Dataset Information

A-to-I editing in the miRNA seed region regulates target mRNA selection and silencing efficiency.

ABSTRACT: Hydrolytic deamination of adenosine to inosine (A-to-I) by adenosine deaminases acting on RNA (ADARs) is a post-transcriptional modification which results in a discrepancy between genomic DNA and the transcribed RNA sequence, thus contributing to the diversity of the transcriptome. Inosine preferentially base pairs with cytidine, meaning that A-to-I modifications in the mRNA sequences may be observed as A-to-G substitutions by the protein-coding machinery. Genome-wide studies have revealed that the majority of editing events occur in non-coding RNA sequences, but little is known about their functional meaning. MiRNAs are small non-coding RNAs that regulate the expression of target mRNAs with complementarities to their seed region. Here, we confirm that A-to-I editing in the miRNA seed duplex globally reassigns their target mRNAs in vivo, and reveal that miRNA containing inosine in the seed region exhibits a different degree of silencing efficiency compared to the corresponding miRNA with guanosine at the same position. The difference in base-pairing stability, deduced by melting temperature measurements, between seed-target duplexes containing either C:G or I:C pairs may account for the observed silencing efficiency. These findings unequivocally show that C:G and I:C pairs are biologically different in terms of gene expression regulation by miRNAs.

SUBMITTER: Kume H

PROVIDER: S-EPMC4150774 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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A-to-I editing in the miRNA seed region regulates target mRNA selection and silencing efficiency.

Kume Hideaki H Hino Kimihiro K Galipon Josephine J Ui-Tei Kumiko K

Nucleic acids research 20140723 15

Hydrolytic deamination of adenosine to inosine (A-to-I) by adenosine deaminases acting on RNA (ADARs) is a post-transcriptional modification which results in a discrepancy between genomic DNA and the transcribed RNA sequence, thus contributing to the diversity of the transcriptome. Inosine preferentially base pairs with cytidine, meaning that A-to-I modifications in the mRNA sequences may be observed as A-to-G substitutions by the protein-coding machinery. Genome-wide studies have revealed that ...[more]

PMID: 25056317

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Project description:miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.

Dataset Information

A-to-I editing in the miRNA seed region regulates target mRNA selection and silencing efficiency.

Publications

A-to-I editing in the miRNA seed region regulates target mRNA selection and silencing efficiency.

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