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The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations

  • Jung, Hyeim (Department of Bioinformatics and Life Science, Soongsil University) ;
  • Han, Seonggyun (Department of Bioinformatics and Life Science, Soongsil University) ;
  • Kim, Sangsoo (Department of Bioinformatics and Life Science, Soongsil University)
  • Received : 2015.07.31
  • Accepted : 2015.09.21
  • Published : 2015.09.30

Abstract

Type 2 diabetes mellitus is a complex metabolic disorder associated with multiple genetic, developmental and environmental factors. The recent advances in gene expression microarray technologies as well as network-based analysis methodologies provide groundbreaking opportunities to study type 2 diabetes mellitus. In the present study, we used previously published gene expression microarray datasets of human skeletal muscle samples collected from 20 insulin sensitive individuals before and after insulin treatment in order to construct insulin-mediated regulatory network. Based on a motif discovery method implemented by iRegulon, a Cytoscape app, we identified 25 candidate regulons, motifs of which were enriched among the promoters of 478 up-regulated genes and 82 down-regulated genes. We then looked for a hierarchical network of the candidate regulators, in such a way that the conditional combination of their expression changes may explain those of their target genes. Using Genomica, a software tool for regulatory network construction, we obtained a hierarchical network of eight regulons that were used to map insulin downstream signaling network. Taken together, the results illustrate the benefits of combining completely different methods such as motif-based regulatory factor discovery and expression level-based construction of regulatory network of their target genes in understanding insulin induced biological processes and signaling pathways.

Keywords

References

  1. Cline GW, Petersen KF, Krssak M, Shen J, Hundal RS, Trajanoski Z, et al. Impaired glucose transport as a cause of decreased insulin-stimulated muscle glycogen synthesis in type 2 diabetes. N Engl J Med 1999;341:240-246. https://doi.org/10.1056/NEJM199907223410404
  2. Pollack JR, Perou CM, Alizadeh AA, Eisen MB, Pergamenschikov A, Williams CF, et al. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat Genet 1999;23:41-46.
  3. Krylov AS, Zasedateleva OA, Prokopenko DV, Rouviere-Yaniv J, Mirzabekov AD. Massive parallel analysis of the binding specificity of histone-like protein HU to single- and double- stranded DNA with generic oligodeoxyribonucleotide microchips. Nucleic Acids Res 2001;29:2654-2660. https://doi.org/10.1093/nar/29.12.2654
  4. Mutarelli M, Cicatiello L, Ferraro L, Grober OM, Ravo M, Facchiano AM, et al. Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells. BMC Bioinformatics 2008;9 Suppl 2:S12.
  5. Wu X, Wang J, Cui X, Maianu L, Rhees B, Rosinski J, et al. The effect of insulin on expression of genes and biochemical pathways in human skeletal muscle. Endocrine 2007;31:5-17. https://doi.org/10.1007/s12020-007-0007-x
  6. Do JH, Choi DK. Clustering approaches to identifying gene expression patterns from DNA microarray data. Mol Cells 2008;25:279-288.
  7. Frith MC, Fu Y, Yu L, Chen JF, Hansen U, Weng Z. Detection of functional DNA motifs via statistical over-representation. Nucleic Acids Res 2004;32:1372-1381. https://doi.org/10.1093/nar/gkh299
  8. Wasserman WW, Sandelin A. Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet 2004;5: 276-287. https://doi.org/10.1038/nrg1315
  9. Aerts S. Computational strategies for the genome-wide identification of cis-regulatory elements and transcriptional targets. Curr Top Dev Biol 2012;98:121-145. https://doi.org/10.1016/B978-0-12-386499-4.00005-7
  10. Bulyk ML. Computational prediction of transcription-factor binding site locations. Genome Biol 2003;5:201. https://doi.org/10.1186/gb-2003-5-1-201
  11. Janky R, Verfaillie A, Imrichová H, Van de Sande B, Standaert L, Christiaens V, et al. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol 2014;10:e1003731. https://doi.org/10.1371/journal.pcbi.1003731
  12. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 2003;34:166-176. https://doi.org/10.1038/ng1165
  13. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Methodol 1995;57:289-300.
  14. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57. https://doi.org/10.1038/nprot.2008.211
  15. Mora S, Pessin JE. The MEF2A isoform is required for striated muscle-specific expression of the insulin-responsive GLUT4 glucose transporter. J Biol Chem 2000;275:16323-16328. https://doi.org/10.1074/jbc.M910259199
  16. Verdeguer F, Blattler SM, Cunningham JT, Hall JA, Chim H, Puigserver P. Decreased genetic dosage of hepatic Yin Yang 1 causes diabetic-like symptoms. Mol Endocrinol 2014;28:308-316. https://doi.org/10.1210/me.2013-1173
  17. Jin W, Goldfine AB, Boes T, Henry RR, Ciaraldi TP, Kim EY, et al. Increased SRF transcriptional activity in human and mouse skeletal muscle is a signature of insulin resistance. J Clin Invest 2011;121:918-929. https://doi.org/10.1172/JCI41940
  18. Stoffel M, Stein R, Wright CV, Espinosa R 3rd, Le Beau MM, Bell GI. Localization of human homeodomain transcription factor insulin promoter factor 1 (IPF1) to chromosome band 13q12.1. Genomics 1995;28:125-126. https://doi.org/10.1006/geno.1995.1120
  19. Yang TT, Suk HY, Yang X, Olabisi O, Yu RY, Durand J, et al. Role of transcription factor NFAT in glucose and insulin homeostasis. Mol Cell Biol 2006;26:7372-7387. https://doi.org/10.1128/MCB.00580-06

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