Prediction of non-coding and antisense RNA genes in Escherichia coli with Gapped Markov Model

Sequence-based bacterial small RNAs prediction using ensemble learning strategies

Background

Bacterial small non-coding RNAs (sRNAs) have emerged as important elements in diverse physiological processes, including growth, development, cell proliferation, differentiation, metabolic reactions and carbon metabolism, and attract great attention. Accurate prediction of sRNAs is important and challenging, and helps to explore functions and mechanism of sRNAs.

Results

In this paper, we utilize a variety of sRNA sequence-derived features to develop ensemble learning methods for the sRNA prediction. First, we compile a balanced dataset and four imbalanced datasets. Then, we investigate various sRNA sequence-derived features, such as spectrum profile, mismatch profile, reverse compliment k-mer and pseudo nucleotide composition. Finally, we consider two ensemble learning strategies to integrate all features for building ensemble learning models for the sRNA prediction. One is the weighted average ensemble method (WAEM), which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs. The other is the neural network ensemble method (NNEM), which trains a deep neural network by combining diverse features. In the computational experiments, we evaluate our methods on these five datasets by using 5-fold cross validation. WAEM and NNEM can produce better results than existing state-of-the-art sRNA prediction methods.

Conclusions

WAEM and NNEM have great potential for the sRNA prediction, and are helpful for understanding the biological mechanism of bacteria.

Small RNA repertoires and their intraspecies variation in Aggregatibacter actinomycetemcomitans

Aggregatibacter actinomycetemcomitans is a major periodontal pathogen that has several virulence factors such as leukotoxin and cytolethal distending toxin. Although the genes responsible for virulence have been identified, little is known about their regulatory mechanisms. Small RNA (sRNA) has been recognized as an important factor for gene regulation. To identify new regulatory mechanisms via sRNA in A. actinomycetemcomitans HK1651, we performed a systematic search for sRNAs by RNA-seq and identified 90 intergenic region sRNAs and 30 antisense sRNAs. Of the 85 analysable sRNAs, we successfully detected and quantified 70 sRNAs by developing an RT-PCR system, and we identified 17 sRNAs that were differentially expressed during different growth phases. In addition, we found notable intraspecies variation in the sRNA repertoire of A. actinomycetemcomitans, thus suggesting that frequent acquisition or deletion of sRNAs occurred during the evolution of this species. The predicted target genes of the intergenic region sRNAs indicated the possibility of sRNA interaction with several virulence genes including leukotoxin and cytolethal distending toxin. Our results should serve as an important genomic and genetic basis for future studies to fully understand the regulatory network in A. actinomycetemcomitans and provide new insights into the intraspecies variation of the bacterial sRNA repertoire in bacteria.

Transcriptional Variation of Diverse Enteropathogenic Escherichia coli Isolates under Virulence-Inducing Conditions

Enteropathogenic Escherichia coli (EPEC) bacteria are a diverse group of pathogens that cause moderate to severe diarrhea in young children in developing countries. EPEC isolates can be further subclassified as typical EPEC (tEPEC) isolates that contain the bundle-forming pilus (BFP) or as atypical EPEC (aEPEC) isolates that do not contain BFP. Comparative genomics studies have recently highlighted the considerable genomic diversity among EPEC isolates. In the current study, we used RNA sequencing (RNA-Seq) to characterize the global transcriptomes of eight tEPEC isolates representing the identified genomic diversity, as well as one aEPEC isolate. The global transcriptomes were determined for the EPEC isolates under conditions of laboratory growth that are known to induce expression of virulence-associated genes. The findings demonstrate that unique genes of EPEC isolates from diverse phylogenomic lineages contribute to variation in their global transcriptomes. There were also phylogroup-specific differences in the global transcriptomes, including genes involved in iron acquisition, which had significant differential expression in the EPEC isolates belonging to phylogroup B2. Also, three EPEC isolates from the same phylogenomic lineage (EPEC8) had greater levels of similarity in their genomic content and exhibited greater similarities in their global transcriptomes than EPEC from other lineages; however, even among closely related isolates there were isolate-specific differences among their transcriptomes. These findings highlight the transcriptional variability that correlates with the previously unappreciated genomic diversity of EPEC. IMPORTANCE Recent studies have demonstrated that there is considerable genomic diversity among EPEC isolates; however, it is unknown if this genomic diversity leads to differences in their global transcription. This study used RNA-Seq to compare the global transcriptomes of EPEC isolates from diverse phylogenomic lineages. We demonstrate that there are lineage- and isolate-specific differences in the transcriptomes of genomically diverse EPEC isolates during growth under in vitro virulence-inducing conditions. This study addressed biological variation among isolates of a single pathovar in an effort to demonstrate that while each of these isolates is considered an EPEC isolate, there is significant transcriptional diversity among members of this pathovar. Future studies should consider whether this previously undescribed transcriptional variation may play a significant role in isolate-specific variability of EPEC clinical presentations.

Differential expression of small RNAs under chemical stress and fed-batch fermentation in E. coli

Background

Bacterial small RNAs (sRNAs) are recognized as posttranscriptional regulators involved in the control of bacterial lifestyle and adaptation to stressful conditions. Although chemical stress due to the toxicity of precursor and product compounds is frequently encountered in microbial bioprocessing applications, the involvement of sRNAs in this process is not well understood. We have used RNA sequencing to map sRNA expression in E. coli under chemical stress and high cell density fermentation conditions with the aim of identifying sRNAs involved in the transcriptional response and those with potential roles in stress tolerance.

Results

RNA sequencing libraries were prepared from RNA isolated from E. coli K-12 MG1655 cells grown under high cell density fermentation conditions or subjected to chemical stress with twelve compounds including four organic solvent-like compounds, four organic acids, two amino acids, geraniol and decanoic acid. We have discovered 253 novel intergenic transcripts with this approach, adding to the roughly 200 intergenic sRNAs previously reported in E. coli. There are eighty-four differentially expressed sRNAs during fermentation, of which the majority are novel, supporting possible regulatory roles for these transcripts in adaptation during different fermentation stages. There are a total of 139 differentially expressed sRNAs under chemical stress conditions, where twenty-nine exhibit significant expression changes in multiple tested conditions, suggesting that they may be involved in a more general chemical stress response. Among those with known functions are sRNAs involved in regulation of outer membrane proteins, iron availability, maintaining envelope homeostasis, as well as sRNAs incorporated into complex networks controlling motility and biofilm formation.

Conclusions

This study has used deep sequencing to reveal a wealth of hitherto undescribed sRNAs in E. coli and provides an atlas of sRNA expression during seventeen different growth and stress conditions. Although the number of novel sRNAs with regulatory functions is unknown, several exhibit specific expression patterns during high cell density fermentation and are differentially expressed in the presence of multiple chemicals, suggesting they may play regulatory roles during these stress conditions. These novel sRNAs, together with specific known sRNAs, are candidates for improving stress tolerance and our understanding of the E. coli regulatory network during fed-batch fermentation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2231-8) contains supplementary material, which is available to authorized users.

Identification of novel regulatory small RNAs in Acinetobacter baumannii

Small RNA (sRNA) molecules are non-coding RNAs that have been implicated in regulation of various cellular processes in living systems, allowing them to adapt to changing environmental conditions. Till date, sRNAs have not been reported in Acinetobacter baumannii (A. baumannii), which has emerged as a significant multiple drug resistant nosocomial pathogen. In the present study, a combination of bioinformatic and experimental approach was used for identification of novel sRNAs. A total of 31 putative sRNAs were predicted by a combination of two algorithms, sRNAPredict and QRNA. Initially 10 sRNAs were chosen on the basis of lower E- value and three sRNAs (designated as AbsR11, 25 and 28) showed positive signal on Northern blot. These sRNAs are novel in nature as they do not have homologous sequences in other bacterial species. Expression of the three sRNAs was examined in various phases of bacterial growth. Further, the effect of various stress conditions on sRNA gene expression was determined. A detailed investigation revealed differential expression profile of AbsR25 in presence of varying amounts of ethidium bromide (EtBr), suggesting that its expression is influenced by environmental or internal signals such as stress response. A decrease in expression of AbsR25 and concomitant increase in the expression of bioinformatically predicted targets in presence of high EtBr was reverberated by the decrease in target gene expression when AbsR25 was overexpressed. This hints at the negative regulation of target genes by AbsR25. Interestingly, the putative targets include transporter genes and the degree of variation in expression of one of them (A1S_1331) suggests that AbsR25 is involved in regulation of a transporter. This study provides a perspective for future studies of sRNAs and their possible involvement in regulation of antibiotic resistance in bacteria specifically in cryptic A. baumannii.

Prediction and characterization of small non-coding RNAs related to secondary metabolites in Saccharopolyspora erythraea

Saccharopolyspora erythraea produces a large number of secondary metabolites with biological activities, including erythromycin. Elucidation of the mechanisms through which the production of these secondary metabolites is regulated may help to identify new strategies for improved biosynthesis of erythromycin. In this paper, we describe the systematic prediction and analysis of small non-coding RNAs (sRNAs) in S. erythraea, with the aim to elucidate sRNA-mediated regulation of secondary metabolite biosynthesis. In silico and deep-sequencing technologies were applied to predict sRNAs in S. erythraea. Six hundred and forty-seven potential sRNA loci were identified, of which 382 cis-encoded antisense RNA are complementary to protein-coding regions and 265 predicted transcripts are located in intergenic regions. Six candidate sRNAs (sernc292, sernc293, sernc350, sernc351, sernc361, and sernc389) belong to four gene clusters (tpc3, pke, pks6, and nrps5) that are involved in secondary metabolite biosynthesis. Deep-sequencing data showed that the expression of all sRNAs in the strain HL3168 E3 (E3) was higher than that in NRRL23338 (M), except for sernc292 and sernc361 expression. The relative expression of six sRNAs in strain M and E3 were validated by qRT-PCR at three different time points (24, 48, and 72 h). The results showed that, at each time point, the transcription levels of sernc293, sernc350, sernc351, and sernc389 were higher in E3 than in M, with the largest difference observed at 72 h, whereas no signals for sernc292 and sernc361 were detected. sernc293, sernc350, sernc351, and sernc389 probably regulate iron transport, terpene metabolism, geosmin synthesis, and polyketide biosynthesis, respectively. The major significance of this study is the successful prediction and identification of sRNAs in genomic regions close to the secondary metabolism-related genes in S. erythraea. A better understanding of the sRNA-target interaction would help to elucidate the complete range of functions of sRNAs in S. erythraea, including sRNA-mediated regulation of erythromycin biosynthesis.

Novel regulatory small RNAs in Streptococcus pyogenes

Streptococcus pyogenes (Group A Streptococcus or GAS) is a Gram-positive bacterial pathogen that has shown complex modes of regulation of its virulence factors to cause diverse diseases. Bacterial small RNAs are regarded as novel widespread regulators of gene expression in response to environmental signals. Recent studies have revealed that several small RNAs (sRNAs) have an important role in S. pyogenes physiology and pathogenesis by regulating gene expression at the translational level. To search for new sRNAs in S. pyogenes, we performed a genomewide analysis through computational prediction followed by experimental verification. To overcome the limitation of low accuracy in computational prediction, we employed a combination of three different computational algorithms (sRNAPredict, eQRNA and RNAz). A total of 45 candidates were chosen based on the computational analysis, and their transcription was analyzed by reverse-transcriptase PCR and Northern blot. Through this process, we discovered 7 putative novel trans-acting sRNAs. Their abundance varied between different growth phases, suggesting that their expression is influenced by environmental or internal signals. Further, to screen target mRNAs of an sRNA, we employed differential RNA sequencing analysis. This study provides a significant resource for future study of small RNAs and their roles in physiology and pathogenesis of S. pyogenes.

Plastid non-coding RNAs: emerging candidates for gene regulation

Recent advances in transcriptomics and bioinformatics, specifically strand-specific RNA sequencing, have allowed high-throughput, comprehensive detection of low-abundance transcripts typical of the non-coding RNAs studied in bacteria and eukaryotes. Before this, few plastid non-coding RNAs (pncRNAs) had been identified, and even fewer had been investigated for any functional role in gene regulation. Relaxed plastid transcription initiation and termination result in full transcription of both chloroplast DNA strands. Following this, post-transcriptional processing produces a pool of metastable RNA species, including distinct pncRNAs. Here we review pncRNA biogenesis and possible functionality, and speculate that this RNA class may have an underappreciated role in plastid gene regulation.

Metatranscriptomic analysis of microbes in an Oceanfront deep-subsurface hot spring reveals novel small RNAs and type-specific tRNA degradation

Studies of small noncoding RNAs (sRNAs) have been conducted predominantly using culturable organisms, and the acquisition of further information about sRNAs from global environments containing uncultured organisms now is very important. In this study, hot spring water (57°C, pH 8.1) was collected directly from the underground environment at depths of 250 to 1,000 m in Yunohama, Japan, and small RNA sequences obtained from the environment were analyzed. A phylogenetic analysis of both archaeal and bacterial 16S rRNA gene sequences was conducted, and the results suggested the presence of unique species in the environment, corresponding to the Archaeal Richmond Mine Acidophilic Nanoorganisms (ARMAN) group and three new Betaproteobacteria. A metatranscriptomic analysis identified 64,194 (20,057 nonredundant) cDNA sequences. Of these cDNAs, 90% were either tRNAs, tRNA fragments, rRNAs, or rRNA fragments, whereas 2,181 reads (10%) were classified as previously uncharacterized putative candidate sRNAs. Among these, 15 were particularly abundant, 14 of which showed no sequence similarity to any known noncoding RNA, and at least six of which form very stable RNA secondary structures. The analysis of a large number of tRNA fragments suggested that unique relationships exist between the anticodons of the tRNAs and the sites of tRNA degradation. Previous bacterial tRNA degradation studies have been limited to specific organisms, such as Escherichia coli and Streptomyces coelicolor, and the current results suggest that specific tRNA decay occurs more frequently than previously expected.

An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains

Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli.

EcoBrowser: a web-based tool for visualizing transcriptome data of Escherichia coli

Background

Escherichia coli has been extensively studied as a prokaryotic model organism whose whole genome was determined in 1997. However, it is difficult to identify all the gene products involved in diverse functions by using whole genome sequencesalone. The high-resolution transcriptome mapping using tiling arrays has proved effective to improve the annotation of transcript units and discover new transcripts of ncRNAs. While abundant tiling array data have been generated, the lack of appropriate visualization tools to accommodate and integrate multiple sources of data has emerged.

Findings

EcoBrowser is a web-based tool for visualizing genome annotations and transcriptome data of E. coli. Important tiling array data of E. coli from different experimental platforms are collected and processed for query. An AJAX based genome browser is embedded for visualization. Thus, genome annotations can be compared with transcript profiling and genome occupancy profiling from independent experiments, which will be helpful in discovering new transcripts including novel mRNAs and ncRNAs, generating a detailed description of the transcription unit architecture, further providing clues for investigation of prokaryotic transcriptional regulation that has proved to be far more complex than previously thought.

Conclusions

With the help of EcoBrowser, users can get a systemic view both from the vertical and parallel sides, as well as inspirations for the design of new experiments which will expand our understanding of the regulation mechanism.

Deep sequencing reveals as-yet-undiscovered small RNAs in Escherichia coli

BACKGROUND

In Escherichia coli, approximately 100 regulatory small RNAs (sRNAs) have been identified experimentally and many more have been predicted by various methods. To provide a comprehensive overview of sRNAs, we analysed the low-molecular-weight RNAs (< 200 nt) of E. coli with deep sequencing, because the regulatory RNAs in bacteria are usually 50-200 nt in length.

RESULTS

We discovered 229 novel candidate sRNAs (≥ 50 nt) with computational or experimental evidence of transcription initiation. Among them, the expression of seven intergenic sRNAs and three cis-antisense sRNAs was detected by northern blot analysis. Interestingly, five novel sRNAs are expressed from prophage regions and we note that these sRNAs have several specific characteristics. Furthermore, we conducted an evolutionary conservation analysis of the candidate sRNAs and summarised the data among closely related bacterial strains.

CONCLUSIONS

This comprehensive screen for E. coli sRNAs using a deep sequencing approach has shown that many as-yet-undiscovered sRNAs are potentially encoded in the E. coli genome. We constructed the Escherichia coli Small RNA Browser (ECSBrowser; http://rna.iab.keio.ac.jp/), which integrates the data for previously identified sRNAs and the novel sRNAs found in this study.

cis-antisense RNA, another level of gene regulation in bacteria

A substantial amount of antisense transcription is a hallmark of gene expression in eukaryotes. However, antisense transcription was first demonstrated in bacteria almost 50 years ago. The transcriptomes of bacteria as different as Helicobacter pylori, Bacillus subtilis, Escherichia coli, Synechocystis sp. strain PCC6803, Mycoplasma pneumoniae, Sinorhizobium meliloti, Geobacter sulfurreducens, Vibrio cholerae, Chlamydia trachomatis, Pseudomonas syringae, and Staphylococcus aureus have now been reported to contain antisense RNA (asRNA) transcripts for a high percentage of genes. Bacterial asRNAs share functional similarities with trans-acting regulatory RNAs, but in addition, they use their own distinct mechanisms. Among their confirmed functional roles are transcription termination, codegradation, control of translation, transcriptional interference, and enhanced stability of their respective target transcripts. Here, we review recent publications indicating that asRNAs occur as frequently in simple unicellular bacteria as they do in higher organisms, and we provide a comprehensive overview of the experimentally confirmed characteristics of asRNA actions and intimately linked quantitative aspects. Emerging functional data suggest that asRNAs in bacteria mediate a plethora of effects and are involved in far more processes than were previously anticipated. Thus, the functional impact of asRNAs should be considered when developing new strategies against pathogenic bacteria and when optimizing bacterial strains for biotechnology.

Genome-wide detection of novel regulatory RNAs in E. coli

The intergenic regions in bacterial genomes can contain regulatory leader sequences and small RNAs (sRNAs), which both serve to modulate gene expression. Computational analyses have predicted the presence of hundreds of these noncoding regulatory RNAs in Escherichia coli; however, only about 80 have been experimentally validated. By applying a deep-sequencing approach, we detected and quantified the vast majority of the previously validated regulatory elements and identified 10 new sRNAs and nine new regulatory leader sequences in the intergenic regions of E. coli. Half of the newly discovered sRNAs displayed enhanced stability in the presence of the RNA-binding protein Hfq, which is vital to the function of many of the known E. coli sRNAs. Whereas previous methods have often relied on phylogenetic conservation to identify regulatory leader sequences, only five of the newly discovered E. coli leader sequences were present in the genomes of other enteric species. For those newly identified regulatory elements having orthologs in Salmonella, evolutionary analyses showed that these regions encoded new noncoding elements rather than small, unannotated protein-coding transcripts. In addition to discovering new noncoding regulatory elements, we validated 53 sRNAs that were previously predicted but never detected and showed that the presence, within intergenic regions, of σ(70) promoters and sequences with compensatory mutations that maintain stable RNA secondary structures across related species is a good predictor of novel sRNAs.

nocoRNAc: characterization of non-coding RNAs in prokaryotes

Background

The interest in non-coding RNAs (ncRNAs) constantly rose during the past few years because of the wide spectrum of biological processes in which they are involved. This led to the discovery of numerous ncRNA genes across many species. However, for most organisms the non-coding transcriptome still remains unexplored to a great extent. Various experimental techniques for the identification of ncRNA transcripts are available, but as these methods are costly and time-consuming, there is a need for computational methods that allow the detection of functional RNAs in complete genomes in order to suggest elements for further experiments. Several programs for the genome-wide prediction of functional RNAs have been developed but most of them predict a genomic locus with no indication whether the element is transcribed or not.

Results

We present NOCORNAc, a program for the genome-wide prediction of ncRNA transcripts in bacteria. NOCORNAc incorporates various procedures for the detection of transcriptional features which are then integrated with functional ncRNA loci to determine the transcript coordinates. We applied RNAz and NOCORNAc to the genome of Streptomyces coelicolor and detected more than 800 putative ncRNA transcripts most of them located antisense to protein-coding regions. Using a custom design microarray we profiled the expression of about 400 of these elements and found more than 300 to be transcribed, 38 of them are predicted novel ncRNA genes in intergenic regions. The expression patterns of many ncRNAs are similarly complex as those of the protein-coding genes, in particular many antisense ncRNAs show a high expression correlation with their protein-coding partner.

Conclusions

We have developed NOCORNAc, a framework that facilitates the automated characterization of functional ncRNAs. NOCORNAc increases the confidence of predicted ncRNA loci, especially if they contain transcribed ncRNAs. NOCORNAc is not restricted to intergenic regions, but it is applicable to the prediction of ncRNA transcripts in whole microbial genomes. The software as well as a user guide and example data is available at http://www.zbit.uni-tuebingen.de/pas/nocornac.htm.

Bacterial antisense RNAs: how many are there, and what are they doing?

Antisense RNAs encoded on the DNA strand opposite another gene have the potential to form extensive base-pairing interactions with the corresponding sense RNA. Unlike other smaller regulatory RNAs in bacteria, antisense RNAs range in size from tens to thousands of nucleotides. The numbers of antisense RNAs reported for different bacteria vary extensively, but hundreds have been suggested in some species. If all of these reported antisense RNAs are expressed at levels sufficient to regulate the genes encoded opposite them, antisense RNAs could significantly impact gene expression in bacteria. Here, we review the evidence for these RNA regulators and describe what is known about the functions and mechanisms of action for some of these RNAs. Important considerations for future research as well as potential applications are also discussed.

Evidence for a major role of antisense RNAs in cyanobacterial gene regulation

Information on the numbers and functions of naturally occurring antisense RNAs (asRNAs) in eubacteria has thus far remained incomplete. Here, we screened the model cyanobacterium Synechocystis sp. PCC 6803 for asRNAs using four different methods. In the final data set, the number of known noncoding RNAs rose from 6 earlier identified to 60 and of asRNAs from 1 to 73 (28 were verified using at least three methods). Among these, there are many asRNAs to housekeeping, regulatory or metabolic genes, as well as to genes encoding electron transport proteins. Transferring cultures to high light, carbon-limited conditions or darkness influenced the expression levels of several asRNAs, suggesting their functional relevance. Examples include the asRNA to rpl1, which accumulates in a light-dependent manner and may be required for processing the L11 r-operon and the SyR7 noncoding RNA, which is antisense to the murF 5′ UTR, possibly modulating murein biosynthesis. Extrapolated to the whole genome, ∼10% of all genes in Synechocystis are influenced by asRNAs. Thus, chromosomally encoded asRNAs may have an important function in eubacterial regulatory networks.

De novo computational prediction of non-coding RNA genes in prokaryotic genomes

Motivation: The computational identification of non-coding RNA (ncRNA) genes represents one of the most important and challenging problems in computational biology. Existing methods for ncRNA gene prediction rely mostly on homology information, thus limiting their applications to ncRNA genes with known homologues.

Results: We present a novel de novo prediction algorithm for ncRNA genes using features derived from the sequences and structures of known ncRNA genes in comparison to decoys. Using these features, we have trained a neural network-based classifier and have applied it to Escherichia coli and Sulfolobus solfataricus for genome-wide prediction of ncRNAs. Our method has an average prediction sensitivity and specificity of 68% and 70%, respectively, for identifying windows with potential for ncRNA genes in E.coli. By combining windows of different sizes and using positional filtering strategies, we predicted 601 candidate ncRNAs and recovered 41% of known ncRNAs in E.coli. We experimentally investigated six novel candidates using Northern blot analysis and found expression of three candidates: one represents a potential new ncRNA, one is associated with stable mRNA decay intermediates and one is a case of either a potential riboswitch or transcription attenuator involved in the regulation of cell division. In general, our approach enables the identification of both cis- and trans-acting ncRNAs in partially or completely sequenced microbial genomes without requiring homology or structural conservation.

Availability: The source code and results are available at http://csbl.bmb.uga.edu/publications/materials/tran/.

Contact:xyn@bmb.uga.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

sRNAMap: genomic maps for small non-coding RNAs, their regulators and their targets in microbial genomes

Small non-coding RNAs (sRNAs) carry out a variety of biological functions and affect protein synthesis and protein activities in prokaryotes. Recently, numerous sRNAs and their targets were identified in Escherichia coli and in other bacteria. It is crucial to have a comprehensive resource concerning the annotation of small non-coding RNAs in microbial genomes. This work presents an integrated database, namely sRNAMap, to collect the sRNA genes, the transcriptional regulators of sRNAs and the sRNA target genes by integrating a variety of biological databases and by surveying literature. In this resource, we collected 397 sRNAs, 62 regulators/sRNAs and 60 sRNAs/targets in 70 microbial genomes. Additionally, more valuable information of the sRNAs, such as the secondary structure of sRNAs, the expressed conditions of sRNAs, the expression profiles of sRNAs, the transcriptional start sites of sRNAs and the cross-links to other biological databases, are provided for further investigation. Besides, various textual and graphical interfaces were designed and implemented to facilitate the data access in sRNAMap. sRNAMap is available at http://sRNAMap.mbc.nctu.edu.tw/.

Small RNA gene identification and mRNA target predictions in bacteria

MOTIVATION

Bacterial small ribonucleic acids (sRNAs) that are not ribosomal and transfer or messenger RNAs were initially identified in the sixties, whereas their molecular functions are still under active investigation today. It is now widely accepted that most play central roles in gene expression regulation in response to environmental changes. Interestingly, some are also implicated in bacterial virulence. Functional studies revealed that a large subset of these sRNAs act by an antisense mechanism thanks to pairing interactions with dedicated mRNA targets, usually around their translation start sites, to modulate gene expression at the posttranscriptional level. Some sRNAs modulate protein activity or mimic the structure of other macromolecules. In the last few years, in silico methods have been developed to detect more bacterial sRNAs. Among these, computational analyses of the bacterial genomes by comparative genomics have predicted the existence of a plethora of sRNAs, some that were confirmed to be expressed in vivo. The prediction accuracy of these computational tools is highly variable and can be perfectible. Here we review the computational studies that have contributed to detecting the sRNA gene and mRNA targets in bacteria and the methods for their experimental testing. In addition, the remaining challenges are discussed.

NcDNAlign: plausible multiple alignments of non-protein-coding genomic sequences

Genome-wide multiple sequence alignments (MSAs) are a necessary prerequisite for an increasingly diverse collection of comparative genomic approaches. Here we present a versatile method that generates high-quality MSAs for non-protein-coding sequences. The NcDNAlign pipeline combines pairwise BLAST alignments to create initial MSAs, which are then locally improved and trimmed. The program is optimized for speed and hence is particulary well-suited to pilot studies. We demonstrate the practical use of NcDNAlign in three case studies: the search for ncRNAs in gammaproteobacteria and the analysis of conserved noncoding DNA in nematodes and teleost fish, in the latter case focusing on the fate of duplicated ultra-conserved regions. Compared to the currently widely used genome-wide alignment program TBA, our program results in a 20- to 30-fold reduction of CPU time necessary to generate gammaproteobacterial alignments. A showcase application of bacterial ncRNA prediction based on alignments of both algorithms results in similar sensitivity, false discovery rates, and up to 100 putatively novel ncRNA structures. Similar findings hold for our application of NcDNAlign to the identification of ultra-conserved regions in nematodes and teleosts. Both approaches yield conserved sequences of unknown function, result in novel evolutionary insights into conservation patterns among these genomes, and manifest the benefits of an efficient and reliable genome-wide alignment package. The software is available under the GNU Public License at http://www.bioinf.uni-leipzig.de/Software/NcDNAlign/.

Identification of chromosomal alpha-proteobacterial small RNAs by comparative genome analysis and detection in Sinorhizobium meliloti strain 1021

BACKGROUND

Small untranslated RNAs (sRNAs) seem to be far more abundant than previously believed. The number of sRNAs confirmed in E. coli through various approaches is above 70, with several hundred more sRNA candidate genes under biological validation. Although the total number of sRNAs in any one species is still unclear, their importance in cellular processes has been established. However, unlike protein genes, no simple feature enables the prediction of the location of the corresponding sequences in genomes. Several approaches, of variable usefulness, to identify genomic sequences encoding sRNA have been described in recent years.

RESULTS

We used a combination of in silico comparative genomics and microarray-based transcriptional profiling. This approach to screening identified ~60 intergenic regions conserved between Sinorhizobium meliloti and related members of the alpha-proteobacteria sub-group 2. Of these, 14 appear to correspond to novel non-coding sRNAs and three are putative peptide-coding or 5' UTR RNAs (ORF smaller than 100 aa). The expression of each of these new small RNA genes was confirmed by Northern blot hybridization.

CONCLUSION

Small non coding RNA (sra) genes can be found in the intergenic regions of alpha-proteobacteria genomes. Some of these sra genes are only present in S. meliloti, sometimes in genomic islands; homologues of others are present in related genomes including those of the pathogens Brucella and Agrobacterium.

Computational approaches for the discovery of bacterial small RNAs

Recent work has uncovered a growing number of bacterial small RNAs (sRNAs), some of which have been shown to regulate critical cellular processes. Computational approaches, in combination with experiments, have played an important role in the discovery of these sRNAs. In this article, we first give an overview of different computational approaches for genome-wide prediction of sRNAs. These approaches have led to the discovery of several novel sRNAs, however the regulatory roles are not yet known for a majority of these sRNAs. By contrast, several recent studies have highlighted the inverse problem where the functional role of the sRNA is already known and the challenge is to identify its genomic location. The focus of this article is on computational tools and strategies for identifying these specific sRNAs which function as key components of known regulatory pathways.

Comparative analysis of cis-encoded antisense RNAs in eukaryotes

Recent large-scale transcriptomic analyses have identified numerous endogenously encoded cis-antisense RNAs that are thought to play important roles in diverse cellular processes although comprehensive comparative studies among multiple species have yet to be performed. To investigate conserved genomic features across various species that may be related to sense-antisense regulation, we performed comparative analysis of approximately 1000-2000 cis-encoded antisense RNA pairs from five model eukaryotes (Homo sapiens, Mus musculus, Drosophila melanogaster, Arabidopsis thaliana, and Oryza sativa). Analysis of overlapping patterns relative to the exon-intron structure revealed that the number of pairs sharing the 3' part of the transcripts was larger than that of the 5'-sharing pairs except in rice. Moreover, most of the well-conserved sense-antisense pairs between human and mouse exhibited 3'-overlaps, suggesting that regulatory mechanisms involving these regions may be important in sense-antisense transcription. Functional classification using Gene Ontology revealed that genes related to catalytic activity, nucleotide binding, DNA metabolism, and mitochondria were preferentially distributed within the set of exon-overlapping sense-antisense genes compared to the non-exon-overlapping group in animals. Despite the numerous sense-antisense pairs identified in human and mouse individually, the number of conserved pairs was extremely small (6.6% of the entire set). Whereas both genes of most of the conserved sense-antisense pairs had protein-coding potential, nearly half of the non-conserved pairs included a non-coding RNA, suggesting that non-coding sense-antisense RNAs may function in species-specific regulatory pathways.

On the interplay of gene positioning and the role of rho-independent terminators in Escherichia coli

The majority of intrinsic rho-independent terminator signals, reported to consist of stable hairpin structures followed by T-rich regions, possess the potential to operate bi-directionally and to induce transcription terminations on both strands of the DNA duplex in Escherichia coli. By using RNAMotif software, we investigated the distributions of termination motifs around the 3'-ends of overlapping and non-overlapping genes at the genomic level. We suggest that the positions of compactly encoded E. coli genes and rho-independent terminators are optimized to terminate the adjoining genes on their antisense strands efficiently, and not to mis-terminate overlapping transcripts, due to their bi-directional properties.