Author_Institution :
Sch. of Knowledge Sci., Japan Adv. Inst. of Sci. & Technol., Nomi, Japan
Abstract :
Eukaryotic gene transcription is a complex process, which requires the orchestrated recruitment of a large number of proteins, such as sequence-specific DNA binding factors, chromatin remodelers and modifiers, and general transcription machinery, to regulatory regions. Previous works have shown that these regulatory proteins favor specific organizational theme along promoters. Details about how they cooperatively regulate transcriptional process, however, remain unclear. We developed a method to reconstruct a Bayesian network (BN) model representing functional relationships among various transcriptional components. The positive/negative influence between these components was measured from protein binding and nucleosome occupancy data and embedded into the model. Application on S.cerevisiae ChIP-Chip data showed that the proposed method can recover confirmed relationships, such as Isw1-Pol II, TFIIH-Pol II, TFIIB-TBP, Pol II-H3K36Me3, H3K4Me3-H3K14Ac, etc. Moreover, it can distinguish colocating components from functionally related ones. Novel relationships, e.g., ones between Mediator and chromatin remodeling complexes (CRCs), and the combinatorial regulation of Pol II recruitment and activity by CRCs and general transcription factors (GTFs), were also suggested. Conclusion: protein binding events during transcription positively influence each other. Among contributing components, GTFs and CRCs play pivotal roles in transcriptional regulation. These findings provide insights into the regulatory mechanism.
Keywords :
Bayes methods; DNA; biology computing; genetics; genomics; lab-on-a-chip; microorganisms; molecular biophysics; proteins; Bayesian network model; H3K4Me3-H3K14Ac; Isw1-Pol II; Pol II-H3K36Me3; S.cerevisiae ChIP-Chip data; TFIIB-TBP; TFIIH-Pol II; chromatin modifier; chromatin remodeler; chromatin remodeling complex; combinatorial regulation; computational reconstruction; eukaryotic gene transcription; general transcription factor; nucleosome occupancy data; protein binding; regulatory protein; sequence-specific DNA binding factor; Bayesian methods; Bioinformatics; Data models; Genomics; Machinery; Proteins; Stability analysis; Bayesian methods; Bayesian network; Bioinformatics; ChIP-Chip data; Data models; Genomics; Machinery; Proteins; Stability analysis; Transcriptional relationship; chromatin remodeling complex; histone modification; nucleosome positioning; Bayes Theorem; Chromatin Assembly and Disassembly; Chromatin Immunoprecipitation; Computational Biology; Gene Regulatory Networks; Histones; Models, Genetic; Oligonucleotide Array Sequence Analysis; Saccharomyces cerevisiae; Saccharomyces cerevisiae Proteins; Transcription Factors; Transcription, Genetic;