Regulation of virulence in Francisella tularensis by small non-coding RNAs

Using a cDNA cloning and sequencing approach we have shown that Francisella tularensis expresses homologues of several small RNAs
(sRNAs) that are well-conserved among diverse bacteria. We have also discovered two abundant putative sRNAs that share no sequence similarity or conserved genomic context with any previously annotated regulatory transcripts. Deletion of either of these two loci led to significant changes in the expression of several mRNAs that likely include the cognate target(s) of these sRNAs. Deletion of these sRNAs did not, however, significantly alter F. tularensis growth under various stress conditions in vitro, its replication in murine cells, or its ability to induce disease in a mouse model of F. tularensis infection

Identification of small RNAs in Francisella tularensis

Background: Regulation of bacterial gene expression by small RNAs (sRNAs) have proved to be important for many biological processes. Francisella tularensis is a highly pathogenic Gram-negative bacterium that causes the disease tularaemia in humans and animals. Relatively little is known about the regulatory networks existing in this organism that allows it to survive in a wide array of environments and no sRNA regulators have been identified so far. Results: We have used a combination of experimental assays and in silico prediction to identify sRNAs in F. tularensis strain LVS. Using a cDNA cloning and sequencing approach we have shown that F. tularensis expresses homologues of several sRNAs that are well-conserved among diverse bacteria. We have also discovered two abundant putative sRNAs that share no sequence similarity or conserved genomic context with any previously annotated regulatory transcripts. Deletion of either of these two loci led to significant changes in the expression of several mRNAs that likely include the cognate target(s) of these sRNAs. Deletion of these sRNAs did not, however, significantly alter F. tularensis growth under various stress conditions in vitro, its replication in murine cells, or its ability to induce disease in a mouse model of F. tularensis infection. We also conducted a genome-wide in silico search for intergenic loci that suggests F. tularensis encodes several other sRNAs in addition to the sRNAs found in our experimental screen. Conclusion: Our findings suggest that F. tularensis encodes a significant number of non-coding regulatory RNAs, including members of well conserved families of structural and housekeeping RNAs and other poorly conserved transcripts that may have evolved more recently to help F. tularensis deal with the unique and diverse set of environments with which it must contend

Characterization of NrnA homologs from Mycobacterium tuberculosis and Mycoplasma pneumoniae.

International audienceProcessive RNases are unable to degrade efficiently very short oligonucleotides, and they are complemented by specific enzymes, nanoRNases, that assist in this process. We previously identified NrnA (YtqI) from Bacillus subtilis as a bifunctional protein with the ability to degrade nanoRNA (RNA oligos ≤5 nucleotides) and to dephosphorylate 3'-phosphoadenosine 5'-phosphate (pAp) to AMP. While the former activity is analogous to that of oligoribonuclease (Orn) from Escherichia coli, the latter corresponds to CysQ. NrnA homologs are widely present in bacterial and archaeal genomes. They are found preferably in genomes that lack Orn or CysQ homologs. Here, we characterize NrnA homologs from important human pathogens, Mpn140 from Mycoplasma pneumoniae, and Rv2837c from Mycobacterium tuberculosis. Like NrnA, these enzymes degrade nanoRNA and dephosphorylate pAp in vitro. However, they show dissimilar preferences for specific nanoRNA substrate lengths. Whereas NrnA prefers RNA 3-mers with a 10-fold higher specific activity compared to 5-mers, Rv2837c shows a preference for nanoRNA of a different length, namely, 2-mers. Mpn140 degrades Cy5-labeled nanoRNA substrates in vitro with activities varying within one order of magnitude as follows: 5-mer>4-mer>3-mer>2-mer. In agreement with these in vitro activities, both Rv2837c and Mpn140 can complement the lack of their functional counterparts in E. coli: CysQ and Orn. The NrnA homolog from Streptococcus mutans, SMU.1297, was previously shown to hydrolyze pAp and to complement an E. coli cysQ mutant. Here, we show that SMU.1297 can complement an E. coli orn(-) mutant, suggesting that having both pAp-phosphatase and nanoRNase activity is a common feature of NrnA homologs

Representations of protein structure for exploring the conformational space: A speed–accuracy trade-off

International audienceThe recent breakthrough in the field of protein structure prediction shows the relevance of using knowledge-based based scoring functions in combination with a low-resolution 3D representation of protein macromolecules. The choice of not using all atoms is barely supported by any data in the literature, and is mostly motivated by empirical and practical reasons, such as the computational cost of assessing the numerous folds of the protein conformational space. Here, we present a comprehensive study, carried on a large and balanced benchmark of predicted protein structures, to see how different types of structural representations rank in either accuracy or calculation speed, and which ones offer the best compromise between these two criteria. We tested ten representations, including lowresolution, high-resolution, and coarse-grained approaches. We also investigated the generalization of the findings to other formalisms than the widely-used ''potential of mean force" (PMF) method. Thus, we observed that representing protein structures by their b carbons-combined or not with Ca-provides the best speed-accuracy trade-off, when using a ''total information gain" scoring function. For statistical PMFs, using MARTINI backbone and side-chains beads is the best option. Finally, we also demonstrated the necessity of training the reference state on all atom types, and of including the Ca atoms of glycine residues, in a Cb-based representation

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction

International audienceAbstract RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality

International audienceRNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality

RNA is a complex macromolecule that plays central roles in the cell. While it is well-known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction

RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but the adaptation to RNA 3D structure prediction remains at stake. In this work, we give a brief review of the ab initio , template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine approaches using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked models, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr

MyPMFs: a simple tool for creating statistical potentials to assess protein structural models

International audienc

An ambiguity principle for assigning protein structural domains

International audienceOur novel algorithm for delimiting protein structural domains provides insights into protein folding, function, and evolution