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Integrated analysis of microRNA and mRNA expression: adding biological significance to microRNA target predictions.

van Iterson M, Bervoets S, de Meijer EJ, Buermans HP, 't Hoen PA, Menezes RX, Boer JM - Nucleic Acids Res. (2013)

Bottom Line: The strongest negatively associated mRNAs as measured by the test were prioritized.We applied our integration method to a well-defined muscle differentiation model.Using the same study, we showed the advantages of the global test over Pearson correlation and lasso.

View Article: PubMed Central - PubMed

Affiliation: Center for Human and Clinical Genetics and Leiden University Medical Center, Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, 2300 ZC Leiden, The Netherlands, Netherlands Bioinformatics Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands, Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands and Department of Pediatric Oncology, Erasmus Medical Center - Sophia Children's Hospital, Dr. Molewaterplein 60, 3015 GJ Rotterdam, The Netherlands.

ABSTRACT
Current microRNA target predictions are based on sequence information and empirically derived rules but do not make use of the expression of microRNAs and their targets. This study aimed to improve microRNA target predictions in a given biological context, using in silico predictions, microRNA and mRNA expression. We used target prediction tools to produce lists of predicted targets and used a gene set test designed to detect consistent effects of microRNAs on the joint expression of multiple targets. In a single test, association between microRNA expression and target gene set expression as well as the contribution of the individual target genes on the association are determined. The strongest negatively associated mRNAs as measured by the test were prioritized. We applied our integration method to a well-defined muscle differentiation model. Validation of our predictions in C2C12 cells confirmed predicted targets of known as well as novel muscle-related microRNAs. We further studied associations between microRNA-mRNA pairs in human prostate cancer, finding some pairs that have been recently experimentally validated by others. Using the same study, we showed the advantages of the global test over Pearson correlation and lasso. We conclude that our integrated approach successfully identifies regulated microRNAs and their targets.

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Related in: MedlinePlus

Validation of miRNA–mRNA target pairs by co-transfection in C2C12 cells for those 3′ UTRs that were predicted to have multiple binding sites. Notice that the y-axis scale of each panel is different, reflecting differences in baseline activity of the luciferase UTR reporters.
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gkt525-F4: Validation of miRNA–mRNA target pairs by co-transfection in C2C12 cells for those 3′ UTRs that were predicted to have multiple binding sites. Notice that the y-axis scale of each panel is different, reflecting differences in baseline activity of the luciferase UTR reporters.

Mentions: The 3′ UTR of Fbxl19 contained two predicted binding sites for mmu-miR-26a and one for mmu-miR-22 (Figure 2A). Indeed, mmu-miR-22 reduced the luciferase activity, 1.7-fold, mmu-miR-26a (two sites) 2.4-fold and their combination 2.6-fold (Figure 4A). The gene Arfip2 is predicted to be regulated by both mmu-miR-133a and mmu-miR-22. Target predictions that were made using an older version of TargetScan Nov 2011 did not identify Arfip2 as a predicted target for mmu-miR-22. Therefore, initially primers were only designed around the binding site of mmu-miR-133a. However, it turned out that the binding site for mmu-miR-22 was included as well (Figure 2B). Both miRNAs downregulated Arfip2 with P-values (Figure 4B).Figure 4.


Integrated analysis of microRNA and mRNA expression: adding biological significance to microRNA target predictions.

van Iterson M, Bervoets S, de Meijer EJ, Buermans HP, 't Hoen PA, Menezes RX, Boer JM - Nucleic Acids Res. (2013)

Validation of miRNA–mRNA target pairs by co-transfection in C2C12 cells for those 3′ UTRs that were predicted to have multiple binding sites. Notice that the y-axis scale of each panel is different, reflecting differences in baseline activity of the luciferase UTR reporters.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3753644&req=5

gkt525-F4: Validation of miRNA–mRNA target pairs by co-transfection in C2C12 cells for those 3′ UTRs that were predicted to have multiple binding sites. Notice that the y-axis scale of each panel is different, reflecting differences in baseline activity of the luciferase UTR reporters.
Mentions: The 3′ UTR of Fbxl19 contained two predicted binding sites for mmu-miR-26a and one for mmu-miR-22 (Figure 2A). Indeed, mmu-miR-22 reduced the luciferase activity, 1.7-fold, mmu-miR-26a (two sites) 2.4-fold and their combination 2.6-fold (Figure 4A). The gene Arfip2 is predicted to be regulated by both mmu-miR-133a and mmu-miR-22. Target predictions that were made using an older version of TargetScan Nov 2011 did not identify Arfip2 as a predicted target for mmu-miR-22. Therefore, initially primers were only designed around the binding site of mmu-miR-133a. However, it turned out that the binding site for mmu-miR-22 was included as well (Figure 2B). Both miRNAs downregulated Arfip2 with P-values (Figure 4B).Figure 4.

Bottom Line: The strongest negatively associated mRNAs as measured by the test were prioritized.We applied our integration method to a well-defined muscle differentiation model.Using the same study, we showed the advantages of the global test over Pearson correlation and lasso.

View Article: PubMed Central - PubMed

Affiliation: Center for Human and Clinical Genetics and Leiden University Medical Center, Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, 2300 ZC Leiden, The Netherlands, Netherlands Bioinformatics Centre, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands, Department of Epidemiology and Biostatistics, VU University Medical Center, De Boelelaan 1118, 1081 HZ Amsterdam, The Netherlands and Department of Pediatric Oncology, Erasmus Medical Center - Sophia Children's Hospital, Dr. Molewaterplein 60, 3015 GJ Rotterdam, The Netherlands.

ABSTRACT
Current microRNA target predictions are based on sequence information and empirically derived rules but do not make use of the expression of microRNAs and their targets. This study aimed to improve microRNA target predictions in a given biological context, using in silico predictions, microRNA and mRNA expression. We used target prediction tools to produce lists of predicted targets and used a gene set test designed to detect consistent effects of microRNAs on the joint expression of multiple targets. In a single test, association between microRNA expression and target gene set expression as well as the contribution of the individual target genes on the association are determined. The strongest negatively associated mRNAs as measured by the test were prioritized. We applied our integration method to a well-defined muscle differentiation model. Validation of our predictions in C2C12 cells confirmed predicted targets of known as well as novel muscle-related microRNAs. We further studied associations between microRNA-mRNA pairs in human prostate cancer, finding some pairs that have been recently experimentally validated by others. Using the same study, we showed the advantages of the global test over Pearson correlation and lasso. We conclude that our integrated approach successfully identifies regulated microRNAs and their targets.

Show MeSH
Related in: MedlinePlus