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) 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
More than 80% of the wheat genome is composed of transposable elements (TEs). Since active TEs can move to different locations and potentially impose a significant mutational load, their expression is suppressed in the genome via small non-coding RNAs (sRNAs). sRNAs guide silencing of TEs at the transcriptional (mainly 24-nt sRNAs) and post-transcriptional (mainly 21-nt sRNAs) levels. In this study, we report the distribution of these two types of sRNAs among the different classes of wheat TEs, the regions targeted within the TEs, and their impact on the methylation patterns of the targeted regions.
We constructed an sRNA library from hexaploid wheat and developed a database that included our library and three other publicly available sRNA libraries from wheat. For five completely-sequenced wheat BAC contigs, most perfectly matching sRNAs represented TE sequences, suggesting that a large fraction of the wheat sRNAs originated from TEs. An analysis of all wheat TEs present in the Triticeae Repeat Sequence database showed that sRNA abundance was correlated with the estimated number of TEs within each class. Most of the sRNAs perfectly matching miniature inverted repeat transposable elements (MITEs) belonged to the 21-nt class and were mainly targeted to the terminal inverted repeats (TIRs). In contrast, most of the sRNAs matching class I and class II TEs belonged to the 24-nt class and were mainly targeted to the long terminal repeats (LTRs) in the class I TEs and to the terminal repeats in CACTA transposons. An analysis of the mutation frequency in potentially methylated sites revealed a three-fold increase in TE mutation frequency relative to intron and untranslated genic regions. This increase is consistent with wheat TEs being preferentially methylated, likely by sRNA targeting.
Our study examines the wheat epigenome in relation to known TEs. sRNA-directed transcriptional and post-transcriptional silencing plays important roles in the short-term suppression of TEs in the wheat genome, whereas DNA methylation and increased mutation rates may provide a long-term mechanism to inactivate TEs.
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
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 trans-encoded RNAs (sRNAs) modulate the translation and decay of mRNAs in bacteria. In Gram-negative species, antisense regulation by trans-encoded sRNAs relies on the Sm-like protein Hfq. In contrast to this, Hfq is dispensable for sRNA-mediated riboregulation in the Gram-positive species studied thus far. Here, we provide evidence for Hfq-dependent translational repression in the Gram-positive human pathogen Listeria monocytogenes, which is known to encode at least 50 sRNAs. We show that the Hfq-binding sRNA LhrA controls the translation and degradation of its target mRNA by an antisense mechanism, and that Hfq facilitates the binding of LhrA to its target. The work presented here provides the first experimental evidence for Hfq-dependent riboregulation in a Gram-positive bacterium. Our findings indicate that modulation of translation by trans-encoded sRNAs may occur by both Hfq-dependent and -independent mechanisms, thus adding another layer of complexity to sRNA-mediated riboregulation in Gram-positive species.
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.
Recently, many small non-coding RNAs (sRNAs) with important regulatory roles have been identified in bacteria. As their eukaryotic counterparts, a major class of bacterial trans-encoded sRNAs acts by basepairing with target mRNAs, resulting in changes in translation and stability of the mRNA. RNA interference (RNAi) has become a powerful gene silencing tool in eukaryotes. However, such an effective RNA silencing tool remains to be developed for prokaryotes. In this study, we described first the use of artificial trans-encoded sRNAs (atsRNAs) for specific gene silencing in bacteria. Based on the common structural characteristics of natural sRNAs in Gram-negative bacteria, we developed the designing principle of atsRNA. Most of the atsRNAs effectively suppressed the expression of exogenous EGFP gene and endogenous uidA gene in Escherichia coli. Further studies demonstrated that the mRNA base pairing region and AU rich Hfq binding site were crucial for the activity of atsRNA. The atsRNA-mediated gene silencing was Hfq dependent. The atsRNAs led to gene silencing and RNase E dependent degradation of target mRNA. We also designed a series of atsRNAs which targeted the toxic genes in Staphyloccocus aureus, but found no significant interfering effect. We established an effective method for specific gene silencing in Gram-negative bacteria.
Recent advances in high-throughput sequencing have facilitated the genome-wide studies of small non-coding RNAs (sRNAs). Numerous studies have highlighted the role of various classes of sRNAs at different levels of gene regulation and disease. The fast growth of sequence data and the diversity of sRNA species have prompted the need to organise them in annotation databases. There are currently several databases that collect sRNA data. Various tools are provided for access, with special emphasis on the well-characterised family of micro-RNAs. The striking heterogeneity of the new classes of sRNAs and the lack of sufficient functional annotation, however, make integration of these datasets a difficult task. This review describes the currently available databases for human sRNAs that are accessible via the internet, and some of the large datasets for human sRNAs from high-throughput sequencing experiments that are so far only available as supplementary data in publications. Some of the main issues related to the integration and annotation of sRNA datasets are also discussed.
miRNAs; small RNAs; non-coding RNAs; high-throughput sequencing; databases
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
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.
The prevalence and characteristics of small regulatory RNAs (sRNAs) have not been well characterized for Bacillus subtilis, an important model system for Gram-positive bacteria. However, B. subtilis was recently found to synthesize many candidate sRNAs during stationary phase. In the current study, we performed deep sequencing on Hfq-associated RNAs and found that a small subset of sRNAs associates with Hfq, an enigmatic RNA-binding protein that stabilizes sRNAs in Gram-negatives, but whose role is largely unknown in Gram-positive bacteria. We also found that Hfq associated with antisense RNAs, antitoxin transcripts, and many mRNA leaders. Several new candidate sRNAs and mRNA leader regions were also discovered by this analysis. Additionally, mRNA fragments overlapping with start or stop codons associated with Hfq, while, in contrast, relatively few full-length mRNAs were recovered. Deletion of hfq reduced the intracellular abundance of several representative sRNAs, suggesting that B. subtilis Hfq-sRNA interactions may be functionally significant in vivo. In general, we anticipate this catalog of Hfq-associated RNAs to serve as a resource in the functional characterization of Hfq in B. subtilis.
A key to the success of Mycobacterium tuberculosis (Mtb) is the bacteria’s ability to survive and thrive in the presence of numerous stresses mounted by the host. Small, non-coding RNAs (sRNAs) have been shown to modulate numerous stress responses in bacteria, yet to date only two studies have screened the Mtb transcriptome to identify sRNA. Their association with oxidative and acid stress has been demonstrated but the cellular function and role of these sRNAs in the pathogenesis of tuberculosis (TB) remains unknown. Here, we have identified an sRNA, ncrMT1302, in a locus involved in cAMP metabolism and demonstrate that expression of ncrMT1302 responds to changes in pH and cAMP concentration. The differential expression of ncrMT1302 observed in wild-type Mtb during growth is abolished in a strain lacking MT1302, an adenylyl cyclase encoding gene. We report that ncrMT1302 is expressed in Mtb residing in the lungs of mice during an active infection.
Small regulatory RNA; acid stress
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
In bacteria, there exist some small non-coding RNAs (sRNAs) with 40–500 nucleotides in length. Most of them function as posttranscriptional regulation of gene expression through
binding to their target mRNAs, in which Hfq protein acts as RNA chaperone. With the increase of identified sRNA genes in the bacterium, prediction of sRNA targets plays a more
important role in determining sRNA functions. However, there are few available computational tools for predicting sRNA targets at present. Here we introduced a web server, sRNATarget,
for genome-scale prediction of bacterial sRNA targets. The server is based on a recently published model which uses Naive Bayes method as the supervised method and take RNA secondary
structure profile as the feature. The prediction results will be returned to the users through E-mail.
sRNATarget web server is freely available athttp://ccb.bmi.ac.cn/srnatarget/
prediction of sRNA target; model; Naive Bayes method
Bacterial small non-coding RNA (sRNA) plays an important role in post-transcriptional gene regulation. Although the number of annotated sRNA is steadily increasing, their functional characterization is still lagging behind. Various computational strategies for finding sRNA–mRNA interactions, and thus putative sRNA targets, were developed. Most of them suffer from a high false positive rate. Here, we present a qualitative model to simulate the effect of an sRNA on the translation initiation of a potential target. Information about the ribosome–mRNA interaction, sRNA–mRNA interaction and expression information from deep sequencing experiments is integrated to calculate the change in translation initiation complex formation, as a proxy for translational activity. This model can be used to post-evaluate predicted targets, hence condensing the list of potential targets. We show that our translation initiation model, under the influence of an sRNA, can successfully simulate thirteen out of fifteen tested sRNA–mRNA interactions in a qualitative manner. To show the gain in specificity, we applied our method to a target search for the Escherichia coli sRNA RyhB. Compared with simple target prediction without post-evaluation, we reduce the number of targets to less than one fourth potential targets, considerably reducing the burden of experimental validation.
sRNA; sRNA target prediction; translation initiation
Small non-coding RNAs (sRNAs) are an emerging class of post-transcriptional regulators of bacterial gene expression. To study sRNAs and their potential protein interaction partners, it is desirable to purify sRNAs from cells in their native form. Here, we used RNA-based affinity chromatography to purify sRNAs following their expression as aptamer-tagged variants in vivo. To this end, we developed a family of plasmids to express sRNAs with any of three widely used aptamer sequences (MS2, boxB, eIF4A), and systematically tested how the aptamer tagging impacted on intracellular accumulation and target regulation of the Salmonella GcvB, InvR or RybB sRNAs. In addition, we successfully tagged the chromosomal rybB gene with MS2 to observe that RybB-MS2 is fully functional as an envelope stress-induced repressor of ompN mRNA following induction of sigmaE. We further demonstrate that the common sRNA-binding protein, Hfq, co-purifies with MS2-tagged sRNAs of Salmonella. The presented affinity purification strategy may facilitate the isolation of in vivo assembled sRNA–protein complexes in a wide range of bacteria.
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.
Regulation of bacterial gene expression by small RNAs (sRNAs) have proved to be important for many biological processes. Francisella tularensis is a highly pathogenic Gram-negative bacterium that causes the disease tularaemia in humans and animals. Relatively little is known about the regulatory networks existing in this organism that allows it to survive in a wide array of environments and no sRNA regulators have been identified so far.
We have used a combination of experimental assays and in silico prediction to identify sRNAs in F. tularensis strain LVS. Using a cDNA cloning and sequencing approach we have shown that F. tularensis expresses homologues of several sRNAs that are well-conserved among diverse bacteria. We have also discovered two abundant putative sRNAs that share no sequence similarity or conserved genomic context with any previously annotated regulatory transcripts. Deletion of either of these two loci led to significant changes in the expression of several mRNAs that likely include the cognate target(s) of these sRNAs. Deletion of these sRNAs did not, however, significantly alter F. tularensis growth under various stress conditions in vitro, its replication in murine cells, or its ability to induce disease in a mouse model of F. tularensis infection. We also conducted a genome-wide in silico search for intergenic loci that suggests F. tularensis encodes several other sRNAs in addition to the sRNAs found in our experimental screen.
Our findings suggest that F. tularensis encodes a significant number of non-coding regulatory RNAs, including members of well conserved families of structural and housekeeping RNAs and other poorly conserved transcripts that may have evolved more recently to help F. tularensis deal with the unique and diverse set of environments with which it must contend.
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.
Small non-coding RNAs (sRNAs) play key roles in plant development, growth and responses to biotic and abiotic stresses. At least four classes of sRNAs have been well characterized in plants, including repeat-associated siRNAs (rasiRNAs), microRNAs (miRNAs), trans-acting siRNAs (tasiRNAs) and natural antisense transcript-derived siRNAs. Chinese fir (Cunninghamia lanceolata) is one of the most important coniferous evergreen tree species in China. No sRNA from Chinese fir has been described to date.
To obtain sRNAs in Chinese fir, we sequenced a sRNA library generated from seeds, seedlings, leaves, stems and calli, using Illumina high throughput sequencing technology. A comprehensive set of sRNAs were acquired, including conserved and novel miRNAs, rasiRNAs and tasiRNAs. With BLASTN and MIREAP we identified a total of 115 conserved miRNAs comprising 40 miRNA families and one novel miRNA with precursor sequence. The expressions of 16 conserved and one novel miRNAs and one tasiRNA were detected by RT-PCR. Utilizing real time RT-PCR, we revealed that four conserved and one novel miRNAs displayed developmental stage-specific expression patterns in Chinese fir. In addition, 209 unigenes were predicted to be targets of 30 Chinese fir miRNA families, of which five target genes were experimentally verified by 5' RACE, including a squamosa promoter-binding protein gene, a pentatricopeptide (PPR) repeat-containing protein gene, a BolA-like family protein gene, AGO1 and a gene of unknown function. We also demonstrated that the DCL3-dependent rasiRNA biogenesis pathway, which had been considered absent in conifers, existed in Chinese fir. Furthermore, the miR390-TAS3-ARF regulatory pathway was elucidated.
We unveiled a complex population of sRNAs in Chinese fir through high throughput sequencing. This provides an insight into the composition and function of sRNAs in Chinese fir and sheds new light on land plant sRNA evolution.
Chinese fir; miRNA; rasiRNA; tasiRNA; Cunninghamia lanceolata
Iron can regulate biofilm formation via non-coding small RNA (sRNA). To determine if iron-regulated sRNAs are involved in biofilm formation by the periodontopathogen Aggregatibacter actinomycetemcomitans, total RNA was isolated from bacteria cultured with iron supplementation or chelation. Transcriptional analysis demonstrated that the expression of four sRNA molecules (JA01-JA04) identified by bioinformatics was significantly up-regulated in iron-stressed medium compared to iron-rich medium. A DNA fragment encoding each sRNA promoter was able to titrate E. coli Fur from a Fur-repressible reporter fusion in an iron uptake regulator titration assay. Cell lysates containing recombinant AaFur shifted the mobility of sRNA-specific DNAs in a gel shift assay. Potential targets of these sRNAs, determined in silico, included genes involved in biofilm formation. A. actinomycetemcomitans overexpressing JA03 sRNA maintained a rough phenotype on agar, but no longer adhered to uncoated polystyrene or glass, although biofilm determinant gene expression was only modestly decreased. In summary, these sRNA have the ability to modulate biofilm formation, but their functional targets genes remain to be confirmed.
iron; Fur; sRNA; biofilm; regulation; transcription
Riboregulation stands for RNA-based control of gene expression. In bacteria, small non-coding RNAs (sRNAs) are a major class of riboregulatory elements, most of which act at the post-transcriptional level by base-pairing target mRNA genes. The RNA chaperone Hfq facilitates antisense interactions between target mRNAs and regulatory sRNAs, thus influencing mRNA stability and/or translation rate. In the α-proteobacterium Sinorhizobium meliloti strain 2011, the identification and detection of multiple sRNAs genes and the broadly pleitropic phenotype associated to the absence of a functional Hfq protein both support the existence of riboregulatory circuits controlling gene expression to ensure the fitness of this bacterium in both free living and symbiotic conditions. In order to identify target mRNAs subject to Hfq-dependent riboregulation, we have compared the proteome of an hfq mutant and the wild type S. meliloti by quantitative proteomics following protein labelling with 15N. Among 2139 univocally identified proteins, a total of 195 proteins showed a differential abundance between the Hfq mutant and the wild type strain; 65 proteins accumulated ≥2-fold whereas 130 were downregulated (≤0.5-fold) in the absence of Hfq. This profound proteomic impact implies a major role for Hfq on regulation of diverse physiological processes in S. meliloti, from transport of small molecules to homeostasis of iron and nitrogen. Changes in the cellular levels of proteins involved in transport of nucleotides, peptides and amino acids, and in iron homeostasis, were confirmed with phenotypic assays. These results represent the first quantitative proteomic analysis in S. meliloti. The comparative analysis of the hfq mutant proteome allowed identification of novel strongly Hfq-regulated genes in S. meliloti.
Translation of the sigma factor RpoS is activated by DsrA, RprA and ArcA, three small non-coding sRNAs (sRNA) that expose the ribosome-binding site (RBS) by opening up an inhibitory loop. In the RpoS network, no sRNAs have been found to pair with the RBS, a most common sRNA target site in bacteria. Here, we generate Ribo-0, an artificial sRNA, which represses rpoS translation by pairing with the RBS. Ribo-0 bypasses the RNA chaperon Hfq but requires the RBS to be loosely blocked. Ribo-0 interacts with DsrA and reshapes the RpoS network. Specifically, in the intact RpoS network, DsrA activates rpoS translation by freeing up the RBS. In the modified RpoS network where Ribo-0 is introduced, the DsrA-caused RBS exposure facilitates Ribo-0 binding, thereby strengthening Ribo-0 inhibition. In other words, Ribo-0 changes DsrA from an activator to an accomplice for repressing rpoS translation. This work presents an artificial mechanism of rpoS regulation, reveals mutual effects of native and synthetic players and demonstrates genetic context-dependency of their functions.
Small non-coding RNAs (sRNAs) are an emerging class of regulators of bacterial gene expression. Most of the regulatory Escherichia coli sRNAs known to date modulate translation of trans-encoded target mRNAs. We studied the specificity of sRNA target interactions using gene fusions to green fluorescent protein (GFP) as a novel reporter of translational control by bacterial sRNAs in vivo. Target sequences were selected from both monocistronic and polycistronic mRNAs. Upon expression of the cognate sRNA (DsrA, GcvB, MicA, MicC, MicF, RprA, RyhB, SgrS and Spot42), we observed highly specific translation repression/activation of target fusions under various growth conditions. Target regulation was also tested in mutants that lacked Hfq or RNase III, or which expressed a truncated RNase E (rne701). We found that translational regulation by these sRNAs was largely independent of full-length RNase E, e.g. despite the fact that ompA fusion mRNA decay could no longer be promoted by MicA. This is the first study in which multiple well-defined E.coli sRNA target pairs have been studied in a uniform manner in vivo. We expect our GFP fusion approach to be applicable to sRNA targets of other bacteria, and also demonstrate that Vibrio RyhB sRNA represses a Vibrio sodB fusion when co-expressed in E.coli.