Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE

BMC Systems Biology

Volumn 6, Issue , 2012, Pages

  Wang, Yuliang ^a,b   Eddy, James A ^a,c   Price, Nathan D ^a,b  

^a INSTITUTE FOR SYSTEMS BIOLOGY   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^c University of Illinois at Urbana Champaign   (United States)

Author keywords

Automated metabolic network reconstruction; Brain; Cancer metabolism; Constraint based modeling; Tissue specific metabolic model

Indexed keywords

TRANSCRIPTOME;

ANTIBODY SPECIFICITY; ARTICLE; BIOLOGICAL MODEL; GENETICS; GENOMICS; HUMAN; METABOLISM; METHODOLOGY; NEOPLASM; TIME;

GENOMICS; HUMANS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; NEOPLASMS; ORGAN SPECIFICITY; TIME FACTORS; TRANSCRIPTOME;

MLCS; MLOWN;

EID: 84870933131     PISSN: None     EISSN: 17520509     Source Type: Journal    
DOI: 10.1186/1752-0509-6-153     Document Type: Article

References (62)

1. Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, Srivas R, Palsson BO. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 2007, 104:1777. 10.1073/pnas.0610772104, 1794290, 17267599.
2. Ma H, Sorokin A, Mazein A, Selkov A, Selkov E, Demin O, Goryanin I. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol 2007, 3:135. 2013923, 17882155.
3. Bordbar A, Palsson BO. Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J Intern Med 2012, 271:131-141. 10.1111/j.1365-2796.2011.02494.x, 3243107, 22142339.
4. Lazar MA, Birnbaum MJ. Physiology. De-meaning of metabolism. Science 2012, 336:1651-1652. 10.1126/science.1221834, 22745413.
5. Ramsköld D, Wang ET, Burge CB, Sandberg R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput Biol 2009, 5:e1000598. 10.1371/journal.pcbi.1000598, 2781110, 20011106.
6. Yuneva Mariia O, Fan Teresa WM, Allen Thaddeus D, Higashi Richard M, Ferraris Dana V, Tsukamoto T, Matés José M, Alonso Francisco J, Wang C, Seo Y, et al. The metabolic profile of tumors depends on both the responsible genetic lesion and tissue type. Cell Metab 2012, 15:157-170. 10.1016/j.cmet.2011.12.015, 3282107, 22326218.
7. Nilsson LM, Plym Forshell TZ, Rimpi S, Kreutzer C, Pretsch W, Bornkamm GW, Nilsson JA. Mouse genetics suggests cell-context dependency for Myc-regulated metabolic enzymes during tumorigenesis. PLoS Genet 2012, 8:e1002573. 10.1371/journal.pgen.1002573, 3305401, 22438825.
8. Possemato R, Marks KM, Shaul YD, Pacold ME, Kim D, Birsoy K, Sethumadhavan S, Woo H-K, Jang HG, Jha AK, et al. Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature 2011, 476:346-350. 10.1038/nature10350, 3353325, 21760589.
9. Locasale JW, Grassian AR, Melman T, Lyssiotis CA, Mattaini KR, Bass AJ, Heffron G, Metallo CM, Muranen T, Sharfi H, et al. Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nat Genet 2011, 43:869-874. 10.1038/ng.890, 21804546.
10. Shlomi T, Cabili MN, Herrgard MJ, Palsson BO, Ruppin E. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 2008, 26:1003-1010. 10.1038/nbt.1487, 18711341.
11. Bordbar A, Lewis NE, Schellenberger J, Palsson BO, Jamshidi N. Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 2010, 6:422. 2990636, 20959820.
12. Chang RL, Xie L, Xie L, Bourne PE, Palsson B. Drug Off-target effects predicted using structural analysis in the context of a metabolic network model. PLoS Comput Biol 2010, 6:e1000938. 10.1371/journal.pcbi.1000938, 2950675, 20957118.
13. Gille C, Bolling C, Hoppe A, Bulik S, Hoffmann S, Hubner K, Karlstadt A, Ganeshan R, Konig M, Rother K, et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol Syst Biol 2010, 6:411. 2964118, 20823849.
14. Jerby L, Shlomi T, Ruppin E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 2010, 6:401. 2964116, 20823844.
15. Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T. Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 2011, 7:501. 3159974, 21694718.
16. Frezza C, Zheng L, Folger O, Rajagopalan KN, MacKenzie ED, Jerby L, Micaroni M, Chaneton B, Adam J, Hedley A, et al. Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 2011, 477:225-228. 10.1038/nature10363, 21849978.
17. Becker SA, Palsson BO. Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 2008, 4:e1000082. 10.1371/journal.pcbi.1000082, 2366062, 18483554.
18. Bordbar A, Feist AM, Usaite-Black R, Woodcock J, Palsson BO, Famili I. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst Biol 2011, 5:180. 10.1186/1752-0509-5-180, 3219569, 22041191.
19. Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N, Yee A, Lewis RA, Eils R, et al. Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat Biotechnol 2010, 28:1279-1285. 10.1038/nbt.1711, 3140076, 21102456.
20. McCall MN, Uppal K, Jaffee HA, Zilliox MJ, Irizarry RA. The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res 2011, 39:D1011-1015. 10.1093/nar/gkq1259, 3013751, 21177656.
21. The price Lab http://price.systemsbiology.net/downloads.php .
22. Rosenthal MD, Glew RH. Medical biochemistry: human metabolism in health and disease 2009, Oxford: Wiley & Sons.
23. Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, Zwahlen M, Kampf C, Wester K, Hober S, et al. Towards a knowledge-based Human Protein Atlas. Nat Biotechnol 2010, 28:1248-1250. 10.1038/nbt1210-1248, 21139605.
24. Ohno S, Nakajin S. Determination of mRNA expression of human UDP-glucuronosyltransferases and application for localization in various human tissues by real-time reverse transcriptase-polymerase chain reaction. Drug Metab Dispos 2009, 37:32-40. 10.1124/dmd.108.023598, 18838504.
25. Shelby MK, Cherrington NJ, Vansell NR, Klaassen CD. Tissue mRNA expression of the rat UDP-glucuronosyltransferase gene family. Drug Metab Dispos 2003, 31:326-333. 10.1124/dmd.31.3.326, 12584160.
26. Mahadevan R, Schilling CH. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 2003, 5:264-276. 10.1016/j.ymben.2003.09.002, 14642354.
27. Gudmundsson S, Thiele I. Computationally efficient flux variability analysis. BMC Bioinforma 2010, 11:489.
28. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10:57-63. 10.1038/nrg2484, 2949280, 19015660.
29. Krupp M, Marquardt JU, Sahin U, Galle PR, Castle J, Teufel A. RNA-Seq Atlas - A reference database for gene expression profiling in normal tissue by next generation sequencing. Bioinformatics 2012, 28:1184-1185. 10.1093/bioinformatics/bts084, 22345621.
30. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, Chen W, Selbach M. Global quantification of mammalian gene expression control. Nature 2011, 473:337-342. 10.1038/nature10098, 21593866.
31. The gene expression barcode website http://barcode.luhs.org/ .
32. Moore SA. Polyunsaturated fatty acid synthesis and release by brain-derived cells in vitro. J Mol Neurosci 2001, 16:195-200. discussion 215-121, 10.1385/JMN:16:2-3:195, 11478374.
33. Moore SA, Yoder E, Murphy S, Dutton GR, Spector AA. Astrocytes, Not neurons, produce docosahexaenoic acid (22:6ω-3) and arachidonic acid (20:4ω-6). J Neurochem 1991, 56:518-524. 10.1111/j.1471-4159.1991.tb08180.x, 1824862.
34. Kuhajda FP. Fatty-acid synthase and human cancer: new perspectives on its role in tumor biology. Nutrition 2000, 16:202-208. 10.1016/S0899-9007(99)00266-X, 10705076.
35. Menendez JA, Lupu R. Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat Rev Cancer 2007, 7:763-777. 10.1038/nrc2222, 17882277.
36. Mashima T, Seimiya H, Tsuruo T. De novo fatty-acid synthesis and related pathways as molecular targets for cancer therapy. Br J Cancer 2009, 100:1369-1372. 10.1038/sj.bjc.6605007, 2694429, 19352381.
37. Wang D, Dubois RN. Eicosanoids and cancer. Nat Rev Cancer 2010, 10:181-193. 10.1038/nrc2809, 2898136, 20168319.
38. Ogretmen B, Hannun YA. Biologically active sphingolipids in cancer pathogenesis and treatment. Nat Rev Cancer 2004, 4:604-616. 10.1038/nrc1411, 15286740.
39. Ye YN, Wu WK, Shin VY, Cho CH. A mechanistic study of colon cancer growth promoted by cigarette smoke extract. Eur J Pharmacol 2005, 519:52-57. 10.1016/j.ejphar.2005.07.009, 16125168.
40. Cianchi F, Cortesini C, Magnelli L, Fanti E, Papucci L, Schiavone N, Messerini L, Vannacci A, Capaccioli S, Perna F, et al. Inhibition of 5-lipoxygenase by MK886 augments the antitumor activity of celecoxib in human colon cancer cells. Mol Cancer Ther 2006, 5:2716-2726. 10.1158/1535-7163.MCT-06-0318, 17121918.
41. Peters-Golden M, Henderson WR. Leukotrienes. N Engl J Med 2007, 357:1841-1854. 10.1056/NEJMra071371, 17978293.
42. Tsopanoglou NE, Pipili-Synetos E, Maragoudakis ME. Leukotrienes C4 and D4 promote angiogenesis via a receptor-mediated interaction. Eur J Pharmacol 1994, 258:151-154. 10.1016/0014-2999(94)90068-X, 7523148.
43. Paruchuri S, Broom O, Dib K, Sjolander A. The pro-inflammatory mediator leukotriene D4 induces phosphatidylinositol 3-kinase and Rac-dependent migration of intestinal epithelial cells. J Biol Chem 2005, 280:13538-13544. 10.1074/jbc.M409811200, 15657050.
44. Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol 2012, 8:e1002518. 10.1371/journal.pcbi.1002518, 3355067, 22615553.
45. Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, et al. NCBI GEO: archive for functional genomics data sets-10 years on. Nucleic Acids Res 2011, 39:D1005-D1010. 10.1093/nar/gkq1184, 3013736, 21097893.
46. Davis S, Meltzer PS. GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics 2007, 23:1846-1847. 10.1093/bioinformatics/btm254, 17496320.
47. Dudley J, Butte AJ. Enabling integrative genomic analysis of high-impact human diseases through text mining. Pac Symp Biocomput 2008, 580-591.
48. Desvergne B, Michalik L, Wahli W. Transcriptional regulation of metabolism. Physiol Rev 2006, 86:465-514. 10.1152/physrev.00025.2005, 16601267.
49. Fajans SS, Bell GI, Polonsky KS. Molecular mechanisms and clinical pathophysiology of maturity-onset diabetes of the young. N Engl J Med 2001, 345:971-980. 10.1056/NEJMra002168, 11575290.
50. Cairns RA, Harris IS, Mak TW. Regulation of cancer cell metabolism. Nat Rev Cancer 2011, 11:85-95.
51. Chandrasekaran S, Price ND. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci USA 2010, 107:17845-17850. 10.1073/pnas.1005139107, 2955152, 20876091.
52. Evans RM, Barish GD, Wang YX. PPARs and the complex journey to obesity. Nat Med 2004, 10:355-361. 10.1038/nm1025, 15057233.
53. Maeda K, Cao H, Kono K, Gorgun CZ, Furuhashi M, Uysal KT, Cao Q, Atsumi G, Malone H, Krishnan B, et al. Adipocyte/macrophage fatty acid binding proteins control integrated metabolic responses in obesity and diabetes. Cell Metab 2005, 1:107-119. 10.1016/j.cmet.2004.12.008, 16054052.
54. Furuhashi M, Fucho R, Görgün CZ, Tuncman G, Cao H, Hotamisligil GS. Adipocyte/macrophage fatty acid-binding proteins contribute to metabolic deterioration through actions in both macrophages and adipocytes in mice. J Clin Invest 2008, 118:2640-2650. 2423863, 18551191.
55. Allaman I, Belanger M, Magistretti PJ. Astrocyte-neuron metabolic relationships: for better and for worse. Trends Neurosci 2011, 34:76-87. 10.1016/j.tins.2010.12.001, 21236501.
56. Schellenberger J, Park JO, Conrad TM, Palsson BO. BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinf 2010, 11:213.
57. Becker SA, Feist AM, Mo ML, Hannum G, Palsson BO, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protocols 2007, 2:727-738.
58. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008, 18:1509-1517. 10.1101/gr.079558.108, 2527709, 18550803.
59. Roth R, Hevezi P, Lee J, Willhite D, Lechner S, Foster A, Zlotnik A. Gene expression analyses reveal molecular relationships among 20 regions of the human CNS. Neurogenetics 2006, 7:67-80. 10.1007/s10048-006-0032-6, 16572319.
60. Liao YL, Sun YM, Chau GY, Chau YP, Lai TC, Wang JL, Horng JT, Hsiao M, Tsou AP. Identification of SOX4 target genes using phylogenetic footprinting-based prediction from expression microarrays suggests that overexpression of SOX4 potentiates metastasis in hepatocellular carcinoma. Oncogene 2008, 27:5578-5589. 10.1038/onc.2008.168, 18504433.
61. Wurmbach E, Chen YB, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J, et al. Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology 2007, 45:938-947. 10.1002/hep.21622, 17393520.
62. Hubbell E, Liu WM, Mei R. Robust estimators for expression analysis. Bioinformatics 2002, 18:1585-1592. 10.1093/bioinformatics/18.12.1585, 12490442.