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Updated: Apr 30, 2026

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
Published on: February 11, 2019
Integrating bioinformatic resources to predict transcription factors interacting with cis-sequences conserved in
Christian Dubos, Zsolt Kelemen, Alvaro Sebastian
1Institut für Genetik, Technische Universität Braunschweig, Spielmannstr, 7, 38106 Braunschweig, Germany. r.hehl@tu-bs.de.
Background:
Using motif detection programs it is fairly straightforward to identify conserved cis-sequences in promoters of co-regulated genes. In contrast, the identification of the transcription factors (TFs) interacting with these cis-sequences is much more elaborate. To facilitate this, we explore the possibility of using several bioinformatic and experimental approaches for TF identification. This starts with the selection of co-regulated gene sets and leads first to the prediction and then to the experimental validation of TFs interacting with cis-sequences conserved in the promoters of these co-regulated genes.
Results:
Using the PathoPlant database, 32 up-regulated gene groups were identified with microarray data for drought-responsive gene expression from Arabidopsis thaliana. Application of the binding site estimation suite of tools (BEST) discovered 179 conserved sequence motifs within the corresponding promoters. Using the STAMP web-server, 49 sequence motifs were classified into 7 motif families for which similarities with known cis-regulatory sequences were identified. All motifs were subjected to a footprintDB analysis to predict interacting DNA binding domains from plant TF families. Predictions were confirmed by using a yeast-one-hybrid approach to select interacting TFs belonging to the predicted TF families. TF-DNA interactions were further experimentally validated in yeast and with a Physcomitrella patens transient expression system, leading to the discovery of several novel TF-DNA interactions.
Conclusions:
The present work demonstrates the successful integration of several bioinformatic resources with experimental approaches to predict and validate TFs interacting with conserved sequence motifs in co-regulated genes.
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