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Background: The SOS response is an almost ubiquitous response of cells to genotoxic stresses. The full complement of genes in the SOS regulon for Vibrio species has only been addressed through bioinformatic analyses predicting LexA binding box consensus and in vitro validation. Here, we perform whole transcriptome sequencing from Vibrio cholerae treated with mitomycin C as an SOS inducer to characterize the SOS regulon and other pathways affected by this treatment., Results: Comprehensive transcriptional profiling allowed us to define the full landscape of promoters and transcripts active in V. cholerae. We performed extensive transcription start site (TSS) mapping as well as detection/quantification of the coding and non-coding RNA (ncRNA) repertoire in strain N16961. To improve TSS detection, we developed a new technique to treat RNA extracted from cells grown in various conditions. This allowed for identification of 3078 TSSs with an average 5'UTR of 116 nucleotides, and peak distribution between 16 and 64 nucleotides; as well as 629 ncRNAs. Mitomycin C treatment induced transcription of 737 genes and 28 ncRNAs at least 2 fold, while it repressed 231 genes and 17 ncRNAs. Data analysis revealed that in addition to the core genes known to integrate the SOS regulon, several metabolic pathways were induced. This study allowed for expansion of the Vibrio SOS regulon, as twelve genes (ubiEJB, tatABC, smpA, cep, VC0091, VC1190, VC1369-1370) were found to be co-induced with their adjacent canonical SOS regulon gene(s), through transcriptional read-through. Characterization of UV and mitomycin C susceptibility for mutants of these newly identified SOS regulon genes and other highly induced genes and ncRNAs confirmed their role in DNA damage rescue and protection., Conclusions: We show that genotoxic stress induces a pervasive transcriptional response, affecting almost 20% of the V. cholerae genes. We also demonstrate that the SOS regulon is larger than previously known, and its syntenic organization is conserved among Vibrio species. Furthermore, this specific co-localization is found in other γ-proteobacteria for genes recN-smpA and rmuC-tatABC, suggesting SOS regulon conservation in this phylum. Finally, we comment on the limitations of widespread NGS approaches for identification of all RNA species in bacteria.

Background: Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods., Results: SARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of an R package and two R script templates (for DESeq2 and edgeR respectively). Tuning a small number of parameters and executing one of the R scripts, users have access to the full results of the analysis, including lists of differentially expressed genes and a HTML report that (i) displays diagnostic plots for quality control and model hypotheses checking and (ii) keeps track of the whole analysis process, parameter values and versions of the R packages used., Conclusions: SARTools provides systematic quality controls of the dataset as well as diagnostic plots that help to tune the model parameters. It gives access to the main parameters of DESeq2 and edgeR and prevents untrained users from misusing some functionalities of both packages. By keeping track of all the parameters of the analysis process it fits the requirements of reproducible research.

Although Yersinia pestis and Yersinia pseudotuberculosis are genetically very similar (97% nucleotide sequence identity for most of the chromosomal genes), they exhibit very different patterns of infection. Y. pestis causes plague which is usually fatal in the absence of treatment, whereas Y. pseudotuberculosis generally triggers non-life-threatening intestinal symptoms. This drastic difference in pathogenicity may result from the acquisition of a few species-specific genes, but also from differences in their transcriptional regulation networks. In this study, we performed an in silico comparative whole-genome transcriptome analysis of Y. pestis and Y. pseudotuberculosis grown in parallel under 8 distinct conditions to determine whether they exhibit differences in their regulatory networks. In this analysis, 304 genes common to both species were found to display significant inter-species differences in transcriptional levels, with 91% of them being more expressed in Y. pestis. Remarkably, 3 major virulence determinants conserved in the 2 species (the pYV virulence plasmid, the High Pathogenicity Island, and the ail locus) were among the genes more expressed in Y. pestis. Furthermore, the induction at 37°C of pYV-borne genes was considerably greater in Y. pestis than in Y. pseudotuberculosis. Conversely, the rovA transcriptional regulator gene was more transcribed in Y. pseudotuberculosis. We also performed a clustering analysis of the transcriptome data of both Y. pestis and Y. pseudotuberculosis, which allowed to group genes according to their expression profiles. This analysis identified groups of genes with unknown functions which, based on regulation patterns similar to those of known virulence genes, are potential new virulence determinants in Y. pestis. In conclusion, this is the first comparative analysis at the whole-genome level of the transcription profiles of Y. pestis and Y. pseudotuberculosis. Our results suggest that the higher pathogenicity of the plague bacillus may not only result from the acquisition of new genetic material, but also from a higher expression level of common crucial virulence genes. This in silico analysis thus opens new avenues for investigating Y. pestis gain of pathogenicity and new potential virulence factors., (Copyright © 2010 Elsevier GmbH. All rights reserved.)

Background: Arsenic is present in numerous ecosystems and microorganisms have developed various mechanisms to live in such hostile environments. Herminiimonas arsenicoxydans, a bacterium isolated from arsenic contaminated sludge, has acquired remarkable capabilities to cope with arsenic. In particular our previous studies have suggested the existence of a temporal induction of arsenite oxidase, a key enzyme in arsenic metabolism, in the presence of As(III)., Results: Microarrays were designed to compare gene transcription profiles under a temporal As(III) exposure. Transcriptome kinetic analysis demonstrated the existence of two phases in arsenic response. The expression of approximatively 14% of the whole genome was significantly affected by an As(III) early stress and 4% by an As(III) late exposure. The early response was characterized by arsenic resistance, oxidative stress, chaperone synthesis and sulfur metabolism. The late response was characterized by arsenic metabolism and associated mechanisms such as phosphate transport and motility. The major metabolic changes were confirmed by chemical, transcriptional, physiological and biochemical experiments. These early and late responses were defined as general stress response and specific response to As(III), respectively., Conclusion: Gene expression patterns suggest that the exposure to As(III) induces an acute response to rapidly minimize the immediate effects of As(III). Upon a longer arsenic exposure, a broad metabolic response was induced. These data allowed to propose for the first time a kinetic model of the As(III) response in bacteria.