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Epistatic relationships reveal the functional organization of yeast transcription factors.

ABSTRACT: The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in Saccharomyces cerevisiae. This allowed us to both reconstruct regulatory models for specific subsets of transcription factors and identify global epistatic patterns. Overall, there was a much stronger preference for negative relative to positive genetic interactions among STFs than there was among GTFs. Negative genetic interactions, which often identify factors working in non-essential, redundant pathways, were also enriched for pairs of STFs that co-regulate similar sets of genes. Microarray analysis demonstrated that pairs of STFs that display negative genetic interactions regulate gene expression in an independent rather than coordinated manner. Collectively, these data suggest that parallel/compensating relationships between regulators, rather than linear pathways, often characterize transcriptional circuits.

SUBMITTER: Zheng J

PROVIDER: S-EPMC2990640 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Epistatic relationships reveal the functional organization of yeast transcription factors.

Zheng Jiashun J Benschop Joris J JJ Shales Michael M Kemmeren Patrick P Greenblatt Jack J Cagney Gerard G Holstege Frank F Li Hao H Krogan Nevan J NJ

Molecular systems biology 20101001

The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in ...[more]

PMID: 20959818

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Project description:BACKGROUND:Current technologies for understanding the transcriptional reprogramming in cells include the transcription factor (TF) chromatin immunoprecipitation (ChIP) experiments and the TF knockout experiments. The ChIP experiments show the binding targets of TFs against which the antibody directs while the knockout techniques find the regulatory gene targets of the knocked-out TFs. However, it was shown that these two complementary results contain few common targets. Researchers have used the concept of TF functional redundancy to explain the low overlap between these two techniques. But the detailed molecular mechanisms behind TF functional redundancy remain unknown. Without knowing the possible molecular mechanisms, it is hard for biologists to fully unravel the cause of TF functional redundancy. RESULTS:To mine out the molecular mechanisms, a novel algorithm to extract TF regulatory modules that help explain the observed TF functional redundancy effect was devised and proposed in this research. The method first searched for candidate TF sets from the TF binding data. Then based on these candidate sets the method utilized the modified Steiner Tree construction algorithm to construct the possible TF regulatory modules from protein-protein interaction data and finally filtered out the noise-induced results by using confidence tests. The mined-out regulatory modules were shown to correlate to the concept of functional redundancy and provided testable hypotheses of the molecular mechanisms behind functional redundancy. And the biological significance of the mined-out results was demonstrated in three different biological aspects: ontology enrichment, protein interaction prevalence and expression coherence. About 23.5% of the mined-out TF regulatory modules were literature-verified. Finally, the biological applicability of the proposed method was shown in one detailed example of a verified TF regulatory module for pheromone response and filamentous growth in yeast. CONCLUSION:In this research, a novel method that mined out the potential TF regulatory modules which elucidate the functional redundancy observed among TFs is proposed. The extracted TF regulatory modules not only correlate the molecular mechanisms to the observed functional redundancy among TFs, but also show biological significance in inferring TF functional binding target genes. The results provide testable hypotheses for biologists to further design subsequent research and experiments.

Project description:BackgroundTranscription factors (TFs) have long been known to be principally activators of transcription in eukaryotes and prokaryotes. The growing awareness of the ubiquity of microRNAs (miRNAs) as suppressive regulators in eukaryotes, suggests the possibility of a mutual, preferential, self-regulatory connectivity between miRNAs and TFs. Here we investigate the connectivity from TFs and miRNAs to other genes and each other using text mining, TF promoter binding site and 6 different miRNA binding site prediction methods.ResultsIn the first approach text mining of PubMed abstracts reveal statistically significant associations between miRNAs and both TFs and signal transduction gene classes. Secondly, prediction of miRNA targets in human and mouse 3'UTRs show enrichment only for TFs but not consistently across prediction methods for signal transduction or other gene classes. Furthermore, a random sample of 986 TarBase entries was scored for experimental evidence by manual inspection of the original papers, and enrichment for TFs was observed to increase with score. Low-scoring TarBase entries, where experimental evidence is anticorrelated miRNA:mRNA expression with predicted miRNA targets, appear not to select for real miRNA targets to any degree. Our manually validated text-mining results also suggests that miRNAs may be activated by more TFs than other classes of genes, as 7% of miRNA:TF co-occurrences in the literature were TFs activating miRNAs. This was confirmed when thirdly, we found enrichment for predicted, conserved TF binding sites in miRNA and TF genes compared to other gene classes.ConclusionsWe see enrichment of connections between miRNAs and TFs using several independent methods, suggestive of a network of mutual activating and suppressive regulation. We have also built regulatory networks (containing 2- and 3-loop motifs) for mouse and human using predicted miRNA and TF binding sites and we have developed a web server to search and display these loops, available for the community at http://rth.dk/resources/tfmirloop.

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Epistatic relationships reveal the functional organization of yeast transcription factors.

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Epistatic relationships reveal the functional organization of yeast transcription factors.

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