- Open Access
Computational discovery and RT-PCR validation of novel Burkholderia conserved and Burkholderia pseudomallei unique sRNAs
© Khoo et al.; licensee BioMed Central Ltd. 2012
- Published: 13 December 2012
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.
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.
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.
- Reverse Transcription Polymerase Chain Reaction
- Burkholderia Pseudomallei
- sRNA Candidate
- sRNA Gene
Small RNAs (sRNAs) are known to function as regulatory or catalytic molecules in bacteria with sequences normally ranging from ~50-250 nt in length and located in the intergenic regions (IGRs) [1, 2]. Although sRNAs with catalytic functions have been reported [3, 4], many of these molecules are known or believed to function as regulatory nucleic acid elements that target near, or at, the translation start site of their dedicated mRNA targets via imperfect sequence complementarity [5–7]. In E. coli, less than 100 sRNAs, accounting for ~0.3% of the genome, have been reported [8–10]. Although these riboregulators represent only a small fraction of the prokaryotic genome, they have been shown to play essential regulatory roles in bacteria, including cell surface modulation , plasmid number control , stress adaptation , quorum sensing  and carbon storage . Other regulatory sRNAs interact with and modulate cellular protein activities .
In pathogenic bacteria, sRNAs have been associated with regulatory networks that modulate the adherence to, and invasion into the host cell [17, 18], environmental adaptation [19, 20] as well as virulence and pathogenicity [17, 18, 20–23]. In several bacterial pathogens, including Salmonella typhimurium , Vibrio cholerae , Yersinia enterocolitica , Brucella abortus  and Pseudomonas aeruginosa , deletion of the hfq gene which encodes the RNA chaperone Hfq, has been shown to severely attenuate virulence. The Hfq protein is known to facilitate the pairing interaction between sRNAs and their target mRNAs . Identification and analysis of sRNAs in pathogenic bacteria may improve current understanding on the molecular mechanisms of host adaptation and virulence. Hence, we carried out a computational based analysis of available Burkholderia spp. genomes to identify potential sRNA sequences and to further delineate sRNAs that are present only in the pathogenic members.
Members of the Burkholderia genus also play important roles as environmental saprophytes. One species of this genus, B. pseudomallei, is the causative agent of melioidosis, a disease endemic to Southeast Asia and northern Australia. This species reportedly has a highly dynamic genome and versatile phenotypes [29–31], thus contributing to its capability to infect nearly all cell types, resulting in a wide spectrum of disease symptoms that confounds diagnosis and delays prompt treatment. B. pseudomallei is an effective pathogen of a broad range of hosts (amoeba , nematodes , dolphins , birds, camels, alpacas, sheep , humans and even plants ). The enigma of B. pseudomallei is further compounded in having an extremely prolonged latent infection capacity  and has been shown to be capable of surviving in a nutrient-free environment for 16 years .
B. pseudomallei is believed to have an array of virulence and pathogenicity factors, including a toxin which is a deamidase named Burkholderia Lethal Factor 1 (BLF1) that targets the translation initiation factor eIF4a . However, the regulation and delivery mechanism of BLF1 to the target protein remains unclear. To date, the mechanisms of adaptation to environmental stress and changes have not been conclusively identified, however a large number of sRNA genes have been reported for B. cenocepacia J2315, another pathogenic member of the Burkholderia genus . These sRNAs were proposed to be responsible for the bacterium's complexity, phenotypic variability and ability to survive in a remarkably wide range of environments .
At present, one can opt for either a knowledge-based approach or a de novo approach for sRNA discovery in a bacterial genome. Knowledge-based techniques search for homologues of known sRNAs based on specific features of the sequences and will usually include upstream regulatory elements, sequence and structural characteristics and downstream targets as a search profile. A number of knowledge-based programs were developed to identify particular sets of sRNAs through homology analysis. One such program, Infernal , was the workhorse used to build the Rfam database . However, predictions relying on homology information limit the applications of such programs to sRNA genes with known homologues and therefore, the methods are insufficient in situations where many if not most bacterial sRNAs remain unidentified. A de novo approach can serve a complementary role in predicting novel sRNA genes that are beyond the profile scope of knowledge-based approaches. The basis of a de novo search lies in the common features of sRNAs in the genomes - sequence and structural conservation, shared physical co-localization, structural stability, existence of transcriptional signals and GC bias - without prior knowledge of the sRNAs to be discovered. Such an approach was applied with various sRNA gene finders such as QRNA , RNAz [42, 43], sRNAPredict [44, 45] and sRNAscanner . In this paper, we report the development of a computational pipeline that integrated successful sRNA prediction programs to identify candidate sRNA genes in B. pseudomallei and subsequent validation by RT-PCR and Sanger sequencing.
Development of the sRNA gene detection pipeline
The intergenic sequences (here, defined as sequences between annotated ORFs) of the replicons were extracted using Artemis v12.0.3  and searched against the Rfam database v10.0 by executing the script rfam_scan.pl v1.0. The supporting software used for the search included BLAST v2.2.22 , Infernal v1.0, Perl v5.10.0 and BioPerl v1.6.0.
SIPHT searches were restricted to detect sRNA genes within the range of 30-550 nucleotides and executed via the web server (URL: http://newbio.cs.wisc.edu/sRNA/). Other parameters were optimized as suggested ; i.e., maximum E value: 1e-15, minimum TransTerm confidence value: 87, maximum FindTerm score: -10, maximum RNAMotif score: -9. All replicons, except the replicon of interest, were included as a partner replicon for the search.
The program sRNAscanner_Ubuntu10 (released 31 August 2010) was used to screen both the forward and reverse strands of the query replicon. The searches were restricted to intergenic regions and the sRNA length for prediction was set to 30-550 nucleotides. All other parameters were left at their default values, i.e. 3 provided input matrices: 35box_sRNA.matrix (cut-off: 2), 10box_sRNA.matrix (cut-off: 2), terminator.txt.matrix (cut-off: 3); spacer range between [-35] & [-10] promoter boxes: 12-18; unique hit value: 200; minimum cumulative sum of score (CSS): 14.
Genome sequences, annotation files and databases
The genome sequences of 11 Burkholderia spp and 3 Ralstonia spp (.fna extensions), annotation files (.gbk and .ptt extensions) and the complete genomic sequences of RefSeq-release47 (.genomic.fna extensions) were obtained from NCBI (Additional file 1). The genome sequences of five local strains of B. pseudomallei (unpublished data) were used for cross-referencing purposes. The Rfam database v10.0, both .fasta and .cm extensions for 1,446 sRNA families, was downloaded from ftp://ftp.sanger.ac.uk/pub/databases/Rfam/.
The intergenic sequences of B. pseudomallei K96243 were compared to sRNA candidates predicted in the Ralstonia and Burkholderia genomes using blastn v2.2.21 (parameters: -e 1e-5 -r 1 -q -1 -G 1 -E 2 -W 9 -F "m D"). The results were visualized using ACT v9.0.3  and the gene physical co-localization for the sRNAs of interest were investigated.
Secondary structure prediction
The secondary structures of the sRNA transcripts were predicted using mfold (unafold v3.8)  and RNAfold (ViennaRNA v1.8.4) . The default parameters or standard conditions for RNA folding were accepted (37°C, 1M NaCl, no divalent ions). The predicted structures were visualized using VARNA v3.7 .
Sequences for sRNAs of interest were globally aligned and consensus secondary structures were predicted using LocARNA  via its web service (URL: http://rna.tbi.univie.ac.at/cgi-bin/LocARNA.cgi). The default parameters for scoring the alignments were accepted (RIBOSUM85_60 matrix, Indel-opening score: -500, Indel score: -350, structure weight: 180, avoid lonely base-pairs). Covariance models representing the alignments with consensus structures were built, calibrated and searched against complete genome sequences in the RefSeq database release 47 using Infernal v1.0 with an E-value ≥ 1e-3.
B. pseudomallei strain and RNA extraction
The B. pseudomallei D286 human isolate was obtained from the Pathogen Laboratory, School of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Malaysia. Stock cultures were stored at -70°C and routinely cultured on brain-heart infusion agar (BHIA) (Pronadisa Hispanlab, South Africa) at 37°C . Bacteria from a stock culture were taken and streaked on Ashdown agar, and incubated at 37°C for 48 hours. A single colony was picked from the plate and inoculated into Brain Heart Infusion broth (BHIB) overnight. The following day, the culture was diluted 1:100 and grown in BHIB until the OD600 reached 0.6 - 1.0. Total RNA was extracted using TRIzol® LS Reagent (Invitrogen, Carlsbad, CA) and purified using Ambion's DNAfree™ DNase Treatment and Removal Reagents (Life Technologies, Carlsbad, CA).
Reverse transcription polymerase chain reaction (RT-PCR) and Sanger sequencing
The purified RNA was reverse transcribed into cDNA with an oligo(dT)18 primer using RevertAid First Strand cDNA Synthesis Kit (Fermentas, Hamburg, Germany). The cDNA produced was used as the template for PCR together with primers that were designed based on the sequences of sRNA candidates (Additional file 2). Amplification reactions were performed in a total volume of 25 μL consisting 10x PCR buffer, 10 mmol/L of dNTP mix, approximately 100 ng of cDNA, 25 pmol of each primer, 1.0 U Taq polymerase (Promega, Madison, WI) and distilled water. Mastercycler® personal (Eppendorf, Hamburg, Germany) was used to perform gradient PCR, with an initial denaturation step of 2 minutes at 95°C, followed by 35 amplification cycles of 30 seconds at 95°C, 30 seconds at 54-62°C, and 30 seconds at 72°C, and a final extension of 2 minutes at 72°C. Amplified products were analyzed by 3% agarose gel electrophoresis with O'GeneRuler™ Low Range DNA Ladder (Fermentas, Vilnius, Lithuania) run in parallel. PCR products were purified with the QIAquick Gel Purification Kit (Qiagen, Germany) and used in the reaction with the BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystem, Foster City, CA). Three biological replicates were carried out for each RT-PCR primer sets. The PCR products were then sequenced on the ABI Prism® 3100 AVANT DNA Sequencer. The sequences obtained were analyzed using BioEdit v184.108.40.206 and compared with the genome sequence of B. pseudomallei D286 human isolate.
Pipeline development and performance assessment
Discovery and verification of bacterial sRNAs in previous studies.
Computational discovery method
Number of sRNAs
Pftools2.2 & RNAMotif
BLAST & TransTermHP
RT-PCR & microarray
Salmonella enterica Typhimurium
RNAz & nocoRNAc
SIPHT, sRNAscanner & Rfam_scan
sRNA searches in B. pseudomallei and other related species
The IGR sequences identified for B. pseudomallei were compared against the 8,920 sRNA candidates using a BLAST-based (blastn) method. The purpose for this comparative analysis is to determine the conservation of sRNA candidates among the closely related bacterial species. As mis-annotations occur in genomes and each of the gene predictors have their own limitations, it was therefore no surprise to detect putative sRNAs from this comparison but not predicted by the sRNA search pipeline. A total of 1,213 out of 4,978 (approximately 24%) B. pseudomallei IGRs were predicted to contain at least one sRNA gene. The complete results list for this comparative analysis is provided as Additional file 4. As two or more sRNA genes could be predicted at the same strand and location, the overlapping candidates were merged before further analysis. For example, if gene A (location: 100 - 200) overlaps with gene B (location 150 - 250), the genes were merged into gene C (location: 100 - 250).
List of B. pseudomallei sRNA sequences with their corresponding sRNA families as reported in Rfam.
Coordinates from Rfam
Excluding the 21 homologues to known sRNAs, 20 previously undescribed candidates (also referred to in this paper as novel sRNAs) that were conserved in at least eight out of the fourteen bacterial genomes analyzed were selected for predicted secondary structure comparison where the calculated secondary structures were visually examined. A total of twelve sRNAs with perceivably conserved secondary structures were selected for experimental validation (discussed in the next section).
List of RT-PCR validated sRNA genes in conserved in Burkholderia and unique to Burkholderia pseudomallei.
Start - end/Length
Conservation (Infernal search)
Highly conserved in Burkholderia
110185 - 110354/170
Bacteria (detected in Proteobacteria, Bacteroidetes, Firmicutes, etc)
2290411 - 2290508/98
2768674 - 2768787/114
Bacteria (detected in Actinobacteria, Cyanobacteria, Firmicutes, etc)
2887980 - 2888055/76
3154052 - 3154260/209
4031759 - 4031986/228
2326038 - 2326224/187
Proteobacteria (predominantly in Burkholderiales, detected in Deltaproteobacteria and Gammaproteobacteria)
Unique to B. pseudomallei
892370 - 892562/193
575285 - 575425/141
Validation of novel sRNAs using RT-PCR
Analysis of novel sRNAs in Burkholderia pseudomallei
Bp1_Cand684_SIPHT was detected in different groups of bacteria, including Actinobacteria, Cyanobacteria and Firmicutes. Physical co-localization analysis showed that the flanking genes were not associated with the same pathways or functions (Figure 6E), suggesting a possible trans-acting role.
Bp1_Cand612_SIPHT, Bp1_Cand697_SIPHT and Bp1_Cand738_SIPHT are RT-PCR validated sRNA candidates that were found to be Burkholderia-specific. These three sRNAs were not detected in bacteria other than Burkholderia spp. during the Infernal search. From the physical co-localization analysis, each of these three sRNA genes has similar flanking genes in different Burkholderia spp. (Figure 6B-D). For Bp1_Cand612_SIPHT and Bp1_Cand697_SIPHT, although R. solanacearum has a similar gene arrangement at the equivalent regions, no such sRNA genes were predicted in that genome.
A total of 1,306 B. pseudomallei sRNA genes were predicted in this study of which: 21 have homologs in Rfam; 15 novel sRNAs were shortlisted due to their conservation in Burkholderia spp. or different B. pseudomallei strains; and 8 of these were verified experimentally. Though the functions for the novel sRNAs obtained in this study remain unknown, their presence in B. pseudomallei is evidence that sRNAs are indeed involved in this bacterium's many different cellular activities that may include regulation of pathogenesis and virulence mechanisms as well as adaptation to environmentally induced changes.
Research funding was provided by Universiti Kebangsaan Malaysia via the UKM research university grants UKM-GUP-KPB-08-33-132 and DIP-2012-13 and the Ministry of Higher Education Malaysia grant ERGS/1/2012/STG08/UKM/02/5. KJS was funded by a National Science Fellowship from the Ministry of Science, Technology and Innovation, Malaysia. CSF was funded by the MyMaster-MyBrain 15 scholarship from the Ministry of Higher Education, Malaysia and the Universiti Kebangsaan Malaysia Zamalah postgraduate research fellowship.
This article has been published as part of BMC Genomics Volume 13 Supplement 7, 2012: Eleventh International Conference on Bioinformatics (InCoB2012): Computational Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/13/S7.
- Argaman L, Hershberg R, Vogel J, Bejerano G, Wagner EG, Margalit H, Altuvia S: Novel small RNA-encoding genes in the intergenic regions of Escherichia coli. Curr Biol. 2001, 11: 941-950. 10.1016/S0960-9822(01)00270-6.View ArticlePubMedGoogle Scholar
- Saito S, Kakeshita H, Nakamura K: Novel small RNA-encoding genes in the intergenic regions of Bacillus subtilis. Gene. 2009, 428: 2-8. 10.1016/j.gene.2008.09.024.View ArticlePubMedGoogle Scholar
- Fedor MJ, Williamson JR: The catalytic diversity of RNAs. Nat Rev Mol Cell Biol. 2005, 6: 399-412. 10.1038/nrm1647.View ArticlePubMedGoogle Scholar
- Wu Q, Huang L, Zhang Y: The structure and function of catalytic RNAs. Sci China C Life Sci. 2009, 52: 232-244. 10.1007/s11427-009-0038-z.View ArticlePubMedGoogle Scholar
- Beisel CL, Storz G: Base pairing small RNAs and their roles in global regulatory networks. FEMS Microbiol Rev. 2010, 34: 866-882.PubMed CentralView ArticlePubMedGoogle Scholar
- Storz G, Opdyke JA, Zhang A: Controlling mRNA stability and translation with small, noncoding RNAs. Curr Opin Microbiol. 2004, 7: 140-144. 10.1016/j.mib.2004.02.015.View ArticlePubMedGoogle Scholar
- Storz G, Vogel J, Wassarman KM: Regulation by small RNAs in bacteria: expanding frontiers. Mol Cell. 2011, 43: 880-891. 10.1016/j.molcel.2011.08.022.PubMed CentralView ArticlePubMedGoogle Scholar
- Hershberg R, Altuvia S, Margalit H: A survey of small RNA-encoding genes in Escherichia coli. Nucleic Acids Res. 2003, 31: 1813-1820. 10.1093/nar/gkg297.PubMed CentralView ArticlePubMedGoogle Scholar
- Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, Wilkinson AC, Finn RD, Griffiths-Jones S, Eddy SR, Bateman A: Rfam: updates to the RNA families database. Nucleic Acids Res. 2009, 37: D136-140. 10.1093/nar/gkn766.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang HY, Chang HY, Chou CH, Tseng CP, Ho SY, Yang CD, Ju YW, Huang HD: sRNAMap: genomic maps for small non-coding RNAs, their regulators and their targets in microbial genomes. Nucleic Acids Res. 2009, 37: D150-154. 10.1093/nar/gkn852.PubMed CentralView ArticlePubMedGoogle Scholar
- Vogel J, Papenfort K: Small non-coding RNAs and the bacterial outer membrane. Current Opinion in Microbiology. 2006, 9: 605-611. 10.1016/j.mib.2006.10.006.View ArticlePubMedGoogle Scholar
- Benito Y, Kolb FA, Romby P, Lina G, Etienne J, Vandenesch F: Probing the structure of RNAIII, the Staphylococcus aureus agr regulatory RNA, and identification of the RNA domain involved in repression of protein A expression. RNA. 2000, 6: 668-679. 10.1017/S1355838200992550.PubMed CentralView ArticlePubMedGoogle Scholar
- Altuvia S, Zhang A, Argaman L, Tiwari A, Storz G: The Escherichia coli OxyS regulatory RNA represses fhlA translation by blocking ribosome binding. EMBO J. 1998, 17: 6069-6075. 10.1093/emboj/17.20.6069.PubMed CentralView ArticlePubMedGoogle Scholar
- Lenz DH, Mok KC, Lilley BN, Kulkarni RV, Wingreen NS, Bassler BL: The small RNA chaperone Hfq and multiple small RNAs control quorum sensing in Vibrio harveyi and Vibrio cholerae. Cell. 2004, 118: 69-82. 10.1016/j.cell.2004.06.009.View ArticlePubMedGoogle Scholar
- Weilbacher T, Suzuki K, Dubey AK, Wang X, Gudapaty S, Morozov I, Baker CS, Georgellis D, Babitzke P, Romeo T: A novel sRNA component of the carbon storage regulatory system of Escherichia coli. Mol Microbiol. 2003, 48: 657-670. 10.1046/j.1365-2958.2003.03459.x.View ArticlePubMedGoogle Scholar
- Barrick JE, Sudarsan N, Weinberg Z, Ruzzo WL, Breaker RR: 6S RNA is a widespread regulator of eubacterial RNA polymerase that resembles an open promoter. RNA. 2005, 11: 774-784. 10.1261/rna.7286705.PubMed CentralView ArticlePubMedGoogle Scholar
- Chabelskaya S, Gaillot O, Felden B: A Staphylococcus aureus small RNA is required for bacterial virulence and regulates the expression of an immune-evasion molecule. PLoS Pathog. 2010, 6: e1000927-10.1371/journal.ppat.1000927.PubMed CentralView ArticlePubMedGoogle Scholar
- Padalon-Brauch G, Hershberg R, Elgrably-Weiss M, Baruch K, Rosenshine I, Margalit H, Altuvia S: Small RNAs encoded within genetic islands of Salmonella typhimurium show host-induced expression and role in virulence. Nucleic Acids Res. 2008, 36: 1913-1927. 10.1093/nar/gkn050.PubMed CentralView ArticlePubMedGoogle Scholar
- Muers M: Small RNAs: microbial metatranscriptomics goes deep. Nat Rev Genet. 2009, 10: 426-427.View ArticleGoogle Scholar
- Song T, Wai SN: A novel sRNA that modulates virulence and environmental fitness of Vibrio cholerae. RNA Biol. 2009, 6: 254-258. 10.4161/rna.6.3.8371.View ArticlePubMedGoogle Scholar
- Camacho EM, Serna A, Madrid C, Marques S, Fernandez R, de la Cruz F, Juarez A, Casadesus J: Regulation of finP transcription by DNA adenine methylation in the virulence plasmid of Salmonella enterica. J Bacteriol. 2005, 187: 5691-5699. 10.1128/JB.187.16.5691-5699.2005.PubMed CentralView ArticlePubMedGoogle Scholar
- Giangrossi M, Prosseda G, Tran CN, Brandi A, Colonna B, Falconi M: A novel antisense RNA regulates at transcriptional level the virulence gene icsA of Shigella flexneri. Nucleic Acids Res. 2010, 38: 3362-3375. 10.1093/nar/gkq025.PubMed CentralView ArticlePubMedGoogle Scholar
- Robertson GT, Roop RM: The Brucella abortus host factor I (HF-I) protein contributes to stress resistance during stationary phase and is a major determinant of virulence in mice. Mol Microbiol. 1999, 34: 690-700. 10.1046/j.1365-2958.1999.01629.x.View ArticlePubMedGoogle Scholar
- Brown L, Elliott T: Efficient translation of the RpoS sigma factor in Salmonella typhimurium requires host factor I, an RNA-binding protein encoded by the hfq gene. J Bacteriol. 1996, 178: 3763-3770.PubMed CentralPubMedGoogle Scholar
- Ding Y, Davis BM, Waldor MK: Hfq is essential for Vibrio cholerae virulence and downregulates sigma expression. Mol Microbiol. 2004, 53: 345-354. 10.1111/j.1365-2958.2004.04142.x.View ArticlePubMedGoogle Scholar
- Nakao H, Watanabe H, Nakayama S, Takeda T: yst gene expression in Yersinia enterocolitica is positively regulated by a chromosomal region that is highly homologous to Escherichia coli host factor 1 gene (hfq). Mol Microbiol. 1995, 18: 859-865. 10.1111/j.1365-2958.1995.18050859.x.View ArticlePubMedGoogle Scholar
- Sonnleitner E, Hagens S, Rosenau F, Wilhelm S, Habel A, Jager KE, Blasi U: Reduced virulence of a hfq mutant of Pseudomonas aeruginosa O1. Microb Pathog. 2003, 35: 217-228. 10.1016/S0882-4010(03)00149-9.View ArticlePubMedGoogle Scholar
- Zhang A, Wassarman KM, Rosenow C, Tjaden BC, Storz G, Gottesman S: Global analysis of small RNA and mRNA targets of Hfq. Mol Microbiol. 2003, 50: 1111-1124. 10.1046/j.1365-2958.2003.03734.x.View ArticlePubMedGoogle Scholar
- Gan YH: Interaction between Burkholderia pseudomallei and the host immune response: sleeping with the enemy?. J Infect Dis. 2005, 192: 1845-1850. 10.1086/497382.View ArticlePubMedGoogle Scholar
- Pitt TL, Trakulsomboon S, Dance DA: Molecular phylogeny of Burkholderia pseudomallei. Acta Trop. 2000, 74: 181-185. 10.1016/S0001-706X(99)00068-6.View ArticlePubMedGoogle Scholar
- Holden MT, Titball RW, Peacock SJ, Cerdeno-Tarraga AM, Atkins T, Crossman LC, Pitt T, Churcher C, Mungall K, Bentley SD, et al: Genomic plasticity of the causative agent of melioidosis, Burkholderia pseudomallei. Proc Natl Acad Sci USA. 2004, 101: 14240-14245. 10.1073/pnas.0403302101.PubMed CentralView ArticlePubMedGoogle Scholar
- Hasselbring BM, Patel MK, Schell MA: Dictyostelium discoideum as a model system for identification of Burkholderia pseudomallei virulence factors. Infect Immun. 2011, 79: 2079-2088. 10.1128/IAI.01233-10.PubMed CentralView ArticlePubMedGoogle Scholar
- O'Quinn AL, Wiegand EM, Jeddeloh JA: Burkholderia pseudomallei kills the nematode Caenorhabditis elegans using an endotoxin-mediated paralysis. Cell Microbiol. 2001, 3: 381-393. 10.1046/j.1462-5822.2001.00118.x.View ArticlePubMedGoogle Scholar
- Huang CT: What is Pseudomonas pseudomallei. Elixir. 1976, 70-72.Google Scholar
- Choy JL, Mayo M, Janmaat A, Currie BJ: Animal melioidosis in Australia. Acta Tropica. 2000, 74: 153-158. 10.1016/S0001-706X(99)00065-0.View ArticlePubMedGoogle Scholar
- Ngauy V, Lemeshev Y, Sadkowski L, Crawford G: Cutaneous melioidosis in a man who was taken as a prisoner of war by the Japanese during World War II. J Clin Microbiol. 2005, 43: 970-972. 10.1128/JCM.43.2.970-972.2005.PubMed CentralView ArticlePubMedGoogle Scholar
- Pumpuang A, Chantratita N, Wikraiphat C, Saiprom N, Day NP, Peacock SJ, Wuthiekanun V: Survival of Burkholderia pseudomallei in distilled water for 16 years. Trans R Soc Trop Med Hyg. 2011, 105: 598-600. 10.1016/j.trstmh.2011.06.004.PubMed CentralView ArticlePubMedGoogle Scholar
- Cruz-Migoni A, Hautbergue GM, Artymiuk PJ, Baker PJ, Bokori-Brown M, Chang C-T, Dickman MJ, Essex-Lopresti A, Harding SV, Mahadi NM, et al: A Burkholderia pseudomallei toxin inhibits helicase activity of translation factor eIF4A. Science. 2011, 334: 821-824. 10.1126/science.1211915.View ArticlePubMedGoogle Scholar
- Coenye T, Drevinek P, Mahenthiralingam E, Shah SA, Gill RT, Vandamme P, Ussery DW: Identification of putative noncoding RNA genes in the Burkholderia cenocepacia J2315 genome. FEMS Microbiol Lett. 2007, 276: 83-92. 10.1111/j.1574-6968.2007.00916.x.View ArticlePubMedGoogle Scholar
- Nawrocki EP, Kolbe DL, Eddy SR: Infernal 1.0: inference of RNA alignments. Bioinformatics. 2009, 25: 1335-1337. 10.1093/bioinformatics/btp157.PubMed CentralView ArticlePubMedGoogle Scholar
- Rivas E, Eddy SR: Noncoding RNA gene detection using comparative sequence analysis. BMC Bioinformatics. 2001, 2: 8-10.1186/1471-2105-2-8.PubMed CentralView ArticlePubMedGoogle Scholar
- Gruber AR, Findeiss S, Washietl S, Hofacker IL, Stadler PF: Rnaz 2.0: improved noncoding RNA detection. Pac Symp Biocomput. 2010, 15: 69-79.Google Scholar
- Washietl S, Hofacker IL, Stadler PF: Fast and reliable prediction of noncoding RNAs. Proc Natl Acad Sci USA. 2005, 102: 2454-2459. 10.1073/pnas.0409169102.PubMed CentralView ArticlePubMedGoogle Scholar
- Livny J, Brencic A, Lory S, Waldor MK: Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2. Nucleic Acids Res. 2006, 34: 3484-3493. 10.1093/nar/gkl453.PubMed CentralView ArticlePubMedGoogle Scholar
- Livny J, Fogel MA, Davis BM, Waldor MK: sRNAPredict: an integrative computational approach to identify sRNAs in bacterial genomes. Nucleic Acids Res. 2005, 33: 4096-4105. 10.1093/nar/gki715.PubMed CentralView ArticlePubMedGoogle Scholar
- Sridhar J, Narmada SR, Sabarinathan R, Ou H-Y, Deng Z, Sekar K, Rafi ZA, Rajakumar K: sRNAscanner: a computational tool for intergenic small RNA detection in bacterial genomes. PLoS One. 2010, 5: e11970-10.1371/journal.pone.0011970.PubMed CentralView ArticlePubMedGoogle Scholar
- Herbig A, Nieselt K: nocoRNAc: characterization of non-coding RNAs in prokaryotes. BMC Bioinformatics. 2011, 12: 40-10.1186/1471-2105-12-40.PubMed CentralView ArticlePubMedGoogle Scholar
- Livny J, Teonadi H, Livny M, Waldor MK: High-throughput, kingdom-wide prediction and annotation of bacterial non-coding RNAs. PLoS One. 2008, 3: e3197-10.1371/journal.pone.0003197.PubMed CentralView ArticlePubMedGoogle Scholar
- Rutherford K, Parkhill J, Crook J, Horsnell T, Rice P, Rajandream MA, Barrell B: Artemis: sequence visualization and annotation. Bioinformatics. 2000, 16: 944-945. 10.1093/bioinformatics/16.10.944.View ArticlePubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralView ArticlePubMedGoogle Scholar
- Carver TJ, Rutherford KM, Berriman M, Rajandream MA, Barrell BG, Parkhill J: ACT: the Artemis Comparison Tool. Bioinformatics. 2005, 21: 3422-3423. 10.1093/bioinformatics/bti553.View ArticlePubMedGoogle Scholar
- Zuker M: Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res. 2003, 31: 3406-3415. 10.1093/nar/gkg595.PubMed CentralView ArticlePubMedGoogle Scholar
- Gruber AR, Lorenz R, Bernhart SH, Neubock R, Hofacker IL: The Vienna RNA websuite. Nucleic Acids Res. 2008, 36: W70-74. 10.1093/nar/gkn188.PubMed CentralView ArticlePubMedGoogle Scholar
- Darty K, Denise A, Ponty Y: VARNA: interactive drawing and editing of the RNA secondary structure. Bioinformatics. 2009, 25: 1974-1975. 10.1093/bioinformatics/btp250.PubMed CentralView ArticlePubMedGoogle Scholar
- Smith C, Heyne S, Richter AS, Will S, Backofen R: Freiburg RNA Tools: a web server integrating INTARNA, EXPARNA and LOCARNA. Nucleic Acids Res. 2010, 38 (Suppl): W373-377.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee SH, Chong CE, Lim BS, Chai SJ, Sam KK, Mohamed R, Nathan S: Burkholderia pseudomallei animal and human isolates from Malaysia exhibit different phenotypic characteristics. Diagn Microbiol Infect Dis. 2007, 58: 263-270. 10.1016/j.diagmicrobio.2007.01.002.View ArticlePubMedGoogle Scholar
- Lu X, Goodrich-Blair H, Tjaden B: Assessing computational tools for the discovery of small RNA genes in bacteria. RNA. 2011, 17: 1635-1647. 10.1261/rna.2689811.PubMed CentralView ArticlePubMedGoogle Scholar
- van Rijsbergen CJ: Information Retrieval. 1979, Butterworths, LondonGoogle Scholar
- Schattner P: Searching for RNA genes using base-composition statistics. Nucleic Acids Res. 2002, 30: 2076-2082. 10.1093/nar/30.9.2076.PubMed CentralView ArticlePubMedGoogle Scholar
- Lorenz C, Gesell T, Zimmermann B, Schoeberl U, Bilusic I, Rajkowitsch L, Waldsich C, von Haeseler A, Schroeder R: Genomic SELEX for Hfq-binding RNAs identifies genomic aptamers predominantly in antisense transcripts. Nucleic Acids Res. 2010, 38: 3794-3808. 10.1093/nar/gkq032.PubMed CentralView ArticlePubMedGoogle Scholar
- Brennan RG, Link TM: Hfq structure, function and ligand binding. Curr Opin Microbiol. 2007, 10: 125-133. 10.1016/j.mib.2007.03.015.View ArticlePubMedGoogle Scholar
- Valentin-Hansen P, Eriksen M, Udesen C: The bacterial Sm-like protein Hfq: a key player in RNA transactions. Mol Microbiol. 2004, 51: 1525-1533. 10.1111/j.1365-2958.2003.03935.x.View ArticlePubMedGoogle Scholar
- Otaka H, Ishikawa H, Morita T, Aiba H: PolyU tail of rho-independent terminator of bacterial small RNAs is essential for Hfq action. Proc Natl Acad Sci USA. 2011, 108: 13059-13064. 10.1073/pnas.1107050108.PubMed CentralView ArticlePubMedGoogle Scholar
- Rivas E, Klein RJ, Jones TA, Eddy SR: Computational identification of noncoding RNAs in E. coli by comparative genomics. Curr Biol. 2001, 11: 1369-1373. 10.1016/S0960-9822(01)00401-8.View ArticlePubMedGoogle Scholar
- Chen S, Lesnik EA, Hall TA, Sampath R, Griffey RH, Ecker DJ, Blyn LB: A bioinformatics based approach to discover small RNA genes in the Escherichia coli genome. Biosystems. 2002, 65: 157-177. 10.1016/S0303-2647(02)00013-8.View ArticlePubMedGoogle Scholar
- Panek J, Bobek J, Mikulik K, Basler M, Vohradsky J: Biocomputational prediction of small non-coding RNAs in Streptomyces. BMC Genomics. 2008, 9: 217-10.1186/1471-2164-9-217.PubMed CentralView ArticlePubMedGoogle Scholar
- Voss B, Georg J, Schon V, Ude S, Hess WR: Biocomputational prediction of non-coding RNAs in model cyanobacteria. BMC Genomics. 2009, 10: 123-10.1186/1471-2164-10-123.PubMed CentralView ArticlePubMedGoogle Scholar
- Geissmann T, Chevalier C, Cros MJ, Boisset S, Fechter P, Noirot C, Schrenzel J, Francois P, Vandenesch F, Gaspin C, Romby P: A search for small noncoding RNAs in Staphylococcus aureus reveals a conserved sequence motif for regulation. Nucleic Acids Res. 2009, 37: 7239-7257. 10.1093/nar/gkp668.PubMed CentralView ArticlePubMedGoogle Scholar
- Tran TT, Zhou F, Marshburn S, Stead M, Kushner SR, Xu Y: De novo computational prediction of non-coding RNA genes in prokaryotic genomes. Bioinformatics. 2009, 25: 2897-2905. 10.1093/bioinformatics/btp537.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.