Cohesin is important for 3D genome organization. Nevertheless, even the complete removal of cohesin has surprisingly little impact on steady-state gene transcription and enhancer activity. Here we show that cohesin is required for the core transcriptional response of primary macrophages to microbial signals, and for inducible enhancer activity that underpins inflammatory gene expression. Consistent with a role for inflammatory signals in promoting myeloid differentiation of hematopoietic stem and progenitor cells (HPSCs), cohesin mutations in HSPCs led to reduced inflammatory gene expression and increased resistance to differentiation-inducing inflammatory stimuli. These findings uncover an unexpected dependence of inducible gene expression on cohesin, link cohesin with myeloid differentiation, and may help explain the prevalence of cohesin mutations in human acute myeloid leukemia.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


  1. 1.

    Fudenberg, G. et al. Formation of chromosomal domains by loop extrusion. Cell Rep. 15, 2038–2049 (2016).

  2. 2.

    Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e22 (2017).

  3. 3.

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

  4. 4.

    Rao, S. S. P. et al. Cohesin loss eliminates all loop domains. Cell 171, 305–320.e24 (2017).

  5. 5.

    Dowen, J. M. et al. Control of cell identity genes occurs in insulated neighborhoods in mammalian chromosomes. Cell 159, 374–387 (2014).

  6. 6.

    Hnisz, D., Day, D. S. & Young, R. A. Insulated neighborhoods: structural and functional units of mammalian gene control. Cell 167, 1188–1200 (2016).

  7. 7.

    Schwarzer, W. et al. Two independent modes of chromatin organization revealed by cohesin removal. Nature 551, 51–56 (2017).

  8. 8.

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

  9. 9.

    Dekker, J. & Heard, E. Structural and functional diversity of topologically associating domains. FEBS Lett. 589, 2877–2884 (2015).

  10. 10.

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

  11. 11.

    Bickmore, W. A. & van Steensel, B. Genome architecture: domain organization of interphase chromosomes. Cell 152, 1270–1284 (2013).

  12. 12.

    Hnisz, D. et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science 351, 1454–1458 (2016).

  13. 13.

    Jiang, Y. et al. The methyltransferase SETDB1 regulates a large neuron-specific topological chromatin domain. Nat. Genet. 49, 1239–1250 (2017).

  14. 14.

    Mansour, M. R. et al. An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science 346, 1373–1377 (2014).

  15. 15.

    Lupiáñez, D. G. et al. Disruptions of topological chromatin domains cause pathogenic rewiring of gene-enhancer interactions. Cell 161, 1012–1025 (2015).

  16. 16.

    Narendra, V. et al. CTCF establishes discrete functional chromatin domains at the Hox clusters during differentiation. Science 347, 1017–1021 (2015).

  17. 17.

    Seitan, V. C. et al. A role for cohesin in T-cell-receptor rearrangement and thymocyte differentiation. Nature 476, 467–471 (2011).

  18. 18.

    Kagey, M. H. et al. Mediator and cohesin connect gene expression and chromatin architecture. Nature 467, 430–435 (2010).

  19. 19.

    Lavagnolli, T. et al. Initiation and maintenance of pluripotency gene expression in the absence of cohesin. Genes Dev. 29, 23–38 (2015).

  20. 20.

    Ing-Simmons, E. et al. Spatial enhancer clustering and regulation of enhancer-proximal genes by cohesin. Genome Res. 25, 504–513 (2015).

  21. 21.

    Kaikkonen, M. U. et al. Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol. Cell 51, 310–325 (2013).

  22. 22.

    Ostuni, R. et al. Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171 (2013).

  23. 23.

    Bhatt, D. M. et al. Transcript dynamics of proinflammatory genes revealed by sequence analysis of subcellular RNA fractions. Cell 150, 279–290 (2012).

  24. 24.

    Chen, X. et al. Requirement for the histone deacetylase Hdac3 for the inflammatory gene expression program in macrophages. Proc. Natl. Acad. Sci. USA 109, E2865–E2874 (2012).

  25. 25.

    Glass, C. K. & Natoli, G. Molecular control of activation and priming in macrophages. Nat. Immunol. 17, 26–33 (2016).

  26. 26.

    Nasmyth, K. & Haering, C. H. Cohesin: its roles and mechanisms. Annu. Rev. Genet. 43, 525–558 (2009).

  27. 27.

    Kon, A. et al. Recurrent mutations in multiple components of the cohesin complex in myeloid neoplasms. Nat. Genet. 45, 1232–1237 (2013).

  28. 28.

    Ley, T. J. et al. Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  29. 29.

    Thota, S. et al. Genetic alterations of the cohesin complex genes in myeloid malignancies. Blood 124, 1790–1798 (2014).

  30. 30.

    Mazumdar, C. et al. Leukemia-associated cohesin mutants dominantly enforce stem cell programs and impair human hematopoietic progenitor differentiation. Cell Stem Cell 17, 675–688 (2015).

  31. 31.

    Mullenders, J. et al. Cohesin loss alters adult hematopoietic stem cell homeostasis, leading to myeloproliferative neoplasms. J. Exp. Med. 212, 1833–1850 (2015).

  32. 32.

    Viny, A. D. et al. Dose-dependent role of the cohesin complex in normal and malignant hematopoiesis. J. Exp. Med. 212, 1819–1832 (2015).

  33. 33.

    Galeev, R. et al. Genome-wide RNAi screen identifies cohesin genes as modifiers of renewal and differentiation in human HSCs. Cell Rep. 14, 2988–3000 (2016).

  34. 34.

    Fisher, J. B. et al. The cohesin subunit Rad21 is a negative regulator of hematopoietic self-renewal through epigenetic repression of Hoxa7 and Hoxa9. Leukemia 31, 712–719 (2017).

  35. 35.

    Baldridge, M. T., King, K. Y., Boles, N. C., Weksberg, D. C. & Goodell, M. A. Quiescent haematopoietic stem cells are activated by IFN-γ in response to chronic infection. Nature 465, 793–797 (2010).

  36. 36.

    Binder, D. et al. Aplastic anemia rescued by exhaustion of cytokine-secreting CD8+ T cells in persistent infection with lymphocytic choriomeningitis virus. J. Exp. Med. 187, 1903–1920 (1998).

  37. 37.

    Dybedal, I., Bryder, D., Fossum, A., Rusten, L. S. & Jacobsen, S. E. Tumor necrosis factor (TNF)-mediated activation of the p55 TNF receptor negatively regulates maintenance of cycling reconstituting human hematopoietic stem cells. Blood 98, 1782–1791 (2001).

  38. 38.

    Essers, M. A. G. et al. IFNα activates dormant haematopoietic stem cells in vivo. Nature 458, 904–908 (2009).

  39. 39.

    Mossadegh-Keller, N. et al. M-CSF instructs myeloid lineage fate in single haematopoietic stem cells. Nature 497, 239–243 (2013).

  40. 40.

    Nagai, Y. et al. Toll-like receptors on hematopoietic progenitor cells stimulate innate immune system replenishment. Immunity 24, 801–812 (2006).

  41. 41.

    Pietras, E. M. et al. Chronic interleukin-1 exposure drives haematopoietic stem cells towards precocious myeloid differentiation at the expense of self-renewal. Nat. Cell Biol. 18, 607–618 (2016).

  42. 42.

    Zhao, J. L. et al. Conversion of danger signals into cytokine signals by hematopoietic stem and progenitor cells for regulation of stress-induced hematopoiesis. Cell Stem Cell 14, 445–459 (2014).

  43. 43.

    Bennett, J. M. et al. Proposals for the classification of the acute leukaemias. Br. J. Haematol. 33, 451–458 (1976).

  44. 44.

    Tachibana-Konwalski, K. et al. Rec8-containing cohesin maintains bivalents without turnover during the growing phase of mouse oocytes. Genes Dev. 24, 2505–2516 (2010).

  45. 45.

    Zlotnik, A., Yoshie, O. & Nomiyama, H. The chemokine and chemokine receptor superfamilies and their molecular evolution. Genome Biol. 7, 243 (2006).

  46. 46.

    Azofeifa, J. G., Allen, M. A., Lladser, M. E. & Dowell, R. D. An annotation agnostic algorithm for detecting nascent RNA transcripts in GRO-Seq. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 1070–1081 (2017).

  47. 47.

    Hakimi, M.-A. et al. A chromatin remodelling complex that loads cohesin onto human chromosomes. Nature 418, 994–998 (2002).

  48. 48.

    Faure, A. J. et al. Cohesin regulates tissue-specific expression by stabilizing highly occupied cis-regulatory modules. Genome Res. 22, 2163–2175 (2012).

  49. 49.

    Yan, J. et al. Transcription factor binding in human cells occurs in dense clusters formed around cohesin anchor sites. Cell 154, 801–813 (2013).

  50. 50.

    Lopez-Serra, L., Kelly, G., Patel, H., Stewart, A. & Uhlmann, F. The Scc2–Scc4 complex acts in sister chromatid cohesion and transcriptional regulation by maintaining nucleosome-free regions. Nat. Genet. 46, 1147–1151 (2014).

  51. 51.

    Schieber, T. A. et al. Information theory perspective on network robustness. Phys. Lett. A 380, 359–364 (2016).

  52. 52.

    Painter, M. W., Davis, S., Hardy, R. R., Mathis, D. & Benoist, C. Transcriptomes of the B and T lineages compared by multiplatform microarray profiling. J. Immunol. 186, 3047–3057 (2011).

  53. 53.

    Matatall, K. A. et al. Chronic infection depletes hematopoietic stem cells through stress-induced terminal differentiation. Cell Rep. 17, 2584–2595 (2016).

  54. 54.

    Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

  55. 55.

    Heidinger-Pauli, J. M., Mert, O., Davenport, C., Guacci, V. & Koshland, D. Systematic reduction of cohesin differentially affects chromosome segregation, condensation, and DNA repair. Curr. Biol. 20, 957–963 (2010).

  56. 56.

    Deardorff, M. A., Noon, S. E. & Krantz, I. D. Cornelia de Lange syndrome. GeneReviews (2018).

  57. 57.

    Seibler, J. et al. Rapid generation of inducible mouse mutants. Nucleic Acids Res. 31, e12 (2003).

  58. 58.

    Cobb, B. S. et al. Targeting of Ikaros to pericentromeric heterochromatin by direct DNA binding. Genes Dev. 14, 2146–2160 (2000).

  59. 59.

    Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

  60. 60.

    Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

  61. 61.

    Splinter, E., de Wit, E., van de Werken, H. J. G., Klous, P. & de Laat, W. Determining long-range chromatin interactions for selected genomic sites using 4C-seq technology: from fixation to computation. Methods 58, 221–230 (2012).

  62. 62.

    van de Werken, H. J. G. et al. Robust 4C-seq data analysis to screen for regulatory DNA interactions. Nat. Methods 9, 969–972 (2012).

  63. 63.

    Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).

  64. 64.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  65. 65.

    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).

  66. 66.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

  67. 67.

    Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).

  68. 68.

    Mi, H. et al. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 45, D183–D189 (2017). D1.

  69. 69.

    Carroll, T. S., Liang, Z., Salama, R., Stark, R. & de Santiago, I. Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. Front. Genet. 5, 75 (2014).

  70. 70.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

  71. 71.

    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).

  72. 72.

    Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

  73. 73.

    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).

  74. 74.

    McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

  75. 75.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

  76. 76.

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

  77. 77.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

  78. 78.

    Phipson, B., Lee, S., Majewski, I. J., Alexander, W. S. & Smyth, G. K. Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Ann. Appl. Stat. 10, 946–963 (2016).

  79. 79.

    Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

  80. 80.

    Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

  81. 81.

    Servant, N. et al. HiTC: exploration of high-throughput ‘C’ experiments. Bioinformatics 28, 2843–2844 (2012).

  82. 82.

    Lun, A. T. L., Perry, M. & Ing-Simmons, E. Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments. F1000Res. 5, 950 (2016).

  83. 83.

    Harmston, N., Ing-Simmons, E., Perry, M., Barešić, A. & Lenhard, B. GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data. BMC Genomics 16, 963 (2015).

  84. 84.

    Hahne, F. & Ivanek, R. Visualizing genomic data using Gviz and Bioconductor. Methods Mol. Biol. 1418, 335–351 (2016).

  85. 85.

    Kruse, K., Hug, C. B., Hernández-Rodríguez, B. & Vaquerizas, J. M. TADtool: visual parameter identification for TAD-calling algorithms. Bioinformatics 32, 3190–3192 (2016).

  86. 86.

    Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263 (2009).

  87. 87.

    Garber, M. et al. A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals. Mol. Cell 47, 810–822 (2012).

Download references


We thank A. Innes, M. Spivakov, L. Rudolf (FLI, Jena) and D. Odom (CRUK Cambridge) for discussions, G. Sauvageau (University of Montreal) and the Leucegene consortium for early access to AML data, L. Game for sequencing, and J. Elliott and the LMS/NIHR Imperial Biomedical Research Centre Flow Cytometry Facility for cell sorting. This work was funded by Wellcome Investigator Award 099276/Z/12/Z (M.M.), Wellcome Project Grant P55504 (B.L.), Fundação de Amparo à Pesquisa do Estado de São Paulo 2014/20861-3 (M.T.A.), National Science Foundation grant ABI1262410 (R.D.D.), Academy of Finland grants 287478 and 294073 (M.U.K), MRC Programme Grant ID 84637 and Wellcome Trust Programme Grant Ref 078241/Z/05/Z (K.A.M.), ERC grant 692789 (G.N.), and core support from the Medical Research Council UK to the London Institute of Medical Sciences.

Author information

Author notes

    • Elizabeth Ing-Simmons

    Present address: Max Planck Institute for Molecular Biomedicine, Muenster, Germany

    • Mariane T. Amano

    Present address: Hospital Sírio-Libanês, Sao Paulo, Brazil

    • Kikuë Tachibana

    Present address: Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna Biocenter, Vienna, Austria

  1. These authors contributed equally: Felix D. Weiss, Gopuraja Dharmalingam, Ya Guo, Elizabeth Ing-Simmons.


  1. Lymphocyte Development Group, Epigenetics Section, MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK

    • Sergi Cuartero
    • , Felix D. Weiss
    • , Ya Guo
    • , Elizabeth Ing-Simmons
    • , Irene Robles-Rebollo
    • , Dounia Djeghloul
    • , Mariane T. Amano
    • , Amanda G. Fisher
    •  & Matthias Merkenschlager
  2. Computational Regulatory Genomics Group, Integrative Biology Section, MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK

    • Elizabeth Ing-Simmons
    •  & Boris Lenhard
  3. MRC London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK

    • Gopuraja Dharmalingam
    • , Xiaolin Xiao
    • , Yi-Fang Wang
    •  & Enrico Petretto
  4. Department of Experimental Oncology, European Institute of Oncology, Milan, Italy

    • Silvia Masella
    •  & Iros Barozzi
  5. Department of Surgery and Cancer, Department of Medicine, Imperial College London, London, UK

    • Iros Barozzi
  6. A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

    • Henri Niskanen
    •  & Minna U. Kaikkonen
  7. Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, Singapore

    • Enrico Petretto
  8. BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO, USA

    • Robin D. Dowell
  9. Department of Biochemistry, University of Oxford, Oxford, UK

    • Kikuë Tachibana
    •  & Kim A. Nasmyth
  10. Humanitas Clinical and Research Center, Milan, Italy

    • Gioacchino Natoli
  11. Humanitas University, Milan, Italy

    • Gioacchino Natoli


  1. Search for Sergi Cuartero in:

  2. Search for Felix D. Weiss in:

  3. Search for Gopuraja Dharmalingam in:

  4. Search for Ya Guo in:

  5. Search for Elizabeth Ing-Simmons in:

  6. Search for Silvia Masella in:

  7. Search for Irene Robles-Rebollo in:

  8. Search for Xiaolin Xiao in:

  9. Search for Yi-Fang Wang in:

  10. Search for Iros Barozzi in:

  11. Search for Dounia Djeghloul in:

  12. Search for Mariane T. Amano in:

  13. Search for Henri Niskanen in:

  14. Search for Enrico Petretto in:

  15. Search for Robin D. Dowell in:

  16. Search for Kikuë Tachibana in:

  17. Search for Minna U. Kaikkonen in:

  18. Search for Kim A. Nasmyth in:

  19. Search for Boris Lenhard in:

  20. Search for Gioacchino Natoli in:

  21. Search for Amanda G. Fisher in:

  22. Search for Matthias Merkenschlager in:


S.C. conceived and designed the study; performed most experiments, including those based on Rad21 deletion; analyzed data; designed figures; and contributed to writing the manuscript. F.D.W. performed experiments based on RAD21-TEV cleavage and contributed to writing the manuscript. G.D., X.X. and Y.-F.W. analyzed data. Y.G. performed and analyzed 4C experiments. E.I.-S. analyzed data, designed figures and contributed to writing the manuscript. S.M. performed 5C experiments. I.R.-R. performed and analyzed immunofluorescence experiments, designed figures and contributed to writing the manuscript. I.B. analyzed data and contributed to writing the manuscript. D.D. designed and performed flow cytometry experiments. M.T.A. performed experiments. H.N. designed and performed GRO-seq experiments. E.P. and B.L. designed and supervised data analysis. R.D.D. conceived and performed analysis of enhancer TSSs. K.T. and K.A.N. generated and provided essential reagents. M.U.K. designed and supervised GRO-seq experiments. G.N. designed and supervised 5C experiments and contributed to writing the manuscript. A.G.F. contributed to study design and writing the manuscript. M.M. conceived and designed the study, wrote the manuscript, made figures and supervised experiments. All authors discussed the results and commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Matthias Merkenschlager.

Integrated supplementary information

  1. Supplementary Figure 1 Cohesin depletion in mature, postmitotic macrophages.

    a) Rad21 deletion in mature macrophages. b) Flow cytometric analysis of the macrophage markers CD11b and F4/80 on control and Rad21-/- macrophages 3d after 4-OHT. Representative of 3 biological replicates. c) DNA content of macrophages at the indicated times after 4-OHT, and % cells in G1 3d after 4-OHT. Mean ± SEM of 6 biological replicates. d) qPCR amplification of the floxed Rad21 exons normalized to genomic sites on chromosome 2 and 3. Mean ± SEM of 3 biological replicates. e) Immunoblot of RAD21 protein normalized to Actin at the indicated times after 4-OHT. Mean ± SEM of 3 biological replicates. f) p53 target gene expression in control and Rad21-deleted macrophages quantified by RNA-seq. Mean ± SEM of 3 biological replicates. g) Enumeration of cells with nuclear-localized NF-κB p65 (>50%) at 1 h post-LPS treatment. Mean ± SEM of 3 biological replicates Mean ± SEM of 113 to 340 cells per genotype in each of 3 biological replicates. Box plots show the median and lower and upper quartiles, whiskers show the maximum and minimum data points up to 1.5 times the interquartile range. h) Quantification of cytokines in media conditioned by LPS-pulsed control and Rad21-deleted macrophages determined by ELISA (for IFN-β) or cytokine array (all other cytokines). Mean ± SE of 3 biological replicates. The secretion of 26 cytokines increased in control macrophages 8 h after LPS (P < 0.05, fold-change > 2). Compared to wild-type, secretion by Rad21-deficient macrophages was significantly increased for 3, and significantly decreased for 13 cytokines (P < 0.05, two-sided t-test).

  2. Supplementary Figure 2 Supporting data for restricted enhancer dynamics in cohesin-deficient macrophages.

    a) Frequency of deregulated constitutive, inducible and repressed enhancers22 by H3K27ac ChIP-seq (adj. P < 0.05) in Rad21-deleted macrophages. H3K27ac ChIP-seq reads were normalized to H3 ChIP-seq and analysed by DESeq2. Based on 2 biological replicates per genotype and condition. b) Volcano plot of chromatin accessibility at constitutive, inducible and repressed enhancers as determined by ATAC-seq 6 h after LPS. Based on 2 biological replicates per genotype and condition. c) Heatmap of eRNA transcription as determined by GRO-seq (left) and frequency of deregulation (DESeq2 adj. P < 0.05) in Rad21-deleted macrophages (right) at intergenic enhancers22 that are constitutively active (n = 3775), inducible (n = 2893) or repressed (n = 4914). Based on 2 biological replicates per genotype and condition. z-scores were determined based on FPKM. d) Correlation between changes in H3K27ac and eRNA transcription (GRO-seq) at intergenic inducible enhancers22 6 h after LPS stimulation of Rad21-deleted versus control macrophages. Of 1461 intergenic inducible enhancers included in the DESeq2 analysis, the number of deregulated (P < 0.05) enhancers was 431 for H3K27ac, 283 for eRNA transcription (GRO-seq), and 185 for both H3K27ac and eRNA transcription. H3K27ac and eRNA transcription were significantly correlated (P < 10e-16, Spearman correlation = 0.52).

  3. Supplementary Figure 3 Deregulated genes and enhancers at the genomic level.

    a) Domain-wide impact of Rad21 deletion on enhancer activity and gene expression. Of 2754 called TADs3, 1830 contained deregulated genes, 689 contained GROseq-deregulated enhancers, and 1451 contained H3K27ac-deregulated enhancers. TADs that contained deregulated genes were enriched for deregulated enhancers (odds ratio = 5.02, P = 4.47e-43 for GRO-seq and odds ratio = 7.23, P = 2.70e-111 for H3K27ac). The figure shows log2 fold-changes in gene expression (RNA-seq, Rad21-deleted versus wild-type macrophages) for TADs with decreased (left) or increased (right) eRNA transcription of intergenic enhancers22 in Rad21-deleted macrophages at the indicated times after LPS stimulation. Based on 2 biological replicates per genotype and condition. b) Top, Hi-C map3 of the ~5Mb region on mouse chr11:80.141.160 to 85.160.410 with TAD annotation. Upper middle, 5C chromatin contacts wild-type macrophages between restriction fragments containing at least one annotated enhancer or promoter (E-P interactions), and chromatin contact changes in response to LPS-stimulation (LPS-responsive E-P interactions). Grey loops: Contacts that change < 2-fold over six time points from 0 to 8 h LPS. Red: > 2-fold induced contacts. Blue: > 2-fold decreased contacts. LPS-responsive chromatin contacts are shown below. Lower middle, the coefficient of variation (CV) is an indicator whether interactions change in response to LPS. Log2 fold-changes in transcripts (RNA-seq), H3K27ac (ChIP-seq), and eRNAs (GRO-seq) are shown for Rad21-/- versus wild-type macrophages. Light, medium and dark colors represent fold-change at 0, 1 and 6 h of LPS treatment for H3K27ac and eRNAs, and 0, 2 and 8 h for transcripts. MCP (the ~260kb MCP/monocyte chemotactic protein cluster containing Ccl1, Ccl2, Ccl7, Ccl11 and Ccl8) and MIP (the ~ 330kb MIP/macrophage inflammatory protein cluster containing Ccl5, Ccl3, Ccl15, Ccl4, Ccl6, and Ccl9) mark domains enriched for downregulated enhancers. Insets show significant E-P interactions and LPS-responsive E-P interactions for two TADs rich in inducible genes, the Slfn gene cluster and the MIP cluster. Based on one time series of 6 5C experiments. c) LPS-induced 5C interactions involving (from left to right) enhancers and promoters, inducible enhancers, and promoters of inducible genes in wild-type macrophages. The normalized strength of each set of interactions was compared at each time point to the normalized strength of all other enhancer-promoter interactions (black line) using a Wilcoxon rank sum test. Red symbols indicate adjusted P < 0.05. Shaded areas represent 95% confidence intervals.

  4. Supplementary Figure 4 4C analysis of chromatin contacts.

    a) Top: Chromosomal coordinates, genes, inducible genes, enhancers22, super-enhancers (SEs), CTCF ChIP-seq and TADs. Below: Main trend at 5kb resolution (mean with 20th to 80th percentile and normalized values in sliding windows sized 2-50kb (colors). Chromatin interactions in wild-type macrophages before and 2 h after LPS, suggesting re-configured interactions of the Egr2 promoter with the downstream super-enhancer. Two merged biological replicates. b) Interactions of the Egr2 promoter with downstream super-enhancer in wild-type and Rad21-/- macrophages before and after LPS (2 h). Two merged biological replicates. c) Interactions of the Egr2, Ifnar1, and Cebpb promoters before and 8 h after TEV induction in RAD-TEV macrophages.

  5. Supplementary Figure 5 Enhancer accessibility in cohesin-depleted macrophages.

    a) The number of ATAC-seq peaks (left) and the percentage of reads in peaks (right) for control and Rad21-/- macrophages before and after LPS induction. Peaks were called based on 2 merged biological replicates. b) Inducible enhancers were classified into maintained (top) or failed (bottom) based on DESeq2 analysis of H3K27ac ChIP-seq replicates (adj. P < 0.05). Transcription start sites within inducible enhancers were identified by analysis of GRO-seq data (inset). ATAC-seq profiles centered on enhancer TSSs are shown for control (grey) and Rad21-/- macrophages (orange) before and after 1 and 6 h of LPS stimulation. Based on 2 independent ATAC-seq experiments per genotype and condition. c) Model for the relationship between cohesin binding and chromatin accessibility based on47,48,49,50 where cohesin binding promotes chromatin accessibility, which in turn facilitates cohesin binding.

  6. Supplementary Figure 6 Vulnerability of inducible gene expression.

    a) Model to illustrate the hierarchical organization of inducible gene expression. Nodes (circles) represent genes and their products, and lines (edges) indicate regulatory relationships. b) Deregulated constitutive and inducible genes after acute degradation of RAD21-TEV are enriched for cohesin binding (RAD21 peaks at baseline within 10kb of gene body, top) and for location within 40 kb of macrophage super-enhancers (bottom). Odds ratios were determined by two-sided Fisher's exact test. Meta-analysis of 3 RNA-seq experiments and 2 RAD21 ChIP-seq experiments per condition. c) Genes involved in the regulation of inducible gene expression are deregulated by cohesin cleavage in RAD21-TEV macrophages. In addition to genes and TADs, tracks show RAD21 ChIP-seq peaks, enhancers, and super-enhancers (SEs) mapped at the indicated times after LPS in wild-type macrophages. Deregulated genes are shown in black. Meta-analysis of 3 RNA-seq experiments and 2 RAD21 ChIP-seq experiments per condition. d) Features of immediate cohesin targets are diluted as more inducible genes become deregulated with time after cohesin depletion. Odds ratios and P-values (Fisher's exact test) for RAD21 binding (ChIP-seq peaks within 10kb) and enhancer proximity (super-enhancer within 40kb) of deregulated genes in RAD21-TEV and Rad21-/- macrophages at baseline (not LPS stimulated). e) Odds ratios and P-values (Fisher's exact test) for the genomic enrichment of inducible genes23 within 40kb of ('near') enhancers22, inducible enhancers, LPS-induced H3K27ac signal of log2 FC >1.5 ('strongly inducible') enhancers, or super-enhancers.

Supplementary information

  1. Supplementary Figures

    Supplementary Figures 1–6

  2. Reporting Summary

  3. Supplementary Text

    Supplementary Table 1

About this article

Publication history