Computational approaches for the discovery of bacterial small RNAs

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach

BACKGROUND

Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class.

METHODS

In this work, we develop novel bioinformatics approaches that integrate sequence and structure-based features to train machine-learning models for the discovery of bacterial sRNAs. We show that features derived from recurrent structural motifs in the ensemble of low energy secondary structures can distinguish the RNA classes with high accuracy.

RESULTS

We apply this approach to predict new members in two broad classes of bacterial small RNAs: 1) sRNAs that bind to the RNA-binding protein RsmA/CsrA in diverse bacterial species and 2) sRNAs regulated by the master regulator of virulence, ToxT, in Vibrio cholerae.

CONCLUSION

The involvement of sRNAs in bacterial adaptation to changing environments is an increasingly recurring theme in current research in microbiology. It is likely that future research, combining experimental and computational approaches, will discover many more examples of sRNAs as components of critical regulatory pathways in bacteria. We have developed a novel approach for prediction of small RNA regulators in important bacterial pathways. This approach can be applied to specific classes of sRNAs for which several members have been identified and the challenge is to identify additional sRNAs.

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.

Discovery and profiling of small RNAs responsive to stress conditions in the plant pathogen Pectobacterium atrosepticum

Background

Small RNAs (sRNAs) have emerged as important regulatory molecules and have been studied in several bacteria. However, to date, there have been no whole-transcriptome studies on sRNAs in any of the Soft Rot Enterobacteriaceae (SRE) group of pathogens. Although the main ecological niches for these pathogens are plants, a significant part of their life cycle is undertaken outside their host within adverse soil environment. However, the mechanisms of SRE adaptation to this harsh nutrient-deficient environment are poorly understood.

Results

In the study reported herein, by using strand-specific RNA-seq analysis and in silico sRNA predictions, we describe the sRNA pool of Pectobacterium atrosepticum and reveal numerous sRNA candidates, including those that are induced during starvation-activated stress responses. Consequently, strand-specific RNA-seq enabled detection of 137 sRNAs and sRNA candidates under starvation conditions; 25 of these sRNAs were predicted for this bacterium in silico. Functional annotations were computationally assigned to 68 sRNAs. The expression of sRNAs in P. atrosepticum was compared under growth-promoting and starvation conditions: 68 sRNAs were differentially expressed with 47 sRNAs up-regulated under nutrient-deficient conditions. Conservation analysis using BLAST showed that most of the identified sRNAs are conserved within the SRE. Subsequently, we identified 9 novel sRNAs within the P. atrosepticum genome.

Conclusions

Since many of the identified sRNAs are starvation-induced, the results of our study suggests that sRNAs play key roles in bacterial adaptive response. Finally, this work provides a basis for future experimental characterization and validation of sRNAs in plant pathogens.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2376-0) contains supplementary material, which is available to authorized users.

Prediction of candidate small non-coding RNAs in Agrobacterium by computational analysis

Small non-coding RNAs with important regulatory roles are not confined to eukaryotes. 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 molecular experiments, have played an important role in the identification of these sRNAs. At present, there is no information on the presence of small non-coding RNAs and their genes in the Agrobacterium tumefaciens genome. To identify potential sRNAs in this important bacterium, deep sequencing of the short RNA populations isolated from Agrobacterium tumefaciens C58 was carried out. From a data set of more than 10,000 short sequences, 16 candidate sRNAs have been tentatively identified based on computational analysis. All of these candidates can form stem-loop structures by RNA folding predictions and the majority of the secondary structures are rich in GC base pairs. Some are followed by a short stretch of U residues, indicative of a rho-independent transcription terminator, whereas some of the short RNAs are found in the stem region of the hairpin, indicative of eukaryotic-like sRNAs. Experimental strategies will need to be used to verify these candidates. The study of an expanded list of candidate sRNAs in Agrobacterium will allow a more complete understanding of the range of roles played by regulatory RNAs in prokaryotes.

Comparative transcriptomics of pathogenic and non-pathogenic Listeria species

Comparative RNA-seq analysis of two related pathogenic and non-pathogenic bacterial strains reveals a hidden layer of divergence in the non-coding genome as well as conserved, widespread regulatory structures called ‘Excludons', which mediate regulation through long non-coding antisense RNAs.

Comparative transcriptome sequencing of two closely related bacterial strains reveals a hidden layer of divergence in the non-coding genome.

Pathogen-specific non-coding RNAs, which might contribute to virulence, are revealed.

The Listeria genome contains a class of unusually long antisense RNAs (lasRNAs) which spans divergent genes and repress expression of the genes located opposite to them while activating the other. The genetic organization of these lasRNAs and operon was named an excludon.

The exhaustive transcriptome information from this publication is provided as an open resource with a web-accessible transcriptome browser.

Listeria monocytogenes is a human, food-borne pathogen. Genomic comparisons between L. monocytogenes and Listeria innocua, a closely related non-pathogenic species, were pivotal in the identification of protein-coding genes essential for virulence. However, no comprehensive comparison has focused on the non-coding genome. We used strand-specific cDNA sequencing to produce genome-wide transcription start site maps for both organisms, and developed a publicly available integrative browser to visualize and analyze both transcriptomes in different growth conditions and genetic backgrounds. Our data revealed conservation across most transcripts, but significant divergence between the species in a subset of non-coding RNAs. In L. monocytogenes, we identified 113 small RNAs (33 novel) and 70 antisense RNAs (53 novel), significantly increasing the repertoire of ncRNAs in this species. Remarkably, we identified a class of long antisense transcripts (lasRNAs) that overlap one gene while also serving as the 5′ UTR of the adjacent divergent gene. Experimental evidence suggests that lasRNAs transcription inhibits expression of one operon while activating the expression of another. Such a lasRNA/operon structure, that we named ‘excludon', might represent a novel form of regulation in bacteria.

Identification and expression of small non-coding RNA, L10-Leader, in different growth phases of Streptococcus mutans

Streptococcus mutans is one of the major cariogenic bacteria in the oral environment. Small non-coding RNAs (sRNAs) play important roles in the regulation of bacterial growth, stress tolerance, and virulence. In this study, we experimentally verified the existence of sRNA, L10-Leader, in S. mutans for the first time. Our results show that the expression level of L10-Leader was growth-phase dependent in S. mutans and varied among different clinical strains of S. mutans. The level of L10-Leader in S. mutans UA159 was closely related to the pH value, but not to the concentrations of glucose and sucrose in culture medium. We predicted target mRNAs of L10-Leader bioinformatically and found that some of these mRNAs were related to growth and stress response. Five predicted mRNA targets were selected and detected by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR), and we found that the expression levels of these mRNAs were closely related to the level of L10-Leader at different growth phases of the bacteria. Our results indicate that L10-Leader may play an important role in the regulation of responses in S. mutans, especially during its growth phase and acid adaption response.

RNA-seq based transcriptional map of bovine respiratory disease pathogen "Histophilus somni 2336"

Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify “novel” genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method.

The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (∼30%) were located in genomic region unique to strain 2336 (∼18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.

TAAPP: Tiling Array Analysis Pipeline for Prokaryotes

High-density tiling arrays provide closer view of transcription than regular microarrays and can also be used for annotating functional elements in genomes. The identified transcripts usually have a complex overlapping architecture when compared to the existing genome annotation. Therefore, there is a need for customized tiling array data analysis tools. Since most of the initial tiling arrays were conducted in eukaryotes, data analysis methods are well suited for eukaryotic genomes. For using whole-genome tiling arrays to identify previously unknown transcriptional elements like small RNA and antisense RNA in prokaryotes, existing data analysis tools need to be tailored for prokaryotic genome architecture. Furthermore, automation of such custom data analysis workflow is necessary for biologists to apply this powerful platform for knowledge discovery. Here we describe TAAPP, a web-based package that consists of two modules for prokaryotic tiling array data analysis. The transcript generation module works on normalized data to generate transcriptionally active regions (TARs). The feature extraction and annotation module then maps TARs to existing genome annotation. This module further categorizes the transcription profile into potential novel non-coding RNA, antisense RNA, gene expression and operon structures. The implemented workflow is microarray platform independent and is presented as a web-based service. The web interface is freely available for acedemic use at http://lims.lsbi.mafes.msstate.edu/TAAPP-HTML/.

Quantifying the sequence-function relation in gene silencing by bacterial small RNAs

Sequence-function relations for small RNA (sRNA)-mediated gene silencing were quantified for the sRNA RyhB and some of its mRNA targets in Escherichia coli. Numerous mutants of RyhB and its targets were generated and their in vivo functions characterized at various levels of target and RyhB expression. Although a core complementary region is required for repression by RyhB, variations in the complementary sequences of the core region gave rise to a continuum of repression strengths, correlated exponentially with the computed free energy of RyhB-target duplex formation. Moreover, sequence variations in the linker region known to interact with the RNA chaperone Hfq also gave rise to a continuum of repression strengths, correlated exponentially with the computed energy cost of keeping the linker region open. These results support the applicability of the thermodynamic model in predicting sRNA-mRNA interaction and suggest that sequences at these locations may be used to fine-tune the degree of repression. Surprisingly, a truncated RyhB without the Hfq-binding region is found to repress multiple targets of the wild-type RyhB effectively, both in the presence and absence of Hfq, even though the former is required for the activity of wild-type RyhB itself. These findings challenge the commonly accepted model concerning the function of Hfq in gene silencing-both in providing stability to the sRNAs and in catalyzing the target mRNAs to take on active conformations-and raise the intriguing question of why many endogenous sRNAs subject their functions to Hfq-dependences.

Small RNAs in the genus Clostridium

The genus Clostridium includes major human pathogens and species important to cellulose degradation, the carbon cycle, and biotechnology. Small RNAs (sRNAs) are emerging as crucial regulatory molecules in all organisms, but they have not been investigated in clostridia. Research on sRNAs in clostridia is hindered by the absence of a systematic method to identify sRNA candidates, thus delegating clostridial sRNA research to a hit-and-miss process. Thus, we wanted to develop a method to identify potential sRNAs in the Clostridium genus to open up the field of sRNA research in clostridia. Using comparative genomics analyses combined with predictions of rho-independent terminators and promoters, we predicted sRNAs in 21 clostridial genomes: Clostridium acetobutylicum, C. beijerinckii, C. botulinum (eight strains), C. cellulolyticum, C. difficile, C. kluyveri (two strains), C. novyi, C. perfringens (three strains), C. phytofermentans, C. tetani, and C. thermocellum. Although more than one-third of predicted sRNAs have Shine-Dalgarno (SD) sequences, only one-sixth have a start codon downstream of SD sequences; thus, most of the predicted sRNAs are noncoding RNAs. Quantitative reverse transcription-PCR (Q-RT-PCR) and Northern analysis were employed to test the presence of a randomly chosen set of sRNAs in C. acetobutylicum and several C. botulinum strains, leading to the confirmation of a large fraction of the tested sRNAs. We identified a conserved, novel sRNA which, together with the downstream gene coding for an ATP-binding cassette (ABC) transporter gene, responds to the antibiotic clindamycin. The number of predicted sRNAs correlated with the physiological function of the species (high for pathogens, low for cellulolytic, and intermediate for solventogenic), but not with 16S rRNA-based phylogeny.

Identification of novel non-coding small RNAs from Streptococcus pneumoniae TIGR4 using high-resolution genome tiling arrays

Background

The identification of non-coding transcripts in human, mouse, and Escherichia coli has revealed their widespread occurrence and functional importance in both eukaryotic and prokaryotic life. In prokaryotes, studies have shown that non-coding transcripts participate in a broad range of cellular functions like gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Streptococcus pneumoniae (pneumococcus), an obligate human respiratory pathogen responsible for significant worldwide morbidity and mortality. Tiling microarrays enable genome wide mRNA profiling as well as identification of novel transcripts at a high-resolution.

Results

Here, we describe a high-resolution transcription map of the S. pneumoniae clinical isolate TIGR4 using genomic tiling arrays. Our results indicate that approximately 66% of the genome is expressed under our experimental conditions. We identified a total of 50 non-coding small RNAs (sRNAs) from the intergenic regions, of which 36 had no predicted function. Half of the identified sRNA sequences were found to be unique to S. pneumoniae genome. We identified eight overrepresented sequence motifs among sRNA sequences that correspond to sRNAs in different functional categories. Tiling arrays also identified approximately 202 operon structures in the genome.

Conclusions

In summary, the pneumococcal operon structures and novel sRNAs identified in this study enhance our understanding of the complexity and extent of the pneumococcal 'expressed' genome. Furthermore, the results of this study open up new avenues of research for understanding the complex RNA regulatory network governing S. pneumoniae physiology and virulence.

Integration of ‘omics’ data: does it lead to new insights into host–microbe interactions?

The interaction between both beneficial and pathogenic microbes and their host has been the subject of many studies. Although the field of systems biology is rapidly evolving, the use of a systems biology approach by means of high-throughput techniques to study host–microbe interactions is just beginning to be explored. In this review, we discuss some of the most recently developed high-throughput ‘omics’ techniques and their use in the context of host–microbe interaction. Moreover, we highlight studies combining several techniques that are pioneering the integration of ‘omics’ data related to host–microbe interactions. Finally, we list the major challenges ahead for successful systems biology research on host–microbe interactions.

MicroRNomics: a newly emerging approach for disease biology

Genomic evidence reveals that gene expression in humans is precisely controlled in cellular, tissue-type, temporal, and condition-specific manners. Completely understanding the regulatory mechanisms of gene expression is therefore one of the most important issues in genomic medicine. Surprisingly, recent analyses of the human and animal genomes have demonstrated that the majority of RNA transcripts are relatively small, noncoding RNAs (sncRNAs), rather than large, protein coding message RNAs (mRNAs). Moreover, these sncRNAs may represent a novel important layer of regulation for gene expression. The most important breakthrough in this new area is the discovery of microRNAs (miRNAs). miRNAs comprise a novel class of endogenous, small, noncoding RNAs that negatively regulate gene expression via degradation or translational inhibition of their target mRNAs. As a group, miRNAs may directly regulate approximately 30% of the genes in the human genome. In keeping with the nomenclature of RNomics, which is to study sncRNAs on the genomic scale, "microRNomics" is coined here to describe a novel subdiscipline of genomics that studies the identification, expression, biogenesis, structure, regulation of expression, targets, and biological functions of miRNAs on the genomic scale. A growing body of exciting evidence suggests that miRNAs are important regulators of cell differentiation, proliferation/growth, mobility, and apoptosis. These miRNAs therefore play important roles in development and physiology. Consequently, dysregulation of miRNA function may lead to human diseases such as cancer, cardiovascular disease, liver disease, immune dysfunction, and metabolic disorders. microRNomics may be a newly emerging approach for human disease biology.