September 18, 2024
Identification of plant transcriptional activation domains – Nature

Identification of plant transcriptional activation domains – Nature

  • Strader, L., Weijers, D. & Wagner, D. Plant transcription factors—being in the right place with the right company. Curr. Opin. Plant Biol. 65, 102136 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • O’Malley, R. C. et al. Cistrome and epicistrome features shape the regulatory DNA landscape. Cell 165, 1280–1292 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Galli, M. et al. The DNA binding landscape of the maize AUXIN RESPONSE FACTOR family. Nat. Commun. 9, 4526 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sanborn, A. L. et al. Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator. eLife 10, e68068 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dyson, H. J. & Wright, P. E. Role of Intrinsic protein disorder in the function and interactions of the transcriptional coactivators CREB-binding protein (CBP) and p300. J. Biol. Chem. 291, 6714–6722 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ferreira, M. E. et al. Mechanism of transcription factor recruitment by acidic activators. J. Biol. Chem. 280, 21779–21784 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hermann, S., Berndt, K. D. & Wright, A. P. How transcriptional activators bind target proteins. J. Biol. Chem. 276, 40127–40132 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kim, J. Y. & Chung, H. S. Disordered proteins follow diverse transition paths as they fold and bind to a partner. Science 368, 1253–1257 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kotha, S. R. & Staller, M. V. Clusters of acidic and hydrophobic residues can predict acidic transcriptional activation domains from protein sequence. Genetics 225, iyad131 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hummel, N. F. C. et al. The trans-regulatory landscape of gene networks in plants. Cell Syst. 14, 501–511 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Staller, M. V. et al. A high-throughput mutational scan of an intrinsically disordered acidic transcriptional activation domain. Cell Syst. 6, 444–455 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Konishi, M. & Yanagisawa, S. The role of protein–protein interactions mediated by the PB1 domain of NLP transcription factors in nitrate-inducible gene expression. BMC Plant Biol. 19, 90 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hahn, S. & Young, E. T. Transcriptional regulation in Saccharomyces cerevisiae: transcription factor regulation and function, mechanisms of initiation, and roles of activators and coactivators. Genetics 189, 705–736 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Emenecker, R. J., Griffith, D. & Holehouse, A. S. Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure. Biophys. J. 120, 4312–4319 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hope, I. A., Mahadevan, S. & Struhl, K. Structural and functional characterization of the short acidic transcriptional activation region of yeast GCN4 protein. Nature 333, 635–640 (1988).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Hope, I. A. & Struhl, K. Functional dissection of a eukaryotic transcriptional activator protein, GCN4 of yeast. Cell 46, 885–894 (1986).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mitchell, P. J. & Tjian, R. Transcriptional regulation in mammalian cells by sequence-specific DNA binding proteins. Science 245, 371–378 (1989).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Mahatma, S. et al. Prediction and functional characterization of transcriptional activation domains. In 57th Annual Conference on Information Sciences and Systems (CISS) 1–6 (2023).

  • Erijman, A. et al. A high-throughput screen for transcription activation domains reveals their sequence features and permits prediction by deep learning. Mol. Cell 78, 890–902 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lundberg, S. & Lee, S.-I. A unified approach to interpreting model predictions. In Proc. 31st International Conference on Neural Information Processing Systems 4768–4777 (2017).

  • Hussain, R. M. F., Sheikh, A. H., Haider, I., Quareshy, M. & Linthorst, H. J. M. Arabidopsis WRKY50 and TGA transcription factors synergistically activate expression of PR1. Front. Plant Sci. 9, 930 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, J. et al. Activation domains for controlling plant gene expression using designed transcription factors. Plant Biotechnol. J. 11, 671–680 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cho, S. et al. Analysis of the C-terminal region of Arabidopsis thaliana APETALA1 as a transcription activation domain. Plant Mol. Biol. 40, 419–429 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sakuma, Y. et al. Functional analysis of an Arabidopsis transcription factor, DREB2A, involved in drought-responsive gene expression. Plant Cell 18, 1292–1309 (2006).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kotak, S., Port, M., Ganguli, A., Bicker, F. & von Koskull-Doring, P. Characterization of C-terminal domains of Arabidopsis heat stress transcription factors (Hsfs) and identification of a new signature combination of plant class A Hsfs with AHA and NES motifs essential for activator function and intracellular localization. Plant J. 39, 98–112 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yoo, C. Y. et al. Direct photoresponsive inhibition of a p53-like transcription activation domain in PIF3 by Arabidopsis phytochrome B. Nat. Commun. 12, 5614 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fernandez-Calvo, P. et al. The Arabidopsis bHLH transcription factors MYC3 and MYC4 are targets of JAZ repressors and act additively with MYC2 in the activation of jasmonate responses. Plant Cell 23, 701–715 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tiwari, S. B., Hagen, G. & Guilfoyle, T. The roles of auxin response factor domains in auxin-responsive transcription. Plant Cell 15, 533–543 (2003).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ulmasov, T., Hagen, G. & Guilfoyle, T. J. Activation and repression of transcription by auxin-response factors. Proc. Natl Acad. Sci. USA 96, 5844–5849 (1999).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pierre-Jerome, E., Jang, S. S., Havens, K. A., Nemhauser, J. L. & Klavins, E. Recapitulation of the forward nuclear auxin response pathway in yeast. Proc. Natl Acad. Sci. USA 111, 9407–2412 (2014).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Powers, S. K. & Strader, L. C. Regulation of auxin transcriptional responses. Dev. Dyn. 249, 483–495 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Choi, H. S., Seo, M. & Cho, H. T. Two TPL-binding motifs of ARF2 are involved in repression of auxin responses. Front. Plant Sci. 9, 372 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hiratsu, K., Matsui, K., Koyama, T. & Ohme-Takagi, M. Dominant repression of target genes by chimeric repressors that include the EAR motif, a repression domain, in Arabidopsis. Plant J. 34, 733–739 (2003).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mutte, S. K. et al. Origin and evolution of the nuclear auxin response system. eLife 7, e33399 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • DelRosso, N. et al. Large-scale mapping and mutagenesis of human transcriptional effector domains. Nature 616, 365–372 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leydon, A. R. et al. Repression by the Arabidopsis TOPLESS corepressor requires association with the core mediator complex. eLife 10, e66739 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Holehouse, A. S., Das, R. K., Ahad, J. N., Richardson, M. O. & Pappu, R. V. CIDER: resources to analyze sequence-ensemble relationships of intrinsically disordered proteins. Biophys. J. 112, 16–21 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kagale, S. & Rozwadowski, K. EAR motif-mediated transcriptional repression in plants: an underlying mechanism for epigenetic regulation of gene expression. Epigenetics 6, 141–146 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boer, D. R. et al. Structural basis for DNA binding specificity by the auxin-dependent ARF transcription factors. Cell 156, 577–589 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Korasick, D. A. et al. Molecular basis for AUXIN RESPONSE FACTOR protein interaction and the control of auxin response repression. Proc. Natl Acad. Sci. USA 111, 5427–5432 (2014).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Havens, K. A. et al. A synthetic approach reveals extensive tunability of auxin signaling. Plant Physiol. 160, 135–142 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hillson, N. J., Rosengarten, R. D. & Keasling, J. D. j5 DNA assembly design automation software. ACS Synth. Biol. 1, 14–21 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Garcia-Nafria, J., Watson, J. F. & Greger, I. H. IVA cloning: a single-tube universal cloning system exploiting bacterial in vivo assembly. Sci. Rep. 6, 27459 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gietz, R. D. & Schiestl, R. H. High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method. Nat. Protoc. 2, 31–34 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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

    MathSciNet 

    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kobak, D. & Berens, P. The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10, 5416 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pierre-Jerome, E., Wright, R. C. & Nemhauser, J. L. Characterizing auxin response circuits in Saccharomyces cerevisiae by flow cytometry. Methods Mol. Biol. 1497, 271–281 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wright, R. C., Bolten, N. & Pierre-Jerome, E. flowTime: annotation and analysis of biological dynamical systems using flow cytometry. R version 1.24.0 https://www.bioconductor.org/packages/release/bioc/html/flowTime.html (2023).

  • White, S. et al. FlowKit: a Python toolkit for integrated manual and automated cytometry analysis workflows. Front. Immunol. 12, 768541 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lotthammer, J. M., Ginell, G. M., Griffith, D., Emenecker, R. J. & Holehouse, A. S. Direct prediction of intrinsically disordered protein conformational properties from sequence. Nat. Methods 21, 465–476 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Source link