Dataset Information

LncRNA-RNA Interactions across the Human Transcriptome.

ABSTRACT: Long non-coding RNAs (lncRNAs) represent a numerous class of non-protein coding transcripts longer than 200 nucleotides. There is possibility that a fraction of lncRNAs are not functional and represent mere transcriptional noise but a growing body of evidence shows they are engaged in a plethora of molecular functions and contribute considerably to the observed diversification of eukaryotic transcriptomes and proteomes. Still, however, only ca. 1% of lncRNAs have well established functions and much remains to be done towards decipherment of their biological roles. One of the least studied aspects of lncRNAs biology is their engagement in gene expression regulation through RNA-RNA interactions. By hybridizing with mate RNA molecules, lncRNAs could potentially participate in modulation of pre-mRNA splicing, RNA editing, mRNA stability control, translation activation, or abrogation of miRNA-induced repression. Here, we implemented a similarity-search based method for transcriptome-wide identification of RNA-RNA interactions, which enabled us to find 18,871,097 lncRNA-RNA base-pairings in human. Further analyses showed that the interactions could be involved in processing, stability control and functions of 57,303 transcripts. An extensive use of RNA-Seq data provided support for approximately one third of the interactions, at least in terms of the two RNA components being co-expressed. The results suggest that lncRNA-RNA interactions are broadly used to regulate and diversify the human transcriptome.

SUBMITTER: Szczesniak MW

PROVIDER: S-EPMC4773119 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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lncRNA-RNA Interactions across the Human Transcriptome.

Szcześniak Michał Wojciech MW Makałowska Izabela I

PloS one 20160301 3

Long non-coding RNAs (lncRNAs) represent a numerous class of non-protein coding transcripts longer than 200 nucleotides. There is possibility that a fraction of lncRNAs are not functional and represent mere transcriptional noise but a growing body of evidence shows they are engaged in a plethora of molecular functions and contribute considerably to the observed diversification of eukaryotic transcriptomes and proteomes. Still, however, only ca. 1% of lncRNAs have well established functions and m ...[more]

PMID: 26930590

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Project description:BackgroundLong noncoding RNAs (lncRNAs) are a recently discovered class of non-protein coding RNAs, which have now increasingly been shown to be involved in a wide variety of biological processes as regulatory molecules. The functional role of many of the members of this class has been an enigma, except a few of them like Malat and HOTAIR. Little is known regarding the regulatory interactions between noncoding RNA classes. Recent reports have suggested that lncRNAs could potentially interact with other classes of non-coding RNAs including microRNAs (miRNAs) and modulate their regulatory role through interactions. We hypothesized that lncRNAs could participate as a layer of regulatory interactions with miRNAs. The availability of genome-scale datasets for Argonaute targets across human transcriptome has prompted us to reconstruct a genome-scale network of interactions between miRNAs and lncRNAs.ResultsWe used well characterized experimental Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) datasets and the recent genome-wide annotations for lncRNAs in public domain to construct a comprehensive transcriptome-wide map of miRNA regulatory elements. Comparative analysis revealed that in addition to targeting protein-coding transcripts, miRNAs could also potentially target lncRNAs, thus participating in a novel layer of regulatory interactions between noncoding RNA classes. Furthermore, we have modeled one example of miRNA-lncRNA interaction using a zebrafish model. We have also found that the miRNA regulatory elements have a positional preference, clustering towards the mid regions and 3' ends of the long noncoding transcripts. We also further reconstruct a genome-wide map of miRNA interactions with lncRNAs as well as messenger RNAs.ConclusionsThis analysis suggests widespread regulatory interactions between noncoding RNAs classes and suggests a novel functional role for lncRNAs. We also present the first transcriptome scale study on miRNA-lncRNA interactions and the first report of a genome-scale reconstruction of a noncoding RNA regulatory interactome involving lncRNAs.

Project description:Motivation:Long non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nt that do not get translated into proteins. Often these transcripts are processed (spliced, capped and polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA. Results:Here, we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined P-value. We applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29 and ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays. Availability and implementation:MechRNA is available for download at https://bitbucket.org/compbio/mechrna. Supplementary information:Supplementary data are available at Bioinformatics online.

Project description:ObjectivesTo explore the long non-coding RNA (lncRNA) expression pattern of congenital lung malformations on a genome-wide scale and investigate their potential biological function in four subtypes of congenital lung malformations.MethodsWe obtained both lesions and normal lung control tissues from the patients diagnosed with CPAM-I, CPAM-II, ILS, and ILS-CPAM, and underwent lobectomy (i.e., surgical removal of the whole lobe which contains the localized lesion as well as normal lung tissue). Then, we performed lncRNA transcriptome profiling in these tissues by RNA sequencing (RNA-seq). A comprehensive bioinformatics analysis was conducted to characterize the expression profiles and relevant biological functions and for multiple comparisons of lncRNA expression in the different subtypes of congenital lung malformation tissues. Furthermore, the lncRNA-mRNA co-expression network was constructed, and dysregulated mRNAs were functionally analyzed. Finally, gene set enrichment analysis (GSEA) was used to predict the potential molecular mechanism of the identified lncRNAs.ResultsA total of 5921 lncRNA transcripts were identified between congenital lung malformations tissues and normal lung control tissues. Compared with normal lung control, 481of these expressed lncRNAs were upregulated and 142 were downregulated in CPAM-I, 91 were upregulated and 14 were downregulated in CPAM-II, 39 were upregulated and 38 were downregulated in ILS, and 201 were upregulated and 38 were downregulated in ILS-CPAM. Unsupervised clustering and principal component analysis of the expressed lncRNAs visualized the differences between normal lung control and different subtypes of congenital lung malformations samples. We also confirmed significant differences in the composition of differentially expressed genes (DEGs) and the differentially expressed lncRNAs (DE lncRNAs) between CPAM-I and other subtypes of congenital lung malformations, as well as in normal lung control tissues, and observed enrichment of DEGs in the regulation of the immune system, cell projection organization, and inflammatory pathways. Finally, we identified the lncRNA FLJ26850 might be related to congenital lung malformations via ZNF473.ConclusionsSignificant differences in lncRNAs expression patterns were observed between different subtypes of congenital lung malformations and normal control. The lncRNA FLJ26850 might be related to congenital lung malformations via ZNF473.

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LncRNA-RNA Interactions across the Human Transcriptome.

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lncRNA-RNA Interactions across the Human Transcriptome.

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