Construction of two mathematical models for prediction of bacterial sRNA targets

Regulatory sRNAs in Cyanobacteria

As the transcriptional and post-transcriptional regulators of gene expression, small RNAs (sRNAs) play important roles in every domain of life in organisms. It has been discovered gradually that bacteria possess multiple means of gene regulation using RNAs. They have been continuously used as model organisms for photosynthesis, metabolism, biotechnology, evolution, and nitrogen fixation for many decades. Cyanobacteria, one of the most ancient life forms, constitute all kinds of photoautotrophic bacteria and exist in almost any environment on this planet. It is believed that a complex RNA-based regulatory mechanism functions in cyanobacteria to help them adapt to changes and stresses in diverse environments. Although lagging far behind other model microorganisms, such as yeast and Escherichia coli, more and more non-coding regulatory sRNAs have been recognized in cyanobacteria during the past decades. In this article, by focusing on cyanobacterial sRNAs, the approaches for detection and targeting of sRNAs will be summarized, four major mechanisms and regulatory functions will be generalized, eight types of cis-encoded sRNA and four types of trans-encoded sRNAs will be reviewed in detail, and their possible physiological functions will be further discussed.

Small RNAs expressed during dimethylsulfoniopropionate degradation by a model marine bacterium

The fate of the sulfur moiety of dimethylsulfoniopropionate (DMSP) depends on the 'bacterial switch', a regulatory point between two metabolic pathways with different biogeochemical endpoints. Studies have focused on transcriptional patterns of known genes to determine physiological and environmental factors affecting this switch, but post-transcriptional regulation has been under-studied. Here we use a model bacterium containing both pathways to look for transcription of non-coding regulatory small RNAs (sRNAs) during DMSP metabolism. RNA-seq analysis of Ruegeria pomeroyi DSS-3 grown with DMSP, metabolic intermediates of DMSP degradation (MMPA or acetate), or methionine revealed 182 putative sRNAs, with 46 showing differential expression during growth on DMSP. A knockout mutant constructed for an upregulated sRNA had a phenotype that differed in its use of the two degradation pathways. Because transcription patterns of many differentially expressed sRNAs were not correlated with the transcription of their putative target gene, their effects on DMSP degradation would not be observable in the transcriptome. Overall, our results indicate that sRNAs are crucial but largely cryptic actors in regulating DMSP metabolism in this model marine bacterium and potentially other bacterial groups involved in the surface ocean sulfur cycle.

Regulation of Hfq mRNA and protein levels in Escherichia coli and Pseudomonas aeruginosa by the Burkholderia cenocepacia MtvR sRNA

Small non-coding RNAs (sRNAs) are important players of gene expression regulation in bacterial pathogens. MtvR is a 136-nucleotide long sRNA previously identified in the human pathogen Burkholderia cenocepacia J2315 and with homologues restricted to bacteria of the Burkholderia cepacia complex. In this work we have investigated the effects of expressing MtvR in Escherichia coli and Pseudomonas aeruginosa. Results are presented showing that MtvR negatively regulates the hfq mRNA levels in both bacterial species. In the case of E. coli, this negative regulation is shown to involve binding of MtvR to the 5′-UTR region of the hfq_Ec mRNA. Results presented also show that expression of MtvR in E. coli and P. aeruginosa originates multiple phenotypes, including reduced resistance to selected stresses, biofilm formation ability, and increased susceptibility to various antibiotics.

Bioinformatics of prokaryotic RNAs

The genome of most prokaryotes gives rise to surprisingly complex transcriptomes, comprising not only protein-coding mRNAs, often organized as operons, but also harbors dozens or even hundreds of highly structured small regulatory RNAs and unexpectedly large levels of anti-sense transcripts. Comprehensive surveys of prokaryotic transcriptomes and the need to characterize also their non-coding components is heavily dependent on computational methods and workflows, many of which have been developed or at least adapted specifically for the use with bacterial and archaeal data. This review provides an overview on the state-of-the-art of RNA bioinformatics focusing on applications to prokaryotes.

Computational prediction of RNA-RNA interactions

We describe different tools and approaches for RNA-RNA interaction prediction. Recognition of ncRNA targets is predominantly governed by two principles, namely the stability of the duplex between the two interacting RNAs and the internal structure of both mRNA and ncRNA. Thus, approaches can be distinguished into different major categories depending on how they consider inter- and intramolecular structure. The first class completely neglects the internal structure and measures only the stability of the duplex. The second class of approaches abstracts from specific intramolecular structures and uses an ensemble-based approach to calculate the effect of internal structure on a putative binding site, thus measuring the accessibility of the binding sites.Since accessibility-based approaches can handle only one continuous interaction site, two addition types of approaches were introduced which predict a joint structure for the interacting RNAs. Since this problem is NP-complete, the approaches can handle only a restricted class of joint structures. The first are co-folding approaches, which predict a joint structure that is nested when the both sequences are concatenated. The last and most complex class of approaches impose only the restriction that they discard zipper-like structures. Finally, we will discuss the use of conservation information in RNA-target prediction.

Identification of novel autoantibodies for detection of malignant mesothelioma

Background

The malignant mesothelioma (MM) survival rate has been hampered by the lack of efficient and accurate early detection methods. The immune system may detect the early changes of tumor progression by responding with tumor-associated autoantibody production. Hence, in this study, we translated the humoral immune response to cancer proteins into a potential blood test for MM.

Methodology/Principal Findings

A T7 phage MM cDNA library was constructed using MM tumor tissues and biopanned for tumor-associated antigens (TAAs) using pooled MM patient and normal serum samples. About 1008 individual phage TAA clones from the biopanned library were subjected to protein microarray construction and tested with 53 MM and 52 control serum samples as a training group. Nine candidate autoantibody markers were selected from the training group using Tclass system and logistic regression statistical analysis, which achieved 94.3% sensitivity and 90.4% specificity with an AUC value of 0.89 in receiver operating characteristic analysis. The classifier was further evaluated with 50 patient and 50 normal serum samples as an independent blind validation, and the sensitivity of 86.0% and the specificity of 86.0% were obtained with an AUC of 0.82. Sequencing and BLASTN analysis of the classifier revealed that five of these nine candidate markers were found to have strong homology to cancer related proteins (PDIA6, MEG3, SDCCAG3, IGHG3, IGHG1).

Conclusions/Significance

Our results indicated that using a panel of 9 autoantibody markers presented a promising accuracy for MM detection. Although the results need further validation in high-risk groups, they provided the potentials in developing a serum-based assay for MM diagnosis.

MtvR is a global small noncoding regulatory RNA in Burkholderia cenocepacia

Burkholderia cenocepacia J2315 is a highly epidemic and transmissible clinical isolate of the Burkholderia cepacia complex (Bcc), a group of bacteria causing life-threatening respiratory infections among cystic fibrosis patients. This work describes the functional analysis of the 136-nucleotide (nt)-long MtvR small noncoding RNA (sRNA) from the Bcc member B. cenocepacia J2315, with homologues restricted to the genus Burkholderia. Bioinformatic target predictions revealed a total of 309 mRNAs to be putative MtvR targets. The mRNA levels corresponding to 17 of 19 selected genes were found to be affected when MtvR was either overexpressed or silenced. Analysis of the interaction between MtvR and the hfq mRNA, one of its targets, showed that the sRNA binds exclusively to the 5' untranslated region (UTR) of the hfq mRNA. This interaction resulted in decreased protein synthesis, suggesting a negative regulatory effect of MtvR on the RNA chaperone Hfq. Bacterial strains with MtvR silenced or overexpressed exhibited pleiotropic phenotypes related to growth and survival after several stresses, swimming and swarming motilities, biofilm formation, resistance to antibiotics, and ability to colonize and kill the nematode Caenorhabditis elegans. Together, the results indicate that the MtvR sRNA is a major posttranscriptional regulator in B. cenocepacia.

Identifying and characterizing Hfq-RNA interactions

To regulate stress responses and virulence, bacteria use small regulatory RNAs (sRNAs). These RNAs can up or down regulate target mRNAs through base pairing by influencing ribosomal access and RNA decay. A large class of these sRNAs, called trans-encoded sRNAs, requires the RNA binding protein Hfq to facilitate base pairing between the regulatory RNA and its target mRNA. The resulting network of regulation is best characterized in Escherichia coli and Salmonella typhimurium, but the importance of Hfq dependent sRNA regulation is recognized in a diverse population of bacteria. In this review we present the approaches and methods used to discover Hfq binding RNAs, characterize their interactions and elucidate their functions.

Predicting sRNAs and their targets in bacteria

Bacterial small RNAs (sRNAs) are an emerging class of regulatory RNAs of about 40–500 nucleotides in length and, by binding to their target mRNAs or proteins, get involved in many biological processes such as sensing environmental changes and regulating gene expression. Thus, identification of bacterial sRNAs and their targets has become an important part of sRNA biology. Current strategies for discovery of sRNAs and their targets usually involve bioinformatics prediction followed by experimental validation, emphasizing a key role for bioinformatics prediction. Here, therefore, we provided an overview on prediction methods, focusing on the merits and limitations of each class of models. Finally, we will present our thinking on developing related bioinformatics models in future.

A modified visual loop-mediated isothermal amplification method for diagnosis and differentiation of main pathogens from Mycobacterium tuberculosis complex

This study was aimed to rapidly identify and differentiate two main pathogens of the Mycobacterium tuberculosis complex: Mycobacterium tuberculosis subsp. tuberculosis and Mycobacterium bovis by a modified loop-mediated isothermal amplification (LAMP) assay. The reaction results could be evaluated by naked eye with two optimized closed tube detection methods as follows: adding the modified fluorescence dye in advance into the reaction mix so as to observe the color changes or putting a tinfoil in the tube and adding the SYBR Green I dye on it, then making the dye drop into the bottom of the tube by centrifuge after reaction. The results showed that the two groups of primers used jointly in this assay could successfully identify and differentiate Mycobacterium tuberculosis subsp. tuberculosis and Mycobacterium tuberculosis bovis. Sensitivity test displayed that the modified LAMP assay with the closed tube system could determine the minimal template concentration of 1 copy/μl, which was more sensitive than that of routine PCR. The advantages of this LAMP method for detection of the Mycobacterium tuberculosis complex included high specificity, high sensitivity, simplicity, and superiority in avoidance of aerosol contamination. The modified LAMP assay would provide a potential for clinical diagnosis and therapy of tuberculosis in the developing countries and the resource-limited areas.

sTarPicker: a method for efficient prediction of bacterial sRNA targets based on a two-step model for hybridization

Background

Bacterial sRNAs are a class of small regulatory RNAs involved in regulation of expression of a variety of genes. Most sRNAs act in trans via base-pairing with target mRNAs, leading to repression or activation of translation or mRNA degradation. To date, more than 1,000 sRNAs have been identified. However, direct targets have been identified for only approximately 50 of these sRNAs. Computational predictions can provide candidates for target validation, thereby increasing the speed of sRNA target identification. Although several methods have been developed, target prediction for bacterial sRNAs remains challenging.

Results

Here, we propose a novel method for sRNA target prediction, termed sTarPicker, which was based on a two-step model for hybridization between an sRNA and an mRNA target. This method first selects stable duplexes after screening all possible duplexes between the sRNA and the potential mRNA target. Next, hybridization between the sRNA and the target is extended to span the entire binding site. Finally, quantitative predictions are produced with an ensemble classifier generated using machine-learning methods. In calculations to determine the hybridization energies of seed regions and binding regions, both thermodynamic stability and site accessibility of the sRNAs and targets were considered. Comparisons with the existing methods showed that sTarPicker performed best in both performance of target prediction and accuracy of the predicted binding sites.

Conclusions

sTarPicker can predict bacterial sRNA targets with higher efficiency and determine the exact locations of the interactions with a higher accuracy than competing programs. sTarPicker is available at http://ccb.bmi.ac.cn/starpicker/.

Accessibility and evolutionary conservation mark bacterial small-rna target-binding regions

Bacterial small noncoding RNAs have attracted much interest in recent years as posttranscriptional regulators of genes involved in diverse pathways. Small RNAs (sRNAs) are 50 to 400 nucleotides long and exert their regulatory function by directly base pairing with mRNA targets to alter their stability and/or affect their translation. This base pairing is achieved through a region of about 10 to 25 nucleotides, which may be located at various positions along different sRNAs. By compiling a data set of experimentally determined target-binding regions of sRNAs and systematically analyzing their properties, we reveal that they are both more evolutionarily conserved and more accessible than random regions. We demonstrate the use of these properties for computational identification of sRNA target-binding regions with high specificity and sensitivity. Our results show that these predicted regions are likely to base pair with known targets of an sRNA, suggesting that pointing out these regions in a specific sRNA can help in searching for its targets.

sRNATarBase: a comprehensive database of bacterial sRNA targets verified by experiments

Bacterial sRNAs are an emerging class of small regulatory RNAs, 40-500 nt in length, which play a variety of important roles in many biological processes through binding to their mRNA or protein targets. A comprehensive database of experimentally confirmed sRNA targets would be helpful in understanding sRNA functions systematically and provide support for developing prediction models. Here we report on such a database--sRNATarBase. The database holds 138 sRNA-target interactions and 252 noninteraction entries, which were manually collected from peer-reviewed papers. The detailed information for each entry, such as supporting experimental protocols, BLAST-based phylogenetic analysis of sRNA-mRNA target interaction in closely related bacteria, predicted secondary structures for both sRNAs and their targets, and available binding regions, is provided as accurately as possible. This database also provides hyperlinks to other databases including GenBank, SWISS-PROT, and MPIDB. The database is available from the web page http://ccb.bmi.ac.cn/srnatarbase/.

The delta subunit of RNA polymerase, RpoE, is a global modulator of Streptococcus mutans environmental adaptation

The delta subunit of RNA polymerase, RpoE, is widespread in low-G+C Gram-positive bacteria and is thought to play a role in enhancing transcriptional specificity by blocking RNA polymerase binding at weak promoter sites and stimulating RNA synthesis by accelerating core enzyme recycling. Despite the well-studied biochemical properties of RpoE, a role for this protein in vivo has not been defined in depth. In this study, we show that inactivation of rpoE in the human dental caries pathogen Streptococcus mutans causes impaired growth and loss of important virulence traits, including biofilm formation, resistance to antibiotics, and tolerance to environmental stresses. Complementation of the mutant with rpoE expressed in trans restored its phenotype to wild type. The luciferase fusion reporter showed that rpoE was highly transcribed throughout growth and that acid and hydrogen peroxide stresses repressed rpoE expression. Transcriptome profiling of wild-type and ΔrpoE cells in the exponential and early stationary phase of growth, under acid and hydrogen peroxide stress and under both stresses combined, revealed that genes involved in histidine synthesis, malolactic fermentation, biofilm formation, and antibiotic resistance were downregulated in the ΔrpoE mutant under all conditions. Moreover, the loss of RpoE resulted in dramatic changes in transport and metabolism of carbohydrates and amino acids. Interestingly, differential expression, mostly upregulation, of 330 noncoding regions was found. In conclusion, this study demonstrates that RpoE is an important global modulator of gene expression in S. mutans which is required for optimal growth and environmental adaptation.

Glutamic acid decarboxylase-derived epitopes with specific domains expand CD4(+)CD25(+) regulatory T cells

Background

CD4⁺CD25⁺ regulatory T cell (Treg)-based immunotherapy is considered a promising regimen for controlling the progression of autoimmune diabetes. In this study, we tested the hypothesis that the therapeutic effects of Tregs in response to the antigenic epitope stimulation depend on the structural properties of the epitopes used.

Methodology/Principal Findings

Splenic lymphocytes from nonobese diabetic (NOD) mice were stimulated with different glutamic acid decarboxylase (GAD)-derived epitopes for 7–10 days and the frequency and function of Tregs was analyzed. We found that, although all expanded Tregs showed suppressive functions in vitro, only p524 (GAD524–538)-expanded CD4⁺CD25⁺ T cells inhibited diabetes development in the co-transfer models, while p509 (GAD509–528)- or p530 (GAD530–543)-expanded CD4⁺CD25⁺ T cells had no such effects. Using computer-guided molecular modeling and docking methods, the differences in structural characteristics of these epitopes and the interaction mode (including binding energy and identified domains in the epitopes) between the above-mentioned epitopes and MHC class II I-A^g7 were analyzed. The theoretical results showed that the epitope p524, which induced protective Tregs, possessed negative surface-electrostatic potential and bound two chains of MHC class II I-A^g7, while the epitopes p509 and p530 which had no such ability exhibited positive surface-electrostatic potential and bound one chain of I-A^g7. Furthermore, p524 bound to I-A^g7 more stably than p509 and p530. Of importance, we hypothesized and subsequently confirmed experimentally that the epitope (GAD570–585, p570), which displayed similar characteristics to p524, was a protective epitope by showing that p570-expanded CD4⁺CD25⁺ T cells suppressed the onset of diabetes in NOD mice.

Conclusions/Significance

These data suggest that molecular modeling-based structural analysis of epitopes may be an instrumental tool for prediction of protective epitopes to expand functional Tregs.

sRNATarget: a web server for prediction of bacterial sRNA targets

In bacteria, there exist some small non-coding RNAs (sRNAs) with 40–500 nucleotides in length. Most of them function as posttranscriptional regulation of gene expression through binding to their target mRNAs, in which Hfq protein acts as RNA chaperone. With the increase of identified sRNA genes in the bacterium, prediction of sRNA targets plays a more important role in determining sRNA functions. However, there are few available computational tools for predicting sRNA targets at present. Here we introduced a web server, sRNATarget, for genome-scale prediction of bacterial sRNA targets. The server is based on a recently published model which uses Naive Bayes method as the supervised method and take RNA secondary structure profile as the feature. The prediction results will be returned to the users through E-mail.