Detection of small RNAs in Pseudomonas aeruginosa by RNomics and structure-based bioinformatic tools

Sonnleitner, Elisabeth; Sorger-Domenigg, Theresa; Madej, M; Findeiß, Sven; Hackermüller, Jörg; Hüttenhofer, Alexander; Stadler, Peter F.; Bläsi, Udo; Moll, Isabella

doi:10.1099/mic.0.2008/019703-0

Cited by 96 publications

(97 citation statements)

References 62 publications

(73 reference statements)

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“…Twenty-two SNPs are transversions, and 17 SNPs are transitions. Nine SNPs are located in intergenic regions, of which SNP 5036891 resides within one of the noncoding RNA loci described so far (3,23,35). Of the 30 SNPs in coding regions, 13 are synonymous substitutions, 16 cause an amino acid exchange, and one changes an arginine into a stop codon.…”

Section: Resultsmentioning

confidence: 99%

Genome Diversity ofPseudomonas aeruginosaPAO1 Laboratory Strains

et al. 2010

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Pseudomonas aeruginosa PAO1 is the most commonly used strain for research on this ubiquitous and metabolically versatile opportunistic pathogen. Strain PAO1, a derivative of the original Australian PAO isolate, has been distributed worldwide to laboratories and strain collections. Over decades discordant phenotypes of PAO1 sublines have emerged. Taking the existing PAO1-UW genome sequence (named after the University of Washington, which led the sequencing project) as a blueprint, the genome sequences of reference strains MPAO1 and PAO1-DSM (stored at the German Collection for Microorganisms and Cell Cultures [DSMZ]) were resolved by physical mapping and deep short read sequencing-by-synthesis. MPAO1 has been the source of near-saturation libraries of transposon insertion mutants, and PAO1-DSM is identical in its SpeI-DpnI restriction map with the original isolate. The major genomic differences of MPAO1 and PAO1-DSM in comparison to PAO1-UW are the lack of a large inversion, a duplication of a mobile 12-kb prophage region carrying a distinct integrase and protein phosphatases or kinases, deletions of 3 to 1,006 bp in size, and at least 39 single-nucleotide substitutions, 17 of which affect protein sequences. The PAO1 sublines differed in their ability to cope with nutrient limitation and their virulence in an acute murine airway infection model. Subline PAO1-DSM outnumbered the two other sublines in late stationary growth phase. In conclusion, P. aeruginosa PAO1 shows an ongoing microevolution of genotype and phenotype that jeopardizes the reproducibility of research. High-throughput genome resequencing will resolve more cases and could become a proper quality control for strain collections.The metabolically versatile Pseudomonas aeruginosa is an opportunistic pathogen of plants, animals, and humans and is ubiquitously distributed in soil and aquatic habitats. The common reference strain is P. aeruginosa PAO1, a spontaneous chloramphenicol-resistant mutant of the original PAO strain (earlier called "P. aeruginosa strain 1") that had been isolated in 1954 from a wound in Melbourne, Australia (9, 10). This PAO1 strain from Bruce Holloway's laboratory has become the reference strain for Pseudomonas genetics and functional analyses of the physiology and metabolism of this gammaproteobacterium. A genetic map of its chromosome was generated by exploiting the mechanisms of gene exchange in bacteria, i.e., transduction and conjugation (11). With the advent of pulsedfield gel electrophoresis (PFGE), a physical map of the PAO1 genome was constructed (32) and later merged with the genetic map information (12). By 2000 the PAO1 strain had been completely sequenced (36). Thereafter, the genome annotation has been continually updated and the database content and functionality have been expanded to facilitate accelerated discovery of P. aeruginosa drug targets and vaccine candidates (38). Two near-saturation libraries of transposon insertion mutants have been constructed in P. aeruginosa PAO1 as a global resource for the scient...

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Section: Resultsmentioning

confidence: 99%

Genome Diversity ofPseudomonas aeruginosaPAO1 Laboratory Strains

et al. 2010

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“…RNAz classifies multiple sequence alignments into ncRNA/non-ncRNA based on the detection of significant conserved secondary structure and base pair (bp) covariation. It has been applied to several organism families, including bacteria, where it led to experimental confirmation of novel ncRNAs (del Val et al 2007;Panek et al 2008;Sonnleitner et al 2008). We compared NAPP clustering and RNAz in their abilities to distinguish true ncRNAs from other conserved elements.…”

Section: Performance Of Napp As An Ncrna Classifiermentioning

confidence: 99%

Single-pass classification of all noncoding sequences in a bacterial genome using phylogenetic profiles

Marchais¹,

Naville²,

Bohn³

et al. 2009

Genome Res.

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Identification and characterization of functional elements in the noncoding regions of genomes is an elusive and timeconsuming activity whose output does not keep up with the pace of genome sequencing. Hundreds of bacterial genomes lay unexploited in terms of noncoding sequence analysis, although they may conceal a wide diversity of novel RNA genes, riboswitches, or other regulatory elements. We describe a strategy that exploits the entirety of available bacterial genomes to classify all noncoding elements of a selected reference species in a single pass. This method clusters noncoding elements based on their profile of presence among species. Most noncoding RNAs (ncRNAs) display specific signatures that enable their grouping in distinct clusters, away from sequence conservation noise and other elements such as promoters. We submitted 24 ncRNA candidates from Staphylococcus aureus to experimental validation and confirmed the presence of seven novel small RNAs or riboswitches. Besides offering a powerful method for de novo ncRNA identification, the analysis of phylogenetic profiles opens a new path toward the identification of functional relationships between co-evolving coding and noncoding elements.[Supplemental material is available online at www.genome.org.]In all living organisms, the genome regions located between protein-coding sequences are home to a wide diversity of functional elements that include noncoding RNA (ncRNA) genes, DNA regulatory elements, untranslated regions (UTRs) of genes, transposable and self-replicating elements, and a variety of other transcribed or nontranscribed functional sequences. As these elements are often key players in gene regulation and thus in the global cell interaction network, their systematic identification and characterization has become a major challenge in biology.Computational protocols developed to collect and characterize noncoding elements in genomic sequences rely, to a large extent, on comparative genomics. The most common strategies involve, first, collecting sequences under selective pressure and, second, analyzing the aligned sequences using various classifiers that exploit criteria such as nucleotide composition, folding potential, fold conservation, or covariation between distant positions (Rivas and Eddy 2001;Washietl et al. 2005;Pedersen et al. 2006;Torarinsson et al. 2006). In general, such classifiers are designed to detect structured RNAs among noncoding elements with no further distinction between regulatory elements, repeats, or artifacts produced by sequence comparison algorithms.Comparative genomics entails a significant amount of expert intervention, especially in obtaining the right genome set to optimize the specificity and sensitivity of RNA detection. Although a number of studies have successfully identified ncRNAs in several animal (Missal et al. 2005;Washietl et al. 2007) and microbial genomes (Altuvia 2007), the pace at which such studies are performed and published lags far behind the rate of genome sequence output. Most of the complete...

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“…Finally, in recent years, small ORFs of a size too short for automated genome annotation (usually Ͻ150 nt) have been discovered at a staggering rate in various bacterial genomes, including E. coli (40), Pseudomonas aeruginosa (41), and marine cyanobacteria (42). In addition, many of the corresponding small peptides were experimentally validated in E. coli (43,44).…”

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confidence: 99%

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

Jäger

Sharma

Thomsen³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

187

243

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Methanosarcina mazei and related mesophilic archaea are the only organisms fermenting acetate, methylamines, and methanol to methane and carbon dioxide, contributing significantly to greenhouse gas production. The biochemistry of these metabolic processes is well studied, and genome sequences are available, yet little is known about the overall transcriptional organization and the noncoding regions representing 25% of the 4.01-Mb genome of M. mazei. We present a genome-wide analysis of transcription start sites (TSS) in M. mazei grown under different nitrogen availabilities. Pyrosequencing-based differential analysis of primary vs. processed 5 ends of transcripts discovered 876 TSS across the M. mazei genome. Unlike in other archaea, in which leaderless mRNAs are prevalent, the majority of the detected mRNAs in M. mazei carry long untranslated 5 regions. Our experimental data predict a total of 208 small RNA (sRNA) candidates, mostly from intergenic regions but also antisense to 5 and 3 regions of mRNAs. In addition, 40 new small mRNAs with ORFs of <30 aa were identified, some of which might have dual functions as mRNA and regulatory sRNA. We confirmed differential expression of several sRNA genes in response to nitrogen availability. Inspection of their promoter regions revealed a unique conserved sequence motif associated with nitrogen-responsive regulation, which might serve as a regulator binding site upstream of the common IIB recognition element. Strikingly, several sRNAs antisense to mRNAs encoding transposases indicate nitrogen-dependent transposition events. This global TSS map in archaea will facilitate a better understanding of transcriptional and posttranscriptional control in the third domain of life.Methanosarcina mazei strain Gö1 is a representative methaneproducing archaeon of ecologic significance because of its role in biogenic methane production in various anaerobic habitats on Earth (1). The genome sequences of M. mazei and its close relatives Methanosarcina acetivorans and Methanosarcina barkeri have recently become available and have revealed an unexpected low proportion of coding region (74.2% in M. acetivorans, 75.15% in M. mazei, and 79.2% in M. barkeri) (2-4). The biochemical basis of methanogenesis has been analyzed in considerable detail (5, 6). In contrast, little is known about global regulatory networks that ensure survival in periods of nutrient starvation or stress in this important group of archaea. More than 50 predicted transcriptional regulators were annotated in the genome of M. mazei. Strikingly, most of them seem to be closely related to bacterial proteins (2), whereas the basic components of the archaeal transcription and translation machineries generally are more similar to those of eukaryotes (7). A recent genetic study (8) discovered the first global transcriptional regulator of M. mazei, the nitrogen regulator NrpR, which was experimentally demonstrated to globally repress transcription of nitrogen fixation and assimilation genes in response to the nitrogen source.Bes...

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Detection of small RNAs in Pseudomonas aeruginosa by RNomics and structure-based bioinformatic tools

Cited by 96 publications

References 62 publications

Genome Diversity ofPseudomonas aeruginosaPAO1 Laboratory Strains

Genome Diversity ofPseudomonas aeruginosaPAO1 Laboratory Strains

Single-pass classification of all noncoding sequences in a bacterial genome using phylogenetic profiles

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

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