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In this study, we compared different computational methods used for genome-wide determination of mRNA half-lives in Escherichia coli with a special focus on the impact on considering a delay before the onset of mRNA decay after transcription arrest. A wide variety of datasets were analyzed coming from different technical methods for mRNA quantification (microarrays, RNA-seq, and RT-qPCR) and different bacterial growth conditions. The exponential decay of mRNA levels was fitted using both linear and exponential models and with or without a delay. We showed that for all the models, independently of mRNA quantification methods and growth conditions, ignoring the delay resulted in only a modest overestimation of the half-life. For approximately 80% of the mRNAs, differences in mRNA half-life values were less than 34s. The correlation between half-lives estimated with and without a delay was extremely high. However, the slope of the linear regression between the half-lives with and without a delay tended to decrease with the delay. For the few mRNAs for which taking into account the delay influenced the estimated half-life, the impact was dependent on the model and the growth condition. The smallest impact was obtained for the linear model., (© 2018 Elsevier Inc. All rights reserved.)

The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA-mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA-mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks., (© The Author 2014. Published by Oxford University Press.)

RNAIII is the intracellular effector of the quorum-sensing system in Staphylococcus aureus. It is one of the largest regulatory RNAs (514 nucleotides long) that are known to control the expression of a large number of virulence genes. Here, we show that the 3' domain of RNAIII coordinately represses at the post-transcriptional level, the expression of mRNAs that encode a class of virulence factors that act early in the infection process. We demonstrate that the 3' domain acts primarily as an antisense RNA and rapidly anneals to these mRNAs, forming long RNA duplexes. The interaction between RNAIII and the mRNAs results in repression of translation initiation and triggers endoribonuclease III hydrolysis. These processes are followed by rapid depletion of the mRNA pool. In addition, we show that RNAIII and its 3' domain mediate translational repression of rot mRNA through a limited number of base pairings involving two loop-loop interactions. Since Rot is a transcriptional regulatory protein, we proposed that RNAIII indirectly acts on many downstream genes, resulting in the activation of the synthesis of several exoproteins. These data emphasize the multitude of regulatory steps affected by RNAIII and its 3' domain in establishing a network of S. aureus virulence factors.

A novel approach aiding in the prediction of RNA secondary structures is presented. Although phylogenetic methods are the most successful at deriving RNA secondary structures, the are not applicable when the number of sequences or the sequence variability is too low. Methods based on energy minimization are therefore of great interest. However, some of the suboptimal RNA secondary structures computed with classic methods are unsaturated structures, i.e. some structures are included into others. Thus, the incorporation of constraints during the process of folding is not possible, while the incorporation of constraints before the process of folding often introduces a bias into the energy function. This paper describes a new procedure which allows for the incorporation of constraints before and during the process of RNA folding. SAPSSARN is an interactive program which offers a framework, both to specify a secondary structure through a set of folding constraints and to compute all the supoptimal saturated RNA secondary structures which satisfy all the folding constraints. At the start, it relies on the computation of the probabilities of pairing of each base with all others according to McCaskill's algorithm. The constraint satisfaction formulation of the problem deals dynamically with a chosen set of folding constraints and, finally, a search algorithm computes all the suboptimal saturated secondary structures which satisfy those folding constraints. Within such a framework, it is possible to test new ideas about RNA folding and secondary structures, including pseudoknots, can be computed. The program is illustrated with RNA sequences on which we obtained results in agreement with known structures by using a protocol which mimics the hierarchical folding of RNA molecules.