Identification of transcription factor targets by activation-based strategies.
Gordon Chua (1), Richelle Sopko (2), Jeff Pootoolal (1), Owen Ryan (1), Armaity Davierwala (1), Stuart Yang (1), Quaid Morris (1), Brenda Andrews (2), Charlie Boone (1), Tim Hughes (1)
(1) B. and B. Medical Genetics, University of Toronto, 112 College Street, Toronto, ON, M5G 1L6, Canada;
(2) Department of Medical Genetics and Microbiology, University of Toronto, 1 Kings College Circle, Medical Sciences Building, Rm 4287, Toronto, Ontario, M5S 1A8, Canada.
A major obstacle to identify physiological transcriptional targets is that the conditions that induce the majority of transcription factors are unknown. Microarray analyses of deletion mutants indicate that most transcription factors are inactive under standard growth conditions. To overcome this we screened an ordered array of yeast ORFs to identify known and putative transcription factors that confer reduced fitness upon overexpression, suggesting that overexpression results in an activated state. A total of 78 yeast transcription factors were examined by microarray expression profiling under activation by overexpression. We observed a strong correlation between growth inhibition and number of genes with altered expression. Candidate targets were identified by a computational method that requires specificity of induction or repression among all experiments, as well as shared sequence elements in the promoters of the induced or repressed genes. Targets we obtained compared favorably with a set of known targets compiled from the literature, and also correlated highly with gene function categories, known microarray expression clusters, and previously-described sets of genes with shared promoter motifs. However, the targets did not correlate well with those previously identified by chromatin immunoprecipitation. In addition to refining the target range of well-characterized transcription factors, we uncovered potential targets of poorly-characterized transcription factors.
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