- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 187509, 11 pages
Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data
1State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
2Graduate School of the Chinese Academy of Sciences, Kunming 650223, China
3Kunming Institute of Zoology, Chinese University of Hongkong Joint Research Center for Bio-resources and Human Disease Mechanisms, Kunming 650223, China
Received 7 June 2013; Revised 23 August 2013; Accepted 26 August 2013
Academic Editor: Zhirong Sun
Copyright © 2013 Ai-Di Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- H. Ma and I. Goryanin, “Human metabolic network reconstruction and its impact on drug discovery and development,” Drug Discovery Today, vol. 13, no. 9-10, pp. 402–408, 2008.
- R. Okun and C. R. Kleeman, “Renal disease secondary to metabolic disorders or physiological deficiency states,” California Medicine, vol. 107, no. 1, pp. 8–10, 1967.
- G. Thomas, A. R. Sehgal, S. R. Kashyap, T. R. Srinivas, J. P. Kirwan, and S. D. Navaneethan, “Metabolic syndrome and kidney disease: a systematic review and meta-analysis,” Clinical Journal of the American Society of Nephrology, vol. 6, no. 10, pp. 2364–2373, 2011.
- K. I. Woroniecka, A. S. D. Park, D. Mohtat, D. B. Thomas, J. M. Pullman, and K. Susztak, “Transcriptome analysis of human diabetic kidney disease,” Diabetes, vol. 60, no. 9, pp. 2354–2369, 2011.
- N. C. Duarte, S. A. Becker, N. Jamshidi et al., “Global reconstruction of the human metabolic network based on genomic and bibliomic data,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 6, pp. 1777–1782, 2007.
- L. Jerby, T. Shlomi, and E. Ruppin, “Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism,” Molecular Systems Biology, vol. 6, no. 401, 2010.
- T. D. Vo, H. J. Greenberg, and B. O. Palsson, “Reconstruction and functional characterization of the human mitochondrial metabolic network based on proteomic and biochemical data,” Journal of Biological Chemistry, vol. 279, no. 38, pp. 39532–39540, 2004.
- R. Agren, S. Bordel, A. Mardinoglu, N. Pornputtapong, I. Nookaew, and J. Nielsen, “Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT,” PLOS Computational Biology, vol. 8, no. 5, Article ID e1002518, 2012.
- Y. Zhao and J. Huang, “Reconstruction and analysis of human heart-specific metabolic network based on transcriptome and proteome data,” Biochemical and Biophysical Research Communications, vol. 415, no. 3, pp. 450–454, 2011.
- M. J. Sarnak, A. S. Levey, A. C. Schoolwerth et al., “Kidney disease as a risk factor for development of cardiovascular disease: a statement from the american heart association councils on kidney in cardiovascular disease, high blood pressure research, clinical cardiology, and epidemiology and prevention,” Circulation, vol. 108, no. 17, pp. 2154–2169, 2003.
- T. Barrett, S. E. Wilhite, P. Ledoux, et al., “NCBI GEO: archive for functional genomics data sets—update,” Nucleic Acids Research, no. D1, pp. D991–D995, 2013.
- M. Uhlen, P. Oksvold, L. Fagerberg et al., “Towards a knowledge-based Human Protein Atlas,” Nature Biotechnology, vol. 28, no. 12, pp. 1248–1250, 2010.
- H. Zur, E. Ruppin, and T. Shlomi, “iMAT: an integrative metabolic analysis tool,” Bioinformatics, vol. 26, no. 24, pp. 3140–3142, 2010.
- S. Garbis, G. Lubec, and M. Fountoulakis, “Limitations of current proteomics technologies,” Journal of Chromatography A, vol. 1077, no. 1, pp. 1–18, 2005.
- A. I. Su, T. Wiltshire, S. Batalov, et al., “A gene atlas of the mouse and human protein-encoding transcriptomes,” Proceedings of the National Academy of Sciences, vol. 101, no. 16, pp. 6062–6067, 2004.
- M. Ringwald, C. L. Wu, and A. I. Su, “BioGPS and GXD: mouse gene expression data-the benefits and challenges of data integration,” Mammalian Genome, vol. 23, no. 9-10, pp. 550–558, 2012.
- X. Dai, T. Erkkilä, O. Yli-Harja, and H. Lähdesmäki, “A joint finite mixture model for clustering genes from independent Gaussian and beta distributed data,” BMC Bioinformatics, vol. 10, article 165, 2009.
- M. H. Radfar, W. Wong, and Q. Morris, “Computational prediction of intronic microRNA targets using host gene expression reveals novel regulatory mechanisms,” PLoS ONE, vol. 6, no. 6, Article ID e19312, 2011.
- A. S. Siddiqui, A. D. Delaney, A. Schnerch, O. L. Griffith, S. J. M. Jones, and M. A. Marra, “Sequence biases in large scale gene expression profiling data,” Nucleic Acids Research, vol. 34, no. 12, article e83, 2006.
- R. Blekhman, A. Oshlack, A. E. Chabot, G. K. Smyth, and Y. Gilad, “Gene regulation in primates evolves under tissue-specific selection pressures,” PLoS Genetics, vol. 4, no. 11, Article ID e1000271, 2008.
- M. Lakshmanan, G. Koh, B. K. Chung, and D. Y. Lee, “Software applications for flux balance analysis,” Briefings in Bioinformatics. In press.
- J. Schellenberger, R. Que, R. M. T. Fleming et al., “Quantitative prediction of cellular metabolism with constraint-based models: the COBRA toolbox v2.0,” Nature Protocols, vol. 6, no. 9, pp. 1290–1307, 2011.
- C.-W. Chang, W.-C. Cheng, C.-R. Chen et al., “Identification of human housekeeping genes and tissue-selective genes by microarray meta-analysis,” PLoS ONE, vol. 6, no. 7, Article ID e22859, 2011.
- T. Shlomi, M. N. Cabili, and E. Ruppin, “Predicting metabolic biomarkers of human inborn errors of metabolism,” Molecular Systems Biology, vol. 5, no. 263, 2009.
- I. Prassas, C. C. Chrystoja, S. Makawita, and E. P. Diamandis, “Bioinformatic identification of proteins with tissue-specific expression for biomarker discovery,” BMC Medicine, vol. 10, article 39, 2012.
- Y. Alam-Faruque, E. C. Dimmer, R. P. Huntley, C. O'Donovan, P. Scambler, and R. Apweiler, “The renal gene ontology annotation initiative,” Organogenesis, vol. 6, no. 2, pp. 71–75, 2010.
- D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009.
- D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists,” Nucleic Acids Research, vol. 37, no. 1, pp. 1–13, 2009.
- D. S. Wishart, T. Jewison, A. C. Guo, et al., “HMDB 3.0—the human metabolome database in 2013,” Nucleic Acids Research, vol. 41, no. D1, 2012.
- N. T. Doncheva, Y. Assenov, F. S. Domingues, and M. Albrecht, “Topological analysis and interactive visualization of biological networks and protein structures,” Nature Protocol, vol. 7, no. 4, pp. 670–685, 2012.
- S. Maere, K. Heymans, and M. Kuiper, “BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks,” Bioinformatics, vol. 21, no. 16, pp. 3448–3449, 2005.
- M. E. Smoot, K. Ono, J. Ruscheinski, P.-L. Wang, and T. Ideker, “Cytoscape 2.8: new features for data integration and network visualization,” Bioinformatics, vol. 27, no. 3, pp. 431–432, 2011.
- G. Schlotterbeck, A. Ross, F. Dieterle, and H. Senn, “Metabolic profiling technologies for biomarker discovery in biomedicine and drug development,” Pharmacogenomics, vol. 7, no. 7, pp. 1055–1075, 2006.
- M. R. Tennant and J. A. Lyon, “Web-based genetics resources for clinicians, researchers, students, and patients: online mendelian inheritance in man (OMIM) and genetests,” Journal of Electronic Resources in Medical Libraries, vol. 3, no. 2, pp. 1–22, 2006.
- K. Tomoeda, H. Awata, T. Matsuura et al., “Mutations in the 4-hydroxyphenylpyruvic acid dioxygenase gene are responsible for tyrosinemia type III and hawkinsinuria,” Molecular Genetics and Metabolism, vol. 71, no. 3, pp. 506–510, 2000.
- J. R. Mead, S. A. Irvine, and D. P. Ramji, “Lipoprotein lipase: structure, function, regulation, and role in disease,” Journal of Molecular Medicine, vol. 80, no. 12, pp. 753–769, 2002.
- J. M. Forbes, K. Fukami, and M. E. Cooper, “Diabetic nephropathy: where hemodynamics meets metabolism,” Experimental and Clinical Endocrinology and Diabetes, vol. 115, no. 2, pp. 69–84, 2007.
- M. Cascante, L. G. Boros, B. Comin-Anduix, P. de Atauri, J. J. Centelles, and P. W. Lee, “Metabolic control analysis in drug discovery and disease,” Nature Biotechnology, vol. 20, no. 3, pp. 243–249, 2002.