sRNA Genes Research Articles

Novel small RNAs from Mycobacterium avium

Posttranscriptional regulation of gene expression by small RNAs was shown for multiple pathogenic microorganisms and plays an important role in virulence. 4 putative sRNA genes located in intergenic loci were identified: MAV_0380-0381 (4.5S RNA), MAV_1034-1035 (trans-encoded sRNA), MAV_1415-1416 (antisense or trans-encoded sRNA) and MAV_1531-1532 (processed 5' UTR of 16S rRNA gene). The revealed sRNAs represent the first small noncoding RNAs identified in M. avium.

FiRePat—Finding Regulatory Patterns between sRNAs and Genes

AbstractSmall RNAs are regulatory RNA fragments which, through RNA silencing, can regulate the expression of genes. Because sRNAs are negative regulators it is generally assumed that expression profiles of sRNAs and their targets are negatively correlated. Recently, examples of positive correlation between the expression of sRNAs and their targets have been discovered. At the moment, it is not known how many sRNA‐target pairs are positively and negatively correlated, and it is also not clear in what situations (e.g., under which treatments) any of these correlations can be observed. To determine this, one of the first steps is to develop tools to carry out a genome wide characterization of covariation of expression levels of sRNAs and genes. We present FiRePat—Finding Regulatory Patterns—an unsupervised data mining tool applicable to large datasets, typically produced by high throughput sequencing of sRNAs and mRNAs or microarray experiments, that detects sRNA‐gene pairs with correlated expression levels. The method consists of three steps: first, we select differentially expressed sRNAs and genes; second, we compute the correlation between sRNA and gene series for all possible sRNA–gene pairs; and third, we cluster the sRNA or gene expression series, simultaneously inducing clusters in the other series. Potential uses of FiRePat are presented using publicly available sRNA and mRNA datasets for both plants and animals. The standard output of FiRePat, a list of correlated pairs formed with sRNAs and mRNAs, can be used to investigate the cause and consequences of the respective expression patterns. © 2012 Wiley Periodicals, Inc.This article is categorized under: Algorithmic Development > Biological Data Mining

Construction and characterization of a null allele of the hfq gene of the dissimilatory metal reducing bacterium Shewanella oneidensis

Shewanella oneidensis is a dissimilatory metal reducing bacterium that can utilize a range of terminal electron acceptors when grown under anaerobic conditions. Our primary goal is to identify and characterize genes that encode small regulatory non‐coding RNAs (sRNAs) in S. oneidensis. sRNA genes are regulated by changes in environmental conditions and can themselves regulate other genes by inexact base pairing to their mRNA targets. We are particularly interested in sRNAs that may regulate the process of metal reduction.The highly conserved RNA chaperone protein Hfq has been widely implicated in bacterial sRNA function. We have constructed a null allele of the S. oneidensis hfq gene. The S. oneidensis hfq mutant has a modest delayed growth phenotype. In addition, our mutant displays reduced survival in stationary phase. Our hfq mutant also exhibits a decreased rate of Cr(VI) reduction when grown under anaerobic conditions compared to the rate of Cr(VI) reduction by wild type S. oneidensis. We are currently evaluating the growth and survival of the Shewanella hfq null mutant. We are also investigating the mutant's sensitivity to a variety of physical stresses, evaluating the capacity of the hfq mutant to reduce metals other than Cr(VI), and determining the role of Shewanella hfq in sRNA function.This research is funded by a grant from the RI‐INBRE program, award # P20RR016457‐10.

Esre: A novel essential non-coding RNA in Escherichia coli

YigP gene (GeneID: 948915) locates between ubiquinone biosynthetic genes ubiE and ubiB in Escherichia coli. GeneBank annotates yigP as a putative protein-coding gene. In this study, we found a new essential sRNA gene, esre, locates within the region of yigP. The E. coli strain with inactive esre must rely on a complementary plasmid to survive. Moreover, RACE experiments showed esre encodes an RNA molecule of 252 nt. Further experiments revealed esre gene is immune to frame shift mutations and the function of esre depends mostly on the RNA secondary structure, which are typical traits of sRNA. Since it is difficult to predict the target of an essential sRNA, more research is needed to reveal the function and mechanism of esre.

Small RNAs of the Bradyrhizobium/Rhodopseudomonas lineage and their analysis

Small RNAs (sRNAs) play a pivotal role in bacterial gene regulation. However, the sRNAs of the vast majority of bacteria with sequenced genomes still remain unknown since sRNA genes are usually difficult to recognize and thus not annotated. Here, expression of seven sRNAs (BjrC2a, BjrC2b, BjrC2c, BjrC68, BjrC80, BjrC174 and BjrC1505) predicted by genome comparison of Bradyrhizobium and Rhodopseudomonas members, was verified by RNA gel blot hybridization, microarray and deep sequencing analyses of RNA from the soybean symbiont Bradyrhizobium japonicum USDA 110. BjrC2a, BjrC2b and BjrC2c belong to the RNA family RF00519, while the other sRNAs are novel. For some of the sRNAs we observed expression differences between free-living bacteria and bacteroids in root nodules. The amount of BjrC1505 was decreased in nodules. By contrast, the amount of BjrC2a, BjrC68, BjrC80, BjrC174 and the previously described 6S RNA was increased in nodules, and accumulation of truncated forms of these sRNAs was observed. Comparative genomics and deep sequencing suggest that BjrC2a is an antisense RNA regulating the expression of inositol-monophosphatase. The analyzed sRNAs show a different degree of conservation in Rhizobiales, and expression of homologs of BjrC2, BjrC68, BjrC1505, and 6S RNA was confirmed in the free-living purple bacterium Rhodopseudomonas palustris 5D.

Diversity of Antisense and Other Non-Coding RNAs in Archaea Revealed by Comparative Small RNA Sequencing in Four Pyrobaculum Species

A great diversity of small, non-coding RNA (ncRNA) molecules with roles in gene regulation and RNA processing have been intensely studied in eukaryotic and bacterial model organisms, yet our knowledge of possible parallel roles for small RNAs (sRNA) in archaea is limited. We employed RNA-seq to identify novel sRNA across multiple species of the hyperthermophilic genus Pyrobaculum, known for unusual RNA gene characteristics. By comparing transcriptional data collected in parallel among four species, we were able to identify conserved RNA genes fitting into known and novel families. Among our findings, we highlight three novel cis-antisense sRNAs encoded opposite to key regulatory (ferric uptake regulator), metabolic (triose-phosphate isomerase), and core transcriptional apparatus genes (transcription factor B). We also found a large increase in the number of conserved C/D box sRNA genes over what had been previously recognized; many of these genes are encoded antisense to protein coding genes. The conserved opposition to orthologous genes across the Pyrobaculum genus suggests similarities to other cis-antisense regulatory systems. Furthermore, the genus-specific nature of these sRNAs indicates they are relatively recent, stable adaptations.

Evolutionary Dynamics of Small RNAs in 27 Escherichia coli and Shigella Genomes

Small RNAs (sRNAs) are widespread in bacteria and play critical roles in regulating physiological processes. They are best characterized in Escherichia coli K-12 MG1655, where 83 sRNAs constitute nearly 2% of the gene complement. Most sRNAs act by base pairing with a target mRNA, modulating its translation and/or stability; many of these RNAs share only limited complementarity to their mRNA target, and require the chaperone Hfq to facilitate base pairing. Little is known about the evolutionary dynamics of bacterial sRNAs. Here, we apply phylogenetic and network analyses to investigate the evolutionary processes and principles that govern sRNA gene distribution in 27 E. coli and Shigella genomes. We identify core (encoded in all 27 genomes) and variable sRNAs; more than two-thirds of the E. coli K-12 MG1655 sRNAs are core, whereas the others show patterns of presence and absence that are principally due to genetic loss, not duplication or lateral genetic transfer. We present evidence that variable sRNAs are less tightly integrated into cellular genetic regulatory networks than are the core sRNAs, and that Hfq facilitates posttranscriptional cross talk between the E. coli–Shigella core and variable genomes. Finally, we present evidence that more than 80% of genes targeted by Hfq-associated core sRNAs have been transferred within the E. coli–Shigella clade, and that most of these genes have been transferred intact. These results suggest that Hfq and sRNAs help integrate laterally acquired genes into established regulatory networks.

A conserved RpoS-dependent small RNA controls the synthesis of major porin OmpD

A remarkable feature of many small non-coding RNAs (sRNAs) of Escherichia coli and Salmonella is their accumulation in the stationary phase of bacterial growth. Several stress response regulators and sigma factors have been reported to direct the transcription of stationary phase-specific sRNAs, but a widely conserved sRNA gene that is controlled by the major stationary phase and stress sigma factor, σS (RpoS), has remained elusive. We have studied in Salmonella the conserved SdsR sRNA, previously known as RyeB, one of the most abundant stationary phase-specific sRNAs in E. coli. Alignments of the sdsR promoter region and genetic analysis strongly suggest that this sRNA gene is selectively transcribed by σS. We show that SdsR down-regulates the synthesis of the major Salmonella porin OmpD by Hfq-dependent base pairing; SdsR thus represents the fourth sRNA to regulate this major outer membrane porin. Similar to the InvR, MicC and RybB sRNAs, SdsR recognizes the ompD mRNA in the coding sequence, suggesting that this mRNA may be primarily targeted downstream of the start codon. The SdsR-binding site in ompD was localized by 3′-RACE, an experimental approach that promises to be of use in predicting other sRNA–target interactions in bacteria.

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.

Bioinformatic prediction and experimental verification of sRNAs in the haloarchaeon Haloferax volcanii

Recently a small-scale RNomics study led to the experimental identification of 21 intergenic and 18 antisense sRNA genes in the haloarchaeon Haloferax volcanii. To broaden the knowledge about sRNAs in haloarchaea, two bioinformatic approaches were used to predict sRNA genes in the genome of H. volcanii. More than 120 putative intergenic sRNA genes were identified by these comparative genomic approaches. The expression of 61 of the predicted genes was analyzed using DNA microarrays, and 37 were found to be expressed under at least one of three conditions tested. Using the results of Northern blot analyses and of a high throughput sequencing study the number of expressed genes was raised to 54 and the small size was verified for 26 predicted sRNAs. An analysis of the coding capacity revealed that the set of predicted sRNAs most likely does not encode proteins or peptides. In two cases it turned out that the predictions had not identified bona fide sRNAs but conserved regions in UTRs of large protein-encoding transcripts. Taken together, the combination of bioinformatic prediction and experimental verification has more than tripled the number of known haloarchaeal sRNAs, underscoring the importance of regulatory RNAs in the third domain of life, the archaea. Further analyses of the biological functions of selected sRNAs, including the construction of deletion mutants, are currently under way.

Phenotyping in the archaea: optimization of growth parameters and analysis of mutants of Haloferax volcanii

A method to grow the halophilic archaeon Haloferax volcanii in microtiter plates has been optimized and now allows the parallel generation of very reproducible growth curves. The doubling time in a synthetic medium with glucose is around 6 h. The method was used to optimize glucose and casamino acid concentrations, to clarify carbon source usage and to analyze vitamin dependence. The characterization of osmotolerance revealed that after a lag phase of 24 h, H. volcanii is able to grow at salt concentrations as low as 0.7 M NaCl, much lower than the 1.4 M NaCl described as the lowest concentration until now. The application of oxidative stresses showed that H. volcanii exhibits a reaction to paraquat that is delayed by about 10 h. Surprisingly, only one of two amino acid auxotrophic mutants could be fully supplemented by the addition of the respective amino acid. Analysis of eight sRNA gene deletion mutants exemplified that the method can be applied for bona fide phenotyping of mutant collections. This method for the parallel analysis of many cultures contributes towards making H. volcanii an archaeal model species for functional genomic approaches.

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

Over the past decade, a number of biocomputational tools have been developed to predict small RNA (sRNA) genes in bacterial genomes. In this study, several of the leading biocomputational tools, which use different methodologies, were investigated. The performance of the tools, both individually and in combination, was evaluated on ten sets of benchmark data, including data from a novel RNA-seq experiment conducted in this study. The results of this study offer insight into the utility as well as the limitations of the leading biocomputational tools for sRNA identification and provide practical guidance for users of the tools.

An Integrated Approach for Finding Overlooked Genes in Shigella

BackgroundThe completion of numerous genome sequences introduced an era of whole-genome study. However, many genes are missed during genome annotation, including small RNAs (sRNAs) and small open reading frames (sORFs). In order to improve genome annotation, we aimed to identify novel sRNAs and sORFs in Shigella, the principal etiologic agents of bacillary dysentery.Methodology/Principal FindingsWe identified 64 sRNAs in Shigella, which were experimentally validated in other bacteria based on sequence conservation. We employed computer-based and tiling array-based methods to search for sRNAs, followed by RT-PCR and northern blots, to identify nine sRNAs in Shigella flexneri strain 301 (Sf301) and 256 regions containing possible sRNA genes. We found 29 candidate sORFs using bioinformatic prediction, array hybridization and RT-PCR verification. We experimentally validated 557 (57.9%) DOOR operon predictions in the chromosomes of Sf301 and 46 (76.7%) in virulence plasmid.We found 40 additional co-expressed gene pairs that were not predicted by DOOR.Conclusions/SignificanceWe provide an updated and comprehensive annotation of the Shigella genome. Our study increased the expected numbers of sORFs and sRNAs, which will impact on future functional genomics and proteomics studies. Our method can be used for large scale reannotation of sRNAs and sORFs in any microbe with a known genome sequence.

Regulatory RNAs in Haloferax volcanii

In organisms of all three domains of life, a plethora of sRNAs (small regulatory RNAs) exists in addition to the well-known RNAs such as rRNAs, tRNAs and mRNAs. Although sRNAs have been well studied in eukaryotes and in bacteria, the sRNA population in archaea has just recently been identified and only in a few archaeal species. In the present paper, we summarize our current knowledge about sRNAs and their function in the halophilic archaeon Haloferax volcanii. Using two different experimental approaches, 111 intergenic and 38 antisense sRNAs were identified, as well as 42 tRFs (tRNA-derived fragments). Observation of differential expression under various conditions suggests that these sRNAs might be active as regulators in gene expression like their bacterial and eukaryotic counterparts. The severe phenotypes observed upon deletion and overexpression of sRNA genes revealed that sRNAs are involved in, and important for, a variety of biological functions in H. volcanii and possibly other archaea. Investigation of the Haloferax Lsm protein suggests that this protein is involved in the archaeal sRNA pathway.

Bacterial Small RNA Regulators: Versatile Roles and Rapidly Evolving Variations

Small RNA regulators (sRNAs) have been identified in a wide range of bacteria and found to play critical regulatory roles in many processes. The major families of sRNAs include true antisense RNAs, synthesized from the strand complementary to the mRNA they regulate, sRNAs that also act by pairing but have limited complementarity with their targets, and sRNAs that regulate proteins by binding to and affecting protein activity. The sRNAs with limited complementarity are akin to eukaryotic microRNAs in their ability to modulate the activity and stability of multiple mRNAs. In many bacterial species, the RNA chaperone Hfq is required to promote pairing between these sRNAs and their target mRNAs. Understanding the evolution of regulatory sRNAs remains a challenge; sRNA genes show evidence of duplication and horizontal transfer but also could be evolved from tRNAs, mRNAs or random transcription.

SRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

BackgroundBacterial 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.Methodology/Principal FindingsHere 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.Conclusions/SignificanceWe 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/.

Abundance of type I toxin–antitoxin systems in bacteria: searches for new candidates and discovery of novel families

Small, hydrophobic proteins whose synthesis is repressed by small RNAs (sRNAs), denoted type I toxin–antitoxin modules, were first discovered on plasmids where they regulate plasmid stability, but were subsequently found on a few bacterial chromosomes. We used exhaustive PSI-BLAST and TBLASTN searches across 774 bacterial genomes to identify homologs of known type I toxins. These searches substantially expanded the collection of predicted type I toxins, revealed homology of the Ldr and Fst toxins, and suggested that type I toxin–antitoxin loci are not spread by horizontal gene transfer. To discover novel type I toxin–antitoxin systems, we developed a set of search parameters based on characteristics of known loci including the presence of tandem repeats and clusters of charged and bulky amino acids at the C-termini of short proteins containing predicted transmembrane regions. We detected sRNAs for three predicted toxins from enterohemorrhagic Escherichia coli and Bacillus subtilis, and showed that two of the respective proteins indeed are toxic when overexpressed. We also demonstrated that the local free-energy minima of RNA folding can be used to detect the positions of the sRNA genes. Our results suggest that type I toxin–antitoxin modules are much more widely distributed among bacteria than previously appreciated.

Prediction of small non-coding RNA in bacterial genomes using support vector machines

Small non-coding RNA genes have been shown to play important regulatory roles in a variety of cellular processes, but prediction of non-coding RNA genes is a great challenge, using either an experimental or a computational approach, due to the characteristics of sRNAs, which are that sRNAs are small in size, are not translated into proteins and show variable stability. Most known sRNAs have been identified in Escherichia coli and have been shown to be conserved in closely related organisms. We have developed an integrative approach that searches highly conserved intergenic regions among related bacterial genomes for combinations of characteristics that have been extracted from known E. coli sRNA genes. Support vector machines (SVM) were then used with these characteristics to predict novel sRNA genes.

Deep sequencing analysis of the Methanosarcina mazei Gö1 transcriptome in response to nitrogen availability

Methanosarcina mazei and related mesophilic archaea are the only organisms fermenting acetate, methylamines, and methanol to methane and carbon dioxide, contributing significantly to greenhouse gas production. The biochemistry of these metabolic processes is well studied, and genome sequences are available, yet little is known about the overall transcriptional organization and the noncoding regions representing 25% of the 4.01-Mb genome of M. mazei. We present a genome-wide analysis of transcription start sites (TSS) in M. mazei grown under different nitrogen availabilities. Pyrosequencing-based differential analysis of primary vs. processed 5' ends of transcripts discovered 876 TSS across the M. mazei genome. Unlike in other archaea, in which leaderless mRNAs are prevalent, the majority of the detected mRNAs in M. mazei carry long untranslated 5' regions. Our experimental data predict a total of 208 small RNA (sRNA) candidates, mostly from intergenic regions but also antisense to 5' and 3' regions of mRNAs. In addition, 40 new small mRNAs with ORFs of < or = 30 aa were identified, some of which might have dual functions as mRNA and regulatory sRNA. We confirmed differential expression of several sRNA genes in response to nitrogen availability. Inspection of their promoter regions revealed a unique conserved sequence motif associated with nitrogen-responsive regulation, which might serve as a regulator binding site upstream of the common IIB recognition element. Strikingly, several sRNAs antisense to mRNAs encoding transposases indicate nitrogen-dependent transposition events. This global TSS map in archaea will facilitate a better understanding of transcriptional and posttranscriptional control in the third domain of life.

Photooxidative stress‐induced and abundant small RNAs in Rhodobacter sphaeroides

Exposure to oxygen and light generates photooxidative stress by the bacteriochlorophyll a mediated formation of singlet oxygen ((1)O(2)) in Rhodobacter sphaeroides. Our study reports the genome-wide search for small RNAs (sRNAs) involved in the regulatory response to (1)O(2). By using 454 pyrosequencing and Northern blot analysis, we identified 20 sRNAs from R. sphaeroides aerobic cultures or following treatment with (1)O(2) or superoxide (O(-)(2)). One sRNA was specifically induced by (1)O(2) and its expression depends on the extracytoplasmic function sigma factor RpoE. Two sRNAs induced by (1)O(2) and O(-)(2) were cotranscribed with upstream genes preceded by promoters with target sequences for the alternative sigma factors RpoH(I) and RpoH(II). The most abundant sRNA was processed in the presence of (1)O(2) but not by O(-)(2). From this and a second sRNA a conserved 3'-segment accumulated from a larger precursor. Absence of the RNA chaperone Hfq changed the half-lives, abundance and processing of (1)O(2)-affected sRNAs. Orthologues of three sRNA genes are present in different alpha-proteobacteria, but the majority was unique to R. sphaeroides or Rhodobacterales species. Our discovery that abundant sRNAs are affected by (1)O(2) exposure extends the knowledge on the role of sRNAs and Hfq in the regulatory response to oxidative stress.