Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE
Tóm tắt
Human tissues perform diverse metabolic functions. Mapping out these tissue-specific functions in genome-scale models will advance our understanding of the metabolic basis of various physiological and pathological processes. The global knowledgebase of metabolic functions categorized for the human genome (
We developed a method called metabolic Context-specificity Assessed by Deterministic Reaction Evaluation (mCADRE). mCADRE is able to infer a tissue-specific network based on gene expression data and metabolic network topology, along with evaluation of functional capabilities during model building. mCADRE produces models with similar or better functionality and achieves dramatic computational speed up over existing methods. Using our method, we reconstructed draft genome-scale metabolic models for 126 human tissue and cell types. Among these, there are models for 26 tumor tissues along with their normal counterparts, and 30 different brain tissues. We performed pathway-level analyses of this large collection of tissue-specific models and identified the eicosanoid metabolic pathway, especially reactions catalyzing the production of leukotrienes from arachidnoic acid, as potential drug targets that selectively affect tumor tissues.
This large collection of 126 genome-scale draft metabolic models provides a useful resource for studying the metabolic basis for a variety of human diseases across many tissues. The functionality of the resulting models and the fast computational speed of the mCADRE algorithm make it a useful tool to build and update tissue-specific metabolic models.
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Tài liệu tham khảo
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.
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-
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.
Lazar MA, Birnbaum MJ: Physiology. De-meaning of metabolism. Science. 2012, 336: 1651-1652. 10.1126/science.1221834.
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.
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., 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.
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.
Possemato R, Marks KM, Shaul YD, Pacold ME, Kim D, Birsoy K, Sethumadhavan S, Woo H-K, Jang HG, Jha AK, et al., et al: Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature. 2011, 476: 346-350. 10.1038/nature10350.
Locasale JW, Grassian AR, Melman T, Lyssiotis CA, Mattaini KR, Bass AJ, Heffron G, Metallo CM, Muranen T, Sharfi H, et al., et al: Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nat Genet. 2011, 43: 869-874. 10.1038/ng.890.
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.
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-
Chang RL, Xie L, Xie L, Bourne PE, Palsson BØ: 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.
Gille C, Bolling C, Hoppe A, Bulik S, Hoffmann S, Hubner K, Karlstadt A, Ganeshan R, Konig M, Rother K, et al., et al: HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol Syst Biol. 2010, 6: 411-
Jerby L, Shlomi T, Ruppin E: Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol. 2010, 6: 401-
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-
Frezza C, Zheng L, Folger O, Rajagopalan KN, MacKenzie ED, Jerby L, Micaroni M, Chaneton B, Adam J, Hedley A, et al., et al: Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature. 2011, 477: 225-228. 10.1038/nature10363.
Becker SA, Palsson BO: Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol. 2008, 4: e1000082-10.1371/journal.pcbi.1000082.
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.
Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N, Yee A, Lewis RA, Eils R, et al., 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.
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.
The price Lab.http://price.systemsbiology.net/downloads.php,
Rosenthal MD, Glew RH: Medical biochemistry: human metabolism in health and disease. 2009, Oxford: Wiley & Sons
Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M, Zwahlen M, Kampf C, Wester K, Hober S, et al., et al: Towards a knowledge-based Human Protein Atlas. Nat Biotechnol. 2010, 28: 1248-1250. 10.1038/nbt1210-1248.
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.
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.
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.
Gudmundsson S, Thiele I: Computationally efficient flux variability analysis. BMC Bioinforma. 2010, 11: 489-10.1186/1471-2105-11-489.
Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10: 57-63. 10.1038/nrg2484.
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.
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.
The gene expression barcode website.http://barcode.luhs.org/,
Moore SA: Polyunsaturated fatty acid synthesis and release by brain-derived cells in vitro. J Mol Neurosci. 2001, 16: 195-200. 10.1385/JMN:16:2-3:195. discussion 215–121
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.
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.
Menendez JA, Lupu R: Fatty acid synthase and the lipogenic phenotype in cancer pathogenesis. Nat Rev Cancer. 2007, 7: 763-777. 10.1038/nrc2222.
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.
Ogretmen B, Hannun YA: Biologically active sphingolipids in cancer pathogenesis and treatment. Nat Rev Cancer. 2004, 4: 604-616. 10.1038/nrc1411.
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.
Cianchi F, Cortesini C, Magnelli L, Fanti E, Papucci L, Schiavone N, Messerini L, Vannacci A, Capaccioli S, Perna F, et al., 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.
Peters-Golden M, Henderson WR: Leukotrienes. N Engl J Med. 2007, 357: 1841-1854. 10.1056/NEJMra071371.
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.
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.
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.
Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, et al., et al: NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res. 2011, 39: D1005-D1010. 10.1093/nar/gkq1184.
Davis S, Meltzer PS: GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics. 2007, 23: 1846-1847. 10.1093/bioinformatics/btm254.
Dudley J, Butte AJ: Enabling integrative genomic analysis of high-impact human diseases through text mining. Pac Symp Biocomput. 2008, 580-591.
Desvergne B, Michalik L, Wahli W: Transcriptional regulation of metabolism. Physiol Rev. 2006, 86: 465-514. 10.1152/physrev.00025.2005.
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.
Cairns RA, Harris IS, Mak TW: Regulation of cancer cell metabolism. Nat Rev Cancer. 2011, 11: 85-95.
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.
Evans RM, Barish GD, Wang YX: PPARs and the complex journey to obesity. Nat Med. 2004, 10: 355-361. 10.1038/nm1025.
Maeda K, Cao H, Kono K, Gorgun CZ, Furuhashi M, Uysal KT, Cao Q, Atsumi G, Malone H, Krishnan B, et al., 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.
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.
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.
Schellenberger J, Park JO, Conrad TM, Palsson BO: BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinf. 2010, 11: 213-10.1186/1471-2105-11-213.
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. 10.1038/nprot.2007.99.
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.
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.
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.
Wurmbach E, Chen YB, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J, et al., et al: Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology. 2007, 45: 938-947. 10.1002/hep.21622.