Identification of differentially expressed small non‐coding RNAs in the legume endosymbiont <i>Sinorhizobium meliloti</i> by comparative genomics

Val, Coral del; Rivas, Elena; Torres‐Quesada, Omar; Toro, Nicolás; Jiménez‐Zurdo, José I.

doi:10.1111/j.1365-2958.2007.05978.x

Cited by 102 publications

(136 citation statements)

References 55 publications

(78 reference statements)

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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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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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“…In recent years, comparative genomics-based predictions have become a standard method to search for ncRNA genes conserved in at least two bacterial genomes (10,53,100,163,210,303,309). Thus, the high number of genome sequences from marine picocyanobacteria provides an excellent data set for the computational prediction of ncRNA genes.…”

Section: Two-component Systems and Crp-type Regulatorsmentioning

confidence: 99%

Ecological Genomics of Marine Picocyanobacteria

Scanlan

Ostrowski

Mazard

et al. 2009

Microbiol Mol Biol Rev

648

824

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SUMMARY Marine picocyanobacteria of the genera Prochlorococcus and Synechococcus numerically dominate the picophytoplankton of the world ocean, making a key contribution to global primary production. Prochlorococcus was isolated around 20 years ago and is probably the most abundant photosynthetic organism on Earth. The genus comprises specific ecotypes which are phylogenetically distinct and differ markedly in their photophysiology, allowing growth over a broad range of light and nutrient conditions within the 45°N to 40°S latitudinal belt that they occupy. Synechococcus and Prochlorococcus are closely related, together forming a discrete picophytoplankton clade, but are distinguishable by their possession of dissimilar light-harvesting apparatuses and differences in cell size and elemental composition. Synechococcus strains have a ubiquitous oceanic distribution compared to that of Prochlorococcus strains and are characterized by phylogenetically discrete lineages with a wide range of pigmentation. In this review, we put our current knowledge of marine picocyanobacterial genomics into an environmental context and present previously unpublished genomic information arising from extensive genomic comparisons in order to provide insights into the adaptations of these marine microbes to their environment and how they are reflected at the genomic level.

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“…Expression of VSsrna24 is repressed by glucose, and is hypothesized to have roles in the central carbohydrate metabolism. 4 The sRNAs sX13, 17 ErsA 18 and Smr7C, 19,20 are found in Xanthomonadaceae, Pseudomonas and Rhizobialez, respectively, but neither has the same function or structure as Spot 42. sX13 and Smr7C share secondary structure features comprising 3 stem-loops with Crich motifs and are Hfq-independent. 17,21 ErsA is Hfq-mediated and regulated by sigma factor 22, in contrast to Spot 42 that is dependent on sigma factor 70.…”

Section: Spot 42 Structure Conservation and Potential Base Pairing Wimentioning

confidence: 99%

The Spot 42 RNA: A regulatory small RNA with roles in the central metabolism

Bækkedal

Haugen

2015

RNA Biology

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T he Spot 42 RNA is a 109 nucleotide long (in Escherichia coli) noncoding small regulatory RNA (sRNA) encoded by the spf (spot fourty-two) gene. spf is found in gamma-proteobacteria and the majority of experimental work on Spot 42 RNA has been performed using E. coli, and recently Aliivibrio salmonicida. In the cell Spot 42 RNA plays essential roles as a regulator in carbohydrate metabolism and uptake, and its expression is activated by glucose, and inhibited by the cAMP-CRP complex. Here we summarize the current knowledge on Spot 42, and present the natural distribution of spf, show family-specific secondary structural features of Spot 42, and link highly conserved structural regions to mRNA target binding.

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Identification of differentially expressed small non‐coding RNAs in the legume endosymbiont Sinorhizobium meliloti by comparative genomics

Cited by 102 publications

References 55 publications

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

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

Ecological Genomics of Marine Picocyanobacteria

The Spot 42 RNA: A regulatory small RNA with roles in the central metabolism

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