Published online 6 February 2006
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Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae
1Wilmer Institute, Johns Hopkins University School of Medicine Baltimore, MD 21287 USA 2Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine Baltimore, MD 21287 USA 3Department of Neuroscience, Johns Hopkins University School of Medicine Baltimore, MD 21287 USA 4McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine Baltimore, MD 21287 USA 5Department of Applied Biochemistry, Faculty of Agriculture, Utsunomiya University Japan
*To whom correspondence should be addressed. Tel: +1 443 287 3882; Fax: +1 410 502 5382; Email: jiang.qian{at}jhmi.edu
Received November 21, 2005. Revised January 10, 2006. Accepted January 18, 2006.
Combinatorial regulation by transcription factor complexes is an important feature of eukaryotic gene regulation. Here, we propose a new method for identification of interactions between transcription factors (TFs) that relies on the relationship of their binding sites, and we test it using Saccharomyces cerevisiae as a model system. The algorithm predicts interacting TF pairs based on the co-occurrence of their binding motifs and the distance between the motifs in promoter sequences. This allows investigation of interactions between TFs without known binding motifs or expression data. With this approach, 300 significant interactions involving 77 TFs were identified. These included more than 70% of the known protein–protein interactions. Approximately half of the detected interacting motif pairs showed strong preferences for particular distances and orientations in the promoter sequences. These one dimensional features may reflect constraints on allowable spatial arrangements for protein–protein interactions. Evidence for biological relevance of the observed characteristic distances is provided by the finding that target genes with the same characteristic distances show significantly higher co-expression than those without preferred distances. Furthermore, the observed interactions were dynamic: most of the TF pairs were not constitutively active, but rather showed variable activity depending on the physiological condition of the cells. Interestingly, some TF pairs active in multiple conditions showed preferences for different distances and orientations depending on the condition. Our prediction and characterization of TF interactions may help to understand the transcriptional regulatory networks in eukaryotic systems.
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