Although the regulation of gene expression has been intensively studied in the yeast S. cerevisiae, much about this process remains unknown. This is exemplified by our inability to predict, as opposed to explain, the expression pattern of any gene given its promoter sequence. Our long-term goal is to provide a comprehensive map of the S. cerevisiae gene regulatory network that can be used to develop predictive models of gene expression. The first task is to complete the catalog of transcription factors and their binding sites. We will use a combination of existing in vitro and in vivo methods to accomplish that goal. We will identify the binding sites of the more than 100 transcription factors of yeast whose specificity remains unknown (Aim 1) using electrophoretic gel mobility shift assays, a yeast one-hybrid assay, and a novel method to probe protein microarrays with DMA oligonucleotides. We will then develop comprehensive weight matrices of the binding sites of yeast transcription factors (Aim 2) using a novel implementation of the SELEX method we have developed. These results will be extended by determining the in vivo targets of selected transcription factors (Aim 3) using genome-wide chromatin immunoprecipitation (ChlP-Chip). We expect that the combination of these approaches will enable us to determine the binding sites and target genes of nearly all transcription factors of yeast. We will then attempt to learn the architectural principles of yeast promoters by determining how transcription factor binding sites contribute to gene expression. By creating large libraries of potential gene promoters in which a set of binding sites have been randomly distributed, we can ascertain the combinations of binding sites that determine specific expression patterns. This approach will be initially developed and tested using a few well characterized binding sites; we expect it will provide a general tool for more comprehensive studies of the logic of gene regulation. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM078222-01A1
Application #
7262888
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Tompkins, Laurie
Project Start
2007-05-01
Project End
2011-04-30
Budget Start
2007-05-01
Budget End
2008-04-30
Support Year
1
Fiscal Year
2007
Total Cost
$444,481
Indirect Cost
Name
Washington University
Department
Biochemistry
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Spivak, Aaron T; Stormo, Gary D (2016) Combinatorial Cis-regulation in Saccharomyces Species. G3 (Bethesda) 6:653-67
White, Michael A; Parker, Davis S; Barolo, Scott et al. (2012) A model of spatially restricted transcription in opposing gradients of activators and repressors. Mol Syst Biol 8:614
Sherman, Marc S; Cohen, Barak A (2012) Thermodynamic state ensemble models of cis-regulation. PLoS Comput Biol 8:e1002407
Spivak, Aaron T; Stormo, Gary D (2012) ScerTF: a comprehensive database of benchmarked position weight matrices for Saccharomyces species. Nucleic Acids Res 40:D162-8
Wang, Haoyi; Mayhew, David; Chen, Xuhua et al. (2011) Calling Cards enable multiplexed identification of the genomic targets of DNA-binding proteins. Genome Res 21:748-55
Parker, David S; White, Michael A; Ramos, Andrea I et al. (2011) The cis-regulatory logic of Hedgehog gradient responses: key roles for gli binding affinity, competition, and cooperativity. Sci Signal 4:ra38
Gertz, Jason; Gerke, Justin P; Cohen, Barak A (2010) Epistasis in a quantitative trait captured by a molecular model of transcription factor interactions. Theor Popul Biol 77:1-5
Mogno, Ilaria; Vallania, Francesco; Mitra, Robi D et al. (2010) TATA is a modular component of synthetic promoters. Genome Res 20:1391-7
Gertz, Jason; Cohen, Barak A (2009) Environment-specific combinatorial cis-regulation in synthetic promoters. Mol Syst Biol 5:244
Zhao, Yue; Granas, David; Stormo, Gary D (2009) Inferring binding energies from selected binding sites. PLoS Comput Biol 5:e1000590

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