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Bacterial Noncoding RNAs Excised from within Protein-Coding Transcripts.

ABSTRACT: Prokaryotic genomes encode a plethora of small noncoding RNAs (ncRNAs) that fine-tune the expression of specific genes. The vast majority of known bacterial ncRNAs are encoded from within intergenic regions, where their expression is controlled by promoter and terminator elements, similarly to protein-coding genes. In addition, recent studies have shown that functional ncRNAs can also be derived from gene 3' untranslated regions (3'UTRs) via an alternative biogenesis pathway, in which the ncRNA segment is separated from the mRNA via RNase cleavage. Here, we report the detection of a large set of decay-generated noncoding RNAs (decRNAs), many of which are completely embedded within protein-coding mRNA regions rather than in the UTRs. We show that these decRNAs are "carved out" of the mRNA through the action of RNase E and that they are predicted to fold into highly stable RNA structures, similar to those of known ncRNAs. A subset of these decRNAs is predicted to interact with Hfq or ProQ or both, which act as ncRNA chaperones, and some decRNAs display evolutionarily conserved sequences and conserved expression patterns between different species. These results suggest that mRNA protein-coding regions may harbor a large set of potentially functional small RNAs.IMPORTANCE Bacteria and archaea utilize regulatory small noncoding RNAs (ncRNAs) to control the expression of specific genetic programs. These ncRNAs are almost exclusively encoded within intergenic regions and are independently transcribed. Here, we report on a large set ncRNAs that are "carved out" from within the protein-coding regions of Escherichia coli mRNAs by cellular RNases. These protected mRNA fragments fold into energetically stable RNA structures, reminiscent of those of intergenic regulatory ncRNAs. In addition, a subset of these ncRNAs coprecipitate with the major ncRNA chaperones Hfq and ProQ and display evolutionarily conserved sequences and conserved expression patterns between different bacterial species. Our data suggest that protein-coding genes can potentially act as a reservoir of regulatory ncRNAs.

SUBMITTER: Dar D

PROVIDER: S-EPMC6156199 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Bacterial Noncoding RNAs Excised from within Protein-Coding Transcripts.

Dar Daniel D Sorek Rotem R

mBio 20180925 5

Prokaryotic genomes encode a plethora of small noncoding RNAs (ncRNAs) that fine-tune the expression of specific genes. The vast majority of known bacterial ncRNAs are encoded from within intergenic regions, where their expression is controlled by promoter and terminator elements, similarly to protein-coding genes. In addition, recent studies have shown that functional ncRNAs can also be derived from gene 3' untranslated regions (3'UTRs) via an alternative biogenesis pathway, in which the ncRNA ...[more]

PMID: 30254125

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Project description:Many viruses produce protein-coding and noncoding subgenomic RNAs (sgRNAs) that are critical for infection. A recently discovered pathway for viral sgRNA production uses exoribonuclease-resistant RNAs (xrRNAs), discrete folded RNA elements that block the processive exoribonucleolytic degradation of RNA. xrRNAs are widespread in animal-infecting flaviviruses but had been found only in three members of the plant virus genus Dianthovirus Also, xrRNAs had been found only in the 3' untranslated regions (3'UTRs) of viral RNAs, where they produce noncoding sgRNAs. The degree to which xrRNA elements exist in other viruses, the conservation of their ring-like fold, and the ability of xrRNAs to operate in diverse contexts were unknown. Using computational tools and biochemical assays, we discovered xrRNA elements pervading two large families of plant-infecting RNA viruses, demonstrating their importance and widespread utility. Comparison of the sequences and functional requirements suggests that all adopt the characteristic ring-like fold. Unexpectedly, many of these newly discovered xrRNAs are located in intergenic regions rather than 3´UTRs, and some are associated with the 5' ends of subgenomic RNAs that encode viral proteins. This suggests that xrRNAs are involved in the production of both coding and noncoding subgenomic RNAs and can operate as part of broader mechanisms to regulate RNA levels and protein expression. These discoveries expand the potential roles for xrRNAs and suggest that xrRNAs may represent a more general strategy for RNA maturation and maintenance than previously known.IMPORTANCE During infection, viruses often produce subgenomic RNAs (sgRNAs) that either serve as the template for protein synthesis or act as "riboregulators" that interact with and influence the viral and cellular machinery. Recently, a mechanism for producing sgRNAs was found that depends on the presence of specifically structured RNA elements (xrRNAs). However, the degree to which this mechanism is used, where the elements are found, their structural diversity, and what types of sgRNAs are produced by this pathway were unclear. This article describes the discovery of these structured RNA elements in two large families of plant viruses and shows that they are used to produce both protein-coding sgRNAs and "riboregulatory" RNAs. These discoveries provide evidence that xrRNA-based RNA maturation pathways may be more widespread than previously anticipated and that they are involved in producing a variety of RNAs of diverse functions.

Project description:BackgroundIn recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts.MethodsThe proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs.ResultsThis method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively.ConclusionsThe SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species.

Dataset Information

Bacterial Noncoding RNAs Excised from within Protein-Coding Transcripts.

Publications

Bacterial Noncoding RNAs Excised from within Protein-Coding Transcripts.

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