cell-sorting services, and P. Faber for high-throughput sequencing services. This work is supported by the US National Institutes of Health grants R01 AI150860 (M.R.C. and M.M.) and R01 AI143778 (M.R.C.).

Author information

Authors and Affiliations

  1. Department of Medicine, Section of Rheumatology and Gwen Knapp Center for Lupus and Immunology Research, University of Chicago, Chicago, IL, USA

    Malay Mandal, Margaret L. Veselits, Nathaniel E. Wright, Michael K. Okoreeh, Young me Yoon, Jacob Veselits & Marcus R. Clark

  2. Core for Research Informatics, University of Illinois at Chicago, Chicago, IL, USA

    Mark Maienschein-Cline

  3. Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA

    Yeguang Hu & Katia Georgopoulos

  4. Program in Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Azam Mohsin

Authors
  1. Malay Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark Maienschein-Cline
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yeguang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Azam Mohsin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Margaret L. Veselits
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nathaniel E. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael K. Okoreeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Young me Yoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jacob Veselits
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Katia Georgopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Marcus R. Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.M. and M.R.C. designed the experiments. M.M. carried out and analyzed most of the experiments, including the ChIP-seqs. Y.H. prepared the HiC libraries. M.M-C. analyzed the high-throughput sequencing and HiC data along with M.M. A.M. helped with the initial analyses of HiC data and view-point analyses. M.L.V. assisted with the ATP-depletion assay. M.L.V., N.E.W., M.K.O., Y.Y. and J.V. conducted genotyping, cell sorting and flow cytometry analyses. N.E.W. and K.G. contributed to editing the manuscript. M.M. and M.R.C. oversaw the entire project and prepared the final draft of the manuscript.

Corresponding authors

Correspondence to Malay Mandal or Marcus R. Clark.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Peer review

Peer review information

Nature Immunology thanks Charalampos Spilianakis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Additional information

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

Extended data

Extended Data Fig. 1 Small pre-B cells have a unique developmental signature.

a, Total and overlapping accessibility peaks (ATAC-seq) in WT pro-B, WT large pre-B, WT small pre-B, WT Immature B, Brwd1-/- small pre-B and Rag1-/-B18i (Igμ) small pre-B cells. b, Differential heatmap showing the up and down-regulated genes in WT small pre-B cells compared to Rag1-/-B18i (Igμ) small pre-B cells. c, HiC interaction matrices along with TAD scores in indicated cell populations for genomic regions containing T cell lineage specific (Bcl11b), B cell lineage specific (Ebf1) and granulocyte lineage specific (Il1f9) genes.

Extended Data Fig. 2 BRWD1 can repress chromatin interactions.

a, Genomic region containing Myc in WT (left) and Brwd1-/- (right) small pre-B cells (top panels). Corresponding HiC interaction matrices of Myc locus (middle panels) and arc plots from the point of view of the Myc promoter region (bottom panels). The height of an arc represents the strength of the interactions. b, Arc and line plots of significant interactions (p < 0.05) strengthened in Brwd1-/- small pre-B cells compared to WT small pre-B cells. Schematic demonstrates promoter-enhancer and enhancer-enhancer interactions detected in the Myc locus of Brwd1-/- small pre-B cells. c, Mki67 gene region in WT small pre-B cells (left) and Brwd1-/- small pre-B cells (right). Corresponding HiC contact matrices and interaction loops as arc plots. The loop region containing Mki67 is shaded. Expression of Mki67 (RNA-seq; n = 4) and Ptpre (gene located in the adjacent loop) in WT and Brwd1-/- small pre-B cells shown as a bar plot (bottom). Data in bar plots are represented as mean ± SD; t-test). d, Genomic interactions at the H2-Q1 gene region were repressed by BRWD1 which is shown and labeled as described in ‘c’. (RNA-seq; n = 4).

Extended Data Fig. 3 BRWD1 regulates active enhancers including super enhancers.

a, ChIP-seq of H3K4me1 and H3K27Ac in WT and Brwd1-/- small pre-B cells across the entire chromosome 3. Data from replicates is shown. b, Identification of super enhancers (SEs) by enrichment of H3K27Ac binding. c, Venn diagram showing total and overlapping number of SEs in WT and Brwd1-/- small pre-B cells. d, Histograms showing the enrichment of enhancer histone marks in SEs upregulated in WT or Brwd1-/- small pre-B cells. e, H3K4me1 and H3K27Ac binding in WT and Brwd1-/- small pre-B cells across the Igk locus from Jk1-5, through the intronic enhancer (iEk) to the 3’ kappa enhancer (3’Ek). f, de novo transcription factor binding motifs identified in active enhancers (as described in Fig. 4b) upregulated in WT at least 2-fold compared to Brwd1-/- small pre-B cells. g, de novo transcription factor binding motifs identified in active enhancers (as described in Fig. 4b) upregulated in Brwd1-/- small pre-B at least 2-fold compared to WT small pre-B cells.

Extended Data Fig. 4 BRWD1 regulates enhancer-promoter interactions.

a, b, BRWD1 repressed cell cycle related genes Smc2 and Rrm2. Corresponding BRWD1 binding (top), HiC interaction matrices at 5 kb resolution, promoter-specific interactions as point-of-view arc plots, ChIP-seq for enhancer histone marks H3K4me1 and H3K27Ac, ATAC-seq and RNA-seq are shown. WT in upper blue and Brwd1-/- in lower red panels. c, BRWD1 induced gene expression of Cxcr4. Corresponding BRWD1 binding profile (ChIP-seq, top), HiC interaction matrices at 5 kb resolution, promoter-specific interactions as point-of-view arc plots, ChIP-seq for enhancer histone marks H3K4me1 and H3K27Ac, ATAC-seq and RNA-seq are shown. WT in upper blue and Brwd1-/- in lower red panels.

Extended Data Fig. 5 BRWD1 modulates cohesin binding to mediate static to dynamic cohesin conversion.

a, Bar diagram showing expression (RNA-seq; n = 4) of indicated cohesin component genes in B cell progenitor subsets. Data are represented as mean ± SD. b, c, Total and coincident genomic peaks for RAD21 and SMC3 in WT (b) and Brwd1-/- (c) small pre-B cells. The total number of peaks for each population is shown in parentheses with the number in each Venn region indicated. d, e, Total and overlapping coincident genomic peaks for RAD21, SMC3 and NIPBL in WT (d) and Brwd1-/- (e) small pre-B cells. f-g, Total and overlapping coincident genomic peaks for RAD21, SMC3 and WAPL in WT (f) and Brwd1-/- (g) small pre-B cells. h-i, Total and overlapping coincident genomic peaks for RAD21 + SMC3 complex to NIPBL and WAPL in WT (h) and Brwd1-/- (i) small pre-B cells. j, HiC interaction matrix for the indicated genomic region displaying BRWD1 binding near or at TAD anchor sites with CTCF binding (black arrowhead). Red and blue arrows indicate CTCF direction. k, HiC interaction matrix for indicated genomic region along with ChIP-seq profile of BRWD1 binding (WT), CTCF, RAD21, SMC3, NIPBL and WAPL binding in WT and Brwd1-/- small pre-B cells for the indicated region shown by a black bar. Pink shaded boxes indicate regions with 4 F (RAD21 + SMC3 + NIPBL + WAPL; dynamic cohesin) binding in both WT and Brwd1-/- small pre-B cells near or at CTCF sites. Yellow boxes denote regions with 4 F sites in WT small pre-B cells but 2 F (RAD21 + SMC3; static cohesin) sites in Brwd1-/- small pre-B cells near/at CTCF binding sites. Green boxes denote 4 F (RAD21 + SMC3 + NIPBL + WAPL, without CTCF; dynamic cohesin) sites in WT small pre-B cells but without any detectable cohesin binding in Brwd1-/- small pre-B cells.

Source data

Extended Data Fig. 6 BRWD1 mediates loop extrusion at Igk locus by recruiting dynamic cohesin.

a, HiC interaction matrix for the Igk locus demonstrating a stripe across the entire WT Igk locus (~3.2 Mb, half arrow) that is anchored at the recombination center. b, ChIP-seq binding profile of BRWD1 (in WT), CTCF, RAD21, SMC3, NIPBL and WAPL along with histone marks H3K4me1 and H3K27Ac in WT and Brwd1-/- small pre-B cells across the Igk locus. Shaded boxes show the presence of dynamic cohesin complex sites without CTCF binding sites in WT small pre-B cells but absent in Brwd1-/- small pre-B cells. c, Point of view arc plots displaying normalized genomic interactions at indicated Vk gene segments in WT (blue) and Brwd1-/- small pre-B cells. The location of the recombination center is shown. The height of the arcs indicates the intensity of interactions.

Extended Data Fig. 7 Cohesin subunit chromatin binding distributions in Brwd1-/- and ATP-depleted WT small pre-B cells.

a, Flow cytometry demonstrating the percentage of live cells in WT small pre-B cells untreated (left) and treated with oligomycin A (126 nM) for 2 hours (right). b, Bar plot displaying ATP depletion (%) in WT small pre-B cells after 2 hours of treatment with oligomycin A (126 nM). c-f, Total and coincident binding peaks for RAD21 (c), SMC3 (d), NIPBL (e) and WAPL (f) in WT, Brwd1-/- and ATP-depleted WT small pre-B cells. g, Total and coincident binding peaks for RAD21 and SMC3 in ATP-depleted WT small pre-B cells. h, Total and coincident binding peaks for RAD21 + SMC3 in WT, Brwd1-/- and ATP-depleted WT small pre-B cells. i, Total and coincident binding peaks for RAD21 + SMC3 with NIPBL and WAPL in ATP-depleted small pre-B cells.

Extended Data Fig. 8 Model for BRWD1-mediated static to dynamic cohesin conversion.

BRWD1 complexes at CTCF sites convert chromatin-bound static cohesin to dynamic complexes competent for loop extrusion. At TAD boundaries this leads to a general increase in intra-TAD looping. In contrast, selective conversion at stripe origins induces directional loop extrusion and apposition of linear chromatin regions onto origin proximate chromatin. In small pre-B cells, cohesin conversion remodeled chromatin topology to mediate Igk contraction and dictate enhancer activation.

Supplementary information

Supplementary Information

Supplementary Tables 1–3. Supplementary Table 1. Raw reads and alignment of in situ HiC sequencing. Supplementary Table 2. Raw reads and alignment of ChIP-seqs from WT and Brwd1-/- small pre-B cells. Supplementary Table 3. Raw reads and alignment of ChIP-seqs from ATP-depleted WT small pre-B cells

Reporting Summary

Source data

Source Data Fig. 1

Raw data for quantification of genomic contraction

Source Data Fig. 2

A and B compartment information.

Source Data Fig. 3

Summary of counting of topologically associated domains by three programs.

Source Data Fig. 7

Counts of CTCF vs stripe anchors.

Source Data Extended Data Fig. 5

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mandal, M., Maienschein-Cline, M., Hu, Y. et al. BRWD1 orchestrates small pre-B cell chromatin topology by converting static to dynamic cohesin. Nat Immunol 25, 129–141 (2024). https://doi.org/10.1038/s41590-023-01666-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-023-01666-z

Search

Advanced search

Quick links

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