Limits...
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.

Show MeSH

Related in: MedlinePlus

miRNA expression profiles of the top 20 that show the highest association with gene expression of their target sets. The normalized  ratio compares the expression from proliferating myoblasts (60–70% confluence), at confluence (100%), 1 and 4 days after induction of differentiation to a pool of proliferating myoblasts (20).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3753644&req=5

gkt525-F1: miRNA expression profiles of the top 20 that show the highest association with gene expression of their target sets. The normalized ratio compares the expression from proliferating myoblasts (60–70% confluence), at confluence (100%), 1 and 4 days after induction of differentiation to a pool of proliferating myoblasts (20).

Mentions: Although we did not, a priori, select for miRNAs that were differentially regulated during C2C12 cell differentiation, the miRNAs mmu-miR-133a and mmu-miR-26a, which are known to be upregulated during muscle differentiation (20,48,49), were ranked among the top 20. This was expected, as the global test models the expression of miRNAs as a function of the expression of the predicted targets, and statistically significant associations require some level of regulation of the miRNA under the tested experimental conditions. Figure 1 shows the expression of the top 20 associated miRNAs during muscle cell differentiation. Among the identified miRNAs highly associated to target gene expression in differentiating C2C12 cells, several have been related to muscle in literature e.g. miR-133a, miR-26a, miR-24 and miR-486 (50). To validate our integrated approach, we selected two known myomirs, mmu-miR-133a (49) and mmu-miR-26a (20), and an miRNA predicted by our approach to be involved in C2C12 cell differentiation, mmu-miR-22. Similar to mmu-miR-133a and mmu-miR-26a, mmu-miR-22 is upregulated during differentiation of C2C12 cells (Figure 1). Four of five most negatively associated target genes of miR-22 are involved in cytoskeleton reorganization, which is a process occurring during myoblast differentiation (51).Figure 1.


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)

miRNA expression profiles of the top 20 that show the highest association with gene expression of their target sets. The normalized  ratio compares the expression from proliferating myoblasts (60–70% confluence), at confluence (100%), 1 and 4 days after induction of differentiation to a pool of proliferating myoblasts (20).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt525-F1: miRNA expression profiles of the top 20 that show the highest association with gene expression of their target sets. The normalized ratio compares the expression from proliferating myoblasts (60–70% confluence), at confluence (100%), 1 and 4 days after induction of differentiation to a pool of proliferating myoblasts (20).
Mentions: Although we did not, a priori, select for miRNAs that were differentially regulated during C2C12 cell differentiation, the miRNAs mmu-miR-133a and mmu-miR-26a, which are known to be upregulated during muscle differentiation (20,48,49), were ranked among the top 20. This was expected, as the global test models the expression of miRNAs as a function of the expression of the predicted targets, and statistically significant associations require some level of regulation of the miRNA under the tested experimental conditions. Figure 1 shows the expression of the top 20 associated miRNAs during muscle cell differentiation. Among the identified miRNAs highly associated to target gene expression in differentiating C2C12 cells, several have been related to muscle in literature e.g. miR-133a, miR-26a, miR-24 and miR-486 (50). To validate our integrated approach, we selected two known myomirs, mmu-miR-133a (49) and mmu-miR-26a (20), and an miRNA predicted by our approach to be involved in C2C12 cell differentiation, mmu-miR-22. Similar to mmu-miR-133a and mmu-miR-26a, mmu-miR-22 is upregulated during differentiation of C2C12 cells (Figure 1). Four of five most negatively associated target genes of miR-22 are involved in cytoskeleton reorganization, which is a process occurring during myoblast differentiation (51).Figure 1.

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