Dataset Information

Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver.

ABSTRACT: Validation of physiologic miRNA targets has been met with significant challenges. We employed HITS-CLIP to identify which miRNAs participate in liver regeneration, and to identify their target mRNAs.miRNA recruitment to the RISC is highly dynamic, changing more than five-fold for several miRNAs. miRNA recruitment to the RISC did not correlate with changes in overall miRNA expression for these dynamically recruited miRNAs, emphasizing the necessity to determine miRNA recruitment to the RISC in order to fully assess the impact of miRNA regulation. We incorporated RNA-seq quantification of total mRNA to identify expression-weighted Ago footprints, and developed a microRNA regulatory element (MRE) prediction algorithm that represents a greater than 20-fold refinement over computational methods alone. These high confidence MREs were used to generate candidate 'competing endogenous RNA' (ceRNA) networks.HITS-CLIP analysis provide novel insights into global miRNA:mRNA relationships in the regenerating liver.

SUBMITTER: Schug J

PROVIDER: S-EPMC3639193 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver.

Schug Jonathan J McKenna Lindsay B LB Walton Gabriel G Hand Nicholas N Mukherjee Sarmistha S Essuman Kow K Shi Zhongjie Z Gao Yan Y Markley Karen K Nakagawa Momo M Kameswaran Vasumathi V Vourekas Anastassios A Friedman Joshua R JR Kaestner Klaus H KH Greenbaum Linda E LE

BMC genomics 20130418

<h4>Background</h4>Validation of physiologic miRNA targets has been met with significant challenges. We employed HITS-CLIP to identify which miRNAs participate in liver regeneration, and to identify their target mRNAs.<h4>Results</h4>miRNA recruitment to the RISC is highly dynamic, changing more than five-fold for several miRNAs. miRNA recruitment to the RISC did not correlate with changes in overall miRNA expression for these dynamically recruited miRNAs, emphasizing the necessity to determine ...[more]

PMID: 23597149

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Project description:BACKGROUND & AIMS:The adult liver is the main detoxification organ and routinely is exposed to environmental insults but retains the ability to restore its mass and function upon tissue damage. However, extensive injury can lead to liver failure, and chronic injury causes fibrosis, cirrhosis, and hepatocellular carcinoma. Currently, the transcriptional regulation of organ repair in the adult liver is incompletely understood. METHODS:We isolated nuclei from quiescent as well as repopulating hepatocytes in a mouse model of hereditary tyrosinemia, which recapitulates the injury and repopulation seen in toxic liver injury in human beings. We then performed the assay for transposase accessible chromatin with high-throughput sequencing specifically in repopulating hepatocytes to identify differentially accessible chromatin regions and nucleosome positioning. In addition, we used motif analysis to predict differential transcription factor occupancy and validated the in silico results with chromatin immunoprecipitation followed by sequencing for hepatocyte nuclear factor 4? (HNF4?) and CCCTC-binding factor (CTCF). RESULTS:Chromatin accessibility in repopulating hepatocytes was increased in the regulatory regions of genes promoting proliferation and decreased in the regulatory regions of genes involved in metabolism. The epigenetic changes at promoters and liver enhancers correspond with the regulation of gene expression, with enhancers of many liver function genes showing a less accessible state during the regenerative process. Moreover, increased CTCF occupancy at promoters and decreased HNF4? binding at enhancers implicate these factors as key drivers of the transcriptomic changes in replicating hepatocytes that enable liver repopulation. CONCLUSIONS:Our analysis of hepatocyte-specific epigenomic changes during liver repopulation identified CTCF and HNF4? as key regulators of hepatocyte proliferation and regulation of metabolic programs. Thus, liver repopulation in the setting of toxic injury makes use of both general transcription factors (CTCF) for promoter activation, and reduced binding by a hepatocyte-enriched factor (HNF4?) to temporarily limit enhancer activity. All sequencing data in this study were deposited to the Gene Expression Omnibus database and can be downloaded with accession number GSE109466.

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

Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver.

Publications

Dynamic recruitment of microRNAs to their mRNA targets in the regenerating liver.

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