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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 of luciferase 3′ UTR reporter constructs and synthetic miRNAs in C2C12 cells. Luciferase activities in cells transfected with the miRNA targeting the cloned 3′ UTR are depicted in color: top row: mmu-miR-22 in green , middle row mmu-miR-133a in red △ and bottom row mmu-miR-26a in dark blue × . Points reflect independent biological replicates (n = 3 per condition). Note that the y-axis scale of each panel is different, reflecting differences in baseline activity of the luciferase UTR reporters. P-values reported at the top of each panel are the results from a one-sided two-sample Wilcoxon rank sum tests for each luciferase 3′ UTR reporter between the miRNA for which a binding site was predicted and the non-binding miRNAs.
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gkt525-F3: Validation of miRNA–mRNA target pairs by co-transfection of luciferase 3′ UTR reporter constructs and synthetic miRNAs in C2C12 cells. Luciferase activities in cells transfected with the miRNA targeting the cloned 3′ UTR are depicted in color: top row: mmu-miR-22 in green , middle row mmu-miR-133a in red △ and bottom row mmu-miR-26a in dark blue × . Points reflect independent biological replicates (n = 3 per condition). Note that the y-axis scale of each panel is different, reflecting differences in baseline activity of the luciferase UTR reporters. P-values reported at the top of each panel are the results from a one-sided two-sample Wilcoxon rank sum tests for each luciferase 3′ UTR reporter between the miRNA for which a binding site was predicted and the non-binding miRNAs.

Mentions: The 3′ untranslated regions (UTR) of 11 top-ranked mRNA targets were cloned behind luciferase reporters (Supplementary Table S1). The UTRs of Arfip2 and Fbxl19 contained predicted binding sites for multiple tested miRNAs (Figure 2). Co-transfection experiments with synthetic mmu-miR-133a, mmu-miR-26a and mmu-miR-22 were performed to evaluate the effect of specific miRNA binding on luciferase protein activity (Figure 3). The three top negatively associated targets—Wasf1, Arpc5 and Nr3c1—were indeed regulated by mmu-miR-22, which we predicted to be involved in C2C12 cell differentiation (Figure 3 upper row). Whsc2, a known target for mmu-miR-133a, was clearly downregulated by the targeting miRNA. Also the predicted targets Foxc1, Ptbp2 and Arfip2 showed significant downregulation by mmu-miR-133a (Figure 3 middle row). For mmu-miR-26a, both known targets Epha2 and Ezh2 and predicted target Thrap3 showed significant downregulation (Figure 3 bottom row).Figure 2.


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 of luciferase 3′ UTR reporter constructs and synthetic miRNAs in C2C12 cells. Luciferase activities in cells transfected with the miRNA targeting the cloned 3′ UTR are depicted in color: top row: mmu-miR-22 in green , middle row mmu-miR-133a in red △ and bottom row mmu-miR-26a in dark blue × . Points reflect independent biological replicates (n = 3 per condition). Note that the y-axis scale of each panel is different, reflecting differences in baseline activity of the luciferase UTR reporters. P-values reported at the top of each panel are the results from a one-sided two-sample Wilcoxon rank sum tests for each luciferase 3′ UTR reporter between the miRNA for which a binding site was predicted and the non-binding miRNAs.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt525-F3: Validation of miRNA–mRNA target pairs by co-transfection of luciferase 3′ UTR reporter constructs and synthetic miRNAs in C2C12 cells. Luciferase activities in cells transfected with the miRNA targeting the cloned 3′ UTR are depicted in color: top row: mmu-miR-22 in green , middle row mmu-miR-133a in red △ and bottom row mmu-miR-26a in dark blue × . Points reflect independent biological replicates (n = 3 per condition). Note that the y-axis scale of each panel is different, reflecting differences in baseline activity of the luciferase UTR reporters. P-values reported at the top of each panel are the results from a one-sided two-sample Wilcoxon rank sum tests for each luciferase 3′ UTR reporter between the miRNA for which a binding site was predicted and the non-binding miRNAs.
Mentions: The 3′ untranslated regions (UTR) of 11 top-ranked mRNA targets were cloned behind luciferase reporters (Supplementary Table S1). The UTRs of Arfip2 and Fbxl19 contained predicted binding sites for multiple tested miRNAs (Figure 2). Co-transfection experiments with synthetic mmu-miR-133a, mmu-miR-26a and mmu-miR-22 were performed to evaluate the effect of specific miRNA binding on luciferase protein activity (Figure 3). The three top negatively associated targets—Wasf1, Arpc5 and Nr3c1—were indeed regulated by mmu-miR-22, which we predicted to be involved in C2C12 cell differentiation (Figure 3 upper row). Whsc2, a known target for mmu-miR-133a, was clearly downregulated by the targeting miRNA. Also the predicted targets Foxc1, Ptbp2 and Arfip2 showed significant downregulation by mmu-miR-133a (Figure 3 middle row). For mmu-miR-26a, both known targets Epha2 and Ezh2 and predicted target Thrap3 showed significant downregulation (Figure 3 bottom row).Figure 2.

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