Inhibition of <i>Escherichia coli</i> RNase P by oligonucleotide directed misfolding of RNA

Childs, Jessica L.; Poole, Alex W.; Turner, Douglas H.

doi:10.1261/rna.5780503

Cited by 18 publications

(13 citation statements)

References 74 publications

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“…We also detect very well conserved (although not very stable) structures within the RNAse P but miss RNAse MRP and telomerase RNA. Here the pseudoknotted structures 23,24 , which are not taken into account by RNAz , appear to be the problem.…”

Section: Selection Of Conserved Sequences and Screening For Structuramentioning

confidence: 99%

Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome

et al. 2005

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In contrast to the fairly reliable and complete annotation of the protein coding genes in the human genome, comparable information is lacking for non-coding RNAs. We present a comparative screen of vertebrate genomes for structural non-coding RNAs, which evaluates sequence conservation, secondary structure conservation, and thermodynamic stability of putative RNA structures. We predict more than 30 000 structured RNA elements in the human genome, almost 1000 of which are conserved across all vertebrates. Roughly a third is found in introns of known genes, a sixth are potential regulatory elements in untranslated regions, about half are located far away of any known gene. Only a small fraction of these sequences has been described previously. EST data demonstrate, however, that the majority of them is at least transcribed. The widespread conservation of secondary structure points to a large number of functional ncRNAs in the human genome, which we estimate to be comparable to the number of protein-coding genes. The recent finishing of the human genome sequence emphasizes the "need for reliable experimental and computational methods for comprehensive identification of non-coding RNAs" 1 . A variety of experimental techniques have been used to uncover the human and mouse transcriptomes, in particular tiling arrays 2-4 , cDNA sequencing 5,6 , and unbiased mapping of transcription factor binding sites 7 . All these studies agree that a substantial fraction of the genome is transcribed and that a large fraction of the transcriptome consists of non-coding RNAs. It is unclear, however, which fraction are functional non-coding RNAs (ncRNAs), and which constitutes "transcriptional noise" 8 .Genome-wide computational surveys of ncRNAs, on the other hand, have been impossible until recently, because ncRNAs do not share common signals that could be detected at the sequence level. A large class of ncRNAs, however, has characteristic structures that are functional and hence are well conserved over evolutionary timescales: most of the "classical" ncRNAs, including rRNAs, tRNAs, snRNAs, snoRNAs, as well as the RNA components of RNAse P and the signal recognition particle, are of this type. The stabilizing selection acting on the secondary structure causes characteristic substitution patterns in the underlying sequences: Consistent and compensatory mutations replace one type of base-pair by another one in the paired regions (helices) of the molecule. In addition, loop regions are more variable than helices. These patterns can be ex-1 ploited in comparative computational approaches 9-12 to discriminate functional RNAs from other types of conserved sequence. Recently, high levels of sequence conservation of non-coding DNA regions have been reported 13, 14 . Here we screen the complete collection of conserved non-coding DNA sequences from mammalian genomes and provide a first annotation of the complement of structurally conserved RNAs in the human genome. ResultsSelection of conserved sequences and screening for structural RNAs We s...

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Section: Selection Of Conserved Sequences and Screening For Structuramentioning

confidence: 99%

Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome

et al. 2005

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“…RNA structural motifs may be targeted with oligonucleotides (Childs et al 2002(Childs et al , 2003Disney et al 2004) or small molecules (Mei et al 1998;Sucheck and Wong 2000;Wilson and Li 2000;Gallego and Varani 2001;Childs-Disney et al 2007;Disney et al 2008; Lee et al 2009;Pushechnikov et al 2009) to disrupt viral function.…”

Section: Introductionmentioning

confidence: 99%

Identification of potential conserved RNA secondary structure throughout influenza A coding regions

Moss¹,

Priore²,

Turner³

2011

RNA

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Influenza A is a negative sense RNA virus of significant public health concern. While much is understood about the life cycle of the virus, knowledge of RNA secondary structure in influenza A virus is sparse. Predictions of RNA secondary structure can focus experimental efforts. The present study analyzes coding regions of the eight viral genome segments in both the (+) and (-) sense RNA for conserved secondary structure. The predictions are based on identifying regions of unusual thermodynamic stabilities and are correlated with studies of suppression of synonymous codon usage (SSCU). The results indicate that secondary structure is favored in the (+) sense influenza RNA. Twenty regions with putative conserved RNA structure have been identified, including two previously described structured regions. Of these predictions, eight have high thermodynamic stability and SSCU, with five of these corresponding to current annotations (e.g., splice sites), while the remaining 12 are predicted by the thermodynamics alone. Secondary structures with high conservation of base-pairing are proposed within the five regions having known function. A combination of thermodynamics, amino acid and nucleotide sequence comparisons along with SSCU was essential for revealing potential secondary structures.

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“…Inhibition of ribozymes by means of oligonucleotide directed RNA misfolding has been demonstrated e.g. for group I introns [11] and RNase P [12]. Oligomeric nucleic acid analogs were recently used to specifically inhibit IRES-dependent translation in hepatitis C virus [54], presumably by interfering with the IRES structure.…”

Section: Introductionmentioning

confidence: 99%

The effect of RNA secondary structures on RNA-ligand binding and the modifier RNA mechanism: a quantitative model

Hackermüller¹,

Meisner²,

Auer³

et al. 2005

Gene

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RNA-ligand binding often depends crucially on the local RNA secondary structure at the binding site. We develop here a model that quantitatively predicts the effect of RNA secondary structure on effective RNA-ligand binding activities based on equilibrium thermodynamics and the explicit computations of partition functions for the RNA structures. A statistical test for the impact of a particular structural feature on the binding affinities follows directly from this approach. The formalism is extended to describing the effects of hybridizing small "modifier RNAs" to a target RNA molecule outside its ligand binding site. We illustrate the applicability of our approach by quantitatively describing the interaction of the mRNA stabilizing protein HuR with AU-rich elements [Meisner et al. (2004), Chem. Biochem. in press]. We discuss our model and recent experimental findings demonstrating the effectivity of modifier RNAs in vitro in the context of the current research activities in the field of non-coding RNAs. We speculate that modifier RNAs might also exist in nature; if so, they present an additional regulatory layer for fine-tuning gene expression that could evolve rapidly, leaving no obvious traces in the genomic DNA sequences.

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Inhibition of Escherichia coli RNase P by oligonucleotide directed misfolding of RNA

Cited by 18 publications

References 74 publications

Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome

Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome

Identification of potential conserved RNA secondary structure throughout influenza A coding regions

The effect of RNA secondary structures on RNA-ligand binding and the modifier RNA mechanism: a quantitative model

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