Small non-coding RNAs (sRNAs) have attracted attention as a new class of gene regulators in both eukaryotes and bacteria. Genome-wide screening methods have been successfully applied in Gram-negative bacteria to identify sRNA regulators. Many sRNAs are well characterized, including their target mRNAs and mode of action. In comparison, little is known about sRNAs in Gram-positive pathogens. In this study, we identified novel sRNAs in the exclusively human pathogen Streptococcus pyogenes M49 (Group A Streptococcus, GAS M49), employing a whole genome intergenic tiling array approach. GAS is an important pathogen that causes diseases ranging from mild superficial infections of the skin and mucous membranes of the naso-pharynx, to severe toxic and invasive diseases.
We identified 55 putative sRNAs in GAS M49 that were expressed during growth. Of these, 42 were novel. Some of the newly-identified sRNAs belonged to one of the common non-coding RNA families described in the Rfam database. Comparison of the results of our screen with the outcome of two recently published bioinformatics tools showed a low level of overlap between putative sRNA genes. Previously, 40 potential sRNAs have been reported to be expressed in a GAS M1T1 serotype, as detected by a whole genome intergenic tiling array approach. Our screen detected 12 putative sRNA genes that were expressed in both strains. Twenty sRNA candidates appeared to be regulated in a medium-dependent fashion, while eight sRNA genes were regulated throughout growth in chemically defined medium. Expression of candidate genes was verified by reverse transcriptase-qPCR. For a subset of sRNAs, the transcriptional start was determined by 5′ rapid amplification of cDNA ends-PCR (RACE-PCR) analysis.
In accord with the results of previous studies, we found little overlap between different screening methods, which underlines the fact that a comprehensive analysis of sRNAs expressed by a given organism requires the complementary use of different methods and the investigation of several environmental conditions. Despite a high conservation of sRNA genes within streptococci, the expression of sRNAs appears to be strain specific.
Streptococcus pyogenes; Small noncoding RNAs; Virulence; Transcriptional regulation; Pathogenesis
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 non-coding RNAs (sRNAs) facilitate host-microbe interactions. They have a central function in the post-transcriptional regulation during pathogenic lifestyles. Hfq, an RNA-binding protein that many sRNAs act in conjunction with, is required for Y. pestis pathogenesis. However, information on how Yersinia pestis modulates the expression of sRNAs during infection is largely unknown.
Methodology and Principal Findings
We used RNA-seq technology to identify the sRNA candidates expressed from Y. pestis grown in
vitro and in the infected lungs of mice. A total of 104 sRNAs were found, including 26 previously annotated sRNAs, by searching against the Rfam database with 78 novel sRNA candidates. Approximately 89% (93/104) of these sRNAs from Y. pestis are shared with its ancestor Y. pseudotuberculosis. Ninety-seven percent of these sRNAs (101/104) are shared among more than 80 sequenced genomes of 135 Y. pestis strains. These 78 novel sRNAs include 62 intergenic and 16 antisense sRNAs. Fourteen sRNAs were selected for verification by independent Northern blot analysis. Results showed that nine selected sRNA transcripts were Hfq-dependent. Interestingly, three novel sRNAs were identified as new members of the transcription factor CRP regulon. Semi-quantitative analysis revealed that Y. pestis from the infected lungs induced the expressions of six sRNAs including RyhB1, RyhB2, CyaR/RyeE, 6S RNA, RybB and sR039 and repressed the expressions of four sRNAs, including CsrB, CsrC, 4.5S RNA and sR027.
Conclusions and Significance
This study is the first attempt to subject RNA from Y. pestis-infected samples to direct high-throughput sequencing. Many novel sRNAs were identified and the expression patterns of relevant sRNAs in Y. pestis during in
vitro growth and in
vivo infection were revealed. The annotated sRNAs accounted for the most abundant sRNAs either expressed in bacteria grown in
vitro or differentially expressed in the infected lungs. These findings suggested these sRNAs may have important functions in Y. pestis physiology or pathogenesis.
Small untranslated RNAs (sRNAs) are widespread regulators of gene expression in bacteria. This study reports on a comprehensive screen for sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti applying deep sequencing of cDNAs and microarray hybridizations.
A total of 1,125 sRNA candidates that were classified as trans-encoded sRNAs (173), cis-encoded antisense sRNAs (117), mRNA leader transcripts (379), and sense sRNAs overlapping coding regions (456) were identified in a size range of 50 to 348 nucleotides. Among these were transcripts corresponding to 82 previously reported sRNA candidates. Enrichment for RNAs with primary 5'-ends prior to sequencing of cDNAs suggested transcriptional start sites corresponding to 466 predicted sRNA regions. The consensus σ70 promoter motif CTTGAC-N17-CTATAT was found upstream of 101 sRNA candidates. Expression patterns derived from microarray hybridizations provided further information on conditions of expression of a number of sRNA candidates. Furthermore, GenBank, EMBL, DDBJ, PDB, and Rfam databases were searched for homologs of the sRNA candidates identified in this study. Searching Rfam family models with over 1,000 sRNA candidates, re-discovered only those sequences from S. meliloti already known and stored in Rfam, whereas BLAST searches suggested a number of homologs in related alpha-proteobacteria.
The screening data suggests that in S. meliloti about 3% of the genes encode trans-encoded sRNAs and about 2% antisense transcripts. Thus, this first comprehensive screen for sRNAs applying deep sequencing in an alpha-proteobacterium shows that sRNAs also occur in high number in this group of bacteria.
Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement.
Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5′-ends of these six Northern-supported sRNA candidates were successfully mapped using 5′-RACE analysis.
We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that ∼40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.
sRNAs are small, non-coding RNA species that control numerous cellular processes. Although it iswidely accepted that sRNAs are encoded by most if not all bacteria, genome-wide annotations for sRNA-encoding genes have been conducted in only a few of the nearly 300 bacterial species sequenced to date. To facilitate the efficient annotation of bacterial genomes for sRNA-encoding genes, we developed a program, sRNAPredict2, that identifies putative sRNAs by searching for co-localization of genetic features commonly associated with sRNA-encoding genes. Using sRNAPredict2, we conducted genome-wide annotations for putative sRNA-encoding genes in the intergenic regions of 11 diverse pathogens. In total, 2759 previously unannotated candidate sRNA loci were predicted. There was considerable range in the number of sRNAs predicted in the different pathogens analyzed, raising the possibility that there are species-specific differences in the reliance on sRNA-mediated regulation. Of 34 previously unannotated sRNAs predicted in the opportunistic pathogen Pseudomonas aeruginosa, 31 were experimentally tested and 17 were found to encode sRNA transcripts. Our findings suggest that numerous genes have been missed in the current annotations of bacterial genomes and that, by using improved bioinformatic approaches and tools, much remains to be discovered in ‘intergenic’ sequences.
In the recent years, the number of drug‐ and multi‐drug‐resistant microbial strains has increased rapidly. Therefore, the need to identify innovative approaches for development of novel anti‐infectives and new therapeutic targets is of high priority in global health care. The detection of small RNAs (sRNAs) in bacteria has attracted considerable attention as an emerging class of new gene expression regulators. Several experimental technologies to predict sRNA have been established for the Gram‐negative model organism Escherichia coli. In many respects, sRNA screens in this model system have set a blueprint for the global and functional identification of sRNAs for Gram‐positive microbes, but the functional role of sRNAs in colonization and pathogenicity for Listeria monocytogenes, Staphylococcus aureus, Streptococcus pyogenes, Enterococcus faecalis and Clostridium difficile is almost completely unknown. Here, we report the current knowledge about the sRNAs of these socioeconomically relevant Gram‐positive pathogens, overview the state‐of‐the‐art high‐throughput sRNA screening methods and summarize bioinformatics approaches for genome‐wide sRNA identification and target prediction. Finally, we discuss the use of modified peptide nucleic acids (PNAs) as a novel tool to inactivate potential sRNA and their applications in rapid and specific detection of pathogenic bacteria.
Erwinia amylovora is a phytopathogenic bacterium and causal agent of fire blight disease in apples and pears. Although many virulence factors have been characterized, the coordination of expression of these virulence factors in E. amylovora is still not clear. Regulatory small RNAs (sRNAs) are important post-transcriptional regulatory components in bacteria. A large number of sRNAs require the RNA chaperone Hfq for both stability and functional activation. In E. amylovora, Hfq was identified as a major regulator of virulence and various virulence traits. However, information is still lacking about Hfq-dependent sRNAs on a genome scale, including the virulence regulatory functions of these sRNAs in E. amylovora.
Using both an RNA-seq analysis and a Rho-independent terminator search, 40 candidate Hfq-dependent sRNAs were identified in E. amylovora. The expression and sizes of 12 sRNAs and the sequence boundaries of seven sRNAs were confirmed by Northern blot and 5’ RACE assay respectively. Sequence conservation analysis identified sRNAs conserved only in the Erwinia genus as well as E. amylovora species-specific sRNAs. In addition, a dynamic re-patterning of expression of Hfq-dependent sRNAs was observed at 6 and 12 hours after induction in Hrp-inducing minimal medium. Furthermore, sRNAs that control virulence traits were characterized, among which ArcZ positively controls the type III secretion system (T3SS), amylovoran exopolysaccahride production, biofilm formation, and motility, and negatively modulates attachment while RmaA (Hrs6) and OmrAB both negatively regulate amylovoran production and positively regulate motility.
This study has significantly enhanced our understanding of the Hfq-dependent sRNAs in E. amylovora at the genome level. The identification of multiple virulence-regulating sRNAs also suggests that post-transcriptional regulation by sRNAs may play a role in the deployment of virulence factors needed during varying stages of pathogenesis during host invasion by E. amylovora.
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RNA-seq; sRNA; Type III secretion system; Biofilm; Amylovoran; Motility; ArcZ
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.
There are abundance of transcripts that code for no particular protein and that remain functionally uncharacterized. Some of these transcripts may have novel functions while others might be junk transcripts. Unfortunately, the experimental validation of such transcripts to find functional non-coding RNA candidates is very costly. Therefore, our primary interest is to computationally mine candidate functional transcripts from a pool of uncharacterized transcripts. We introduce fRNAdb: a novel database service that hosts a large collection of non-coding transcripts including annotated/non-annotated sequences from the H-inv database, NONCODE and RNAdb. A set of computational analyses have been performed on the included sequences. These analyses include RNA secondary structure motif discovery, EST support evaluation, cis-regulatory element search, protein homology search, etc. fRNAdb provides an efficient interface to help users filter out particular transcripts under their own criteria to sort out functional RNA candidates. fRNAdb is available at
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.
Clostridia include major human pathogens and species important to human physiology, cellulose degradation, the carbon cycle, and biotechnology. Small RNAs (sRNAs) are increasingly recognized as crucial regulatory molecules in all organisms, but they remain virtually unexplored in clostridia. We provide the first comprehensive list of computationally identified and experimentally verified small RNAs in the genus Clostridium aiming to accelerate interest in and studies of small RNA molecules in a very important genus. The higher number of sRNAs found in clostridial pathogens suggests a good correlation between the physiological function or niche of the species and the number of predicted and conserved sRNAs. Our list of predicted sRNAs displays a strong enrichment of sRNAs upstream or downstream of ATP-binding cassette (ABC) transporter genes. This, combined with the identification of a conserved sRNA apparently involved in clindamycin resistance, provides a new perspective for future studies of possible regulation of antibiotic resistance genes by sRNAs in bacteria.
Diverse bacterial genomes encode numerous small non-coding RNAs (sRNAs) that regulate myriad biological processes. While bioinformatic algorithms have proven effective in identifying sRNA-encoding loci, the lack of tools and infrastructure with which to execute these computationally demanding algorithms has limited their utilization. Genome-wide predictions of sRNA-encoding genes have been conducted in less than 3% of all sequenced bacterial strains, leading to critical gaps in current annotations. The relative paucity of genome-wide sRNA prediction represents a critical gap in current annotations of bacterial genomes and has limited examination of larger issues in sRNA biology, such as sRNA evolution.
We have developed and deployed SIPHT, a high throughput computational tool that utilizes workflow management and distributed computing to effectively conduct kingdom-wide predictions and annotations of intergenic sRNA-encoding genes. Candidate sRNA-encoding loci are identified based on the presence of putative Rho-independent terminators downstream of conserved intergenic sequences, and each locus is annotated for several features, including conservation in other species, association with one of several transcription factor binding sites and homology to any of over 300 previously identified sRNAs and cis-regulatory RNA elements. Using SIPHT, we conducted searches for putative sRNA-encoding genes in all 932 bacterial replicons in the NCBI database. These searches yielded nearly 60% of previously confirmed sRNAs, hundreds of previously annotated cis-encoded regulatory RNA elements such as riboswitches, and over 45,000 novel candidate intergenic loci.
Candidate loci were identified across all branches of the bacterial evolutionary tree, suggesting a central and ubiquitous role for RNA-mediated regulation among bacterial species. Annotation of candidate loci by SIPHT provides clues into the potential biological function of thousands of previously confirmed and candidate regulatory RNAs and affords new insights into the evolution of bacterial riboregulation.
In bacteria, small non-coding RNAs (sRNAs) have been recognized as important regulators of various cellular processes. Approximately 200 bacterial sRNAs in total have been reported. However, very few sRNAs have been identified from phytopathogenic bacteria.
Xanthomons campestris pathovar campestris (Xcc) is the causal agent of black rot disease of cruciferous crops. In this study, a cDNA library was constructed from the low-molecular weight RNA isolated from the Xcc strain 8004 grown to exponential phase in the minimal medium XVM2. Seven sRNA candidates were obtained by sequencing screen of 2,500 clones from the library and four of them were confirmed to be sRNAs by Northern hybridization, which were named sRNA-Xcc1, sRNA-Xcc2, sRNA-Xcc3, and sRNA-Xcc4. The transcription start and stop sites of these sRNAs were further determined. BLAST analysis revealed that the four sRNAs are novel. Bioinformatics prediction showed that a large number of genes with various known or unknown functions in Xcc 8004 are potential targets of sRNA-Xcc1, sRNA-Xcc3 and sRNA-Xcc4. In contrast, only a few genes were predicted to be potential targets of sRNA-Xcc2.
We have identified four novel sRNAs from Xcc by a large-scale screen. Bioinformatics analysis suggests that they may perform various functions. This work provides the first step toward understanding the role of sRNAs in the molecular mechanisms of Xanthomonas campestris pathogenesis.
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.
Small non-coding bacterial RNAs (sRNAs) play important regulatory roles in a variety of cellular processes. Nearly all known sRNAs have been identified in Escherichia coli and most of these are not conserved in the majority of other bacterial species. Many of the E.coli sRNAs were initially predicted through bioinformatic approaches based on their common features, namely that they are encoded between annotated open reading frames and are flanked by predictable transcription signals. Because promoter consensus sequences are undetermined for most species, the successful use of bioinformatics to identify sRNAs in bacteria other than E.coli has been limited. We have created a program, sRNAPredict, which uses coordinate-based algorithms to integrate the respective positions of individual predictive features of sRNAs and rapidly identify putative intergenic sRNAs. Relying only on sequence conservation and predicted Rho-independent terminators, sRNAPredict was used to search for sRNAs in Vibrio cholerae. This search identified 9 of the 10 known or putative V.cholerae sRNAs and 32 candidates for novel sRNAs. Small transcripts for 6 out of 9 candidate sRNAs were observed by Northern analysis. Our findings suggest that sRNAPredict can be used to efficiently identify novel sRNAs even in bacteria for which promoter consensus sequences are not available.
Bacterial small RNAs (sRNAs) regulate gene expression by base-pairing with downstream target mRNAs to attenuate translation of mRNA into protein at the post-transcriptional level. In response to specific environmental changes, sRNAs can modulate the expression levels of target genes, thus enabling adaptation of cellular physiology.
We profiled sRNA expression in the Gram-negative bacteria Burkholderia thailandensis cultured under 54 distinct growth conditions using a Burkholderia-specific microarray that contains probe sets to all intergenic regions greater than 90 bases. We identified 38 novel sRNAs and performed experimental validation on five sRNAs that play a role in adaptation of Burkholderia to cell stressors. In particular, the trans-encoded BTH_s1 and s39 exhibited differential expression profiles dependent on growth phase and cell stimuli, such as antibiotics and serum. Furthermore, knockdown of the highly-expressed BTH_s39 by antisense transcripts reduced B. thailandensis cell growth and attenuated host immune response upon infection, indicating that BTH_s39 functions in bacterial metabolism and adaptation to the host. In addition, expression of cis-encoded BTH_s13 and s19 found in the 5′ untranslated regions of their cognate genes correlated with tight regulation of gene transcript levels. This sRNA-mediated downregulation of gene expression may be a conserved mechanism of post-transcriptional gene dosage control.
These studies provide a broad analysis of differential Burkholderia sRNA expression profiles and illustrate the complexity of bacterial gene regulation in response to different environmental stress conditions.
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Small RNAs; Burkholderia; Microarray; Stress conditions; Bacterial adaptation; Gene expression
We have performed a computational comparative analysis of six small non-coding RNA (sRNA) families in α-proteobacteria. Members of these families were first identified in the intergenic regions of the nitrogen-fixing endosymbiont S. meliloti by a combined bioinformatics screen followed by experimental verification. Consensus secondary structures inferred from covariance models for each sRNA family evidenced in some cases conserved motifs putatively relevant to the function of trans-encoded base-pairing sRNAs i.e., Hfq-binding signatures and exposed anti Shine-Dalgarno sequences. Two particular family models, namely αr15 and αr35, shared own sub-structural modules with the Rfam model suhB (RF00519) and the uncharacterized sRNA family αr35b, respectively. A third sRNA family, termed αr45, has homology to the cis-acting regulatory element speF (RF00518). However, new experimental data further confirmed that the S. meliloti αr45 representative is an Hfq-binding sRNA processed from or expressed independently of speF, thus refining the Rfam speF model annotation. All the six families have members in phylogenetically related plant-interacting bacteria and animal pathogens of the order of the Rhizobiales, some occurring with high levels of paralogy in individual genomes. In silico and experimental evidences predict differential regulation of paralogous sRNAs in S. meliloti 1021. The distribution patterns of these sRNA families suggest major contributions of vertical inheritance and extensive ancestral duplication events to the evolution of sRNAs in plant-interacting bacteria.
Sinorhizobium meliloti; Brucella; Hfq; rhizobia; riboregulation; RNome; speF; suhB; symbiotic nitrogen fixation
Recent discoveries on bacterial transcriptomes gave evidence that small RNAs (sRNAs) have important regulatory roles in prokaryotic cells. Modern high-throughput sequencing approaches (RNA-Seq) enable the most detailed view on transcriptomes offering an unmatched comprehensiveness and single-base resolution. Whole transcriptome data obtained by RNA-Seq can be used to detect and characterize all transcript species, including small RNAs. Here, we describe an RNA-Seq approach for comprehensive detection and characterization of small RNAs from Corynebacterium glutamicum, an actinobacterium of high industrial relevance and model organism for medically important Corynebacterianeae, such as C. diphtheriae and Mycobacterium tuberculosis.
In our RNA-Seq approach, total RNA from C. glutamicum ATCC 13032 was prepared from cultures grown in minimal medium at exponential growth or challenged by physical (heat shock, cold shock) or by chemical stresses (diamide, H2O2, NaCl) at this time point. Total RNA samples were pooled and sequencing libraries were prepared from the isolated small RNA fraction. High throughput short read sequencing and mapping yielded over 800 sRNA genes. By determining their 5′- and 3′-ends and inspection of their locations, these potential sRNA genes were classified into UTRs of mRNAs (316), cis-antisense sRNAs (543), and trans-encoded sRNAs (262). For 77 of trans-encoded sRNAs significant sequence and secondary structure conservation was found by a computational approach using a whole genome alignment with the closely related species C. efficiens YS-314 and C. diphtheriae NCTC 13129. Three selected trans-encoded sRNAs were characterized by Northern blot analysis and stress-specific transcript patterns were found.
The study showed comparable numbers of sRNAs known from genome-wide surveys in other bacteria. In detail, our results give deep insight into the comprehensive equipment of sRNAs in C. glutamicum and provide a sound basis for further studies concerning the functions of these sRNAs.
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Bacteria; Non-coding RNA; High-throughput sequencing; RNA regulation
Small RNA (sRNA) has been described as a regulator of gene expression. In order to understand the role of maize sRNA (Zea mays – hybrid UENF 506-8) during association with endophytic nitrogen-fixing bacteria, we analyzed the sRNA regulated by its association with two diazotrophic bacteria, Herbaspirillum seropedicae and Azospirillum brasilense.
Deep sequencing analysis was done with RNA extracted from plants inoculated with H. seropedicae, allowing the identification of miRNA and siRNA. A total of 25 conserved miRNA families and 15 novel miRNAs were identified. A dynamic regulation in response to inoculation was also observed. A hypothetical model involving copper-miRNA is proposed, emphasizing the fact that the up-regulation of miR397, miR398, miR408 and miR528, which is followed by inhibition of their targets, can facilitate association with diazotrophic bacteria. Similar expression patterns were observed in samples inoculated with A. brasilense. Moreover, novel miRNA and siRNA were classified in the Transposable Elements (TE) database, and an enrichment of siRNA aligned with TE was observed in the inoculated samples. In addition, an increase in 24-nt siRNA mapping to genes was observed, which was correlated with an increase in methylation of the coding regions and a subsequent reduction in transcription.
Our results show that maize has RNA-based silencing mechanisms that can trigger specific responses when plants interact with beneficial endophytic diazotrophic bacteria. Our findings suggest important roles for sRNA regulation in maize, and probably in other plants, during association with diazotrophic bacteria, emphasizing the up-regulation of Cu-miRNA.
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Herbaspirillum seropedicae; miRNA; siRNA; Azospirillum brasilense; Epigenetics
We developed a pair of databases that support two important tasks: annotation of anonymous RNA transcripts and discovery of novel non-coding RNAs. The database combo is called the Functional RNA Database and consists of two databases: a rewrite of the original version of the Functional RNA Database (fRNAdb) and the latest version of the UCSC GenomeBrowser for Functional RNA. The former is a sequence database equipped with a powerful search function and hosts a large collection of known/predicted non-coding RNA sequences acquired from existing databases as well as novel/predicted sequences reported by researchers of the Functional RNA Project. The latter is a UCSC Genome Browser mirror with large additional custom tracks specifically associated with non-coding elements. It also includes several functional enhancements such as a presentation of a common secondary structure prediction at any given genomic window ⩽500 bp. Our GenomeBrowser supports user authentication and user-specific tracks. The current version of the fRNAdb is a complete rewrite of the former version, hosting a larger number of sequences and with a much friendlier interface. The current version of UCSC GenomeBrowser for Functional RNA features a larger number of tracks and richer features than the former version. The databases are available at http://www.ncrna.org/.
Small RNAs (sRNAs) are widespread among bacteria and have diverse regulatory roles. Most of these sRNAs have been discovered by a combination of computational and experimental methods. In Pseudomonas aeruginosa, a ubiquitous Gram-negative bacterium and opportunistic human pathogen, the GacS/GacA two-component system positively controls the transcription of two sRNAs (RsmY, RsmZ), which are crucial for the expression of genes involved in virulence. In the biocontrol bacterium Pseudomonas fluorescens CHA0, three GacA-controlled sRNAs (RsmX, RsmY, RsmZ) regulate the response to oxidative stress and the expression of extracellular products including biocontrol factors. RsmX, RsmY and RsmZ contain multiple unpaired GGA motifs and control the expression of target mRNAs at the translational level, by sequestration of translational repressor proteins of the RsmA family.
A combined computational and experimental approach enabled us to identify 14 intergenic regions encoding sRNAs in P. aeruginosa. Eight of these regions encode newly identified sRNAs. The intergenic region 1698 was found to specify a novel GacA-controlled sRNA termed RgsA. GacA regulation appeared to be indirect. In P. fluorescens CHA0, an RgsA homolog was also expressed under positive GacA control. This 120-nt sRNA contained a single GGA motif and, unlike RsmX, RsmY and RsmZ, was unable to derepress translation of the hcnA gene (involved in the biosynthesis of the biocontrol factor hydrogen cyanide), but contributed to the bacterium's resistance to hydrogen peroxide. In both P. aeruginosa and P. fluorescens the stress sigma factor RpoS was essential for RgsA expression.
The discovery of an additional sRNA expressed under GacA control in two Pseudomonas species highlights the complexity of this global regulatory system and suggests that the mode of action of GacA control may be more elaborate than previously suspected. Our results also confirm that several GGA motifs are required in an sRNA for sequestration of the RsmA protein.
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.
Small non-coding RNAs (sRNAs) are regarded as important regulators in prokaryotes and play essential roles in diverse cellular processes. Xanthomonas oryzae pathovar oryzae (Xoo) is an important plant pathogenic bacterium which causes serious bacterial blight of rice. However, little is known about the number, genomic distribution and biological functions of sRNAs in Xoo.
Here, we performed a systematic screen to identify sRNAs in the Xoo strain PXO99. A total of 850 putative non-coding RNA sequences originated from intergenic and gene antisense regions were identified by cloning, of which 63 were also identified as sRNA candidates by computational prediction, thus were considered as Xoo sRNA candidates. Northern blot hybridization confirmed the size and expression of 6 sRNA candidates and other 2 cloned small RNA sequences, which were then added to the sRNA candidate list. We further examined the expression profiles of the eight sRNAs in an hfq deletion mutant and found that two of them showed drastically decreased expression levels, and another exhibited an Hfq-dependent transcript processing pattern. Deletion mutants were obtained for seven of the Northern confirmed sRNAs, but none of them exhibited obvious phenotypes. Comparison of the proteomic differences between three of the ΔsRNA mutants and the wild-type strain by two-dimensional gel electrophoresis (2-DE) analysis showed that these sRNAs are involved in multiple physiological and biochemical processes.
We experimentally verified eight sRNAs in a genome-wide screen and uncovered three Hfq-dependent sRNAs in Xoo. Proteomics analysis revealed Xoo sRNAs may take part in various metabolic processes. Taken together, this work represents the first comprehensive screen and functional analysis of sRNAs in rice pathogenic bacteria and facilitates future studies on sRNA-mediated regulatory networks in this important phytopathogen.
The importance of small RNA (sRNA) regulators has been recognized across all domains of life. In bacteria, sRNAs typically control the expression of virulence and stress response genes via antisense base pairing with mRNA targets. Originally dubbed “non-coding RNAs,” a number of bacterial antisense sRNAs have been found to encode functional proteins. Although very few of these dual-function sRNAs have been characterized, they have been found in both gram-negative and gram-positive organisms. Among the few known examples, the functions and mechanisms of regulation by dual-function sRNAs are variable. Some dual-function sRNAs depend on the RNA chaperone Hfq for base pairing-dependent regulation (riboregulation); this feature appears so far exclusive to gram-negative bacterial sRNAs. Other variations can be found in the spatial organization of the coding region with respect to the riboregulation determinants. How the functions of encoded proteins relate to riboregulation is for the most part not understood. However, in one case it appears that there is physiological redundancy between protein and riboregulation functions. This mini-review focuses on the two best-studied bacterial dual-function sRNAs: RNAIII from Staphylococcus aureus and SgrS from Escherichia coli and includes a discussion of what is known about the structure, function and physiological roles of these sRNAs as well as what questions remain outstanding.
Hfq; RNAIII; glucose phosphate stress; SgrS; riboregulation
In the last several years, bacterial gene regulation via small RNAs (sRNAs) has been recognized as an important mechanism controlling expression of essential proteins that are critical to bacterial growth and metabolism. Technologies such as RNA-seq are rapidly expanding the field of sRNAs and are enabling a global view of the “sRNAome” of several bacterial species. While numerous sRNAs have been identified in a variety of both Gram-negative and Gram-positive bacteria, only a very small number have been fully characterized in the human pathogen Neisseria gonorrhoeae, the etiological agent of the STD gonorrhea. Here we present the first analysis of N. gonorrhoeae specifically focused on the identification of sRNAs through RNA-seq analysis of the organism cultured under different in vitro growth conditions. Using a new computational program, Rockhopper, to analyze prokaryotic RNA-seq data obtained from N. gonorrhoeae we identified several putative sRNAs and confirmed their expression and size through Northern blot analysis. In addition, RNA was collected from four different growth conditions (iron replete and deplete, as well as with and without co-culture with human endocervical cells). Many of the putative sRNAs identified shoed varying expression levels relative to the different growth conditions examine or were detected only under certain conditions but not others. Comparisons of identified sRNAs with the regulatory pattern of putative mRNA targets revealed possible functional roles for these sRNAs. These studies are the first to carry out a global analysis of N. gonorrhoeae specifically focused on sRNAs and show that RNA-mediated regulation may be an important mechanism of gene control in this human pathogen.
small RNA; Neisseria gonorrhoeae; RNA-seq; host cell; iron