Identification of plant transcriptional activation domains

Nicholas Morffy, Lisa Broeck, Caelan Miller, Ryan J. Emenecker, John A. Bryant, Tyler M. Lee, Katelyn Sageman-Furnas, Edward G. Wilkinson, Sunita Pathak, Sanjana R. Kotha, Angelica Lam, Saloni Mahatma, Vikram Pande, Aman Waoo, R. Clay Wright, Alex S. Holehouse, Max V. Staller, Rosangela Sozzani and Lucia C. Strader ()
Additional contact information
Nicholas Morffy: Duke University
Lisa Broeck: North Carolina State University
Caelan Miller: Duke University
Ryan J. Emenecker: Washington University School of Medicine
John A. Bryant: Virginia Tech
Tyler M. Lee: Duke University
Katelyn Sageman-Furnas: Duke University
Edward G. Wilkinson: Duke University
Sunita Pathak: Duke University
Sanjana R. Kotha: University of California, Berkeley
Angelica Lam: University of California, Berkeley
Saloni Mahatma: North Carolina State University
Vikram Pande: North Carolina State University
Aman Waoo: North Carolina State University
R. Clay Wright: Virginia Tech
Alex S. Holehouse: Washington University School of Medicine
Max V. Staller: University of California, Berkeley
Rosangela Sozzani: North Carolina State University
Lucia C. Strader: Duke University

Nature, 2024, vol. 632, issue 8023, 166-173

Abstract: Abstract Gene expression in Arabidopsis is regulated by more than 1,900 transcription factors (TFs), which have been identified genome-wide by the presence of well-conserved DNA-binding domains. Activator TFs contain activation domains (ADs) that recruit coactivator complexes; however, for nearly all Arabidopsis TFs, we lack knowledge about the presence, location and transcriptional strength of their ADs1. To address this gap, here we use a yeast library approach to experimentally identify Arabidopsis ADs on a proteome-wide scale, and find that more than half of the Arabidopsis TFs contain an AD. We annotate 1,553 ADs, the vast majority of which are, to our knowledge, previously unknown. Using the dataset generated, we develop a neural network to accurately predict ADs and to identify sequence features that are necessary to recruit coactivator complexes. We uncover six distinct combinations of sequence features that result in activation activity, providing a framework to interrogate the subfunctionalization of ADs. Furthermore, we identify ADs in the ancient AUXIN RESPONSE FACTOR family of TFs, revealing that AD positioning is conserved in distinct clades. Our findings provide a deep resource for understanding transcriptional activation, a framework for examining function in intrinsically disordered regions and a predictive model of ADs.

Date: 2024
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DOI: 10.1038/s41586-024-07707-3

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