The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations
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 diab...
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BioMed Central
2015-09-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gni-13-76.pdf |
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| author | Hyeim Jung Seonggyun Han Sangsoo Kim |
| author_facet | Hyeim Jung Seonggyun Han Sangsoo Kim |
| author_sort | Hyeim Jung |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a70f6c2ae5794c7e8cc5f20897cd885a |
| institution | Kabale University |
| issn | 1598-866X 2234-0742 |
| language | English |
| publishDate | 2015-09-01 |
| publisher | BioMed Central |
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| series | Genomics & Informatics |
| spelling | doaj-art-a70f6c2ae5794c7e8cc5f20897cd885a2025-02-02T19:45:11ZengBioMed CentralGenomics & Informatics1598-866X2234-07422015-09-01133768010.5808/GI.2015.13.3.76155The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression VariationsHyeim Jung0Seonggyun Han1Sangsoo Kim2Department of Bioinformatics and Life Science, Soongsil University, Seoul 06978, Korea.Department of Bioinformatics and Life Science, Soongsil University, Seoul 06978, Korea.Department of Bioinformatics and Life Science, Soongsil University, Seoul 06978, Korea.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.http://genominfo.org/upload/pdf/gni-13-76.pdfnetworkregulationregulatorsequence motif |
| spellingShingle | Hyeim Jung Seonggyun Han Sangsoo Kim The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations Genomics & Informatics network regulation regulator sequence motif |
| title | The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations |
| title_full | The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations |
| title_fullStr | The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations |
| title_full_unstemmed | The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations |
| title_short | The Construction of Regulatory Network for Insulin-Mediated Genes by Integrating Methods Based on Transcription Factor Binding Motifs and Gene Expression Variations |
| title_sort | construction of regulatory network for insulin mediated genes by integrating methods based on transcription factor binding motifs and gene expression variations |
| topic | network regulation regulator sequence motif |
| url | http://genominfo.org/upload/pdf/gni-13-76.pdf |
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