DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression 1 . Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation 2-6 , but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.We used genome-wide location analysis 7-10 to determine the genomic occupancy of 203 DNA-binding transcriptional regulators in rich media conditions and, for 84 of these regulators, in at least 1 of 12 other environmental conditions (Supplementary Table 1, Supplementary Fig. 1; http://web.wi.mit.edu/young/regulatory_code). These 203 proteins are likely to include nearly all of the DNA-binding transcriptional regulators encoded in the yeast genome. Regulators were selected for profiling in an additional environment if they were essential for growth in that environment or if there was other evidence implicating them in the regulation of gene expression in that environment. The genome-wide location data identified 11,000 unique interactions between regulators and promoter regions at high confidence (P ≤ 0.001).

We describe an algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets. A gene module is defined as a set of coexpressed genes to which the same set of transcription factors binds. Unlike previous approaches that relied primarily on functional information from expression data, the GRAM algorithm explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions. We use the GRAM algorithm to describe a genome-wide regulatory network in Saccharomyces cerevisiae using binding information for 106 transcription factors profiled in rich medium conditions data from over 500 expression experiments. We also present a genome-wide location analysis data set for regulators in yeast cells treated with rapamycin, and use the GRAM algorithm to provide biological insights into this regulatory network

In Saccharomyces cerevisiae, glucose depletion causes a profound alteration in metabolism, mediated in part by global transcriptional changes. Many of the transcription factors that regulate these changes act combinatorially. We have analyzed combinatorial regulation by Adr1 and Cat8, two transcription factors that act during glucose depletion, by combining genome-wide expression and genome-wide binding data. We identified 32 genes that are directly activated by Adr1, 28 genes that are directly activated by Cat8, and 14 genes that are directly regulated by both. Our analysis also uncovered promoters that Adr1 binds but does not regulate and promoters that are indirectly regulated by Cat8, stressing the advantage of combining global expression and global localization analysis to find directly regulated targets. At most of the coregulated promoters, the in vivo binding of one factor is independent of the other, but Adr1 is required for optimal Cat8 binding at two promoters with a poor match to the Cat8 binding consensus. In addition, Cat8 is required for Adr1 binding at promoters where Adr1 is not required for transcription. These data provide a comprehensive analysis of the direct, indirect, and combinatorial requirements for these two global transcription factors.Cells respond to stresses such as variations in temperature, pH or osmotic or nutrient conditions by altering the expression of genes that allow the cell to respond to the new environment (10). These transcriptional changes are initiated by signals that converge on a limited number of transcription factors, which then act combinatorially to control many genes in multiple pathways. Microarrays can be used to identify these coordinated, combinatorial responses by detecting both the localization of key transcription factors and the corresponding changes in the transcriptome (1).