σ factors are considered as positive regulators of gene expression. Here we reveal the opposite, inhibitory role of these proteins. We used a combination of molecular biology methods and computational modeling to analyze the regulatory activity of the extracytoplasmic σE factor from Streptomyces coelicolor. The direct activator/repressor function of σE was then explored by experimental analysis of selected promoter regions in vivo. Additionally, the σE interactome was defined. Taken together, the results characterize σE, its regulation, regulon, and suggest its direct inhibitory function (as a repressor) in gene expression, a phenomenon that may be common also to other σ factors and organisms.

• MeSH
• Computer Simulation MeSH
• Sigma Factor genetics   MeSH
• Streptomyces coelicolor * genetics   MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Sigma Factor MeSH

The exponential increase in the number of conducted studies combined with the development of sequencing methods have led to an enormous accumulation of partially processed experimental data in the past two decades. Here, we present an approach using literature-mined data complemented with gene expression kinetic modeling and promoter sequence analysis. This approach allowed us to identify the regulon of Bacillus subtilis sigma factor SigB of RNA polymerase (RNAP) specifically expressed during germination and outgrowth. SigB is critical for the cell's response to general stress but is also expressed during spore germination and outgrowth, and this specific regulon is not known. This approach allowed us to (i) define a subset of the known SigB regulon controlled by SigB specifically during spore germination and outgrowth, (ii) identify the influence of the promoter sequence binding motif organization on the expression of the SigB-regulated genes, and (iii) suggest additional sigma factors co-controlling other SigB-dependent genes. Experiments then validated promoter sequence characteristics necessary for direct RNAP-SigB binding. In summary, this work documents the potential of computational approaches to unravel new information even for a well-studied system; moreover, the study specifically identifies the subset of the SigB regulon, which is activated during germination and outgrowth.

• Keywords
• Bacillus subtilis, SigB, computational modeling, gene regulatory networks, promoter sequence analysis,
• Publication type
• Journal Article MeSH

RNase J1 is the major 5'-to-3' bacterial exoribonuclease. We demonstrate that in its absence, RNA polymerases (RNAPs) are redistributed on DNA, with increased RNAP occupancy on some genes without a parallel increase in transcriptional output. This suggests that some of these RNAPs represent stalled, non-transcribing complexes. We show that RNase J1 is able to resolve these stalled RNAP complexes by a "torpedo" mechanism, whereby RNase J1 degrades the nascent RNA and causes the transcription complex to disassemble upon collision with RNAP. A heterologous enzyme, yeast Xrn1 (5'-to-3' exonuclease), is less efficient than RNase J1 in resolving stalled Bacillus subtilis RNAP, suggesting that the effect is RNase-specific. Our results thus reveal a novel general principle, whereby an RNase can participate in genome-wide surveillance of stalled RNAP complexes, preventing potentially deleterious transcription-replication collisions.

• Keywords
• RNAP, RNase J1, stalling, torpedo, transcription-replication collision,
• MeSH
• Bacillus subtilis enzymology   genetics   MeSH
• Bacterial Proteins metabolism   MeSH
• RNA, Bacterial genetics   metabolism   MeSH
• DNA-Directed RNA Polymerases metabolism   MeSH
• Exoribonucleases metabolism   MeSH
• Transcription, Genetic MeSH
• RNA, Messenger genetics   metabolism   MeSH
• Gene Expression Regulation, Bacterial MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Bacterial Proteins MeSH
• RNA, Bacterial MeSH
• DNA-Directed RNA Polymerases MeSH
• Exoribonucleases MeSH
• RNA, Messenger MeSH

BACKGROUND: Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks. RESULTS: We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package. CONCLUSIONS: Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.

• Keywords
• Cytoscape, Gene network inference, Time series, Transcription regulation,
• MeSH
• Bacteria genetics   MeSH
• Time Factors MeSH
• Databases, Genetic * MeSH
• Eukaryota genetics   MeSH
• Gene Regulatory Networks * MeSH
• Humans MeSH
• Gene Expression Regulation * MeSH
• Regulon genetics   MeSH
• Reproducibility of Results MeSH
• Saccharomyces cerevisiae genetics   MeSH
• Software * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH