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This article is part of the supplement: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2008

Open Access Proceedings

Identifying promoter features of co-regulated genes with similar network motifs

Oscar Harari1, Coral del Val1, Rocío Romero-Zaliz1, Dongwoo Shin2, Henry Huang3, Eduardo A Groisman4 and Igor Zwir14*

Author Affiliations

1 Department of Computer Science and Artificial Intelligence, University of Granada, c/. Daniel Saucedo Aranda, s/n 18071, Granada, Spain

2 Department of Molecular Cell Biology, Samsung Biomedical Research Institute, Sungkyunkwan University School of Medicine, Suwon 440-746, South Korea

3 Department of Molecular Microbiology, Washington University School of Medicine, Campus Box 8230, 660 S. Euclid Ave., St. Louis, Missouri, 63110, USA

4 Department of Molecular Microbiology, Washington University School of Medicine, Howard Hughes Medical Institute, Campus Box 8230, 660 South Euclid Avenue, St. Louis, Missouri, 63110-1093, USA

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BMC Bioinformatics 2009, 10(Suppl 4):S1  doi:10.1186/1471-2105-10-S4-S1

Published: 29 April 2009

Abstract

Background

A large amount of computational and experimental work has been devoted to uncovering network motifs in gene regulatory networks. The leading hypothesis is that evolutionary processes independently selected recurrent architectural relationships among regulators and target genes (motifs) to produce characteristic expression patterns of its members. However, even with the same architecture, the genes may still be differentially expressed. Therefore, to define fully the expression of a group of genes, the strength of the connections in a network motif must be specified, and the cis-promoter features that participate in the regulation must be determined.

Results

We have developed a model-based approach to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We provide methods for annotating regulatory regions by detecting their subjacent cis-features. This includes identifying binding sites for a transcriptional regulator, distinguishing between activation and repression sites, direct and reverse orientation, and among sequences that weakly reflect a particular pattern; binding sites for the RNA polymerase, characterizing different classes, and locations relative to the transcription factor binding sites; the presence of riboswitches in the 5'UTR, and for other transcription factors. We applied our approach to characterize network motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identified key features that enable the PhoP protein to control its target genes, and distinct features may produce different expression patterns even within the same network motif.

Conclusion

Global transcriptional regulators control multiple promoters by a variety of network motifs. This is clearly the case for the regulatory protein PhoP. In this work, we studied this regulatory protein and demonstrated that understanding gene expression does not only require identifying a set of connexions or network motif, but also the cis-acting elements participating in each of these connexions.