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.

Selective Mediator dependence of cell-type-specifying transcription

Nature Genetics volume 52, pages 719–727 (2020)Cite this article

Subjects

Abstract

The Mediator complex directs signals from DNA-binding transcription factors to RNA polymerase II (Pol II). Despite this pivotal position, mechanistic understanding of Mediator in human cells remains incomplete. Here we quantified Mediator-controlled Pol II kinetics by coupling rapid subunit degradation with orthogonal experimental readouts. In agreement with a model of condensate-driven transcription initiation, large clusters of hypophosphorylated Pol II rapidly disassembled upon Mediator degradation. This was accompanied by a selective and pronounced disruption of cell-type-specifying transcriptional circuits, whose constituent genes featured exceptionally high rates of Pol II turnover. Notably, the transcriptional output of most other genes was largely unaffected by acute Mediator ablation. Maintenance of transcriptional activity at these genes was linked to an unexpected CDK9-dependent compensatory feedback loop that elevated Pol II pause release rates across the genome. Collectively, our work positions human Mediator as a globally acting coactivator that selectively safeguards the functionality of cell-type-specifying transcriptional networks.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

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

Fig. 1: Acute Mediator loss selectively abrogates the functionality of cell-type-specifying transcriptional circuits.
Fig. 2: Mediator is dispensable for maintenance of enhancer–promoter contacts.
Fig. 3: Mediator organizes Pol II clusters to optimize the transcriptional dynamics of cell-type-specifying gene regulatory networks.
Fig. 4: Compensatory P-TEFb activation boosts non-super-enhancer output to shape the Mediator hyperdependence of cell-type-specifying transcription.

Data availability

Next-generation sequencing data are available through the NCBI Gene Expression Omnibus under accession code GSE139468. Chromatin proteomics data have been deposited at PRIDE under dataset identifier PXD017611. Source data for Figs. 1 and 4 and Extended Data Figs. 1–4 and 6 are presented with the paper.

Code availability

Custom code used to analyze the data in this study is available at https://github.com/GWinterLab/Jaeger_Mediator_NatureGenetics_2020.

References

  1. Kelleher, R. J. 3rd, Flanagan, P. M. & Kornberg, R. D. A novel mediator between activator proteins and the RNA polymerase II transcription apparatus. Cell 61, 1209–1215 (1990).

    CAS  PubMed  Google Scholar 

  2. Kornberg, R. D. Mediator and the mechanism of transcriptional activation. Trends Biochem. Sci. 30, 235–239 (2005).

    CAS  PubMed  Google Scholar 

  3. Thompson, C. M., Koleske, A. J., Chao, D. M. & Young, R. A. A multisubunit complex associated with the RNA polymerase II CTD and TATA-binding protein in yeast. Cell 73, 1361–1375 (1993).

    CAS  PubMed  Google Scholar 

  4. Kim, Y. J., Bjorklund, S., Li, Y., Sayre, M. H. & Kornberg, R. D. A multiprotein mediator of transcriptional activation and its interaction with the C-terminal repeat domain of RNA polymerase II. Cell 77, 599–608 (1994).

    CAS  PubMed  Google Scholar 

  5. Fondell, J. D., Ge, H. & Roeder, R. G. Ligand induction of a transcriptionally active thyroid hormone receptor coactivator complex. Proc. Natl Acad. Sci. USA 93, 8329–8333 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Jiang, Y. W. et al. Mammalian mediator of transcriptional regulation and its possible role as an end-point of signal transduction pathways. Proc. Natl Acad. Sci. USA 95, 8538–8543 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Malik, S. & Roeder, R. G. Dynamic regulation of Pol II transcription by the mammalian Mediator complex. Trends Biochem. Sci. 30, 256–263 (2005).

    CAS  PubMed  Google Scholar 

  8. Conaway, R. C., Sato, S., Tomomori-Sato, C., Yao, T. & Conaway, J. W. The mammalian Mediator complex and its role in transcriptional regulation. Trends Biochem. Sci. 30, 250–255 (2005).

    CAS  PubMed  Google Scholar 

  9. Kim, Y. J. & Lis, J. T. Interactions between subunits of Drosophila Mediator and activator proteins. Trends Biochem. Sci. 30, 245–249 (2005).

    CAS  PubMed  Google Scholar 

  10. Allen, B. L. & Taatjes, D. J. The Mediator complex: a central integrator of transcription. Nat. Rev. Mol. Cell Biol. 16, 155–166 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Soutourina, J. Transcription regulation by the Mediator complex. Nat. Rev. Mol. Cell Biol. 19, 262–274 (2018).

    CAS  PubMed  Google Scholar 

  12. Jeronimo, C. & Robert, F. The Mediator complex: at the nexus of RNA polymerase II transcription. Trends Cell Biol. 27, 765–783 (2017).

    CAS  PubMed  Google Scholar 

  13. Eychenne, T., Werner, M. & Soutourina, J. Toward understanding of the mechanisms of Mediator function in vivo: focus on the preinitiation complex assembly. Transcription 8, 328–342 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Malik, S. & Roeder, R. G. The metazoan Mediator co-activator complex as an integrative hub for transcriptional regulation. Nat. Rev. Genet. 11, 761–772 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Holstege, F. C. et al. Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95, 717–728 (1998).

    CAS  PubMed  Google Scholar 

  16. Petrenko, N., Jin, Y., Wong, K. H. & Struhl, K. Evidence that Mediator is essential for Pol II transcription, but is not a required component of the preinitiation complex in vivo. eLife 6, e28447 (2017).

    PubMed  PubMed Central  Google Scholar 

  17. Jeronimo, C. et al. Tail and kinase modules differently regulate core Mediator recruitment and function in vivo. Mol. Cell 64, 455–466 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Petrenko, N., Jin, Y., Wong, K. H. & Struhl, K. Mediator undergoes a compositional change during transcriptional activation. Mol. Cell 64, 443–454 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Shlyueva, D., Stampfel, G. & Stark, A. Transcriptional enhancers: from properties to genome-wide predictions. Nat. Rev. Genet. 15, 272–286 (2014).

    CAS  PubMed  Google Scholar 

  20. 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 

  21. 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 

  22. Spitz, F. & Furlong, E. E. Transcription factors: from enhancer binding to developmental control. Nat. Rev. Genet. 13, 613–626 (2012).

    CAS  PubMed  Google Scholar 

  23. Lee, T. I. & Young, R. A. Transcriptional regulation and its misregulation in disease. Cell 152, 1237–1251 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Davidson, E. H. Emerging properties of animal gene regulatory networks. Nature 468, 911–920 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science 361, eaar3958 (2018).

    PubMed  PubMed Central  Google Scholar 

  26. Cho, W.-K. et al. Mediator and RNA polymerase II clusters associate in transcription-dependent condensates. Science 361, 412 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Levine, M., Cattoglio, C. & Tjian, R. Looping back to leap forward: transcription enters a new era. Cell 157, 13–25 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Kim, S. & Shendure, J. Mechanisms of interplay between transcription factors and the 3D genome. Mol. Cell 76, 306–319 (2019).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  30. El Khattabi, L. et al. A pliable Mediator acts as a functional rather than an architectural bridge between promoters and enhancers. Cell 178, 1145–1158 (2019).

    CAS  PubMed  Google Scholar 

  31. Takahashi, H. et al. Human Mediator subunit MED26 functions as a docking site for transcription elongation factors. Cell 146, 92–104 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Wang, W. et al. Mediator MED23 regulates basal transcription in vivo via an interaction with P-TEFb. Transcription 4, 39–51 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Dahlberg, O., Shilkova, O., Tang, M., Holmqvist, P. H. & Mannervik, M. P-TEFb, the super elongation complex and Mediator regulate a subset of non-paused genes during early Drosophila embryo development. PLoS Genet. 11, e1004971 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. Conaway, R. C. & Conaway, J. W. The Mediator complex and transcription elongation. Biochim. Biophys. Acta 1829, 69–75 (2013).

    CAS  PubMed  Google Scholar 

  35. 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 

  36. 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 

  37. Cevher, M. A. et al. Reconstitution of active human core Mediator complex reveals a critical role of the MED14 subunit. Nat. Struct. Mol. Biol. 21, 1028–1034 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Winter, G. E. et al. BET bromodomain proteins function as master transcription elongation factors independent of CDK9 recruitment. Mol. Cell 67, 5–18 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Olson, C. M. et al. Pharmacological perturbation of CDK9 using selective CDK9 inhibition or degradation. Nat. Chem. Biol. 14, 163–170 (2018).

    CAS  PubMed  Google Scholar 

  40. Muhar, M. et al. SLAM-seq defines direct gene-regulatory functions of the BRD4–MYC axis. Science 360, 800–805 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Plaschka, C. et al. Architecture of the RNA polymerase II–Mediator core initiation complex. Nature 518, 376–380 (2015).

    CAS  PubMed  Google Scholar 

  42. Nozawa, K., Schneider, T. R. & Cramer, P. Core Mediator structure at 3.4 Å extends model of transcription initiation complex. Nature 545, 248–251 (2017).

    CAS  PubMed  Google Scholar 

  43. Tsai, K. L. et al. Mediator structure and rearrangements required for holoenzyme formation. Nature 544, 196–201 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Kato, M. et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell 149, 753–767 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Schwalb, B. et al. TT-seq maps the human transient transcriptome. Science 352, 1225–1228 (2016).

    CAS  PubMed  Google Scholar 

  46. Saint-Andre, V. et al. Models of human core transcriptional regulatory circuitries. Genome Res. 26, 385–396 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Nabet, B. et al. Rapid and direct control of target protein levels with VHL-recruiting dTAG molecules. Preprint at bioRxiv https://doi.org/10.1101/2020.03.13.980946 (2020).

  48. Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Li, W., Notani, D. & Rosenfeld, M. G. Enhancers as non-coding RNA transcription units: recent insights and future perspectives. Nat. Rev. Genet. 17, 207–223 (2016).

    CAS  PubMed  Google Scholar 

  51. Kwak, H., Fuda, N. J., Core, L. J. & Lis, J. T. Precise maps of RNA polymerase reveal how promoters direct initiation and pausing. Science 339, 950–953 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Wissink, E. M., Vihervaara, A., Tippens, N. D. & Lis, J. T. Nascent RNA analyses: tracking transcription and its regulation. Nat. Rev.Genet. 20, 705–723 (2019).

  53. Cramer, P. Organization and regulation of gene transcription. Nature 573, 45–54 (2019).

    CAS  PubMed  Google Scholar 

  54. Hnisz, D., Shrinivas, K., Young, R. A., Chakraborty, A. K. & Sharp, P. A. A phase separation model for transcriptional control. Cell 169, 13–23 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Guo, Y. E. et al. Pol II phosphorylation regulates a switch between transcriptional and splicing condensates. Nature 572, 543–548 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Gressel, S. et al. CDK9-dependent RNA polymerase II pausing controls transcription initiation. eLife 6, e29736 (2017).

  57. Shao, W. & Zeitlinger, J. Paused RNA polymerase II inhibits new transcriptional initiation. Nat. Genet. 49, 1045–1051 (2017).

    CAS  PubMed  Google Scholar 

  58. Adelman, K. & Lis, J. T. Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat. Rev. Genet. 13, 720–731 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Ehrensberger, A. H., Kelly, G. P. & Svejstrup, J. Q. Mechanistic interpretation of promoter-proximal peaks and RNAPII density maps. Cell 154, 713–715 (2013).

    CAS  PubMed  Google Scholar 

  60. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    CAS  PubMed  Google Scholar 

  61. Zhou, Q., Li, T. & Price, D. H. RNA polymerase II elongation control. Annu. Rev. Biochem. 81, 119–143 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Sanso, M. et al. P-TEFb regulation of transcription termination factor Xrn2 revealed by a chemical genetic screen for Cdk9 substrates. Genes Dev. 30, 117–131 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Parua, P. K. et al. A Cdk9–PP1 switch regulates the elongation–termination transition of RNA polymerase II. Nature 558, 460–464 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Bradner, J. E., Hnisz, D. & Young, R. A. Transcriptional addiction in cancer. Cell 168, 629–643 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Krebs, A. R. et al. Genome-wide single-molecule footprinting reveals high RNA polymerase II turnover at paused promoters. Mol. Cell 67, 411–422 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Li, Y., Liu, M., Chen, L. F. & Chen, R. P-TEFb: finding its ways to release promoter-proximally paused RNA polymerase II. Transcription 9, 88–94 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Mir, M., Bickmore, W., Furlong, E. E. M. & Narlikar, G. Chromatin topology, condensates and gene regulation: shifting paradigms or just a phase? Development 146, dev182766 (2019).

  68. Chong, S. et al. Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science 361, eaar2555 (2018).

    PubMed  PubMed Central  Google Scholar 

  69. Roeder, R. G. 50+ years of eukaryotic transcription: an expanding universe of factors and mechanisms. Nat. Struct. Mol. Biol. 26, 783–791 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Sakuma, T., Nakade, S., Sakane, Y., Suzuki, K.-I. T. & Yamamoto, T. MMEJ-assisted gene knock-in using TALENs and CRISPR–Cas9 with the PITCh systems. Nat. Protoc. 11, 118–133 (2016).

    CAS  PubMed  Google Scholar 

  71. 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  Google Scholar 

  72. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  74. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  75. Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).

    CAS  PubMed  Google Scholar 

  76. Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    CAS  PubMed  Google Scholar 

  77. 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).

    PubMed  PubMed Central  Google Scholar 

  78. Cui, Y. et al. BioCircos.js: an interactive Circos JavaScript library for biological data visualization on web applications. Bioinformatics 32, 1740–1742 (2016).

    CAS  PubMed  Google Scholar 

  79. Aranda-Orgilles, B. et al. MED12 regulates HSC-specific enhancers independently of Mediator kinase activity to control hematopoiesis. Cell Stem Cell 19, 784–799 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  81. Peng, K., Radivojac, P., Vucetic, S., Dunker, A. K. & Obradovic, Z. Length-dependent prediction of protein intrinsic disorder. BMC Bioinformatics 7, 208 (2006).

    PubMed  PubMed Central  Google Scholar 

  82. Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Waskom, M. et al. mwaskom/seaborn: v0.9.0. Zenodo https://doi.org/10.5281/ZENODO.1313201 (2018).

  84. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Google Scholar 

  85. McNamara, R. P. et al. KAP1 recruitment of the 7SK snRNP complex to promoters enables transcription elongation by RNA polymerase II. Mol. Cell 61, 39–53 (2016).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank R. Fisher (Icahn School of Medicine at Mount Sinai) for sharing antibody to SPT5 phosphorylated at Thr806. We thank the Biomedical Sequencing Facility at CeMM and the MPIMG sequencing core for assistance with next-generation sequencing. We thank the imaging core facility of the Medical University of Vienna for assistance with microscopy. We thank P. Lenart for critical review of the image quantification procedures. We thank A. Mayer and M. Erb for feedback on this manuscript. M.G.J. was supported by a Boehringer Ingelheim Fonds PhD fellowship. T.V. was supported by the International Max Planck Research School for Genome Science, part of the Göttingen Graduate Center for Neurosciences, Biophysics and Molecular Biosciences. B.A. is supported by the Austrian Science Fund (FWF) and the Medical University of Vienna’s joint PhD program in Inflammation and Immunity (FWF1212). C.B. is supported by a New Frontiers Group award of the Austrian Academy of Sciences and by an ERC Starting Grant (European Union’s Horizon 2020 research and innovation programme, grant agreement 679146). B.N. was supported by an American Cancer Society Postdoctoral Fellowship (PF-17-010-01-CDD). B.N. and N.S.G. were supported by the Katherine L. and Steven C. Pinard Research Fund. D.H. is supported by the SPP2202 Priority Program Grant (HN 4/1-1) from the Deutsche Forschungsgemeinschaft (DFG). This project was further supported by an FWF Stand-Alone grant (P31690-B) awarded to the Winter laboratory.

Author information

Authors and Affiliations

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

    Martin G. Jaeger, Alexander Hanzl, Hana Imrichova, Matthias Brand, Benedikt Agerer, Someth Chorn, André C. Müller, Andreas Bergthaler, Christoph Bock & Georg E. Winter

  2. Department of Molecular Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany

    Björn Schwalb, Taras Velychko & Patrick Cramer

  3. Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany

    Sebastian D. Mackowiak & Denes Hnisz

  4. Department of Cancer Biology, Dana­-Farber Cancer Institute, Boston, MA, USA

    Behnam Nabet, Fleur M. Ferguson & Nathanael S. Gray

  5. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

    Behnam Nabet, Fleur M. Ferguson & Nathanael S. Gray

  6. Novartis Institutes for BioMedical Research, Cambridge, MA, USA

    James E. Bradner

  7. Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria

    Christoph Bock

Authors
  1. Martin G. Jaeger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Björn Schwalb
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sebastian D. Mackowiak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Taras Velychko
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexander Hanzl
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hana Imrichova
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Matthias Brand
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Benedikt Agerer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Someth Chorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Behnam Nabet
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Fleur M. Ferguson
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. André C. Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Andreas Bergthaler
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Nathanael S. Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. James E. Bradner
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Christoph Bock
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Denes Hnisz
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Patrick Cramer
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Georg E. Winter
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.G.J. and G.E.W. conceptualized this project. M.G.J., T.V., A.H., M.B., B.A. and D.H. designed and conducted experiments. M.G.J., B.S., S.D.M., A.H., H.I. and M.B. analyzed and interpreted original and published omics data. M.G.J., M.B., B.A. and S.C. generated cell lines. M.G.J., B.S., S.D.M. and M.B. visualized data. B.N. and F.M.F. synthesized the dTAGV-1 reagent. A.M., A.B., J.E.B., N.S.G., C.B., D.H., P.C. and G.E.W. supervised the work. M.G.J. and G.E.W. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Patrick Cramer or Georg E. Winter.

Ethics declarations

Competing interests

G.E.W., J.E.B. and B.N. are inventors on patent applications related to the dTAG system (WO/2017/024318, WO/2017/024319, WO/2018/148443, WO/2018/148440). The dTAGV-1 molecule is the subject of a patent application filed by Dana-Farber Cancer Institute. N.S.G. is a scientific founder, member of the scientific advisory board (SAB) and equity holder for C4 Therapeutics, Syros, Soltego, B2S, Gatekeeper and Petra Pharmaceuticals. The Gray laboratory receives or has received research funding from Novartis, Takeda, Astellas, Taiho, Janssen, Kinogen, Voroni, Her2llc, Deerfield and Sanofi. J.E.B. is now an executive and shareholder of Novartis and has been a founder and shareholder of SHAPE (acquired by Medivir), Acetylon (acquired by Celgene), Tensha (acquired by Roche), Syros, Regenacy and C4 Therapeutics.

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 Extended characterization of chemically degradable MED-dTAG alleles.

a, MED-dTAG depletion mean of two independent image quantifications of the Fig. 1b immunoblot. b, Degrader treatment selectively destabilizes the tagged Mediator subunit without affecting other complex members. c, Time-resolved immunoblot of MED10-dTAG and direct pharmacologic degradation of CDK9 (dCDK9; THAL-SNS-032) or BRD4 (dBET6). d, Pearson correlation of average 3’ mRNA-seq log2 fold changes after 6 h (n = 3 independent drug treatments). For dTAG-carrying cell lines (only gene names shown), we compare dTAG7 vs. vehicle control in the same cell line. Other conditions represent drug vs. vehicle control in wild-type cells. e, Gene ontology (GO) terms enriched among negative PC2 loadings in Fig. 1c. Enrichment was calculated using the GSEAPreranked tool75. Negative enrichment indicates a strong influence of these terms on PC2 diversity and that the underlying genes are downregulated. f, Gene set enrichment analysis of top 100 core MYC target genes from (ref. 40) among PC2 loadings. g, Time-resolved immunoblot of MED14-dTAG degradation kinetics and its influence on MYC protein levels. Unprocessed western blots shown in Source Data.

Source Data

Extended Data Fig. 2 MED14 degradation disrupts overall Mediator complex integrity.

a, Influence of long-term MED14 degradation on cell growth. b, Size-exclusion chromatography and western blotting of nuclear extracts after MED14 degradation. Mediator subunits of each submodule shifted to lower apparent molecular weight, indicating complex disassembly. BAF complex member BRD9 serves as negative control. c, Image quantification related to Fig. 1f. Pie chart: percent of n = 125 MED1 foci with overlapping MED14-dTAG foci. Middle dot plot: mean±s.e.m number of foci per cell. Swarm plot: mean±s.e.m integrated nuclear fluorescence intensity. Unpaired, two-sided t-tests. 163 nuclei were quantified for DMSO and 105 for dTAG7. d, 20-residue running average-smoothed PONDR-VSL2 disorder prediction for human Mediator. Subunits are ordered by ascending index numbers from MED1 to MED31, followed by CDK8, CDK19, and CCNC. e, Influence of MED14 or MED1 degradation on co-precipitation of other Mediator subunits with biotinylated isoxazole pellets. MED14, but not MED1 degradation, prevents Mediator co-precipitation with IDR-enriching hydrogels. f, Cell identity gene sets enriched among downregulated transcription units in TT-seq after 1 h MED14 degradation. Enrichment was calculated using the GSEAPreranked tool75. Unprocessed western blots shown in Source Data.

Source Data

Extended Data Fig. 3 Acute transcriptional consequences of MED14 degradation in HCT-116 cells.

a, CellTiter-Glo viability-based 72 h dose-response of dTAG7 and dTAGV-1 in HCT-116 MED14-dTAG cells. Mean±s.d. of n = 3 drug treatments. b, Time-resolved immunoblot of MED14-dTAG degradation. c, Differences in TT-seq nascent transcript levels (n = 2 independent treatments). Significantly deregulated (DESeq2 q < 0.01; dark grey), SE-proximal (blue), and auto-regulatory TF genes (red) are highlighted. Dark grey line: median log2 fold change of all n = 21,629 transcription units. d, TT-seq signal of two auto-regulatory TFs, and an expression-matched control gene. H3K27ac and H3K4me3 ChIP-seq signals are from publically available data (GSE72622; see Supplementary Table 7)85. e, Fold-change (color) and significance (size) of SE-driven HCT-116 cell identity and expression-matched control gene sets (data as in c). f, Regulatory wiring of 17 auto-regulatory TFs in the HCT-116 cell type-specifying gene regulatory network. Arrows: the given TF has binding motifs in the target TF’s SE region(s). Edge weight mirrors number of motifs. g, Overlap of KBM7 and HCT-116 auto-regulatory TFs. h, Cell type-specific impact of 1 h MED14 degradation. Auto-regulatory TFs in KBM7 (blue, for example MYB), HCT-116 (orange, for example TGIF1), or MYC (black) as the only shared TF are highlighted. Colored lines: median log2FC in the respective cell line. i, Mean steady state expression of auto-regulatory TFs in merged 1 h and 2 h DMSO TT-seq conditions and transcriptional defects after 1 h MED14 degradation. Unprocessed western blot shown in Source Data.

Source Data

Extended Data Fig. 4 Impact of MED14 degradation on overall chromatin architecture.

a, Genomic feature classes at H3K27ac HiChIP contact anchors. Only significant interactions called by hichipper/mango were used for anchor identification. Arcs indicate the percentage of anchor-anchor pairs annotated with the indicated feature in each of the samples. b, Total number of interactions common to DMSO and dTAG7 samples, which were used for quantification (E: enhancer, P: promoter, SE: constituent). c, Impact of Mediator loss on CTCF-CTCF contact strength as negative control. Bracket: number of quantified contacts. Violin plot elements: approximated density distribution with internal box plots showing medians with interquartile range and 1.5x whiskers. d, Impact of MED14 degradation on H3K27 acetylation. e, Pulldown-independent 4C-seq analysis of MYB SE constituent viewpoint (VP) after 2 h MED14 degradation in triplicates. Top track shows KBM7 wild-type H3K27ac ChIP-seq. TE: typical enhancer, SE: super-enhancer f, Analogous to (e) with a SATB1 SE viewpoint. Unprocessed western blot shown in Source Data.

Source Data

Extended Data Fig. 5 Impact of MED14 degradation on Pol II clusters and nascent transcription dynamics.

a, Image quantification related to Fig. 3c. Pie chart: percent of n = 100 large Pol II foci, which overlap MED14-dTAG foci. Mean±s.e.m. with two-sided, unpaired t-test (n = 40 nuclei in DMSO; n = 36 nuclei in dTAG7 condition). b, Control imaging experiment related to Fig. 3c, omitting anti-HA primary antibody to rule out that Pol II foci are an HA channel bleed through artifact. c, Immunofluorescence of large hypo-phosphorylated Pol II foci (8WG16; arrows) in MED14-dTAG KBM7 cells. Maximum intensity projections of 3D images. Scale bars 1 µm. Pie chart: percent of n = 60 large Pol II foci, which overlap MED14-dTAG foci. Dot plots: changes in number of large Pol II foci per cell and integrated nuclear fluorescence intensity. Mean±s.e.m. with unpaired, two-sided t-tests (n = 94 nuclei for DMSO; n = 89 for dTAG7). d, PRO-seq signal of auto-regulatory TFs MYC and MYB, and the expression-matched control gene RAB3GAP1 after 1 h MED14 degradation. Arrows highlight loss of promoter-proximal signal. H3K4me3 and H3K27ac ChIP-seq signal from KBM7 wild-type cells. e, Aggregated PRO-seq coverage over an SE-proximal metagene. TSS, transcription start site; TES, transcription end site. f, Changes in PRO-seq pausing index at n = 7,643 genes after 1 h MED14 degradation. g, Observed vs. expected median Euclidean distance of auto-regulatory TFs from the pause-initiation limit in Fig. 3f. The expected distribution was generated by randomly selecting the same number of genes from bulk. h, Changes in productive initiation rate and pause duration of all 6,791 genes vs. the 24 auto-regulatory TFs. Productive initiation rates selectively decrease for auto-regulatory TFs, while pause duration decreases globally. Box plot elements: medians with interquartile range and 1.5x whiskers.

Extended Data Fig. 6 Unbiased proteomics reveal increased P-TEFb levels on chromatin.

a, Overlap of three independent data analyses to detect high-confidence differentially chromatin-bound proteins (p < 0.1; see Supplementary Note). b,c, Differential chromatin binding of transcription regulators. Class averages are shown in b. Scratched boxes indicate missing values. GTFs: general transcription factors. d, Salt-based fractionation of 7SK- and chromatin-bound P-TEFb complexes. Unprocessed western blot shown in Source Data.

Source Data

Extended Data Fig. 7 P-TEFb activation shapes the transcriptional response to Mediator loss.

a, PRO-seq read-through upon 2 h MED14 degradation. Additionally inhibiting CDK9 with 500 nM NVP2 in the last 30 min reverses the read-through. Zoom-ins show 30 kb windows around the polyadenylation site. Arrows highlight transcription start site (TSS) regions shown in g. b, Aggregated PRO-seq coverages show read-through even for long genes, where newly initiated Pol II has not yet reached the termination site. Mean±bootstrapped confidence region. c, Aggregated TT-seq coverages show read-through transcription after MED14 degradation also in HCT-116 cells. d,e, Changes in PRO-seq pausing index of all n = 5,558 genes (d) and calculated pause duration at all n = 6,954 transcription units (e) after MED14/CDK9 perturbation. f, Changes in productive initiation rates for all n = 6,954 transcription units. Box plot elements: medians with interquartile range, 1.5x whiskers and confidence region notches. g, PRO-seq signal around transcription start sites (TSS) of two non-SE and one auto-regulatory TF gene. Paused polymerase does not re-accumulate at the MYB TSS upon combined MED14/CDK9 perturbation. h, TT-seq SE-gene set enrichment upon combined MED14/CDK9 perturbation. Less significant enrichment confirms that CDK9 activity aggravated the SE-selectivity of Mediator disruption.

Supplementary information

Supplementary Information

Supplementary Note

Reporting Summary

Supplementary Tables

Supplementary Tables 1–8

Source data

Source Data Fig. 1

Unprocessed western blots for Fig. 1b.

Source Data Fig. 4

Unprocessed western blots for Fig. 4b,c.

Source Data Extended Data Fig. 1

Unprocessed western blots for Extended Data Fig. 1b,c,g.

Source Data Extended Data Fig. 2

Unprocessed western blots for Extended Data Fig. 2b,e.

Source Data Extended Data Fig. 3

Unprocessed western blots for Extended Data Fig. 3b.

Source Data Extended Data Fig. 4

Unprocessed western blots for Extended Data Fig. 4d.

Source Data Extended Data Fig. 6

Unprocessed western blots for Extended Data Fig. 6d.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jaeger, M.G., Schwalb, B., Mackowiak, S.D. et al. Selective Mediator dependence of cell-type-specifying transcription. Nat Genet 52, 719–727 (2020). https://doi.org/10.1038/s41588-020-0635-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-020-0635-0

This article is cited by

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