Selective Mediator dependence of cell-type-specifying transcription


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.

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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. 14 and 6 are presented with the paper.

Code availability

Custom code used to analyze the data in this study is available at


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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




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.

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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.

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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.

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Jaeger, M.G., Schwalb, B., Mackowiak, S.D. et al. Selective Mediator dependence of cell-type-specifying transcription. Nat Genet 52, 719–727 (2020).

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