A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

BMC Systems Biology

Royston Goodacre³  Kaye J Williams¹  Warwick B Dunn⁵  James W Allwood²  Kieran J Sharkey⁴  Emily G Armitage⁷  Helen L Kotze⁶ 

[1] Manchester Pharmacy School, University of Manchester, Oxford Road, Manchester M13 9PT, UK;Current address: School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK;Department of Mathematical Sciences, The University of Liverpool, Peach Street, Liverpool L69 7ZL, UK;Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, York Place, off Oxford Road, Manchester M13 9WL, UK;School of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK;Current address: Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de Farmacia, Campus Monteprincipe, Universidad CEU San Pablo, Boadilla del Monte, Madrid 28668, Spain

关键词: Hypoxia;    Cancer;    Network analysis;    Correlation analysis;    Metabolomics;   

Others  :  1141972
DOI  :  10.1186/1752-0509-7-107

 received in 2013-03-25, accepted in 2013-09-04,  发布年份 2013

【 摘 要 】

Background

Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments.

Results

Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer.

Conclusions

Metabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics.

【 授权许可】

   
2013 Kotze et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Wold S, Esbensen K, Geladi P: Principal component analysis. Chemom Intell Lab Syst 1987, 2:37-52.
• [2]Jolliffe IT: Principal Component Analysis 2nd edn. New York: Springer; 2002.
• [3]Steuer R: On the analysis and interpretation of correlations in metabolomic data. Brief Bioinform 2006, 7:151-158.
• [4]Ugarte M, Brown M, Hollywood KA, Cooper GJ, Bishop PN, Dunn WB: Metabolomic analysis of rat serum in streptozotocin-induced diabetes and after treatment with oral triethylenetetramine (TETA). Genome Medicine 2012, 4:35. BioMed Central Full Text
• [5]Camacho D, de la Fuente A, Mendes P: The origin of correlations in metabolomics data. Metabolomics 2005, 1:53-63.
• [6]Valcárcel B, Würtz P, al Basatena N-K S, Tukiainen T, Kangas AJ, Soininen P, Järvelin M-R, Ala-Korpela M, Ebbels TM, de Iorio M, Valcárcel B, Würtz P, al Basatena N-K S, Tukiainen T, Kangas AJ, Soininen P, Järvelin M-R, Ala-Korpela M, Ebbels TM, de Iorio M: A differential network approach to exploring differences between biological states: an application to prediabetes. Plos One 2011, 6:e24702.
• [7]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 U S A 2007, 104:1777-1782.
• [8]Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, Haraldsdottir H, Mo ML, Rolfsson O, Stobbe MD, et al.: A community-driven global reconstruction of human metabolism. Nat Biotech 2013, 31:419-425. advance online publication
• [9]Ma HW, 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.
• [10]Hao T, Ma H-W, Zhao X-M, Goryanin I: Compartmentalization of the Edinburgh human metabolic network. BMC Bioinforma 2010, 11:393. BioMed Central Full Text
• [11]Arita M: The metabolic world of Escherichia coli is not small. Proc Natl Acad Sci U S A 2004, 101:1543-1547.
• [12]Barabási A-L, Gulbahce N, Loscalzo J: Network medicine: a network-based approach to human disease. Nat Rev Genet 2011, 12:56-68.
• [13]DeBerardinis RJ, Cheng T: Q’s next: the diverse functions of glutamine in metabolism, cell biology and cancer. Oncogene 2010, 29:313-324.
• [14]Warburg O: Origin of cancer cells. Science 1956, 123:309-314.
• [15]Ruan K, Song G, Ouyang GL: Role of hypoxia in the hallmarks of human cancer. J Cell Biochem 2009, 107:1053-1062.
• [16]Griffiths JR, McSheehy PMJ, Robinson SP, Troy H, Chung YL, Leek RD, Williams KJ, Stratford IJ, Harris AL, Stubbs M: Metabolic changes detected by in vivo magnetic resonance studies of HEPA-1 wild-type tumors and tumors deficient in hypoxia-inducible factor-1 beta (HIF-1 beta): evidence of an anabolic role for the HIF-1 pathway. Cancer Res 2002, 62:688-695.
• [17]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-U132.
• [18]Sumner LW, Amberg A, Barrett D, Beger R, Beale MH, Daykin C, Fan TW-M, Fiehn O, Goodacre R, Griffin JL, Hardy N, Higashi R, Kopka J, Lindon JC, Lane AN, Marriott P, Nicholls AW, Reily MD, Viant M: Proposed minimum reporting standards for chemical analysis. Metabolomics 2007, 3:211-221.
• [19]Steuer R, Morgenthal K, Weckwerth W, Selbig J: A Gentle Guide to the Analysis of Metabolomic Data Metabolomics. In Volume 358. In Methods in Molecular Biology. Edited by Walker JM. New York: Humana Press; 2007:105-126.
• [20]Kim JW, Dang CV: Cancer’s molecular sweet tooth and the Warburg effect. Cancer Res 2006, 66:8927-8930.
• [21]Dang CV: Glutaminolysis: supplying carbon or nitrogen or both for cancer cells? Cell Cycle 2010, 9:3884-3886.
• [22]Jiang WG, Bryce RP, Horrobin DF: Essential fatty acids: molecular and cellular basis of their anti-cancer action and clinical implications. Crit Rev Oncol Hematol 1998, 27:179-209.
• [23]Knox WE, Tremblay GC, Spanier BB, Friedell GH: Glutaminase activities in normal and neoplastic tissues of the rat. Cancer Res 1967, 27:1456-1458.
• [24]Brown M, Dunn WB, Dobson P, Patel Y, Winder CL, Francis-McIntyre S, Begley P, Carroll K, Broadhurst D, Tseng A, et al.: Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 2009, 134:1322-1332.
• [25]Herrgard MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Bluethgen N, Borger S, Costenoble R, Heinemann M, et al.: A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol 2008, 26:1155-1160.
• [26]Aledo JC: Glutamine breakdown in rapidly dividing cells: waste or investment? Bioessays 2004, 26:778-785.
• [27]Begley P, Francis-McIntyre S, Dunn WB, Broadhurst DI, Halsall A, Tseng A, Knowles J, Goodacre R, Kell DB, Consortium H: Development and performance of a Gas chromatography-time-of-flight mass spectrometry analysis for large-scale nontargeted metabolomic studies of human serum. Anal Chem 2009, 81:7038-7046.
• [28]Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, et al.: Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 2011, 6:1060-1083.
• [29]Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel B, Bergmuller E, Dormann P, Weckwerth W, Gibon Y, Stitt M, et al.: [e-mail]: the golm metabolome database. Bioinformatics 2005, 21:1635-1638.
• [30]Fiehn O, Robertson D, Griffin J, van der Werf M, Nikolau B, Morrison N, Sumner LW, Goodacre R, Hardy NW, Taylor C, et al.: The metabolomics standards initiative (MSI). Metabolomics 2007, 3:175-178.
• [31]Rodgers JL, Nicewander WA: Thirteen ways to look at the correlation coefficient. Am Stat 1988, 42:59-66.
• [32]Steuer R: Nonlinear dynamics and molecular biology: From gene expression to metabolic networks. University of Potsdam, Institute of physics; 2005. [PhD Thesis]
• [33]Sachs L: Angewandte Statistik. achte auflage edition edn. Berlin: Speinger-Verlag; 1997.
• [34]Keating SM, Bornstein BJ, Finney A, Hucka M: SBMLToolbox: an SBML toolbox for MATLAB users. Bioinformatics 2006, 22:1275-1277.
• [35]Ma H, Zeng A-P: Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 2003, 19:270-277.