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Comparative cofactor screens show the influence of transactivation domains and core promoters on the mechanisms of transcription

Nature Genetics (2024)Cite this article

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Abstract

Eukaryotic transcription factors (TFs) activate gene expression by recruiting cofactors to promoters. However, the relationships between TFs, promoters and their associated cofactors remain poorly understood. Here we combine GAL4-transactivation assays with comparative CRISPR–Cas9 screens to identify the cofactors used by nine different TFs and core promoters in human cells. Using this dataset, we associate TFs with cofactors, classify cofactors as ubiquitous or specific and discover transcriptional co-dependencies. Through a reductionistic, comparative approach, we demonstrate that TFs do not display discrete mechanisms of activation. Instead, each TF depends on a unique combination of cofactors, which influences distinct steps in transcription. By contrast, the influence of core promoters appears relatively discrete. Different promoter classes are constrained by either initiation or pause-release, which influences their dynamic range and compatibility with cofactors. Overall, our comparative cofactor screens characterize the interplay between TFs, cofactors and core promoters, identifying general principles by which they influence transcription.

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Fig. 1: Comparative CRISPR screens identify the cofactors needed by nine different ADs.
Fig. 2: TFs display a diverse range of activation mechanisms.
Fig. 3: A direct co-dependent relationship between the tail 2 and kinase modules of the Mediator complex.
Fig. 4: TFs use different cofactors to regulate different steps in transcription.
Fig. 5: Comparative screens show discrete cofactor preferences dictated by core promoter elements.
Fig. 6: Cofactor–promoter compatibility is influenced by the rate-limiting step in transcription.

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Data availability

All high-throughput sequencing data relevant to this study have been deposited in the NCBI Gene Expression Omnibus under primary accession code GSE198944. All of the relevant source data has been provided with the paper. Source data are provided with this paper.

Code availability

The paper does not include any custom code beyond the implementation of pre-existing publicly available software packages. All computational analysis can be reproduced from the descriptions provided in Methods using the listed publicly available software.

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Acknowledgements

We thank all members of the Dawson Lab for their support and intellectual input throughout the project. We would also like to acknowledge the Peter MacCallum Cancer Centre Flow Cytometry and Genomics core facilities for their assistance with the research. This research was supported by Cancer Council Victoria Postdoctoral Fellowship (C.C.B.), NHMRC Investigator Grant (1196749) (M.A.D.), Cancer Council Victoria Dunlop Fellowship (M.A.D.), Howard Hughes Medical Institute international research scholarship (55008729) (M.A.D.) and ARC project grant (DP220103927) (M.A.D.).

Author information

Author notes

  1. These authors contributed equally: Jesse J. Balic, Laure Talarmain.

Authors and Affiliations

  1. Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Charles C. Bell, Jesse J. Balic, Laure Talarmain, Andrea Gillespie, Laura Scolamiero, Enid Y. N. Lam, Omer Gilan & Mark A. Dawson

  2. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia

    Charles C. Bell, Jesse J. Balic, Laure Talarmain, Laura Scolamiero & Mark A. Dawson

  3. Mater Research Institute, University of Queensland, TRI Building, Woolloongabba, Queensland, Australia

    Charles C. Bell & Geoffrey J. Faulkner

  4. Bio21 Mass Spectrometry and Proteomics Facility, The University of Melbourne, Parkville, Victoria, Australia

    Ching-Seng Ang

  5. Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia

    Geoffrey J. Faulkner

  6. Australian Centre for Blood Diseases, Monash University, Melbourne, Victoria, Australia

    Omer Gilan

  7. Department of Haematology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Mark A. Dawson

  8. Centre for Cancer Research, University of Melbourne, Melbourne, Victoria, Australia

    Mark A. Dawson

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Contributions

C.C.B., O.G. and M.A.D. designed the research and interpreted data. M.A.D. supervised the research, with assistance from C.C.B. C.C.B. and M.A.D. wrote the paper with helpful input from all the authors. C.C.B. performed the experiments with assistance from J.J.B., L.S., C.-S.A. and O.G. G.J.F. provided critical research support and input. L.T. performed the bioinformatic analysis with assistance from A.G. and E.Y.N.L. and input from C.C.B. and M.A.D.

Corresponding authors

Correspondence to Charles C. Bell or Mark A. Dawson.

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Competing interests

M.A.D. has been a member of advisory boards for GSK, CTX CRC, Storm Therapeutics, Celgene and Cambridge Epigenetix and receives research funding from Pfizer. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Establishing the screening system.

(a) Schematic of the GAL4-based transactivation screening platform. ADs of interest are cloned downstream of the GAL4 DNA binding domain and recruit their respective cofactors to activate a reporter containing a fast-degrading GFP. Both constructs are lentivirally integrated. (b) Alpha-fold predictions of the structure of the 9 transcription factors used in the CRISPR screens. The specific AD region used is highlighted in green. (c) Flow cytometry analysis of demonstrating loss of GFP prior to cell death based on FSC-A and SSC-A and GFP signal at different timepoints after addition of triptolide (10uM). (d) Quantification of the reduction in GFP signal (average fluorescent signal, M.F.I) at different timepoints after the addition of triptolide (10uM). (e) Flow cytometry analysis of GFP signal at day 5 after infection with indicated sgRNAs in GAL4-VP64 and GAL4-MYB AD lines. FSC-A and SSC-A plots shown below to demonstrate predominantly live cells at the timepoint analysed. All flow analysis is performed on at least 10000 cells.

Source data

Extended Data Fig. 2 Reproducibility and validity of AD screening system.

(a) Quantification of change in GFP at day 5 after infection with sgRNAs targeting indicated cofactors in two independent AAVS reporter knock-in clones activated by different ADs. CDK8i was applied at a dose of 10uM for 24 hrs. Change in GFP in polyclonal reporter lines provided for direct comparison. Analysis of polyclonal lines was also performed 5 days after infection with sgRNAs targeting indicated cofactors. Error bars represent S.E.M of two independent sgRNAs or drug treatments. (b) Correlation plot of the fold enrichment between two independent biological replicates of the GAL4-NF-kB mCMV screens. Each replicate was performed using an independently derived reporter line likely to contain different lentiviral integration sites. r = Pearson correlation. Error bands reflect 95% confidence interval of Pearson correlation. (c) Correlation plot of the average fold enrichment scores between two independent biological replicates of VP64-AD screens. One replicate was performed using the GAL4-VP64 and a lentiviral reporter. The second replicate was performed using ZFP-VP64 and an insulated piggyBac reporter. r = Pearson correlation. Error bands reflect 95% confidence interval of Pearson correlation. (d) Flow cytometry analysis of GFP signal in each of the GAL4-AD cell lines at day 5 after infection with control sgRNAs and two independent sgRNAs targeting GAL4.

Source data

Extended Data Fig. 3 Comparative CRISPR screens across 9 ADs.

(a) Schematic of the screening strategy used to identify the cofactors required for each AD. Dropout analysis assesses the guides depleted over time in the screen to identify cofactors required for cell viability. (b) STRING analysis on the highest stringency setting using all the genes classified as a hit for at least one of the ADs. (c) Heatmap of the fold enrichment for all of the genes classified as a hit across any of the 9 AD screens. n = 239 genes. (d) Heatmap of fold enrichment for the genes defined as essential for cell viability based on dropout analysis of day 14 samples. n = 230 genes. (e) Correlation between the average fold enrichment and average effect on cell growth (across all cell lines in the DEPMAP database). Components of the RNA polymerase 2 complex are labelled. Genes in red box with blue dot are required for cell viability, but not enriched in any AD screen. n = 239 genes which are hits. (f) Quantification of the change in representation (guide counts) for essential genes and non-essential genes at each of the screen timepoints. Essential genes defined using day 14 samples, same list as (d). n = 230 essential genes. Line extends from minima to maxima, with box representing 25th percentile, 50th percentile, 75th percentile. (g) Quantification of the change in representation (guide counts) for essential genes and non-essential genes at each of the screen timepoints. Essential genes defined using DEPMAP database. Any gene with an average reduction of −0.5 across all cell lines in DEPMAP was defined as essential. n= ~2000 genes. Line extends from minima to maxima, with box representing 25th percentile, 50th percentile, 75th percentile. (h) Coefficient of variation of essential and non-essential genes across the 9 AD screens. Genes defined as essential by either day 14 dropout (left) (n = 230 genes) or DEPMAP (right) (n= ~2000 genes). > indicates that non-essential genes are higher than essential genes. n.s. = not significant, *=p-value < 0.05, paired two-sided t-test. Line extends from minima to maxima, with box representing 25th percentile, 50th percentile, 75th percentile.

Extended Data Fig. 4 INTS5 KO prevents NF-κB activity.

(a) Spoke and wheel plot of Integrator subunits across each of the ADs, demonstrating a disproportionate requirement for the NF-kB-AD. (b) Flow cytometry analysis of two TNF target genes, ICAM1 and CD69 in K562 cells with and without TNF treatment at day 4 after infection with control or INTS5 sgRNAs. (c) RNA-seq heatmap (left) and NF-kB ChIP-seq (right) in K562 cells treated for 6 hrs with TNF. RNA-seq heatmap displays genes upregulated by at least 1.5-fold, with an associated p65 ChIP-seq peak that increases by at least 2-fold. ChIP-seq heatmap displays all increased p65 peaks within 10 kb of a TNF target gene (defined above). (d) Quantification of change in RNA polymerase 2 ChIP-seq signal across TNF target genes (defined as above) and other genes, with and without TNF treatment (6 hrs) at day 4 after infection with SAFE or INTS5 sgRNAs. P-value calculated using a two-sided paired t-test between Safe + TNF and INTS5 KO + TNF. n = 1 sgRNA per gene. (e) Waterfall plot of change RNA polymerase 2 levels in INTS5 KO K562 cells treated with TNF, relative to SAFE guide control K562 cells treated with TNF. TNF target genes are highlighted in red. (f) IGV snapshot of NF-kB and RNA polymerase 2 occupancy at a target gene (ICAM1) in control and INTS5 KO cells, with and without TNF. (g) IGV snapshot of NF-kB or RNA polymerase 2 occupancy at a non-target gene (MYC) in control and INTS5 KO cells, with and without TNF.

Extended Data Fig. 5 Flow cytometry validation of heterogeneous cofactors.

Flow cytometry plots of GFP signal in each of the GAL4-AD lines at day 5 after infection with safe guide control or guides targeting a heterogeneously required cofactor. All plots are presented on the same scale and were analysed concurrently. These raw data were used to calculate the fold change in the validation heatmap in Fig. 2d. All analyses were performed on at least 10000 cells. n = two sgRNAs per gene.

Extended Data Fig. 6 Validation of cofactor heterogeneity at early timepoint.

(a) Quantification of change in GFP signal in each of the GAL4-AD lines after CDK8i treatment (24 hrs) or infection with guides targeting CDK8 and CCNC (day 5 after sgRNA infection). Fold change was calculated by comparison to SAFE guide control or DMSO treatment. N = 3 guides or independent treatments. Error bars = S.E.M. Representative FACS plots that were used to calculate the change in GFP signal are shown. (b) Quantification demonstrating concordant change in GFP signal in each of the GAL4-AD lines at 24 hrs after treatment with dTAGV-1 to degrade MED25, and infection with guides targeting MED25 at day 5 after sgRNA infection. Fold change was calculated by comparison to SAFE guide control or DMSO treatment. N = 3 guides or independent treatments. Error bars = S.E.M. Representative FACS plots that were used to calculate the change in GFP signal are shown.

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Extended Data Fig. 7 Cofactor heterogeneity unlikely to be explained by regulation of GAL4 expression, differences in protein expression, stability or chromatin occupancy.

(a) Schematic of design and rationale behind EF1a screen. (b) Heatmap displaying all genes that were identified as significantly enriched in the EF1a screen and the corresponding enrichment across each of the AD screens. Box indicates the many cofactors required for EF1a expression that were not required for all ADs and did not display simple patterns that would be indicative of indirect effects. (c) Heatmap displaying the 50 most heterogeneously required cofactors across the 9 AD screens ranked by their degree of enrichment in the EF1a screen. Box highlights the large proportion of the heterogeneously required hits that are not significantly enriched in the EF1a screen, suggesting that heterogeneity is not the result of indirect effects on regulation of the GAL4-ADs. Validated hits are highlighted in bold. (d) Western blot to assess expression of the GAL4-ADs. FLAG-GAL4-AD lines were treated for 24 hrs with DMSO or cycloheximide (100 mg/ml) to assess GAL4-AD protein expression and stability. Lamin-B used as loading control. Representative blot of two biological replicates. (e) ChIP-qPCR for GAL4-AD occupancy at the UAS binding site of the reporter construct, and at a negative control region in each of the GAL4-AD cell lines. Cells where GAL4 had been removed by KO were used as a negative control. Error bars represent S.E.M of 3 technical replicates.

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Extended Data Fig. 8 Co-dependencies are largely restricted to large coactivator complexes.

(a) Pearson correlation matrix of the 100 cofactors with the highest coefficient of variation across the 9 AD screens. Correlation score based on Pearson correlation distance. Clear co-dependent clusters are highlighted. Other independently validated, heterogeneous hits that do not display correlated patterns of requirement are also highlighted. (b) Fold enrichment for individual cofactors contained in co-dependent gene clusters. r = average Pearson correlation between subunits. (c) Western blot showing levels of MED12, MED14, MED25, MYC and aTubulin (loading control) after 4 hrs of addition of dTAGV-1 (500 nM) or CDK8i (10uM) in the respective dTAG knock-in cell lines. Representative blot of two biological replicates. (d) Quantification of the number of metabolically labelled SLAM-seq reads in an unlabelled control and MED14 dTAG cell line treated with DMSO or dTAGV-1 (500 nM) for 4 hrs. * = p < 0.05, two-sided paired t-test. Error bars = S.E.M of 3 biological replicates. (e) Correlation plot between fold change in RNA polymerase 2 ChIP-seq signal upon MED25 or MED14 degradation with red dots indicating which genes defined as significantly downregulated at the nascent transcriptional level upon MED14 degradation (SLAM-seq). (f) Quantification of the change in RNA polymerase 2 ChIP-seq signal upon MED12, MED14 or MED25 degradation at genes downregulated at the nascent transcriptional level upon MED14 degradation (SLAM-seq).

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Extended Data Fig. 9 Elongation ratio correlates with requirement for CDK8 after accounting for the p53-AD.

(a) Correlation between the elongation index (calculated by ChIP-nexus) and the reduction in GFP signal upon CDK8i treatment (10uM), or infection with sgRNAs targeting CDK8 or CCNC (day 5 after sgRNA infection). p53 is indicated as a clear outlier. Fold reduction calculated by comparing GFP signal to matched SAFE guide or DMSO control. N = 3 guides or independent treatments. Error bars = S.E.M. (b) Same as (a) without P53 on the plot, which in a dramatically improves the correlation. N = 3 guides or independent treatments. Error bars = S.E.M. n.s = not significant, *=p < 0.05, **=p < 0.01, ***=p < 0.001.

Extended Data Fig. 10 Assessing the impact of altering the core promoter on cofactor use.

(a) Schematic of the GAL4-based promoter screening platform. Core promoters of interest are cloned downstream of the UAS and are activated by NF-kB and its associated cofactors. Both constructs are lentivirally integrated. (b) Sequences of the different core promoters that were screened. The TSS is indicated with an arrow. TATA box sequences are shown in bold and the Initiator sequence is underlined. (c) Luciferase assays performed with different promoter constructs activated by GAL4-NF-kB. Error bars represent the S.E.M of 4 replicates.

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Supplementary information

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Supplementary Table 1

Table 1. AD amino acid composition. Table 2. AD and promoter sequences. Table 3. Genes in sgRNA library. Table 4. AD screen data. Table 5. Promoter screen data. Table 6. Oligonucleotides and sgRNAs. Table 7. MOI of screens

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Bell, C.C., Balic, J.J., Talarmain, L. et al. Comparative cofactor screens show the influence of transactivation domains and core promoters on the mechanisms of transcription. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01749-z

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