Assessing computational tools for the discovery of small RNA genes in bacteria

Apex Predator Nematodes and Meso-Predator Bacteria Consume Their Basal Insect Prey through Discrete Stages of Chemical Transformations

Microbial symbiosis drives physiological processes of higher-order systems, including the acquisition and consumption of nutrients that support symbiotic partner reproduction. Metabolic analytics provide new avenues to examine how chemical ecology, or the conversion of existing biomass to new forms, changes over a symbiotic life cycle. We applied these approaches to the nematode Steinernema carpocapsae, its mutualist bacterium, Xenorhabdus nematophila, and the insects they infect. The nematode-bacterium pair infects, kills, and reproduces in an insect until nutrients are depleted. To understand the conversion of insect biomass over time into either nematode or bacterium biomass, we integrated information from trophic, metabolomic, and gene regulation analyses. Trophic analysis established bacteria as meso-predators and primary insect consumers. Nematodes hold a trophic position of 4.6, indicative of an apex predator, consuming bacteria and likely other nematodes. Metabolic changes associated with Galleria mellonella insect bioconversion were assessed using multivariate statistical analyses of metabolomics data sets derived from sampling over an infection time course. Statistically significant, discrete phases were detected, indicating the insect chemical environment changes reproducibly during bioconversion. A novel hierarchical clustering method was designed to probe molecular abundance fluctuation patterns over time, revealing distinct metabolite clusters that exhibit similar abundance shifts across the time course. Composite data suggest bacterial tryptophan and nematode kynurenine pathways are coordinated for reciprocal exchange of tryptophan and NAD+ and for synthesis of intermediates that can have complex effects on bacterial phenotypes and nematode behaviors. Our analysis of pathways and metabolites reveals the chemistry underlying the recycling of organic material during carnivory. IMPORTANCE The processes by which organic life is consumed and reborn in a complex ecosystem were investigated through a multiomics approach applied to the tripartite Xenorhabdus bacterium-Steinernema nematode-Galleria insect symbiosis. Trophic analyses demonstrate the primary consumers of the insect are the bacteria, and the nematode in turn consumes the bacteria. This suggests the Steinernema-Xenorhabdus mutualism is a form of agriculture in which the nematode cultivates the bacterial food sources by inoculating them into insect hosts. Metabolomics analysis revealed a shift in biological material throughout progression of the life cycle: active infection, insect death, and conversion of cadaver tissues into bacterial biomass and nematode tissue. We show that each phase of the life cycle is metabolically distinct, with significant differences including those in the tricarboxylic acid cycle and amino acid pathways. Our findings demonstrate that symbiotic life cycles can be defined by reproducible stage-specific chemical signatures, enhancing our broad understanding of metabolic processes that underpin a three-way symbiosis.

PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region

Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.

The Novel ncRNA OsiR Positively Regulates Expression of katE2 and Is Required for Oxidative Stress Tolerance in Deinococcus radiodurans

Deinococcus radiodurans is a polyextremophilic bacterium well known for its extreme resistance to irradiation, oxidative stress, and other damaging conditions. Many small noncoding RNAs (ncRNAs) in D. radiodurans have been identified by deep sequencing analysis and computational predictions. However, the precise roles of ncRNAs and their target genes in the oxidative stress response have not been investigated. Here, we report the identification and characterization of a novel ncRNA named OsiR (for oxidative stress-induced ncRNA). Oxidative stress tolerance analysis showed that deleting osiR significantly decreased viability, total antioxidant capacity, and catalase activity in D. radiodurans under oxidative stress conditions. Comparative phenotypic and qRT-PCR analyses of an osiR mutant identify a role of OsiR in regulating the expression of the catalase gene katE2. Microscale thermophoresis and genetic complementation showed that a 21-nt sequence in the stem–loop structure of OsiR (204–244 nt) directly base pairs with its counterpart in the coding region of katE2 mRNA (843–866 nt) via a 19 nt region. In addition, deletion of katE2 caused a significant reduction of catalase activity and oxidative stress tolerance similar to that observed in an osiR mutant. Our results show that OsiR positively regulates oxidative stress tolerance in D. radiodurans by increasing the mRNA stability and translation efficiency of katE2. This work provides a new regulatory pathway mediated by ncRNA for the oxidative stress response that most likely contributes to the extreme tolerances of D. radiodurans.

Prioritizing bona fide bacterial small RNAs with machine learning classifiers

Bacterial small (sRNAs) are involved in the control of several cellular processes. Hundreds of putative sRNAs have been identified in many bacterial species through RNA sequencing. The existence of putative sRNAs is usually validated by Northern blot analysis. However, the large amount of novel putative sRNAs reported in the literature makes it impractical to validate each of them in the wet lab. In this work, we applied five machine learning approaches to construct twenty models to discriminate bona fide sRNAs from random genomic sequences in five bacterial species. Sequences were represented using seven features including free energy of their predicted secondary structure, their distances to the closest predicted promoter site and Rho-independent terminator, and their distance to the closest open reading frames (ORFs). To automatically calculate these features, we developed an sRNA Characterization Pipeline (sRNACharP). All seven features used in the classification task contributed positively to the performance of the predictive models. The best performing model obtained a median precision of 100% at 10% recall and of 64% at 40% recall across all five bacterial species, and it outperformed previous published approaches on two benchmark datasets in terms of precision and recall. Our results indicate that even though there is limited sRNA sequence conservation across different bacterial species, there are intrinsic features in the genomic context of sRNAs that are conserved across taxa. We show that these features are utilized by machine learning approaches to learn a species-independent model to prioritize bona fide bacterial sRNAs.

Genome-Wide Detection of Small Regulatory RNAs in Deep-Sea Bacterium Shewanella piezotolerans WP3

Shewanella are one of the most abundant Proteobacteria in the deep-sea and are renowned for their versatile electron accepting capacities. The molecular mechanisms involved in their adaptation to diverse and extreme environments are not well understood. Small non-coding RNAs (sRNAs) are known for modulating the gene expression at transcriptional and posttranscriptional levels, subsequently playing a key role in microbial adaptation. To understand the potential roles of sRNAs in the adaptation of Shewanella toward deep-sea environments, here an in silico approach was utilized to detect the sRNAs in the genome of Shewanella piezotolerans WP3, a piezotolerant and psychrotolerant deep-sea iron reducing bacterium. After scanning 3673 sets of 5' and 3' UTRs of orthologous genes, 209 sRNA candidates were identified with high confidence in S. piezotolerans WP3. About 92% (193 out of 209) of these putative sRNAs belong to the class trans-encoded RNAs, suggesting that trans-regulatory RNAs are the dominant class of sRNAs in S. piezotolerans WP3. The remaining 16 cis-regulatory RNAs were validated through quantitative polymerase chain reaction. Five cis-sRNAs were further shown to act as cold regulated sRNAs. Our study provided additional evidence at the transcriptional level to decipher the microbial adaptation mechanisms to extreme environmental conditions.

Transcriptome sequencing analysis of novel sRNAs of Kineococcus radiotolerans in response to ionizing radiation

Kineococcus radiotolerans is a Gram-positive, radio-resistant bacterium isolated from a radioactive environment. The small noncoding RNAs (sRNAs) in bacteria are reported to play roles in the immediate response to stress and/or the recovery from stress. The analysis of K. radiotolerans transcriptome sequencing results can identify these sRNAs in a genome-wide detection, using RNA sequencing (RNA-seq) by the deep sequencing technique. In this study, the raw data of radiation-exposed samples (RS) and control samples (CS) were acquired separately from the sequencing platform. There were 217 common sRNA candidates in the two samples screened in the genome-wide scale by bioinformatics analysis. There were 43 differentially expressed sRNA candidates, including 28 up-regulated and 15 down-regulated ones. The down-regulated sRNAs were selected for the sRNA target prediction, of which 12 sRNAs that may modulate the genes related to the transcription regulation and DNA repair were considered as the candidates involved in the radio-resistance regulation system.

In silico prediction and qPCR validation of novel sRNAs in Propionibacterium acnes KPA171202

Propionibacterium acnes is an anaerobic, Gram-positive, opportunistic pathogen known to be involved in a wide variety of diseases ranging from mild acne to prostate cancer. Bacterial small non-coding RNAs are novel regulators of gene expression and are known to be involved in, virulence, pathogenesis, stress tolerance and adaptation to environmental changes in bacteria. The present study was undertaken keeping in view the lack of predicted sRNAs of P. acnes KPA171202 in databases. This report represents the first attempt to identify sRNAs in P. acnes KPA171202. A total of eight potential candidate sRNAs were predicted using SIPHT, one was found to have a Rfam homolog and seven were novel. Out of these seven predicted sRNAs, five were validated by reverse transcriptase-polymerase chain reaction (RT-PCR) and sequencing. The expression of these sRNAs was quantified in different growth phases by qPCR (quantitative PCR). They were found to be expressed in both exponential and stationary stages of growth but with maximum expression in stationary phase which points to a regulatory role for them. Further investigation of their targets and regulatory functions is in progress.

An assessment of bacterial small RNA target prediction programs

Most bacterial regulatory RNAs exert their function through base-pairing with target RNAs. Computational prediction of targets is a busy research field that offers biologists a variety of web sites and software. However, it is difficult for a non-expert to evaluate how reliable those programs are. Here, we provide a simple benchmark for bacterial sRNA target prediction based on trusted E. coli sRNA/target pairs. We use this benchmark to assess the most recent RNA target predictors as well as earlier programs for RNA-RNA hybrid prediction. Moreover, we consider how the definition of mRNA boundaries can impact overall predictions. Recent algorithms that exploit both conservation of targets and accessibility information offer improved accuracy over previous software. However, even with the best predictors, the number of true biological targets with low scores and non-targets with high scores remains puzzling.

Bacterial small RNAs in the Genus Rickettsia

Background

Rickettsia species are obligate intracellular Gram-negative pathogenic bacteria and the etiologic agents of diseases such as Rocky Mountain spotted fever (RMSF), Mediterranean spotted fever, epidemic typhus, and murine typhus. Genome sequencing revealed that R. prowazekii has ~25 % non-coding DNA, the majority of which is thought to be either “junk DNA” or pseudogenes resulting from genomic reduction. These characteristics also define other Rickettsia genomes. Bacterial small RNAs, whose biogenesis is predominantly attributed to either the intergenic regions (trans-acting) or to the antisense strand of an open reading frame (cis-acting), are now appreciated to be among the most important post-transcriptional regulators of bacterial virulence and growth. We hypothesize that intergenic regions in rickettsial species encode for small, non-coding RNAs (sRNAs) involved in the regulation of its transcriptome, leading to altered virulence and adaptation depending on the host niche.

Results

We employed a combination of bioinformatics and in vitro approaches to explore the presence of sRNAs in a number of species within Genus Rickettsia. Using the sRNA Identification Protocol using High-throughput Technology (SIPHT) web interface, we predicted over 1,700 small RNAs present in the intergenic regions of 16 different strains representing 13 rickettsial species. We further characterized novel sRNAs from typhus (R. prowazekii and R. typhi) and spotted fever (R. rickettsii and R. conorii) groups for their promoters and Rho-independent terminators using Bacterial Promoter Prediction Program (BPROM) and TransTermHP prediction algorithms, respectively. Strong σ70 promoters were predicted upstream of all novel small RNAs, indicating the potential for transcriptional activity. Next, we infected human microvascular endothelial cells (HMECs) with R. prowazekii for 3 h and 24 h and performed Next Generation Sequencing to experimentally validate the expression of 26 sRNA candidates predicted in R. prowazekii. Reverse transcriptase PCR was also used to further verify the expression of six putative novel sRNA candidates in R. prowazekii.

Conclusions

Our results yield clear evidence for the expression of novel R. prowazekii sRNA candidates during infection of HMECs. This is the first description of novel small RNAs for a highly pathogenic species of Rickettsia, which should lead to new insights into rickettsial virulence and adaptation mechanisms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2293-7) contains supplementary material, which is available to authorized users.

Rapid and high-throughput construction of microbial cell-factories with regulatory noncoding RNAs

Due to global crises such as pollution and depletion of fossil fuels, sustainable technologies based on microbial cell-factories have been garnering great interest as an alternative to chemical factories. The development of microbial cell-factories is imperative in cutting down the overall manufacturing cost. Thus, diverse metabolic engineering strategies and engineering tools have been established to obtain a preferred genotype and phenotype displaying superior productivity. However, these tools are limited to only a handful of genes with permanent modification of a genome and significant labor costs, and this is one of the bottlenecks associated with biofactory construction. Therefore, a groundbreaking rapid and high-throughput engineering tool is needed for efficient construction of microbial cell-factories. During the last decade, copious small noncoding RNAs (ncRNAs) have been discovered in bacteria. These are involved in substantial regulatory roles like transcriptional and post-transcriptional gene regulation by modulating mRNA elongation, stability, or translational efficiency. Because of their vulnerability, ncRNAs can be used as another layer of conditional control over gene expression without modifying chromosomal sequences, and hence would be a promising high-throughput tool for metabolic engineering. Here, we review successful design principles and applications of ncRNAs for high-throughput metabolic engineering or physiological studies of diverse industrially important microorganisms.

Transcriptional analysis of Deinococcus radiodurans reveals novel small RNAs that are differentially expressed under ionizing radiation

Small noncoding RNAs (sRNAs) are posttranscriptional regulators that have been identified in multiple species and shown to play essential roles in responsive mechanisms to environmental stresses. The natural ability of specific bacteria to resist high levels of radiation has been of high interest to mechanistic studies of DNA repair and biomolecular protection. Deinococcus radiodurans is a model extremophile for radiation studies that can survive doses of ionizing radiation of >12,000 Gy, 3,000 times higher than for most vertebrates. Few studies have investigated posttranscriptional regulatory mechanisms of this organism that could be relevant in its general gene regulatory patterns. In this study, we identified 199 potential sRNA candidates in D. radiodurans by whole-transcriptome deep sequencing analysis and confirmed the expression of 41 sRNAs by Northern blotting and reverse transcriptase PCR (RT-PCR). A total of 8 confirmed sRNAs showed differential expression during recovery after acute ionizing radiation (15 kGy). We have also found and confirmed 7 sRNAs in Deinococcus geothermalis, a closely related radioresistant species. The identification of several novel sRNAs in Deinococcus bacteria raises important questions about the evolution and nature of global gene regulation in radioresistance.

Genome-wide analyses in bacteria show small-RNA enrichment for long and conserved intergenic regions

Interest in finding small RNAs (sRNAs) in bacteria has significantly increased in recent years due to their regulatory functions. Development of high-throughput methods and more sophisticated computational algorithms has allowed rapid identification of sRNA candidates in different species. However, given their various sizes (50 to 500 nucleotides [nt]) and their potential genomic locations in the 5' and 3' untranslated regions as well as in intergenic regions, identification and validation of true sRNAs have been challenging. In addition, the evolution of bacterial sRNAs across different species continues to be puzzling, given that they can exert similar functions with various sequences and structures. In this study, we analyzed the enrichment patterns of sRNAs in 13 well-annotated bacterial species using existing transcriptome and experimental data. All intergenic regions were analyzed by WU-BLAST to examine conservation levels relative to species within or outside their genus. In total, more than 900 validated bacterial sRNAs and 23,000 intergenic regions were analyzed. The results indicate that sRNAs are enriched in intergenic regions, which are longer and more conserved than the average intergenic regions in the corresponding bacterial genome. We also found that sRNA-coding regions have different conservation levels relative to their flanking regions. This work provides a way to analyze how noncoding RNAs are distributed in bacterial genomes and also shows conserved features of intergenic regions that encode sRNAs. These results also provide insight into the functions of regions surrounding sRNAs and into optimization of RNA search algorithms.

Discovery and validation of novel and distinct RNA regulators for ribosomal protein S15 in diverse bacterial phyla

Background

Autogenous cis-regulators of ribosomal protein synthesis play a critical role in maintaining the stoichiometry of ribosome components. Structured portions within an mRNA transcript typically interact with specific ribosomal proteins to prevent expression of the entire operon, thus balancing levels of ribosomal proteins across transcriptional units. Three distinct RNA structures from different bacterial phyla have demonstrated interactions with S15 to regulate gene expression; however, these RNAs are distributed across a small fraction of bacterial diversity.

Results

We used comparative genomics in combination with analysis of existing transcriptomic data to identify three novel putative RNA structures associated with the S15 coding region in microbial genomes. These structures are completely distinct from those previously published and encompass potential regulatory regions including ribosome-binding sites. To validate the biological relevance of our findings, we demonstrate that an example of the Alphaproteobacterial RNA from Rhizobium radiobacter specifically interacts with S15 in vitro, and allows in vivo regulation of gene expression in an E. coli reporter system. In addition, structural probing and nuclease protection assays confirm the predicted secondary structure and indicate nucleotides required for protein interaction.

Conclusions

This work illustrates the importance of integrating comparative genomic and transcriptomic approaches during de novo ncRNA identification and reveals a diversity of distinct natural RNA regulators that support analogous biological functions. Furthermore, this work indicates that many additional uncharacterized RNA regulators likely exist within bacterial genomes and that the plasticity of RNA structure allows unique, and likely independently derived, solutions to the same biological problem.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-657) contains supplementary material, which is available to authorized users.

Small RNAs in mycobacteria: an unfolding story

Mycobacteria represent a class of powerful pathogens, including those causing tuberculosis and leprosy, which continue to be worldwide health challenges. In the last 20 years, an abundance of non-coding, small RNAs (sRNAs) have been discovered in model bacteria and gained significant attention as regulators of cellular responses, including pathogenesis. Naturally, a search in mycobacteria followed, revealing over 200 sRNAs thus far. Characterization of these sRNAs is only beginning, but differential expression under environmental stresses suggests relevance to mycobacterial pathogenesis. This review provides a comprehensive overview of the current knowledge of sRNAs in mycobacteria, including historical perspective and techniques used for identification and characterization.

A multiobjective method for robust identification of bacterial small non-coding RNAs

MOTIVATION

Small non-coding RNAs (sRNAs) have major roles in the post-transcriptional regulation in prokaryotes. The experimental validation of a relatively small number of sRNAs in few species requires developing computational algorithms capable of robustly encoding the available knowledge and using this knowledge to predict sRNAs within and across species.

RESULTS

We present a novel methodology designed to identify bacterial sRNAs by incorporating the knowledge encoded by different sRNA prediction methods and optimally aggregating them as potential predictors. Because some of these methods emphasize specificity, whereas others emphasize sensitivity while detecting sRNAs, their optimal aggregation constitutes trade-off solutions between these two contradictory objectives that enhance their individual merits. Many non-redundant optimal aggregations uncovered by using multiobjective optimization techniques are then combined into a multiclassifier, which ensures robustness during detection and prediction even in genomes with distinct nucleotide composition. By training with sRNAs in Salmonella enterica Typhimurium, we were able to successfully predict sRNAs in Sinorhizobium meliloti, as well as in multiple and poorly annotated species. The proposed methodology, like a meta-analysis approach, may begin to lay a possible foundation for developing robust predictive methods across a wide spectrum of genomic variability.

AVAILABILITY AND IMPLEMENTATION

Scripts created for the experimentation are available at http://m4m.ugr.es/SupInfo/sRNAOS/sRNAOSscripts.zip.

CONTACT

delval@decsai.ugr.es

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Construction of a Pseudomonas aeruginosa genomic DNA library

Although the completion and annotation of the entire genomic DNA sequence of Pseudomonas aeruginosa PAO1 strain has been carried out, an important number of genes are still of unknown function and many genetic elements involved in various regulatory pathways like small RNA are still unrevealed. One of the strategies to identify gene function or genetic elements is the construction and utilization of DNA genomic library. Here, we describe the construction a P. aeruginosa DNA genomic library.

Identification of novel sRNAs in mycobacterial species

Bacterial small RNAs (sRNAs) are short transcripts that typically do not encode proteins and often act as regulators of gene expression through a variety of mechanisms. Regulatory sRNAs have been identified in many species, including Mycobacterium tuberculosis, the causative agent of tuberculosis. Here, we use a computational algorithm to predict sRNA candidates in the mycobacterial species M. smegmatis and M. bovis BCG and confirmed the expression of many sRNAs using Northern blotting. Thus, we have identified 17 and 23 novel sRNAs in M. smegmatis and M. bovis BCG, respectively. We have also applied a high-throughput technique (Deep-RACE) to map the 5' and 3' ends of many of these sRNAs and identified potential regulators of sRNAs by analysis of existing ChIP-seq datasets. The sRNAs identified in this work likely contribute to the unique biology of mycobacteria.

Computational analysis of bacterial RNA-Seq data

Recent advances in high-throughput RNA sequencing (RNA-seq) have enabled tremendous leaps forward in our understanding of bacterial transcriptomes. However, computational methods for analysis of bacterial transcriptome data have not kept pace with the large and growing data sets generated by RNA-seq technology. Here, we present new algorithms, specific to bacterial gene structures and transcriptomes, for analysis of RNA-seq data. The algorithms are implemented in an open source software system called Rockhopper that supports various stages of bacterial RNA-seq data analysis, including aligning sequencing reads to a genome, constructing transcriptome maps, quantifying transcript abundance, testing for differential gene expression, determining operon structures and visualizing results. We demonstrate the performance of Rockhopper using 2.1 billion sequenced reads from 75 RNA-seq experiments conducted with Escherichia coli, Neisseria gonorrhoeae, Salmonella enterica, Streptococcus pyogenes and Xenorhabdus nematophila. We find that the transcriptome maps generated by our algorithms are highly accurate when compared with focused experimental data from E. coli and N. gonorrhoeae, and we validate our system’s ability to identify novel small RNAs, operons and transcription start sites. Our results suggest that Rockhopper can be used for efficient and accurate analysis of bacterial RNA-seq data, and that it can aid with elucidation of bacterial transcriptomes.

Computational small RNA prediction in bacteria

Bacterial, small RNAs were once regarded as potent regulators of gene expression and are now being considered as essential for their diversified roles. Many small RNAs are now reported to have a wide array of regulatory functions, ranging from environmental sensing to pathogenesis. Traditionally, noncoding transcripts were rarely detected by means of genetic screens. However, the availability of approximately 2200 prokaryotic genome sequences in public databases facilitates the efficient computational search of those molecules, followed by experimental validation. In principle, the following four major computational methods were applied for the prediction of sRNA locations from bacterial genome sequences: (1) comparative genomics, (2) secondary structure and thermodynamic stability, (3) ‘Orphan’ transcriptional signals and (4) ab initio methods regardless of sequence or structure similarity; most of these tools were applied to locate the putative genomic sRNA locations followed by experimental validation of those transcripts. Therefore, computational screening has simplified the sRNA identification process in bacteria. In this review, a plethora of small RNA prediction methods and tools that have been reported in the past decade are discussed comprehensively and assessed based on their attributes, compatibility, and their prediction accuracy.

Computational discovery and RT-PCR validation of novel Burkholderia conserved and Burkholderia pseudomallei unique sRNAs

BACKGROUND

The sRNAs of bacterial pathogens are known to be involved in various cellular roles including environmental adaptation as well as regulation of virulence and pathogenicity. It is expected that sRNAs may also have similar functions for Burkholderia pseudomallei, a soil bacterium that can adapt to diverse environmental conditions, which causes the disease melioidosis and is also able to infect a wide variety of hosts.

RESULTS

By integrating several proven sRNA prediction programs into a computational pipeline, available Burkholderia spp. genomes were screened to identify sRNA gene candidates. Orthologous sRNA candidates were then identified via comparative analysis. From the total prediction, 21 candidates were found to have Rfam homologs. RT-PCR and sequencing of candidate sRNA genes of unknown functions revealed six putative sRNAs which were highly conserved in Burkholderia spp. and two that were unique to B. pseudomallei present in a normal culture conditions transcriptome. The validated sRNAs include potential cis-acting elements associated with the modulation of methionine metabolism and one B. pseudomallei-specific sRNA that is expected to bind to the Hfq protein.

CONCLUSIONS

The use of the pipeline developed in this study and subsequent comparative analysis have successfully aided in the discovery and shortlisting of sRNA gene candidates for validation. This integrated approach identified 29 B. pseudomallei sRNA genes - of which 21 have Rfam homologs and 8 are novel.

BSRD: a repository for bacterial small regulatory RNA

In bacteria, small regulatory non-coding RNAs (sRNAs) are the most abundant class of post-transcriptional regulators. They are involved in diverse processes including quorum sensing, stress response, virulence and carbon metabolism. Recent developments in high-throughput techniques, such as genomic tiling arrays and RNA-Seq, have allowed efficient detection and characterization of bacterial sRNAs. However, a comprehensive repository to host sRNAs and their annotations is not available. Existing databases suffer from a limited number of bacterial species or sRNAs included. In addition, these databases do not have tools to integrate or analyse high-throughput sequencing data. Here, we have developed BSRD (http://kwanlab.bio.cuhk.edu.hk/BSRD), a comprehensive bacterial sRNAs database, as a repository for published bacterial sRNA sequences with annotations and expression profiles. BSRD contains over nine times more experimentally validated sRNAs than any other available databases. BSRD also provides combinatorial regulatory networks of transcription factors and sRNAs with their common targets. We have built and implemented in BSRD a novel RNA-Seq analysis platform, sRNADeep, to characterize sRNAs in large-scale transcriptome sequencing projects. We will update BSRD regularly.

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.

Comparative analysis of regulatory elements between Escherichia coli and Klebsiella pneumoniae by genome-wide transcription start site profiling

Genome-wide transcription start site (TSS) profiles of the enterobacteria Escherichia coli and Klebsiella pneumoniae were experimentally determined through modified 5′ RACE followed by deep sequencing of intact primary mRNA. This identified 3,746 and 3,143 TSSs for E. coli and K. pneumoniae, respectively. Experimentally determined TSSs were then used to define promoter regions and 5′ UTRs upstream of coding genes. Comparative analysis of these regulatory elements revealed the use of multiple TSSs, identical sequence motifs of promoter and Shine-Dalgarno sequence, reflecting conserved gene expression apparatuses between the two species. In both species, over 70% of primary transcripts were expressed from operons having orthologous genes during exponential growth. However, expressed orthologous genes in E. coli and K. pneumoniae showed a strikingly different organization of upstream regulatory regions with only 20% identical promoters with TSSs in both species. Over 40% of promoters had TSSs identified in only one species, despite conserved promoter sequences existing in the other species. 662 conserved promoters having TSSs in both species resulted in the same number of comparable 5′ UTR pairs, and that regulatory element was found to be the most variant region in sequence among promoter, 5′ UTR, and ORF. In K. pneumoniae, 48 sRNAs were predicted and 36 of them were expressed during exponential growth. Among them, 34 orthologous sRNAs between two species were analyzed in depth, and the analysis showed that many sRNAs of K. pneumoniae, including pleiotropic sRNAs such as rprA, arcZ, and sgrS, may work in the same way as in E. coli. These results reveal a new dimension of comparative genomics such that a comparison of two genomes needs to be comprehensive over all levels of genome organization.

In order to investigate similarities and differences of closely related species, most of the comparative genomics studies focus on comparing the gene contents either shared or specific for each genome. However, it is also important to investigate the differences in non-coding regulatory elements because they influence the transcriptional and post-transcriptional processes. Thus, we performed a genome-wide profiling of transcription start sites (TSSs) in two species, E. coli K-12 MG1655 and K. pneumoniae MGH78578. Experimental identification of TSSs is important for precise definition of promoter regions and 5′ untranslated regions upstream of coding genes. Comparative analysis of these regulatory elements revealed the use of multiple TSSs, identical sequence motifs of promoter and Shine-Dalgarno sequence. However, we observed that the upstream regulatory regions of the majority of operons having orthologous genes were organized with different usage of promoters and TSSs, resulting in diverse and complex gene regulation. We also found that the 5′ UTR is the least conserved regulatory element in sequence between the two species. Moreover, 34 orthologous sRNAs between E. coli and K. pneumoniae were analyzed in depth. The analysis suggested many of K. pneumoniae sRNAs might regulate the target genes as in E. coli.

Computational approaches to discovering noncoding RNA

New developments are being brought to the field of molecular biology with the mounting evidence that RNA transcripts not translated into protein (noncoding RNAs, ncRNAs) hold a variety of biological functions. Computational discovery of ncRNAs is one of these developments, fueled not only by the urge to characterize these sequences but also by necessity to prioritize ones with the most relevant functions for experimental verification. The heterogeneity in size and mode of activity of ncRNAs is reflected in the corresponding diversity of computational methods for their study. Sequence and structural analysis, conservation across species, and relative position to other genomic elements are being used for ncRNA detection. In addition, the recent development of techniques that allow deep sequencing of cell transcripts either globally or from isolated ncRNA-related material is leading the field toward increased use of such high-throughput data. We expect that imminent breakthroughs will include the classification of newer types of ncRNA and new insights into miRNA and piRNA biology, eventually leading toward the completion of a catalog of all human ncRNAs.

Bioinformatic discovery of bacterial regulatory RNAs using SIPHT

Diverse bacteria encode RNAs that are not translated into proteins but are instead involved in regulating a wide variety of cellular functions. Computational approaches have proven successful in identifying numerous regulatory RNAs in myriad bacterial species but the difficultly of implementing most of these approaches has limited their accessibility to many researchers. Moreover, few of these approaches provide annotations of predicted loci to guide downstream experimental validation and characterization. Here I describe the implementation of SIPHT, a web-accessible program that enables screens for putative loci encoding regulatory RNAs to be conducted in any of nearly 2,000 sequenced bacterial replicons. SIPHT identifies candidate loci by searching for regions of intergenic sequence conservation upstream of predicted intrinsic transcription terminators. Each locus is then annotated for numerous features that provide clues about its potential function and/or enable the most reliable candidates to be identified.

NAPP: the Nucleic Acid Phylogenetic Profile Database

Nucleic acid phylogenetic profiling (NAPP) classifies coding and non-coding sequences in a genome according to their pattern of conservation across other genomes. This procedure efficiently distinguishes clusters of functional non-coding elements in bacteria, particularly small RNAs and cis-regulatory RNAs, from other conserved sequences. In contrast to other non-coding RNA detection pipelines, NAPP does not require the presence of conserved RNA secondary structure and therefore is likely to identify previously undetected RNA genes or elements. Furthermore, as NAPP clusters contain both coding and non-coding sequences with similar occurrence profiles, they can be analyzed under a functional perspective. We recently improved the NAPP pipeline and applied it to a collection of 949 bacterial and 68 archaeal species. The database and web interface available at http://napp.u-psud.fr/ enable detailed analysis of NAPP clusters enriched in non-coding RNAs, graphical display of phylogenetic profiles, visualization of predicted RNAs in their genome context and extraction of predicted RNAs for use with genome browsers or other software.