Biocomputational prediction of small non-coding RNAs in Streptomyces

Pánek, Josef; Bobek, Jan; Mikulı́k, Karel; Basler, Marek; Vohradský, Jiřı́

doi:10.1186/1471-2164-9-217

Cited by 60 publications

(65 citation statements)

References 38 publications

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“…As mentioned in the introduction, two studies of sRNAs in S. coelicolor A3(2) were reported during our study of sRNA in S. griseus (23,27). Panek et al (23) validated the expression of 20 sRNAs by RT-PCR and DNA microarray analyses.…”

Section: Vol 191 2009mentioning

confidence: 99%

“…The complete genome sequences of several species of Actinobacteria, including two Streptomyces species, S. coelicolor A3(2) (3) and Streptomyces avermitilis (11), facilitated bioinformatics prediction of candidate regions for sRNAs in Streptomyces. Recently, two studies (23,27) reported the identification of sRNAs in S. coelicolor A3(2). Panek et al (23) predicted 32 potential sRNAs, and the expression of 20 of them was detected by reverse transcription (RT)-PCR and DNA microarray analyses.…”

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confidence: 99%

“…Recently, two studies (23,27) reported the identification of sRNAs in S. coelicolor A3(2). Panek et al (23) predicted 32 potential sRNAs, and the expression of 20 of them was detected by reverse transcription (RT)-PCR and DNA microarray analyses. Swiercz et al (27) identified nine sRNAs in S. coelicolor A3(2) through a combination of bioinformatics and experimental approaches.…”

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confidence: 99%

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Identification and Gene Disruption of Small Noncoding RNAs in Streptomyces griseus

et al. 2009

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Small noncoding RNAs (sRNAs) have been shown to control diverse cellular processes in prokaryotes. To identify and characterize novel bacterial sRNAs, a gram-positive, soil-inhabiting, filamentous bacterium, Streptomyces griseus, was examined, on the assumption that Streptomyces should express sRNAs as important regulators of morphological and physiological differentiation. By bioinformatics investigation, 54 sRNA candidates, which were encoded on intergenic regions of the S. griseus chromosome and were highly conserved in those of both Streptomyces coelicolor A3(2) and Streptomyces avermitilis, were selected. Of these 54 sRNA candidates, 17 transcripts were detected by Northern blot analysis of the total RNAs isolated from cells grown on solid medium. Then, the direction of transcription of each sRNA candidate gene was determined by S1 nuclease mapping, followed by exclusion of four sRNA candidates that were considered riboswitches of their downstream open reading frames (ORFs). Finally, a further sRNA candidate was excluded because it was cotranscribed with the upstream ORF determined by reverse transcription-PCR. Thus, 12 sRNAs ranging in size from 40 to 300 nucleotides were identified in S. griseus. Seven of them were apparently transcribed in a growth phase-dependent manner. Furthermore, of the 12 sRNAs, the expression profiles of 7 were significantly influenced by a mutation of adpA, which encodes the central transcriptional regulator of the A-factor regulatory cascade involved in both morphological differentiation and secondary metabolism in S. griseus. However, disruption of all 12 sRNA genes showed no detectable phenotypic changes; all the disruptants grew and formed aerial mycelium and spores with the same time course as the wild-type strain on various media and produced streptomycin similarly to the wild-type strain.

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Section: Vol 191 2009mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Identification and Gene Disruption of Small Noncoding RNAs in Streptomyces griseus

et al. 2009

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“…Combinations of bioinformatic predictions and experimental approaches have facilitated the identification of many sRNAs in S. coelicolor and other species (52,(269)(270)(271), and what little is known about their functions points to a boost in the perceived importance of sRNA-mediated regulation of antibiotic production in the near future. Thus, the overexpression of scr5239, an sRNA recently identified in S. coelicolor, decreased ACT production, whereas depletion of scr5239 increased production (272), and overexpression of cnc2198.1as significantly decreased RED production (273).…”

Section: Srnasmentioning

confidence: 99%

Molecular Regulation of Antibiotic Biosynthesis in Streptomyces

Liu

Chater

Chandra

et al. 2013

Microbiol Mol Biol Rev

595

536

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SUMMARY Streptomycetes are the most abundant source of antibiotics. Typically, each species produces several antibiotics, with the profile being species specific. Streptomyces coelicolor , the model species, produces at least five different antibiotics. We review the regulation of antibiotic biosynthesis in S. coelicolor and other, nonmodel streptomycetes in the light of recent studies. The biosynthesis of each antibiotic is specified by a large gene cluster, usually including regulatory genes (cluster-situated regulators [CSRs]). These are the main point of connection with a plethora of generally conserved regulatory systems that monitor the organism's physiology, developmental state, population density, and environment to determine the onset and level of production of each antibiotic. Some CSRs may also be sensitive to the levels of different kinds of ligands, including products of the pathway itself, products of other antibiotic pathways in the same organism, and specialized regulatory small molecules such as gamma-butyrolactones. These interactions can result in self-reinforcing feed-forward circuitry and complex cross talk between pathways. The physiological signals and regulatory mechanisms may be of practical importance for the activation of the many cryptic secondary metabolic gene cluster pathways revealed by recent sequencing of numerous Streptomyces genomes.

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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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Biocomputational prediction of small non-coding RNAs in Streptomyces

Cited by 60 publications

References 38 publications

Identification and Gene Disruption of Small Noncoding RNAs in Streptomyces griseus

Identification and Gene Disruption of Small Noncoding RNAs in Streptomyces griseus

Molecular Regulation of Antibiotic Biosynthesis in Streptomyces

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

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