Dataset Information

Kinetic analysis reveals the fate of a microRNA following target regulation in mammalian cells.

ABSTRACT: Considerable details about microRNA (miRNA) biogenesis and regulation have been uncovered, but little is known about the fate of the miRNA subsequent to target regulation. To gain insight into this process, we carried out kinetic analysis of a miRNA's turnover following termination of its biogenesis and during regulation of a target that is not subject to Ago2-mediated catalytic cleavage. By quantitating the number of molecules of the miRNA and its target in steady state and in the course of its decay, we found that each miRNA molecule was able to regulate at least two target transcripts, providing in vivo evidence that the miRNA is not irreversibly sequestered with its target and that the nonslicing pathway of miRNA regulation is multiple-turnover. Using deep sequencing, we further show that miRNA recycling is limited by target regulation, which promotes posttranscriptional modifications to the 3' end of the miRNA and accelerates the miRNA's rate of decay. These studies provide new insight into the efficiency of miRNA regulation that help to explain how a miRNA can regulate a vast number of transcripts and that identify one of the mechanisms that impart specificity to miRNA decay in mammalian cells.

SUBMITTER: Baccarini A

PROVIDER: S-EPMC3088433 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Kinetic analysis reveals the fate of a microRNA following target regulation in mammalian cells.

Baccarini Alessia A Chauhan Hemangini H Gardner Thomas J TJ Jayaprakash Anitha D AD Sachidanandam Ravi R Brown Brian D BD

Current biology : CB 20110225 5

Considerable details about microRNA (miRNA) biogenesis and regulation have been uncovered, but little is known about the fate of the miRNA subsequent to target regulation. To gain insight into this process, we carried out kinetic analysis of a miRNA's turnover following termination of its biogenesis and during regulation of a target that is not subject to Ago2-mediated catalytic cleavage. By quantitating the number of molecules of the miRNA and its target in steady state and in the course of its ...[more]

PMID: 21353554

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Project description:MicroRNAs (miRNAs) are single-stranded non-coding RNAs known to regulate a wide range of cellular processes by silencing the gene expression at the protein and/or mRNA levels. Computational prediction of miRNA targets is essential for elucidating the detailed functions of miRNA. However, the prediction specificity and sensitivity of the existing algorithms are still poor to generate meaningful, workable hypotheses for subsequent experimental testing. Constructing a richer and more reliable training data set and developing an algorithm that properly exploits this data set would be the key to improve the performance current prediction algorithms.A comprehensive training data set is constructed for mammalian miRNAs with its positive targets obtained from the most up-to-date miRNA target depository called miRecords and its negative targets derived from 20 microarray data. A new algorithm SVMicrO is developed, which assumes a 2-stage structure including a site support vector machine (SVM) followed by a UTR-SVM. SVMicrO makes prediction based on 21 optimal site features and 18 optimal UTR features, selected by training from a comprehensive collection of 113 site and 30 UTR features. Comprehensive evaluation of SVMicrO performance has been carried out on the training data, proteomics data, and immunoprecipitation (IP) pull-down data. Comparisons with some popular algorithms demonstrate consistent improvements in prediction specificity, sensitivity and precision in all tested cases. All the related materials including source code and genome-wide prediction of human targets are available at http://compgenomics.utsa.edu/svmicro.html.A 2-stage SVM based new miRNA target prediction algorithm called SVMicrO is developed. SVMicrO is shown to be able to achieve robust performance. It holds the promise to achieve continuing improvement whenever better training data that contain additional verified or high confidence positive targets and properly selected negative targets are available.

Dataset Information

Kinetic analysis reveals the fate of a microRNA following target regulation in mammalian cells.

Publications

Kinetic analysis reveals the fate of a microRNA following target regulation in mammalian cells.

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