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Screening miRNA and their target genes related to tetralogy of Fallot with microarray

Published online by Cambridge University Press:  17 May 2013

Xian-min Wang ,
Kui Zhang ,
Yan Li ,
Kun Shi ,
Yi-ling Liu ,
Yan-feng Yang ,
Yu Fang  and
Meng Mao
Xian-min Wang
Affiliation:
Department of Padiatric Cardiology, Chengdu Women's and Children's Central Hospital, Chengdu, Sichuan Province, People's Republic of China
Kui Zhang
Affiliation:
Department of Forensic Medicine, Zun Yi Medical College, Zunyi, People's Republic of China
Yan Li
Affiliation:
Department of Padiatric Cardiology, Chengdu Women's and Children's Central Hospital, Chengdu, Sichuan Province, People's Republic of China
Kun Shi
Affiliation:
Department of Padiatric Cardiology, Chengdu Women's and Children's Central Hospital, Chengdu, Sichuan Province, People's Republic of China
Yi-ling Liu
Affiliation:
Department of Padiatric Cardiology, Chengdu Women's and Children's Central Hospital, Chengdu, Sichuan Province, People's Republic of China
Yan-feng Yang
Affiliation:
Department of Padiatric Cardiology, Chengdu Women's and Children's Central Hospital, Chengdu, Sichuan Province, People's Republic of China
Yu Fang
Affiliation:
Department of Padiatric Cardiology, Chengdu Women's and Children's Central Hospital, Chengdu, Sichuan Province, People's Republic of China
Meng Mao*
Affiliation:
Department of Padiatric Cardiology, Chengdu Women's and Children's Central Hospital, Chengdu, Sichuan Province, People's Republic of China
*
Correspondence to: M. Meng, Chengdu Women's and Children's Central Hospital, No. 1617, Riyue Avenue, Chengdu, Sichuan Province, P.R. China 610091. Tel: +86-028-61866050; Fax: +86-028-61866050; E-mail: dffmmao@126.com
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Abstract

Our aim is to screen miRNAs and genes related to tetralogy of Fallot and construct a co-expression network based on integrating miRNA and gene microarrays. We downloaded the gene expression profile GSE35490 (miRNA) and GSE35776 (mRNA) of tetralogy of Fallot from the Gene Expression Omnibus database, which includes eight normal and 15 disease samples from infants, and screened differentially expressed miRNAs and genes between normal and disease samples (cut-off: p < 0.05; FDR < 0.05; and log FC > 2 or log FC < −2); in addition, we downloaded human miRNA and their targets, which were collected in the miRNA targets prediction database TargetScan, and selected ones that also appeared in our differentially expressed miRNAs and their predicted targets (score >0.9) and then made a relationship of diff_miRNAs and diff_genes of our results. Finally, we uploaded all the diff_target genes into String, constructed a co-expression network regulated by diff_miRNAs, and performed functional analysis with the software DAVID. Comparing normal and disease lesion tissue, we got 32 and 875 differentially expressed miRNAs and genes, respectively, and found hsa-miR-124 with 34 diff_target genes and hsa-miR-138 with two diff_target genes. Then we constructed a co-expression network that contains 231 pairs of genes. Genes in the network were enriched into 14 function clusters, and the most significant one is protein localisation. We screened the tetralogy of Fallot-related hsa-miR-124 and hsa-miR-138 with their direct and indirect differentially expressed target genes, and found that protein localisation is the significant cause affecting tetralogy of Fallot. Our approach may provide the groundwork for a new therapy approach to treating tetralogy of Fallot.

Keywords

Tetralogy of Fallotdifferentially expressed miRNAs and genesco-expression networkfunctional analysis
Type
Original Articles
Information
Cardiology in the Young , Volume 24 , Issue 3 , June 2014 , pp. 442 - 446
Copyright
Copyright © Cambridge University Press 2013 

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Footnotes

†

Co-first author.

References

1. Ho, S, McCarthy, K, Josen, M, Rigby, M. Anatomic–echocardiographic correlates: an introduction to normal and congenitally malformed hearts. Heart 2001; 86: ii3–ii11.Google ScholarPubMed
2. Becker, AE, Connor, M, Anderson, RH. Tetralogy of Fallot: a morphometric and geometric study. Am J Cardiol 1975; 35: 402–412.CrossRefGoogle ScholarPubMed
3. Child, JS. Fallot's tetralogy and pregnancy: prognostication and prophesy. J Am Coll Cardiol 2004; 44: 181–183.Google Scholar
4. Hoffman, JIE, Kaplan, S. The incidence of congenital heart disease. J Am Coll Cardiol 2002; 39: 1890–1900.Google Scholar
5. Khositseth, A, Tocharoentanaphol, C, Khowsathit, P, Ruangdaraganon, N. Chromosome 22q11 deletions in patients with conotruncal heart defects. Pediatr Cardiol 2005; 26: 570–573.Google Scholar
6. Maeda, J, Yamagishi, H, Matsuoka, R, et al. Frequent association of 22q11. 2 deletion with tetralogy of Fallot. Am J Med Genet A 2000; 92: 269–272.Google Scholar
7. O'Brien, JE, Kibiryeva, N, Zhou, XG, et al. Noncoding RNA expression in myocardium from infants with tetralogy of Fallot clinical perspective. Circ: Cardiovasc Genet 2012; 5: 279–286.Google Scholar
8. Troyanskaya, O, Cantor, M, Sherlock, G, et al. Missing value estimation methods for DNA microarrays. Bioinformatics 2001; 17: 520–525.Google Scholar
9. Fujita, A, Sato, JR, Rodrigues, LO, Ferreira, CE, Sogayar, MC. Evaluating different methods of microarray data normalization. BMC Bioinformatics 2006; 7: 469.CrossRefGoogle ScholarPubMed
10. Toedling, J, Sklyar, O, Huber, W. Ringo–an R/bioconductor package for analyzing ChIP-chip readouts. BMC Bioinformatics 2007; 8: 221.Google Scholar
11. Benjamini, Y, Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol 1995: 289–300.Google Scholar
12. Lewis, BP, Shih, I, Jones-Rhoades, MW, Bartel, DP, Burge, CB. Prediction of mammalian microRNA targets. Cell 2003; 115: 787–798.Google Scholar
13. Lewis, BP, Burge, CB, Bartel, DP. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 2005; 120: 15–20.CrossRefGoogle ScholarPubMed
14. Szklarczyk, D, Franceschini, A, Kuhn, M, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011; 39: D561–D568.Google Scholar
15. Da Wei Huang, BTS, Lempicki, RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2008; 4: 44–57.Google Scholar
16. Kaynak, B, von Heydebreck, A, Mebus, S, et al. Genome-wide array analysis of normal and malformed human hearts. Circulation 2003; 107: 2467–2474.CrossRefGoogle ScholarPubMed
17. Konstantinov, IE, Coles, JG, Boscarino, C, et al. Gene expression profiles in children undergoing cardiac surgery for right heart obstructive lesions. J Thorac Cardiovasc Surg 2004; 127: 746–754.CrossRefGoogle ScholarPubMed
18. Pozzi, M, Trivedi, DB, Kitchiner, D, Arnold, RA. Tetralogy of Fallot: what operation, at which age. Eur J Cardiothorac Surg 2000; 17: 631–636.Google Scholar
19. Paron, I, D'elia, A, D'ambrosio, C, et al. A proteomic approach to identify early molecular targets of oxidative stress in human epithelial lens cells. Biochem J 2004; 378: 929.Google Scholar
20. Vázquez, I, Maicas, M, Marcotegui, N, et al. Silencing of hsa-miR-124 by EVI1 in cell lines and patients with acute myeloid leukemia. Proc Natl Acad Sci 2010; 107: E167–E168.Google Scholar
21. Ye, D, Wang, G, Liu, Y, et al. MiR-138 promotes induced pluripotent stem cell generation through the regulation of the p53 signaling. Stem Cells 2012; 30: 1645–1654.Google Scholar