MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity

BMC Bioinformatics

Oliver Fiehn¹  Kent E Pinkerton²  Shanker L Kothari³  Tobias Kind¹  Gert Wohlgemuth¹  Pradeep K Haldiya¹  Dinesh K Barupal¹ 

[1]UC Davis Genome Center, Metabolomics, Davis, 95616, CA, USA

[2]UC Davis Center for Health and the Environmental, Davis, 95616, CA, USA

[3]DBT-Bioinformatics Infrastructure Facility, University of Rajasthan, Jaipur, India

关键词: Lung surfactants;    Perinatal lung development;    Enzymatic pathways;    Metabolic networks;   

Others  :  1088271
DOI  :  10.1186/1471-2105-13-99

 received in 2011-12-24, accepted in 2012-04-25,  发布年份 2012

【 摘 要 】

Background

Exposure to environmental tobacco smoke (ETS) leads to higher rates of pulmonary diseases and infections in children. To study the biochemical changes that may precede lung diseases, metabolomic effects on fetal and maternal lungs and plasma from rats exposed to ETS were compared to filtered air control animals. Genome- reconstructed metabolic pathways may be used to map and interpret dysregulation in metabolic networks. However, mass spectrometry-based non-targeted metabolomics datasets often comprise many metabolites for which links to enzymatic reactions have not yet been reported. Hence, network visualizations that rely on current biochemical databases are incomplete and also fail to visualize novel, structurally unidentified metabolites.

Results

We present a novel approach to integrate biochemical pathway and chemical relationships to map all detected metabolites in network graphs (MetaMapp) using KEGG reactant pair database, Tanimoto chemical and NIST mass spectral similarity scores. In fetal and maternal lungs, and in maternal blood plasma from pregnant rats exposed to environmental tobacco smoke (ETS), 459 unique metabolites comprising 179 structurally identified compounds were detected by gas chromatography time of flight mass spectrometry (GC-TOF MS) and BinBase data processing. MetaMapp graphs in Cytoscape showed much clearer metabolic modularity and complete content visualization compared to conventional biochemical mapping approaches. Cytoscape visualization of differential statistics results using these graphs showed that overall, fetal lung metabolism was more impaired than lungs and blood metabolism in dams. Fetuses from ETS-exposed dams expressed lower lipid and nucleotide levels and higher amounts of energy metabolism intermediates than control animals, indicating lower biosynthetic rates of metabolites for cell division, structural proteins and lipids that are critical for in lung development.

Conclusions

MetaMapp graphs efficiently visualizes mass spectrometry based metabolomics datasets as network graphs in Cytoscape, and highlights metabolic alterations that can be associated with higher rate of pulmonary diseases and infections in children prenatally exposed to ETS. The MetaMapp scripts can be accessed at http://metamapp.fiehnlab.ucdavis.edu webcite.

【 授权许可】

   
2012 Barupal et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1 .

Figure 2 .

Figure 3 .

Figure 4 .

Figure 5 .

【 参考文献 】

• [1]Mukhopadhyay P, Horn KH, Greene RM, Michele Pisano M: Prenatal exposure to environmental tobacco smoke alters gene expression in the developing murine hippocampus. Reprod Toxicol 2010, 29:164-175.
• [2]Gilmour MI, Jaakkola MS, London SJ, Nel AE, Rogers CA: How exposure to environmental tobacco smoke, outdoor air pollutants, and increased pollen burdens influences the incidence of asthma. Environ Health Perspect 2006, 114:627-633.
• [3]Gilliland FD, Berhane K, McConnell R, Gauderman WJ, Vora H, Rappaport EB, Avol E, Peters JM: Maternal smoking during pregnancy, environmental tobacco smoke exposure and childhood lung function. Thorax 2000, 55:271-276.
• [4]Zhong CY, Zhou YM, Joad JP, Pinkerton KE: Environmental tobacco smoke suppresses nuclear factor-kappaB signaling to increase apoptosis in infant monkey lungs. Am J Respir Crit Care Med 2006, 174:428-436.
• [5]Gairola CG, Wu H, Gupta RC, Diana JN: Mainstream and sidestream cigarette smoke-induced DNA adducts in C7Bl and DBA mice. Environ Health Perspect 1993, 99:253-255.
• [6]Flouris AD, Metsios GS, Carrillo AE, Jamurtas AZ, Gourgoulianis K, Kiropoulos T, Tzatzarakis MN, Tsatsakis AM, Koutedakis Y: Acute and short-term effects of secondhand smoke on lung function and cytokine production. Am J Respir Crit Care Med 2009, 179:1029-1033.
• [7]DiFranza JR, Aligne CA, Weitzman M: Prenatal and postnatal environmental tobacco smoke exposure and children's health. Pediatrics 2004, 113:1007-1015.
• [8]Rehan VK, Asotra K, Torday JS: The effects of smoking on the developing lung: insights from a biologic model for lung development, homeostasis, and repair. Lung 2009, 187:281-289.
• [9]Majeti R, Becker MW, Tian Q, Lee TL, Yan X, Liu R, Chiang JH, Hood L, Clarke MF, Weissman IL: Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proc Natl Acad Sci U S A 2009, 106:3396-3401.
• [10]Perez-Plasencia C, Vazquez-Ortiz G, Lopez-Romero R, Pina-Sanchez P, Moreno J, Salcedo M: Genome wide expression analysis in HPV16 cervical cancer: identification of altered metabolic pathways. Infect Agent Cancer 2007, 2:16. BioMed Central Full Text
• [11]Fiehn O, Kopka J, Dormann P, Altmann T, Trethewey RN, Willmitzer L: Metabolite profiling for plant functional genomics. Nat Biotechnol 2000, 18:1157-1161.
• [12]Xia J, Psychogios N, Young N, Wishart DS: MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 2009, 37:W652-660.
• [13]Kleijn RJ, Buescher JM, Le Chat L, Jules M, Aymerich S, Sauer U: Metabolic fluxes during strong carbon catabolite repression by malate in Bacillus subtilis. J Biol Chem 2010, 285:1587-1596.
• [14]Lu X, Bennet B, Mu E, Rabinowitz J, Kang Y: Metabolomic changes accompanying transformation and acquisition of metastatic potential in a syngeneic mouse mammary tumor model. J Biol Chem 2010, 285:9317-9321.
• [15]Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, Kaipa P, Karthikeyan AS, Kothari A, Krummenacker M, et al.: The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 2010, 38:D473-479.
• [16]Kanehisa M: KEGG for linking genomes to life and the environment. Nucleic Acids Res 2008, 36:D480-D484.
• [17]Fiehn O: Extending the breadth of metabolite profiling by gas chromatography coupled to mass spectrometry. Trends Analyt Chem 2008, 27:261-269.
• [18]Brauer MJ, Yuan J, Bennett BD, Lu W, Kimball E, Botstein D, Rabinowitz JD: Conservation of the metabolomic response to starvation across two divergent microbes. Proc Natl Acad Sci U S A 2006, 103:19302-19307.
• [19]Ohashi Y, Hirayama A, Ishikawa T, Nakamura S, Shimizu K, Ueno Y, Tomita M, Soga T: Depiction of metabolome changes in histidine-starved Escherichia coli by CE-TOFMS. Mol Biosyst 2008, 4:135-147.
• [20]Tolstikov VV, Fiehn O: Analysis of highly polar compounds of plant origin: Combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry. Anal Biochem 2002, 301:298-307.
• [21]Kind T, Wohlgemuth G, Lee do Y, Lu Y, Palazoglu M, Shahbaz S, Fiehn O: FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal Chem 2009, 81:10038-10048.
• [22]Adams RP: Identification of Essential Oil Components by Gas Chromatography/Mass Spectroscopy. Allured Publishing, IL, USA; 2007.
• [23]Fiehn O, Wohlgemuth G, Scholz M, Kind T, Lee do Y, Lu Y, Moon S, Nikolau B: Quality control for plant metabolomics: reporting MSI-compliant studies. Plant J 2008, 53:691-704.
• [24]Denkert C, Budczies J, Weichert W, Wohlgemuth G, Scholz M, Kind T, Niesporek S, Noske A, Buckendahl A, Dietel M, Fiehn O: Metabolite profiling of human colon carcinoma–deregulation of TCA cycle and amino acid turnover. Mol Cancer 2008, 7:72. BioMed Central Full Text
• [25]Zhang B, Tolstikov V, Turnbull C, Hicks LM, Fiehn O: Divergent metabolome and proteome suggest functional independence of dual phloem transport systems in cucurbits. Proc Natl Acad Sci U S A 2010, 107:13532-13537.
• [26]Hartman AL, Lough DM, Barupal DK, Fiehn O, Fishbein T, Zasloff M, Eisen JA: Human gut microbiome adopts an alternative state following small bowel transplantation. Proc Natl Acad Sci U S A 2009, 106:17187-17192.
• [27]Shin MH, Lee do Y, Wohlgemuth G, Choi IG, Fiehn O, Kim KH: Global metabolite profiling of agarose degradation by Saccharophagus degradans 2–40. N Biotechnol 2010, 27:156-168.
• [28]Seifert EL, Fiehn O, Bezaire V, Bickel DR, Wohlgemuth G, Adams SH, Harper ME: Long-chain fatty acid combustion rate is associated with unique metabolite profiles in skeletal muscle mitochondria. PLoS One 2010, 5:e9834.
• [29]Adriaens ME, Jaillard M, Waagmeester A, Coort SL, Pico AR, Evelo CT: The public road to high- quality curated biological pathways. Drug Discov Today 2008, 13:856-862.
• [30]Bader GD, Cary MP, Sander C: Pathguide: a pathway resource list. Nucleic Acids Res 2006, 34:D504-506.
• [31]Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Cheng D, Jewell K, Arndt D, Sawhney S, Fung C, Nikolai L, Lewis M, Coutouly MA, Forsythe I, Tang P, Shrivastava S, Jeroncic K, Stothard P, Amegbey G, Block D, Hau DD, Wagner J, Miniaci J, Clements M, Gebremedhin M, Guo N, Zhang Y, Duggan GE, Macinnis GD, Weljie AM, Dowlatabadi R, Bamforth F, Clive D, Greiner R, Li L, Marrie T, Sykes BD, Vogel HJ, Querengesser L: HMDB: The human metabolome database. Nucleic Acids Res 2007, 35:D521-526.
• [32]Green ML, Karp PD: A Bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinforma 2004, 5:76. BioMed Central Full Text
• [33]Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C: WikiPathways: pathway editing for the people. PLoS Biol 2008, 6:e184.
• [34]Khersonsky O, Tawfik DS: Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu Rev Biochem 2010, 79:471-505.
• [35]Babtie A, Tokuriki N, Hollfelder F: What makes an enzyme promiscuous? Curr Opin Chem Biol 2010, 14:200-207.
• [36]Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BØ: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 2007, 3:121.
• [37]Steuer R, Kurths J, Fiehn O, Weckwerth W: Observing and interpreting correlations in metabolomic networks. Bioinformatics 2003, 19:1019-1026.
• [38]de la Fuente A, Bing N, Hoeschele I, Mendes P: Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 2004, 20:3565-3574.
• [39]Camacho D, de la Fuente A, Mendes P: The origin of correlations in metabolomics data. Metabolomics 2005, 1:53-63.
• [40]Atkinson HJ, Morris JH, Ferrin TE, Babbitt PC: Using sequence similarity networks for visualization of relationships across diverse protein superfamilies. PLoS One 2009, 4:e4345.
• [41]Theocharidis A, van Dongen S, Enright AJ, Freeman TC: Network visualization and analysis of gene expression data using BioLayout Express(3D). Nat Protoc 2009, 4:1535-1550.
• [42]Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL: The large-scale organization of metabolic networks. Nature 2000, 407:651-654.
• [43]Spirin V, Gelfand MS, Mironov AA, Mirny LA: A metabolic network in the evolutionary context: multiscale structure and modularity. Proc Natl Acad Sci U S A 2006, 103:8774-8779.
• [44]Zhang Y, Li S, Skogerbo G, Zhang Z, Zhu X, Sun S, Lu H, Shi B, Chen R: Phylophenetic properties of metabolic pathway topologies as revealed by global analysis. BMC Bioinforma 2006, 7:252. BioMed Central Full Text
• [45]Peregrin-Alvarez JM, Sanford C, Parkinson J: The conservation and evolutionary modularity of metabolism. Genome Biol 2009, 10:R63. BioMed Central Full Text
• [46]Croes D, Couche F, Wodak SJ, van Helden J: Metabolic PathFinding: inferring relevant pathways in biochemical networks. Nucleic Acids Res 2005, 33:W326-330.
• [47]Blank LM, Kuepfer L, Sauer U: Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast. Genome Biol 2005, 6:R49. BioMed Central Full Text
• [48]Kanehisa M, Goto S, Kawashima S, Nakaya A: Thed KEGG databases at GenomeNet. Nucleic Acids Res 2002, 30:42-46.
• [49]Karp PD, Riley M, Saier M, Paulsen IT, Paley SM, Pellegrini-Toole A: The EcoCyc and MetaCyc databases. Nucleic Acids Res 2000, 28:56-59.
• [50]Vastrik I, D'Eustachio P, Schmidt E, Gopinath G, Croft D, de Bono B, Gillespie M, Jassal B, Lewis S, Matthews L, et al.: Reactome: a knowledge base of biologic pathways and processes. Genome Biol 2007, 8:R39. BioMed Central Full Text
• [51]Schellenberger J, Park JO, Conrad TM, Palsson BO: BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinforma 2010, 11:213. BioMed Central Full Text
• [52]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.
• [53]Khersonsky O, Roodveldt C, Tawfik DS: Enzyme promiscuity: evolutionary and mechanistic aspects. Curr Opin Chem Biol 2006, 10:498-508.
• [54]Degtyarenko K, de Matos P, Ennis M, Hastings J, Zbinden M, McNaught A, Alcantara R, Darsow M, Guedj M, Ashburner M: ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 2008, 36:D344-350.
• [55]Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009, 37:W623-633.
• [56]Kono N, Arakawa K, Ogawa R, Kido N, Oshita K, Ikegami K, Tamaki S, Tomita M: Pathway projector: web-based zoomable pathway browser using KEGG atlas and Google Maps API. PLoS One 2009, 4:e7710.
• [57]Letunic I, Yamada T, Kanehisa M, Bork P: iPath: interactive exploration of biochemical pathways and networks. Trends Biochem Sci 2008, 33:101-103.
• [58]Okuda S, Yamada T, Hamajima M, Itoh M, Katayama T, Bork P, Goto S, Kanehisa M: KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res 2008, 36:W423-426.
• [59]Geer LY, Marchler-Bauer A, Geer RC, Han L, He J, He S, Liu C, Shi W, Bryant SH: The NCBI BioSystems database. Nucleic Acids Res 2010, 38:D492-496.
• [60]Cottret L, Wildridge D, Vinson F, Barrett MP, Charles H, Sagot MF, Jourdan F: MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks. Nucleic Acids Res 2010, 38(Suppl):W132-137.
• [61]Frolkis A, Knox C, Lim E, Jewison T, Law V, Hau DD, Liu P, Gautam B, Ly S, Guo AC, Xia J, Liang Y, Shrivastava S, Wishart DS: SMPDB: The Small Molecule Pathway Database. Nucleic Acids Res 2010, 38:D480-D487.
• [62]Fiehn O, Kind T, Barupal DK: Data Processing, Metabolomic Databases and Pathway Analysis. In Annual Plant Reviews Volume 43. Wiley-Blackwell; 2011:367-406.
• [63]Kotera M, McDonald AG, Boyce S, Tipton KF: Eliciting possible reaction equations and metabolic pathways involving orphan metabolites. J Chem Inf Model 2008, 48:2335-2349.
• [64]Pitkanen E, Jouhten P, Rousu J: Inferring branching pathways in genome-scale metabolic networks. BMC Syst Biol 2009, 3:103. BioMed Central Full Text
• [65]Heath AP, Bennett GN, Kavraki LE: Finding metabolic pathways using atom tracking. Bioinformatics 2010, 26:1548-1555.
• [66]Arita M: In silico atomic tracing by substrate-product relationships in Escherichia coli intermediary metabolism. Genome Res 2003, 13:2455-2466.
• [67]Arita M: The metabolic world of Escherichia coli is not small. Proc Natl Acad Sci 2004, 101:1543.
• [68]Mu F, Williams RF, Unkefer CJ, Unkefer PJ, Faeder JR, Hlavacek WS: Carbon-fate maps for metabolic reactions. Bioinformatics 2007, 23:3193-3199.
• [69]Faust K, Croes D, van Helden J: Metabolic pathfinding using RPAIR annotation. J Mol Biol 2009, 388:390-414.
• [70]Moriya Y, Shigemizu D, Hattori M, Tokimatsu T, Kotera M, Goto S, Kanehisa M: PathPred: an enzyme-catalyzed metabolic pathway prediction server. Nucleic Acids Res 2010, 38(Suppl):W138-143.
• [71]Willett P, Barnard J, Downs G: Chemical similarity searching. J Chem Inf Comput Sci 1998, 38:983-996.
• [72]Hummel J, Strehmel N, Selbig J, Walther D, Kopka J: Decision tree supported substructure prediction of metabolites from GC-MS profiles. Metabolomics 2010, 6:322-333.
• [73]Stein SE: Chemical substructure identification by mass spectral library searching. Journal of the American Society for Mass Spectrometry 1995, 6:644-655.
• [74]Varmuza K, Werther W: Mass Spectral Classifiers for Supporting Systematic Structure Elucidation†. J Chem Inf Comput Sci 1996, 36:323-333.
• [75]Stein SE, Scott DR: Optimization and testing of mass spectral library search algorithms for compound identification. Journal of the American Society for Mass Spectrometry 1994, 5:859-866.
• [76]Viscardi RM: Role of fatty acids in lung development. J Nutr 1995, 125:1645S-1651S.
• [77]Maniscalco WM, Finkelstein JN, Parkhurst AB: De novo fatty acid synthesis in developing rat lung. Biochimica et Biophysica Acta (BBA)-Lipids and Lipid Metabolism 1982, 711:49-58.
• [78]Gross I, Warshaw JB: Enzyme activities related to fatty acid synthesis in developing mammalian lung. Pediatr Res 1974, 8:193-199.
• [79]Smith PMC, Atkins CA: Purine biosynthesis. Big in cell division, even bigger in nitrogen assimilation. Plant Physiol 2002, 128:793-802.
• [80]Peden DB, Hohman R, Brown ME, Mason RT, Berkebile C, Fales HM, Kaliner MA: Uric acid is a major antioxidant in human nasal airway secretions. Proc Natl Acad Sci U S A 1990, 87:7638-7642.
• [81]Fisher AB: Intermediary metabolism of the lung. Environ Health Perspect 1984, 55:149-158.
• [82]Havens CG, Ho A, Yoshioka N, Dowdy SF: Regulation of late G1/S phase transition and APC Cdh1 by reactive oxygen species. Mol Cell Biol 2006, 26:4701-4711.
• [83]Vizan P, Alcarraz-Vizan G, Diaz-Moralli S, Solovjeva ON, Frederiks WM, Cascante M: Modulation of pentose phosphate pathway during cell cycle progression in human colon adenocarcinoma cell line HT29. Int J Cancer 2009, 124:2789-2796.
• [84]Lodrup Carlsen KC, Jaakkola JJ, Nafstad P, Carlsen KH: In utero exposure to cigarette smoking influences lung function at birth. Eur Respir J 1997, 10:1774-1779.
• [85]Naeye RL: Influence of maternal cigarette smoking during pregnancy on fetal and childhood growth. Obstet Gynecol 1981, 57:18-21.
• [86]Scholz M, Fiehn O: SetupX--a public study design database for metabolomic projects. Pac Symp Biocomput 2007, 12:169-180.
• [87]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13:2498-2504.