nocoRNAc: characterization of non-coding RNAs in prokaryotes

Comparative virulome analysis of four Staphylococcus epidermidis strains from human skin and platelet concentrates using whole genome sequencing

Staphylococcus epidermidis is one of the predominant bacterial contaminants in platelet concentrates (PCs), a blood component used to treat bleeding disorders. PCs are a unique niche that triggers biofilm formation, the main pathomechanism of S. epidermidis infections. We performed whole genome sequencing of four S. epidermidis strains isolated from skin of healthy human volunteers (AZ22 and AZ39) and contaminated PCs (ST10002 and ST11003) to unravel phylogenetic relationships and decipher virulence mechanisms compared to 24 complete S. epidermidis genomes in GenBank. AZ39 and ST11003 formed a separate unique lineage with strains 14.1 .R1 and SE95, while AZ22 formed a cluster with 1457 and ST10002 closely grouped with FDAAGOS_161. The four isolates were assigned to sequence types ST1175, ST1174, ST73 and ST16, respectively. All four genomes exhibited biofilm-associated genes ebh, ebp, sdrG, sdrH and atl. Additionally, AZ22 had sdrF and aap, whereas ST10002 had aap and icaABCDR. Notably, AZ39 possesses truncated ebh and sdrG and harbours a toxin-encoding gene. All isolates carry multiple antibiotic resistance genes conferring resistance to fosfomycin (fosB), β-lactams (blaZ) and fluoroquinolones (norA). This study reveales a unique lineage for S. epidermidis and provides insight into the genetic basis of virulence and antibiotic resistance in transfusion-associated S. epidermidis strains.

Biocomputational Identification of sRNAs in Leptospira interrogans Serovar Lai

Regulatory small RNAs (sRNA) are RNA transcripts that are not translated into proteins but act as functional RNAs. Pathogenic Leptospira cause an epidemic spirochaetal zoonosis, Leptospirosis. It is speculated that Leptospiral sRNAs are involved in orchestrating their pathogenicity. In this study, biocomputational approach was adopted to identify Leptospiral sRNAs. In this study, two sRNA prediction programs, i.e., RNAz and nocoRNAc, were employed to screen the reference genome of Leptospira interrogans serovar Lai. Out of 126 predicted sRNAs, there are 96 cis-antisense sRNAs, 28 trans-encoded sRNAs and 2 sRNAs that partially overlap with protein-coding genes in a sense orientation. To determine whether these candidates are expressed in the pathogen, they were compared with the coverage files generated from our RNA-seq datasets. It was found out that 7 predicted sRNAs are expressed in mid-log phase, stationary phase, serum stress, temperature stress and iron stress while 2 sRNAs are expressed in mid-log phase, stationary phase, serum stress, and temperature stress. Besides, their expressions were also confirmed experimentally via RT-PCR. These experimentally validated candidates were also subjected to mRNA target prediction using TargetRNA2. Taken together, our study demonstrated that biocomputational strategy can serve as an alternative or as a complementary strategy to the laborious and expensive deep sequencing methods not only to uncover putative sRNAs but also to predict their targets in bacteria. In fact, this is the first study that integrates computational approach to predict putative sRNAs in L. interrogans serovar Lai.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-022-01050-9.

Global Transcriptomic Analysis of Bacteriophage-Host Interactions between a Kayvirus Therapeutic Phage and Staphylococcus aureus

Kayviruses are polyvalent broad host range staphylococcal phages with a potential to combat staphylococcal infections. However, the implementation of rational phage therapy in medicine requires a thorough understanding of the interactions between bacteriophages and pathogens at omics level. To evaluate the effect of a phage used in therapy on its host bacterium, we performed differential transcriptomic analysis by RNA-Seq from bacteriophage K of genus Kayvirus infecting two Staphylococcus aureus strains, prophage-less strain SH1000 and quadruple lysogenic strain Newman. The temporal transcriptional profile of phage K was comparable in both strains except for a few loci encoding hypothetical proteins. Stranded sequencing revealed transcription of phage noncoding RNAs that may play a role in the regulation of phage and host gene expression. The transcriptional response of S. aureus to phage K infection resembles a general stress response with differential expression of genes involved in a DNA damage response. The host transcriptional changes involved upregulation of nucleotide, amino acid and energy synthesis and transporter genes and downregulation of host transcription factors. The interaction of phage K with variable genetic elements of the host showed slight upregulation of gene expression of prophage integrases and antirepressors. The virulence genes involved in adhesion and immune evasion were only marginally affected, making phage K suitable for therapy. IMPORTANCE Bacterium Staphylococcus aureus is a common human and veterinary pathogen that causes mild to life-threatening infections. As strains of S. aureus are becoming increasingly resistant to multiple antibiotics, the need to search for new therapeutics is urgent. A promising alternative to antibiotic treatment of staphylococcal infections is a phage therapy using lytic phages from the genus Kayvirus. Here, we present a comprehensive view on the phage-bacterium interactions on transcriptomic level that improves the knowledge of molecular mechanisms underlying the Kayvirus lytic action. The results will ensure safer usage of the phage therapeutics and may also serve as a basis for the development of new antibacterial strategies.

In Silico Prediction for ncRNAs in Prokaryotes

The identification and characterization of non-coding RNAs (ncRNAs) in prokaryotes is an important step in the study of the interaction of these molecules with mRNAs-or target proteins, in the post-transcriptional regulation process. Here, we describe one of the main in silico prediction methods in prokaryotes, using the TargetRNA2 tool to predict target mRNAs.

Intergenic lnc-LEP-2:6 and lnc-LEP-2:7 as novel biomarkers associated with type 2 diabetes mellitus

CONTEXT

Type 2 diabetes mellitus (T2DM) has undeniably become a significant threat to public health in the modern world.

OBJECTIVE

To identify molecules involved in the development of T2DM, the expression patterns of cis-lincRNAs-LEP were investigated in T2DM patients.

MATERIALS AND METHODS

The expression levels of 11 lncRNAs-LEP and LEP mRNA in 71 diabetic patients and 32 controls were determined using qRT-PCR. The association between lncRNAs-LEP and T2DM was performed using ROC curve analysis.

RESULTS

The expression analysis of lnc-LEP-2:6 and lnc-LEP-2:7 was significantly different (p≤ .0001) in diabetic patients compared to the controls. The ROC curve data showed that lnc-LEP-2:6 and lnc-LEP-2:7 have a high AUC, with 0.940 (95% CI: 0.875-0.977) and 0.958 (95% CI: 0.899-0.988), respectively.

CONCLUSIONS

Our results revealed two novel biomarkers associated with the T2DM and suggest that the circulating intergenic, lnc-LEP-2:6 and lnc-LEP-2:7, might have a regulatory role in the disease pathogenesis.

PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region

Bacterial small-RNA (sRNA) sequences are functional RNAs, which play an important role in regulating the expression of a diverse class of genes. It is thus critical to identify such sRNA sequences and their probable mRNA targets. Here, we discuss new procedures to identify and characterize sRNA and their targets via the introduction of an integrated online platform 'PresRAT'. PresRAT uses the primary and secondary structural attributes of sRNA sequences to predict sRNA from a given sequence or bacterial genome. PresRAT also finds probable target mRNAs of sRNA sequences from a given bacterial chromosome and further concentrates on the identification of the probable sRNA-mRNA binding regions. Using PresRAT, we have identified a total of 66,209 potential sRNA sequences from 292 bacterial genomes and 2247 potential targets from 13 bacterial genomes. We have also implemented a protocol to build and refine 3D models of sRNA and sRNA-mRNA duplex regions and generated 3D models of 50 known sRNAs and 81 sRNA-mRNA duplexes using this platform. Along with the server part, PresRAT also contains a database section, which enlists the predicted sRNA sequences, sRNA targets, and their corresponding 3D models with structural dynamics information.

The Genome of Staphylococcus epidermidis O47

The skin colonizing coagulase-negative Staphylococcus epidermidis causes nosocomial infections and is an important opportunistic and highly adaptable pathogen. To gain more insight into this species, we sequenced the genome of the biofilm positive, methicillin susceptible S. epidermidis O47 strain (hereafter O47). This strain belongs to the most frequently isolated sequence type 2. In comparison to the RP62A strain, O47 can be transformed, which makes it a preferred strain for molecular studies. S. epidermidis O47’s genome has a single chromosome of about 2.5 million base pairs and no plasmid. Its oriC sequence has the same directionality as S. epidermidis RP62A, S. carnosus, S. haemolyticus, S. saprophyticus and is inverted in comparison to Staphylococcus aureus and S. epidermidis ATCC 12228. A phylogenetic analysis based on all S. epidermidis genomes currently available at GenBank revealed that O47 is closest related to DAR1907. The genome of O47 contains genes for the typical global regulatory systems known in staphylococci. In addition, it contains most of the genes encoding for the typical virulence factors for S. epidermidis but not for S. aureus with the exception of a putative hemolysin III. O47 has the typical S. epidermidis genetic islands and several mobile genetic elements, which include staphylococcal cassette chromosome (SCC) of about 54 kb length and two prophages φO47A and φO47B. However, its genome has no transposons and the smallest number of insertion sequence (IS) elements compared to the other known S. epidermidis genomes. By sequencing and analyzing the genome of O47, we provide the basis for its utilization in genetic and molecular studies of biofilm formation.

In silico prediction and expression profile analysis of small non-coding RNAs in Herbaspirillum seropedicae SmR1

BACKGROUND

Herbaspirillum seropedicae is a diazotrophic bacterium from the β-proteobacteria class that colonizes endophytically important gramineous species, promotes their growth through phytohormone-dependent stimulation and can express nif genes and fix nitrogen inside plant tissues. Due to these properties this bacterium has great potential as a commercial inoculant for agriculture. The H. seropedicae SmR1 genome is completely sequenced and annotated but despite the availability of diverse structural and functional analysis of this genome, studies involving small non-coding RNAs (sRNAs) has not yet been done. We have conducted computational prediction and RNA-seq analysis to select and confirm the expression of sRNA genes in the H. seropedicae SmR1 genome, in the presence of two nitrogen independent sources and in presence of naringenin, a flavonoid secreted by some plants.

RESULTS

This approach resulted in a set of 117 sRNAs distributed in riboswitch, cis-encoded and trans-encoded categories and among them 20 have Rfam homologs. The housekeeping sRNAs tmRNA, ssrS and 4.5S were found and we observed that a large number of sRNAs are more expressed in the nitrate condition rather than the control condition and in the presence of naringenin. Some sRNAs expression were confirmed in vitro and this work contributes to better understand the post transcriptional regulation in this bacterium.

CONCLUSIONS

H. seropedicae SmR1 express sRNAs in the presence of two nitrogen sources and/or in the presence of naringenin. The functions of most of these sRNAs remains unknown but their existence in this bacterium confirms the evidence that sRNAs are involved in many different cellular activities to adapt to nutritional and environmental changes.

Applying a New REFINE Approach in Zymomonas mobilis Identifies Novel sRNAs That Confer Improved Stress Tolerance Phenotypes

As global controllers of gene expression, small RNAs represent powerful tools for engineering complex phenotypes. However, a general challenge prevents the more widespread use of sRNA engineering strategies: mechanistic analysis of these regulators in bacteria lags far behind their high-throughput search and discovery. This makes it difficult to understand how to efficiently identify useful sRNAs to engineer a phenotype of interest. To help address this, we developed a forward systems approach to identify naturally occurring sRNAs relevant to a desired phenotype: RNA-seq Examiner for Phenotype-Informed Network Engineering (REFINE). This pipeline uses existing RNA-seq datasets under different growth conditions. It filters the total transcriptome to locate and rank regulatory-RNA-containing regions that can influence a metabolic phenotype of interest, without the need for previous mechanistic characterization. Application of this approach led to the uncovering of six novel sRNAs related to ethanol tolerance in non-model ethanol-producing bacterium Zymomonas mobilis. Furthermore, upon overexpressing multiple sRNA candidates predicted by REFINE, we demonstrate improved ethanol tolerance reflected by up to an approximately twofold increase in relative growth rate compared to controls not expressing these sRNAs in 7% ethanol (v/v) RMG-supplemented media. In this way, the REFINE approach informs strain-engineering strategies that we expect are applicable for general strain engineering.

Whole genome sequence of moderate halophilic marine bacterium Marinobacter litoralis SW-45: Abundance of non-coding RNAs

A report on the de novo Whole Genome Sequence (WGS) of Marinobacter litoralis SW-45, a moderately salt-tolerant bacterium isolated from the seawater in Malaysia is presented. The strain has a genome size of 3.45 Mb and is capable of producing halophilic lipase, protease and esterase enzymes. Computational prediction of non-coding RNA (ncRNA) genes in M. litoralis SW-45 was performed using standalone software known as the non-coding RNA characterization (nocoRNAc). In addition, a phylogenetic tree showing the evolutionary relationship between the strain and other members of the genus Marinobacter was constructed using 16SrRNA sequence information. A total of 385 ncRNA transcripts, 1124 terminator region, and 2350 Stress Induced Duplex Destabilization sites were predicted. The current WGS shotgun project has provided the relevant genetic information that may be useful for the strain's improvement studies. This manuscript gives the first description of M. litoralis with a complete genome.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

BACKGROUND

Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.

OBJECTIVE

The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA's roles in cellular processes and drugs design, briefly.

METHOD

In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.

RESULTS

The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.

CONCLUSION

ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Functional annotation and distribution overview of RNA families in 27 Streptococcus agalactiae genomes

Background

Streptococcus agalactiae, also known as Group B Streptococcus (GBS), is a Gram-positive bacterium that colonizes the gastrointestinal and genitourinary tract of humans. This bacterium has also been isolated from various animals, such as fish and cattle. Non-coding RNAs (ncRNAs) can act as regulators of gene expression in bacteria, such as Streptococcus pneumoniae and Streptococcus pyogenes. However, little is known about the genomic distribution of ncRNAs and RNA families in S. agalactiae.

Results

Comparative genome analysis of 27 S. agalactiae strains showed more than 5 thousand genomic regions identified and classified as Core, Exclusive, and Shared genome sequences. We identified 27 to 89 RNA families per genome distributed over these regions, from these, 25 were in Core regions while Shared and Exclusive regions showed variations amongst strains. We propose that the amount and type of ncRNA present in each genome can provide a pattern to contribute in the identification of the clonal types.

Conclusions

The identification of RNA families provides an insight over ncRNAs, sRNAs and ribozymes function, that can be further explored as targets for antibiotic development or studied in gene regulation of cellular processes. RNA families could be considered as markers to determine infection capabilities of different strains. Lastly, pan-genome analysis of GBS including the full range of functional transcripts provides a broader approach in the understanding of this pathogen.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4951-z) contains supplementary material, which is available to authorized users.

Non-Coding RNAs are Differentially Expressed by Nocardia brasiliensis in Vitro and in Experimental Actinomycetoma

Introduction:

Nocardia spp. are common soil-inhabiting bacteria that frequently infect humans through traumatic injuries or inhalation routes and cause infections, such as actinomycetoma and nocardiosis, respectively. Nocardia brasiliensis is the main aetiological agent of actinomycetoma in various countries. Many bacterial non-coding RNAs are regulators of genes associated with virulence factors.

Objective:

The aim of this work was to identify non-coding RNAs (ncRNAs) expressed during infection conditions and in free-living form (in vitro) in Nocardia brasiliensis.

Methods and Result:

The N. brasiliensis transcriptome (predominately < 200 nucleotides) was determined by RNA next-generation sequencing in both conditions. A total of seventy ncRNAs were identified in both conditions. Among these ncRNAs, 18 were differentially expressed, 12 were located within intergenic regions, and 2 were encoded as antisense of 2 different genes. Finally, 10 of these ncRNAs were studied by rapid amplification of cDNA ends and/or quantitative reverse transcription polymerase chain reaction. Interestingly, 3 transcripts corresponded to tRNA-derived fragments (tRNAs^{Cys, Met, Thr}), and one transcript was overlapped between an intergenic region and the 5´end of the 23S rRNA. Expression of these last four transcripts was increased during N. brasiliensis infection compared with the in vitro conditions.

Conclusion:

The results of this work suggest a possible role for these transcripts in the regulation of virulence genes in actinomycetoma pathogenesis.

Differentiation of ncRNAs from small mRNAs in Escherichia coli O157:H7 EDL933 (EHEC) by combined RNAseq and RIBOseq - ryhB encodes the regulatory RNA RyhB and a peptide, RyhP

Background

While NGS allows rapid global detection of transcripts, it remains difficult to distinguish ncRNAs from short mRNAs. To detect potentially translated RNAs, we developed an improved protocol for bacterial ribosomal footprinting (RIBOseq). This allowed distinguishing ncRNA from mRNA in EHEC. A high ratio of ribosomal footprints per transcript (ribosomal coverage value, RCV) is expected to indicate a translated RNA, while a low RCV should point to a non-translated RNA.

Results

Based on their low RCV, 150 novel non-translated EHEC transcripts were identified as putative ncRNAs, representing both antisense and intergenic transcripts, 74 of which had expressed homologs in E. coli MG1655. Bioinformatics analysis predicted statistically significant target regulons for 15 of the intergenic transcripts; experimental analysis revealed 4-fold or higher differential expression of 46 novel ncRNA in different growth media. Out of 329 annotated EHEC ncRNAs, 52 showed an RCV similar to protein-coding genes, of those, 16 had RIBOseq patterns matching annotated genes in other enterobacteriaceae, and 11 seem to possess a Shine-Dalgarno sequence, suggesting that such ncRNAs may encode small proteins instead of being solely non-coding. To support that the RIBOseq signals are reflecting translation, we tested the ribosomal-footprint covered ORF of ryhB and found a phenotype for the encoded peptide in iron-limiting condition.

Conclusion

Determination of the RCV is a useful approach for a rapid first-step differentiation between bacterial ncRNAs and small mRNAs. Further, many known ncRNAs may encode proteins as well.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3586-9) contains supplementary material, which is available to authorized users.

Whole Genome Shotgun Sequencing Shows Selection on Leptospira Regulatory Proteins During in vitro Culture Attenuation

Leptospirosis is the most common zoonotic disease worldwide with an estimated 500,000 severe cases reported annually, and case fatality rates of 12–25%, due primarily to acute kidney and lung injuries. Despite its prevalence, the molecular mechanisms underlying leptospirosis pathogenesis remain poorly understood. To identify virulence-related genes in Leptospira interrogans, we delineated cumulative genome changes that occurred during serial in vitro passage of a highly virulent strain of L. interrogans serovar Lai into a nearly avirulent isogenic derivative. Comparison of protein coding and computationally predicted noncoding RNA (ncRNA) genes between these two polyclonal strains identified 15 nonsynonymous single nucleotide variant (nsSNV) alleles that increased in frequency and 19 that decreased, whereas no changes in allelic frequency were observed among the ncRNA genes. Some of the nsSNV alleles were in six genes shown previously to be transcriptionally upregulated during exposure to in vivo-like conditions. Five of these nsSNVs were in evolutionarily conserved positions in genes related to signal transduction and metabolism. Frequency changes of minor nsSNV alleles identified in this study likely contributed to the loss of virulence during serial in vitro culture. The identification of new virulence-associated genes should spur additional experimental inquiry into their potential role in Leptospira pathogenesis.

The Pathway-Specific Regulator ClaR of Streptomyces clavuligerus Has a Global Effect on the Expression of Genes for Secondary Metabolism and Differentiation

Streptomyces clavuligerus claR::aph is a claR-defective mutant, but in addition to its claR defect it also carries fewer copies of the resident linear plasmids pSCL2 and pSCL4 (on the order of 4 × 10(5)-fold lower than the wild-type strain), as shown by qPCR. To determine the function of ClaR without potential interference due to plasmid copy number, a new strain, S. clavuligerus ΔclaR::aac, with claR deleted and carrying the wild-type level of plasmids, was constructed. Transcriptomic analyses were performed in S. clavuligerus ΔclaR::aac and S. clavuligerus ATCC 27064 as the control strain. The new ΔclaR mutant did not produce clavulanic acid (CA) and showed a partial expression of genes for the early steps of the CA biosynthesis pathway and a very poor expression (1 to 8%) of the genes for the late steps of the CA pathway. Genes for cephamycin C biosynthesis were weakly upregulated (1.7-fold at 22.5 h of culture) in the ΔclaR mutant, but genes for holomycin biosynthesis were expressed at levels from 3- to 572-fold higher than in the wild-type strain, supporting the observed overproduction of holomycin by S. clavuligerus ΔclaR::aac. Interestingly, three secondary metabolites produced by gene clusters SMCp20, SMCp22, and SMCp24, encoding still-cryptic compounds, had partially or totally downregulated their genes in the mutant, suggesting a regulatory role for ClaR wider than previously reported. In addition, the amfR gene was downregulated, and consequently, the mutant did not produce aerial mycelium. Expression levels of about 100 genes in the genome were partially up- or downregulated in the ΔclaR mutant, many of them related to the upregulation of the sigma factor-encoding rpoE gene.

MicroRNA target and gene validation in viruses and bacteria

Noncoding RNAs (ncRNAs) constitute an evolutionary conserved system involved in the regulation of biological functions at posttranscriptional level. The capability to rapidly adapt their metabolism is essential for the survival of organisms. NcRNAs are a valuable means used by cells to rapidly transfer and internalize an external signal. NcRNAs are capable not only to influence the translational phase but also to affect epigenetic processes. They have been identified in almost all kingdoms of life (from archaea to human and plants). In this chapter we outline the currently available resources that could be used for the screening of viral and bacterial ncRNAs.

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

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

Computational small RNA prediction in bacteria

Bacterial, small RNAs were once regarded as potent regulators of gene expression and are now being considered as essential for their diversified roles. Many small RNAs are now reported to have a wide array of regulatory functions, ranging from environmental sensing to pathogenesis. Traditionally, noncoding transcripts were rarely detected by means of genetic screens. However, the availability of approximately 2200 prokaryotic genome sequences in public databases facilitates the efficient computational search of those molecules, followed by experimental validation. In principle, the following four major computational methods were applied for the prediction of sRNA locations from bacterial genome sequences: (1) comparative genomics, (2) secondary structure and thermodynamic stability, (3) ‘Orphan’ transcriptional signals and (4) ab initio methods regardless of sequence or structure similarity; most of these tools were applied to locate the putative genomic sRNA locations followed by experimental validation of those transcripts. Therefore, computational screening has simplified the sRNA identification process in bacteria. In this review, a plethora of small RNA prediction methods and tools that have been reported in the past decade are discussed comprehensively and assessed based on their attributes, compatibility, and their prediction accuracy.

Computational discovery and RT-PCR validation of novel Burkholderia conserved and Burkholderia pseudomallei unique sRNAs

BACKGROUND

The sRNAs of bacterial pathogens are known to be involved in various cellular roles including environmental adaptation as well as regulation of virulence and pathogenicity. It is expected that sRNAs may also have similar functions for Burkholderia pseudomallei, a soil bacterium that can adapt to diverse environmental conditions, which causes the disease melioidosis and is also able to infect a wide variety of hosts.

RESULTS

By integrating several proven sRNA prediction programs into a computational pipeline, available Burkholderia spp. genomes were screened to identify sRNA gene candidates. Orthologous sRNA candidates were then identified via comparative analysis. From the total prediction, 21 candidates were found to have Rfam homologs. RT-PCR and sequencing of candidate sRNA genes of unknown functions revealed six putative sRNAs which were highly conserved in Burkholderia spp. and two that were unique to B. pseudomallei present in a normal culture conditions transcriptome. The validated sRNAs include potential cis-acting elements associated with the modulation of methionine metabolism and one B. pseudomallei-specific sRNA that is expected to bind to the Hfq protein.

CONCLUSIONS

The use of the pipeline developed in this study and subsequent comparative analysis have successfully aided in the discovery and shortlisting of sRNA gene candidates for validation. This integrated approach identified 29 B. pseudomallei sRNA genes - of which 21 have Rfam homologs and 8 are novel.

Characterization of a mazEF toxin-antitoxin homologue from Staphylococcus equorum

Toxin-antitoxin (TA) systems encoded in prokaryotic genomes fall into five types, typically composed of two distinct small molecules, an endotoxic protein and a cis-encoded antitoxin of ribonucleic or proteinaceous nature. In silico analysis revealed seven putative type I and three putative type II TA systems in the genome of the nonpathogenic species strain Staphylococcus equorum SE3. Among these, a MazEF system orthologue termed MazEF(seq) was further characterized. 5' rapid amplification of cDNA ends (RACE) revealed the expression and the transcriptional start site of mazE(seq), indicating an immediately upstream promoter. Heterologous expression of the putative toxin-encoding mazF(seq) gene imposed growth cessation but not cell death on Escherichia coli. In vivo and in vitro, MazF(seq) was shown to cleave at UACAU motifs, which are remarkably abundant in a number of putative metabolic and regulatory S. equorum gene transcripts. Specific interaction between MazF(seq) and the putative cognate antitoxin MazE(seq) was demonstrated by bacterial two-hybrid analyses. These data strongly suggest that MazEF(seq) represents the first characterized TA system in a nonpathogenic Staphylococcus species and indicate that MazEF modules in staphylococci may also control processes beyond pathogenicity.

Long non-coding RNAs coordinate cellular responses to stress

Following the initial discovery of the heat shock RNA omega (hsrω) gene of Drosophila melanogaster to be non-coding (nc) and also inducible by cell stress, other stress-inducible long non-coding RNAs (lncRNA) have been described in diverse organisms. In view of the rapid sequence divergence of lncRNAs, present knowledge of stress trasncriptome is limited and fragmented. Several known stress-related lncRNAs, associated with specific nuclear speckled domains or nucleolus, provide structural base for sequestering diverse RNA-processing/regulatory proteins. Others have roles in transcriptional or translational inhibition during stress or in signaling pathways; functions of several other lncRNAs are not yet known. Most stress-related lncRNAs act primarily by modulating activity of the proteins to which they bind or by sequestering specific sets of proteins away from the active pool. A common emerging theme is that a given lncRNA targets one or more protein/s with key role/s in the cascade of events triggered by the stress and therefore has a widespread integrative effect. Since proteins associate with RNA through short sequence motifs, the overall base sequence of functionally similar ncRNAs is often not conserved except for specific motifs. The rapid evolvability of ncRNA sequences provides elegant modules for adaptability to changing environment as binding of one or the other protein to ncRNA can alter its structure and functions in distinct ways. Thus the stress-related lncRNAs act as hubs in the cellular networks to coordinate activities of the members within and between different networks to maintain cellular homeostasis for survival or to trigger cell death.

Computational approaches to discovering noncoding RNA

New developments are being brought to the field of molecular biology with the mounting evidence that RNA transcripts not translated into protein (noncoding RNAs, ncRNAs) hold a variety of biological functions. Computational discovery of ncRNAs is one of these developments, fueled not only by the urge to characterize these sequences but also by necessity to prioritize ones with the most relevant functions for experimental verification. The heterogeneity in size and mode of activity of ncRNAs is reflected in the corresponding diversity of computational methods for their study. Sequence and structural analysis, conservation across species, and relative position to other genomic elements are being used for ncRNA detection. In addition, the recent development of techniques that allow deep sequencing of cell transcripts either globally or from isolated ncRNA-related material is leading the field toward increased use of such high-throughput data. We expect that imminent breakthroughs will include the classification of newer types of ncRNA and new insights into miRNA and piRNA biology, eventually leading toward the completion of a catalog of all human ncRNAs.