Although global analyses of transcription factor binding provide one view of potential transcriptional regulatory networks, regulation also occurs at levels distinct from transcription factor binding. Here, we use a genetic approach to identify targets of transcription factors in yeast and reconstruct a functional regulatory network. First, we profiled transcriptional responses in S. cerevisiae strains with individual deletions of 263 transcription factors. Then we used directed-weighted graph modeling and regulatory epistasis analysis to identify indirect regulatory relationships between these transcription factors, and from this we reconstructed a functional transcriptional regulatory network. The enrichment of promoter motifs and Gene Ontology annotations provide insight into the biological functions of the transcription factors.

Heat shock transcription factor (HSF) and the promoter heat shock element (HSE) are among the most highly conserved transcriptional regulatory elements in nature. HSF mediates the transcriptional response of eukaryotic cells to heat, infection and inflammation, pharmacological agents, and other stresses. While HSF is essential for cell viability in Saccharomyces cerevisiae, oogenesis and early development in Drosophila melanogaster, extended life span in Caenorhabditis elegans, and extraembryonic development and stress resistance in mammals, little is known about its full range of biological target genes. We used whole-genome analyses to identify virtually all of the direct transcriptional targets of yeast HSF, representing nearly 3% of the genomic loci. The majority of the identified loci are heat-inducibly bound by yeast HSF, and the target genes encode proteins that have a broad range of biological functions including protein folding and degradation, energy generation, protein trafficking, maintenance of cell integrity, small molecule transport, cell signaling, and transcription. This genome-wide identification of HSF target genes provides novel insights into the role of HSF in growth, development, disease, and aging and in the complex metabolic reprogramming that occurs in all cells in response to stress.

The yeast heme activator protein Hap1 binds to DNA and activates transcription of genes encoding functions required for respiration and for controlling oxidative damage, in response to heme. Hap1 contains a DNA-binding domain with a C6 zinc cluster motif, a coiled-coil dimerization element, typical of the members of the yeast Gal4 family, and an acidic activation domain. The regulation of Hap1 transcription-activating activity is controlled by two classes of Hap1 elements, repression modules (RPM1-3) and heme-responsive motifs (HRM1-7). Previous indirect evidence indicates that Hap1 may repress transcription directly. Here we show, by promoter analysis, by chromatin immunoprecipitation, and by electrophoretic mobility shift assay, that Hap1 binds directly to DNA and represses transcription of its own gene by at least 20-fold. We found that Hap1 repression of the HAP1 gene occurs independently of heme concentrations. While DNA binding is required for transcriptional repression by Hap1, deletion of Hap1 activation domain and hemeregulatory elements has varying effects on repression. Further, we found that repression by Hap1 requires the function of Hsp70 (Ssa), but not Hsp90. These results show that Hap1 binds to its own promoter and represses transcription in a heme-independent but Hsp70-dependent manner.

Background: The application of global gene expression profiling to saliva samples is hampered by the presence of partially fragmented and degraded RNAs that are difficult to amplify and detect with the prevailing technologies. Moreover, the often limited volume of saliva samples is a challenge to quantitative PCR (qPCR) validation of multiple candidates. The aim of this study was to provide proof-of-concept data on the combination of a universal mRNA-amplification method with exon arrays for candidate selection and a multiplex preamplification method for easy validation. Methods: We used a universal mRNA–specific linear-amplification strategy in combination with Affymetrix Exon Arrays to amplify salivary RNA from 18 healthy individuals on the nanogram scale. Multiple selected candidates were preamplified in one multiplex reverse transcription PCR reaction, cleaned up enzymatically, and validated by qPCR. Results: We defined a salivary exon core transcriptome (SECT) containing 851 transcripts of genes that have highly similar expression profiles in healthy individuals. A subset of the SECT transcripts was verified by qPCR analysis. Informatics analysis of the SECT revealed several functional clusters and sequence motifs. Sex-specific salivary exon biomarkers were identified and validated in tests with samples from healthy individuals. Conclusions: It is feasible to use samples containing fragmented RNAs to conduct high-resolution expression profiling with coverage of the entire transcriptome and to validate multiple targets from limited amounts of sample.