Identification of plant transcriptional activation domains

• 成果类型: Article
• 署名作者: Morffy, Nicholas; Van den Broeck, Lisa; Miller, Caelan; Emenecker, Ryan J.; Bryant, John A., Jr.; Lee, Tyler M.; Sageman-Furnas, Katelyn; Wilkinson, Edward G.; Pathak, Sunita; Kotha, Sanjana R.; Lam, Angelica; Mahatma, Saloni; Pande, Vikram; Waoo, Aman; Wright, R. Clay; Holehouse, Alex S.; Staller, Max V.; Sozzani, Rosangela; Strader, Lucia C.
• 署名单位: Duke University; North Carolina State University; Washington University (WUSTL); Washington University (WUSTL); Virginia Polytechnic Institute & State University; University of California System; University of California Berkeley
• 刊物名称: Nature
• ISSN/ISSBN: 0028-5035
• DOI: 10.1038/s41586-024-07707-3
• 发表日期: 2024-08-01
• 页码: 166-+
• 关键词: saccharomyces-cerevisiae; ear motif; repression; protein; mechanism; features; reveals; region; roles; BIND

摘要

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 ADs(1). 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.