Integrated Analysis of MicroRNA and MRNA Expression: Adding Biological Significance to MicroRNA Target Predictions

Overview

Journal Nucleic Acids Res

Publisher Oxford University Press

Specialty Biochemistry

Date 2013 Jun 18

PMID 23771142

Citations 34

Authors

Maarten van Iterson

Sander Bervoets

Emile J de Meijer

Henk P Buermans

Peter A C t Hoen

Renee X Menezes

Judith M Boer

Affiliations

Soon will be listed here.

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.

Citing Articles

MicroRNAs in atrial fibrillation target genes in structural remodelling.

van den Berg N, Kawasaki M, Nariswari F, Fabrizi B, Neefs J, van der Made I Cell Tissue Res. 2023; 394(3):497-514.

PMID: 37833432 PMC: 10697892. DOI: 10.1007/s00441-023-03823-0.

Magnetique: an interactive web application to explore transcriptome signatures of heart failure.

Britto-Borges T, Ludt A, Boileau E, Gjerga E, Marini F, Dieterich C J Transl Med. 2022; 20(1):513.

PMID: 36345035 PMC: 9641957. DOI: 10.1186/s12967-022-03694-z.

New Tricks with Old Dogs: Computational Identification and Experimental Validation of New miRNA-mRNA Regulation in hiPSC-CMs.

Bencun M, Britto-Borges T, Eschenbach J, Dieterich C Biomedicines. 2022; 10(2).

PMID: 35203600 PMC: 8962266. DOI: 10.3390/biomedicines10020391.

Huntington's disease mice and human brain tissue exhibit increased G3BP1 granules and TDP43 mislocalization.

Sanchez I, Nguyen T, England W, Lim R, Vu A, Miramontes R J Clin Invest. 2021; 131(12).

PMID: 33945510 PMC: 8203471. DOI: 10.1172/JCI140723.

miRTissue : extending miRTissue web service with the analysis of ceRNA-ceRNA interactions.

Fiannaca A, La Paglia L, La Rosa M, Rizzo R, Urso A BMC Bioinformatics. 2020; 21(Suppl 8):199.

PMID: 32938402 PMC: 7493844. DOI: 10.1186/s12859-020-3520-z.

References

Krek A, Grun D, Poy M, Wolf R, Rosenberg L, Epstein E . Combinatorial microRNA target predictions. Nat Genet. 2005; 37(5):495-500. DOI: 10.1038/ng1536. View

Gamazon E, Im H, Duan S, Lussier Y, Cox N, Dolan M . Exprtarget: an integrative approach to predicting human microRNA targets. PLoS One. 2010; 5(10):e13534. PMC: 2958831. DOI: 10.1371/journal.pone.0013534. View

Friedman R, Farh K, Burge C, Bartel D . Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2008; 19(1):92-105. PMC: 2612969. DOI: 10.1101/gr.082701.108. View

Yang Y, Wang Y, Li K . MiRTif: a support vector machine-based microRNA target interaction filter. BMC Bioinformatics. 2008; 9 Suppl 12:S4. PMC: 2638144. DOI: 10.1186/1471-2105-9-S12-S4. View

Menezes R, Boetzer M, Sieswerda M, van Ommen G, Boer J . Integrated analysis of DNA copy number and gene expression microarray data using gene sets. BMC Bioinformatics. 2009; 10:203. PMC: 2753845. DOI: 10.1186/1471-2105-10-203. View

Kong S, Pu W, Park P . A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinformatics. 2006; 22(19):2373-80. PMC: 2813864. DOI: 10.1093/bioinformatics/btl401. View

Buffa F, Camps C, Winchester L, Snell C, Gee H, Sheldon H . microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer. Cancer Res. 2011; 71(17):5635-45. DOI: 10.1158/0008-5472.CAN-11-0489. View

Xin F, Li M, Balch C, Thomson M, Fan M, Liu Y . Computational analysis of microRNA profiles and their target genes suggests significant involvement in breast cancer antiestrogen resistance. Bioinformatics. 2008; 25(4):430-4. PMC: 2642642. DOI: 10.1093/bioinformatics/btn646. View

Wong C, Tellam R . MicroRNA-26a targets the histone methyltransferase Enhancer of Zeste homolog 2 during myogenesis. J Biol Chem. 2008; 283(15):9836-43. DOI: 10.1074/jbc.M709614200. View

10.

Liu H, Brannon A, Reddy A, Alexe G, Seiler M, Arreola A . Identifying mRNA targets of microRNA dysregulated in cancer: with application to clear cell Renal Cell Carcinoma. BMC Syst Biol. 2010; 4:51. PMC: 2876063. DOI: 10.1186/1752-0509-4-51. View

11.

Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T . miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2008; 37(Database issue):D105-10. PMC: 2686554. DOI: 10.1093/nar/gkn851. View

12.

Liu Y, Chu A, Chakroun I, Islam U, Blais A . Cooperation between myogenic regulatory factors and SIX family transcription factors is important for myoblast differentiation. Nucleic Acids Res. 2010; 38(20):6857-71. PMC: 2978361. DOI: 10.1093/nar/gkq585. View

13.

Bossel Ben-Moshe N, Avraham R, Kedmi M, Zeisel A, Yitzhaky A, Yarden Y . Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets. Nucleic Acids Res. 2012; 40(21):10614-27. PMC: 3505984. DOI: 10.1093/nar/gks841. View

14.

Miranda K, Huynh T, Tay Y, Ang Y, Tam W, Thomson A . A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell. 2006; 126(6):1203-17. DOI: 10.1016/j.cell.2006.07.031. View

15.

Hassan O, Ahmad A, Sethi S, Sarkar F . Recent updates on the role of microRNAs in prostate cancer. J Hematol Oncol. 2012; 5:9. PMC: 3313897. DOI: 10.1186/1756-8722-5-9. View

16.

Lu Y, Zhou Y, Qu W, Deng M, Zhang C . A Lasso regression model for the construction of microRNA-target regulatory networks. Bioinformatics. 2011; 27(17):2406-13. DOI: 10.1093/bioinformatics/btr410. View

17.

Bartel D, Chen C . Micromanagers of gene expression: the potentially widespread influence of metazoan microRNAs. Nat Rev Genet. 2004; 5(5):396-400. DOI: 10.1038/nrg1328. View

18.

Muniategui A, Pey J, Planes F, Rubio A . Joint analysis of miRNA and mRNA expression data. Brief Bioinform. 2012; 14(3):263-78. DOI: 10.1093/bib/bbs028. View

19.

Vergoulis T, Vlachos I, Alexiou P, Georgakilas G, Maragkakis M, Reczko M . TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res. 2011; 40(Database issue):D222-9. PMC: 3245116. DOI: 10.1093/nar/gkr1161. View

20.

Edgar R, Domrachev M, Lash A . Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2001; 30(1):207-10. PMC: 99122. DOI: 10.1093/nar/30.1.207. View