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Combinatorial cis-regulation in Saccharomyces species

ABSTRACT: Transcriptional control of gene expression is a result of complex interactions between the cis-regulatory elements (CRE) at gene promoters. To understand the regulatory logic of a cell, we need to identify the CRE combinations that regulate gene expression. We developed a sensitive computational method to identify phylogenetically conserved CRE combinations for any species of interest. In contrast to previous methods, we do not need to align genomes to identify these combinations. We applied the method in 7 sensu stricto and sensu lato Saccharomyces species. 80% of the predictions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including 1) ChIP-chip data for co-localization of transcription factors, 2) gene expression data for co-expression of predicted regulatory targets, and 3) gene ontology databases for common pathway membership of predicted regulatory targets. To establish definitive evidence that these CRE interactions influence TF occupancy, we performed ChIPseq experiments on transcription factors in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments showed that TF occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor.

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE60281 | GEO | 2014/08/11

SECONDARY ACCESSION(S): PRJNA257935

REPOSITORIES: GEO

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Project description:Contemporary high throughput technologies permit the rapid identification of transcription factor (TF) target genes on a genome-wide scale, yet the functional significance of TFs requires knowledge of target gene expression patterns, cooperating TFs and cis-regulatory element (CRE) structures. Here we investigated the myogenic regulatory network downstream of the Drosophila zinc finger TF Lame duck (Lmd) by combining both previously published and newly performed genomic data sets, including chromatin immunoprecipitation sequencing (ChIP-seq), genome-wide mRNA profiling, cell-specific expression patterns of putative transcriptional targets, analysis of histone mark signatures, studies of TF co-occupancy by additional mesodermal regulators, TF binding site determination using protein binding microarrays (PBMs), and machine learning of candidate CRE motif compositions. Our findings suggest that Lmd orchestrates an extensive myogenic regulatory network, a conclusion supported by the identification of Lmd-dependent genes, histone signatures of Lmd-bound genomic regions, and the relationship of these features to cell-specific gene expression patterns. The heterogeneous co-occupancy of Lmd-bound regions with additional mesodermal regulators revealed that different transcriptional inputs are used to mediate similar myogenic gene expression patterns. Machine learning further demonstrated diverse combinatorial motif patterns within tissue-specific Lmd-bound regions. PBM analysis established the complete spectrum of Lmd DNA binding specificities, and site-directed mutagenesis of Lmd and additional, newly discovered motifs in known enhancers demonstrated the critical role of these TF binding sites in supporting full enhancer activity. Collectively, these findings provide new insights into the transcriptional codes regulating muscle gene expression, and offer a generalizable approach for similar studies in other systems. Examination of Lmd occupancy to genomic DNA from sorted mesodermal cells

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Combinatorial cis-regulation in Saccharomyces species

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