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Differential association of microRNAs with polysomes reflects distinct strengths of interactions with their mRNA targets.

ABSTRACT: While microRNAs have been shown to copurify with polysomes, their relative fraction in the translation pool (polysome occupancy) has not yet been measured. Here, we introduce a high-throughput method for quantifying polysome occupancies of hundreds of microRNAs and use it to investigate factors affecting these occupancies. Analysis in human embryonic stem cells (hESCs) and foreskin fibroblasts (hFFs) revealed microRNA-specific preferences for low, medium, or high polysome occupancy. Bioinformatics and functional analysis based on overexpression of endogenous and chimeric microRNAs showed that the polysome occupancy of microRNAs is specified by its mature sequence and depends on the choice of seed. Nuclease treatment further suggested that the differential occupancy of the microRNAs reflects interactions with their mRNA targets. Indeed, analysis of microNRA•mRNA duplexes showed that pairs involving high occupancy microRNAs exhibit significantly higher binding energy compared to pairs with low occupancy microRNAs. Since mRNAs reside primarily in polysomes, strong interactions lead to high association of microRNAs with polysomes and vice versa for weak interactions. Comparison between hESCs and hFFs data revealed that hESCs tend to express lower occupancy microRNAs, suggesting that cell type-dependent translational features may be affected by expression of a particular set of microRNAs.

SUBMITTER: Molotski N

PROVIDER: S-EPMC3425777 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Differential association of microRNAs with polysomes reflects distinct strengths of interactions with their mRNA targets.

Molotski Natali N Soen Yoav Y

RNA (New York, N.Y.) 20120726 9

While microRNAs have been shown to copurify with polysomes, their relative fraction in the translation pool (polysome occupancy) has not yet been measured. Here, we introduce a high-throughput method for quantifying polysome occupancies of hundreds of microRNAs and use it to investigate factors affecting these occupancies. Analysis in human embryonic stem cells (hESCs) and foreskin fibroblasts (hFFs) revealed microRNA-specific preferences for low, medium, or high polysome occupancy. Bioinformati ...[more]

PMID: 22836355

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Project description:BACKGROUND: MicroRNAs (miRNAs) are small non-coding RNAs affecting the expression of target genes via translational repression or mRNA degradation mechanisms. With the increasing availability of mRNA and miRNA expression data, it might be possible to assess functional targets using the fact that a miRNA might down-regulate its target mRNAs. In this work we computed the correlation of expression profiles between miRNAs and target mRNAs using the NCI-60 expression data. The aim is to investigate whether the correlations between miRNA and mRNA expression profiles, either positive or negative, can be used to assist the identification of functional miRNA-mRNA relationships. RESULTS: Predicted miRNA-mRNA interactions were taken from TargetScan 4.1 and miRBase release 5. Pearson correlation coefficients between the miRNA and the mRNA expression profiles were computed using NCI-60 data. The correlation coefficients were then subject to the Benjamini and Hochberg correction. Our results show that the percentage of TargetScan-predicted miRNA-mRNA interactions having negative correlation in expression profiles is higher than that of miRBase-predicted pairs. Using the experimentally validated miRNA targets listed in TarBase, genes involved in mRNA degradation show more negative correlations between miRNA and mRNA expression profiles, comparing with genes involved in translational repression. Furthermore, correlation analysis for miRNAs and mRNAs transcribed from the same genes shows that correlations of expression profiles between intronic miRNAs and host genes tend to be positive. Finally we found that a target gene might be down-regulated by more than one miRNAs sharing the same seed region. CONCLUSION: Our results suggest that expression profiles can be used in the computational identification of functional miRNA-target associations. One can expect a higher chance of finding negatively correlated expression profiles for TargetScan-predicted interactions than for miRBase-predicted ones. With limited experimentally validated miRNA-target interactions, expression profiles can only serve as a supplementary role in finding interactions between miRNAs and mRNAs.

Dataset Information

Differential association of microRNAs with polysomes reflects distinct strengths of interactions with their mRNA targets.

Publications

Differential association of microRNAs with polysomes reflects distinct strengths of interactions with their mRNA targets.

Similar Datasets

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