TY - JOUR
T1 - Identification of plant transcriptional activation domains
AU - Morffy, Nicholas
AU - Van den Broeck, Lisa
AU - Miller, Caelan
AU - Emenecker, Ryan J.
AU - Bryant, John A.
AU - Lee, Tyler M.
AU - Sageman-Furnas, Katelyn
AU - Wilkinson, Edward G.
AU - Pathak, Sunita
AU - Kotha, Sanjana R.
AU - Lam, Angelica
AU - Mahatma, Saloni
AU - Pande, Vikram
AU - Waoo, Aman
AU - Wright, R. Clay
AU - Holehouse, Alex S.
AU - Staller, Max V.
AU - Sozzani, Rosangela
AU - Strader, Lucia C.
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Limited 2024.
PY - 2024/8/1
Y1 - 2024/8/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85198824000&partnerID=8YFLogxK
U2 - 10.1038/s41586-024-07707-3
DO - 10.1038/s41586-024-07707-3
M3 - Article
C2 - 39020176
AN - SCOPUS:85198824000
SN - 0028-0836
VL - 632
SP - 166
EP - 173
JO - Nature
JF - Nature
IS - 8023
ER -