Context transcription factors establish cooperative environments and mediate enhancer communication
Language English Country United States Media print-electronic
Document type Journal Article
Grant support
310030_197082
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
860002
EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
895426
EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
101026623
EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
2020-895
European Molecular Biology Organization (EMBO)
1139-2019
European Molecular Biology Organization (EMBO)
PubMed
39363017
PubMed Central
PMC11525195
DOI
10.1038/s41588-024-01892-7
PII: 10.1038/s41588-024-01892-7
Knihovny.cz E-resources
- MeSH
- Chromatin * genetics metabolism MeSH
- Humans MeSH
- Quantitative Trait Loci * MeSH
- Mice MeSH
- Gene Expression Regulation MeSH
- Transcription Factors * metabolism genetics MeSH
- Protein Binding MeSH
- Binding Sites genetics MeSH
- Enhancer Elements, Genetic * MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Chromatin * MeSH
- Transcription Factors * MeSH
Many enhancers control gene expression by assembling regulatory factor clusters, also referred to as condensates. This process is vital for facilitating enhancer communication and establishing cellular identity. However, how DNA sequence and transcription factor (TF) binding instruct the formation of high regulatory factor environments remains poorly understood. Here we developed a new approach leveraging enhancer-centric chromatin accessibility quantitative trait loci (caQTLs) to nominate regulatory factor clusters genome-wide. By analyzing TF-binding signatures within the context of caQTLs and comparing episomal versus endogenous enhancer activities, we discovered a class of regulators, 'context-only' TFs, that amplify the activity of cell type-specific caQTL-binding TFs, that is, 'context-initiator' TFs. Similar to super-enhancers, enhancers enriched for context-only TF-binding sites display high coactivator binding and sensitivity to bromodomain-inhibiting molecules. We further show that binding sites for context-only and context-initiator TFs underlie enhancer coordination, providing a mechanistic rationale for how a loose TF syntax confers regulatory specificity.
See more in PubMed
Lambert, S. A. et al. The human transcription factors. Cell172, 650–665 (2018). PubMed
Rube, H. T. et al. Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning. Nat. Biotechnol.40, 1520–1527 (2022). PubMed PMC
Kulakovskiy, I. V. et al. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP–seq analysis. Nucleic Acids Res.46, D252–D259 (2018). PubMed PMC
Castro-Mondragon, J. A. et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res.50, D165–D173 (2022). PubMed PMC
Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell158, 1431–1443 (2014). PubMed PMC
Slattery, M. et al. Absence of a simple code: how transcription factors read the genome. Trends Biochem. Sci.39, 381–399 (2014). PubMed PMC
Kaluscha, S. et al. Evidence that direct inhibition of transcription factor binding is the prevailing mode of gene and repeat repression by DNA methylation. Nat. Genet.54, 1895–1906 (2022). PubMed PMC
Neumayr, C. et al. Differential cofactor dependencies define distinct types of human enhancers. Nature606, 406–413 (2022). PubMed PMC
Jolma, A. et al. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature527, 384–388 (2015). PubMed
Isbel, L., Grand, R. S. & Schübeler, D. Generating specificity in genome regulation through transcription factor sensitivity to chromatin. Nat. Rev. Genet.23, 728–740 (2022). PubMed
Avsec, Ž. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet.53, 354–366 (2021). PubMed PMC
de Almeida, B. P., Reiter, F., Pagani, M. & Stark, A. DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers. Nat. Genet.54, 613–624 (2022). PubMed
Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods18, 1196–1203 (2021). PubMed PMC
Karbalayghareh, A., Sahin, M. & Leslie, C. S. Chromatin interaction—aware gene regulatory modeling with graph attention networks. Genome Res.32, 930–944 (2022). PubMed PMC
Zhang, Z., Feng, F., Qiu, Y. & Liu, J. A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome. Nucleic Acids Res.51, 5931–5947 (2023). PubMed PMC
Karollus, A., Mauermeier, T. & Gagneur, J. Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers. Genome Biol.24, 56 (2023). PubMed PMC
Sasse, A. et al. Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings. Nat. Genet.55, 2060–2064 (2023). PubMed
Kim, S. & Wysocka, J. Deciphering the multi-scale, quantitative cis-regulatory code. Mol. Cell.83, 373–392 (2023). PubMed PMC
Liu, Z. & Tjian, R. Visualizing transcription factor dynamics in living cells. J. Cell Biol.217, 1181–1191 (2018). PubMed PMC
Neikes, H. K. et al. Quantification of absolute transcription factor binding affinities in the native chromatin context using BANC-seq. Nat. Biotechnol.41, 1801–1809 (2023). PubMed
Simicevic, J. & Deplancke, B. Transcription factor proteomics—tools, applications, and challenges. Proteomics17, 1600317 (2017). PubMed
Kribelbauer, J. F., Rastogi, C., Bussemaker, H. J. & Mann, R. S. Low-affinity binding sites and the transcription factor specificity paradox in eukaryotes. Annu. Rev. Cell Dev. Biol.35, 357–379 (2019). PubMed PMC
Liu, Z. et al. 3D imaging of Sox2 enhancer clusters in embryonic stem cells. eLife3, e04236 (2014). PubMed PMC
Mir, M. et al. Dynamic multifactor hubs interact transiently with sites of active transcription in Drosophila embryos. eLife7, e40497 (2018). PubMed PMC
Tsai, A. et al. Nuclear microenvironments modulate transcription from low-affinity enhancers. eLife6, e28975 (2017). PubMed PMC
Wollman, A. J. et al. Transcription factor clusters regulate genes in eukaryotic cells. eLife6, e27451 (2017). PubMed PMC
Hayward-Lara, G., Fischer, M. D. & Mir, M. Dynamic microenvironments shape nuclear organization and gene expression. Curr. Opin. Genet. Dev.86, 102177 (2024). PubMed PMC
Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science361, eaar3958 (2018). PubMed PMC
Boija, A. et al. Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell175, 1842–1855 (2018). PubMed PMC
Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell153, 307–319 (2013). PubMed PMC
Hnisz, D. et al. Super-enhancers in the control of cell identity and disease. Cell155, 934–947 (2013). PubMed PMC
Uyehara, C. M. & Apostolou, E. 3D enhancer-promoter interactions and multi-connected hubs: organizational principles and functional roles. Cell Rep.42, 112068 (2023). PubMed PMC
Cheng, L., De, C., Li, J. & Pertsinidis, A. Mechanisms of transcription control by distal enhancers from high-resolution single-gene imaging. Preprint at bioRxiv10.1101/2023.03.19.533190 (2023).
Brzovic, P. S. et al. The acidic transcription activator Gcn4 binds the mediator subunit Gal11/Med15 using a simple protein interface forming a fuzzy complex. Mol. Cell44, 942–953 (2011). PubMed PMC
Chong, S. et al. Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science361, eaar2555 (2018). PubMed PMC
Shrinivas, K. et al. Enhancer features that drive formation of transcriptional condensates. Mol. Cell75, 549–561 (2019). PubMed PMC
Morin, J. A. et al. Sequence-dependent surface condensation of a pioneer transcription factor on DNA. Nat. Phys.18, 271–276 (2022).
Meeussen, J. V. W. et al. Transcription factor clusters enable target search but do not contribute to target gene activation. Nucleic Acids Res.51, 5449–5468 (2023). PubMed PMC
Chong, S. et al. Tuning levels of low-complexity domain interactions to modulate endogenous oncogenic transcription. Mol. Cell82, 2084–2097 (2022). PubMed
Trojanowski, J. et al. Transcription activation is enhanced by multivalent interactions independent of phase separation. Mol. Cell82, 1878–1893 (2022). PubMed
Alberti, S., Gladfelter, A. & Mittag, T. Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell176, 419–434 (2019). PubMed PMC
Degner, J. F. et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature482, 390–394 (2012). PubMed PMC
Kumasaka, N., Knights, A. J. & Gaffney, D. J. Fine-mapping cellular QTLs with RASQUAL and ATAC–seq. Nat. Genet.48, 206–213 (2016). PubMed PMC
Gate, R. E. et al. Genetic determinants of co-accessible chromatin regions in activated T cells across humans. Nat. Genet.50, 1140–1150 (2018). PubMed PMC
Kumasaka, N., Knights, A. J. & Gaffney, D. J. High-resolution genetic mapping of putative causal interactions between regions of open chromatin. Nat. Genet.51, 128–137 (2019). PubMed PMC
Llimos, G. et al. A leukemia-protective germline variant mediates chromatin module formation via transcription factor nucleation. Nat. Commun.13, 2042 (2022). PubMed PMC
Van Mierlo, G., Pushkarev, O., Kribelbauer, J. F. & Deplancke, B. Chromatin modules and their implication in genomic organization and gene regulation. Trends Genet.39, 140–153 (2023). PubMed
Zhao, Y. et al. ‘Stripe’ transcription factors provide accessibility to co-binding partners in mammalian genomes. Mol. Cell82, 3398–3411 (2022). PubMed PMC
Zamudio, A. V. et al. Mediator condensates localize signaling factors to key cell identity genes. Mol. Cell76, 753–766 (2019). PubMed PMC
Meuleman, W. et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature584, 244–251 (2020). PubMed PMC
Ibarra, I. L. et al. Mechanistic insights into transcription factor cooperativity and its impact on protein-phenotype interactions. Nat. Commun.11, 124 (2020). PubMed PMC
Van Arensbergen, J. et al. Genome-wide mapping of autonomous promoter activity in human cells. Nat. Biotechnol.35, 145–153 (2017). PubMed PMC
Staller, M. V. et al. Directed mutational scanning reveals a balance between acidic and hydrophobic residues in strong human activation domains. Cell Syst.13, 334–345 (2022). PubMed PMC
Arnold, C. D. et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science339, 1074–1077 (2013). PubMed
Minderjahn, J. et al. Mechanisms governing the pioneering and redistribution capabilities of the non-classical pioneer PU.1. Nat. Commun.11, 402 (2020). PubMed PMC
Van Mierlo, G. et al. Predicting protein condensate formation using machine learning. Cell Rep.34, 108705 (2021). PubMed
Gibson, B. A. et al. Organization of chromatin by intrinsic and regulated phase separation. Cell179, 470–484.e21 (2019). PubMed PMC
Ott, C. J. et al. Enhancer architecture and essential core regulatory circuitry of chronic lymphocytic leukemia. Cancer Cell34, 982–995 (2018). PubMed PMC
Lovén, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell153, 320–334 (2013). PubMed PMC
Chapuy, B. et al. Discovery and characterization of super-enhancer-associated dependencies in diffuse large B cell lymphoma. Cancer Cell24, 777–790 (2013). PubMed PMC
Crump, N. T. et al. BET inhibition disrupts transcription but retains enhancer-promoter contact. Nat. Commun.12, 223 (2021). PubMed PMC
Chen, C. et al. SEA version 3.0: a comprehensive extension and update of the super-enhancer archive. Nucleic Acids Res.48, D198–D203 (2020). PubMed PMC
Blayney, J. W. et al. Super-enhancers include classical enhancers and facilitators to fully activate gene expression. Cell186, 5826–5839 (2023). PubMed PMC
Batut, P. J. et al. Genome organization controls transcriptional dynamics during development. Science375, 566–570 (2022). PubMed PMC
Brosh, R. et al. Synthetic regulatory genomics uncovers enhancer context dependence at the Sox2 locus. Mol. Cell83, 1140–1152 (2023). PubMed PMC
Lyons, H. et al. Functional partitioning of transcriptional regulators by patterned charge blocks. Cell186, 327–345 (2023). PubMed PMC
Wang, Q. et al. Exploring epigenomic datasets by ChIPseeker. Curr. Protoc.2, e585 (2022). PubMed
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at 10.48550/arXiv.1303.3997 (2013).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics25, 2078–2079 (2009). PubMed PMC
Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res.44, W160–W165 (2016). PubMed PMC
Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics25, 1422–1423 (2009). PubMed PMC
Van den Berge, K. et al. Normalization benchmark of ATAC–seq datasets shows the importance of accounting for GC-content effects. Cell Rep. Methods2, 100321 (2022). PubMed PMC
Luo, Y. et al. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res.48, D882–D889 (2020). PubMed PMC
Picard toolkit. GitHubhttps://broadinstitute.github.io/picard/ (2019).
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at 10.48550/arXiv.1207.3907 (2012).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics29, 15–21 (2013). PubMed PMC
Liao, Y., Smyth, G. K. & Shi, W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res.47, e47 (2019). PubMed PMC
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 PMC
Gardeux, V. & Jukri. DeplanckeLab/Context-TFs: initial release (v1.0). Zenodo10.5281/zenodo.12732162 (2024).