Skip to main content
SpringerLink
Log in
Book cover

Unraveling the Exposome pp 147–181Cite as

Metabonomics

  • Chapter
  • First Online:

Abstract

The exposome concept places substantial weight on the internal chemical milieu of individuals, as this is the primary integrator of the human genome and the wider external environment. Small molecule metabolites of both endogenous and exogenous origin are involved in a plethora of cellular and systemic functions, and collectively contribute to the mechanistic linkage of exposures, responses, and associated adverse outcomes. Temporal and spatial responses of metabolic phenotypes to various environmental stimuli provide a direct report on multiple interacting and conditional processes that are modulated by numerous factors including diet, lifestyle, pharmaceutical use, microbial activity, age, sex, and many others. Measuring and integrating information about the human metabolome represents a critical part of the path toward understanding the environmental determinants of chronic disease.

The size and diversity of the chemical space that metabolites occupy means that measuring the human metabolome via serum, urine, or other biofluids or tissues represents a huge analytical challenge, addressed by the application of high-resolution platforms, typically incorporating liquid- and/or gas-chromatography for separation, and nuclear magnetic resonance spectroscopy and/or mass spectrometry for detection. Advances in the performance of these platforms now permit the measurement of many hundreds or thousands of metabolites, in either targeted or untargeted assays. The focus of this chapter is on the utility of the different analytical platforms, their complementarity, and application to large-scale sample set analysis. Considerations for data analysis and integration with other omics, exposure, and outcome data are discussed, alongside approaches for interpreting findings in the context of the human exposome.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Abbiss H, Rawlinson C, Maker GL, Trengove R (2015) Assessment of automated trimethylsilyl derivatization protocols for GC–MS-based untargeted metabolomic analysis of urine. Metabolomics 11:1908–1921

    Article  CAS  Google Scholar 

  • Anton G, Wilson R, Yu Z-H, Prehn C, Zukunft S, Adamski J et al (2015) Pre-analytical sample quality: metabolite ratios as an intrinsic marker for prolonged room temperature exposure of serum samples. Kim KH, editor. PLoS One e0121495:10

    Google Scholar 

  • Athersuch T (2016) Metabolome analyses in exposome studies: profiling methods for a vast chemical space. Arch Biochem Biophys 589:177–186

    Article  CAS  Google Scholar 

  • Balog J, Szaniszlo T, Schaefer K-C, Dénes J, Lopata A, Godorhazy L et al (2010) Identification of biological tissues by rapid evaporative ionization mass spectrometry. Anal Chem 82:7343–7350

    Article  CAS  Google Scholar 

  • Bamba T, Shimonishi N, Matsubara A, Hirata K, Nakazawa Y, Kobayashi A et al (2008) High throughput and exhaustive analysis of diverse lipids by using supercritical fluid chromatography-mass spectrometry for metabolomics. J Biosci Bioeng 105:460–469

    Article  CAS  Google Scholar 

  • Bamba T, Lee JW, Matsubara A, Fukusaki E (2012) Metabolic profiling of lipids by supercritical fluid chromatography/mass spectrometry. J Chromatogr A 1250:212–219

    Article  CAS  Google Scholar 

  • Bartel J, Krumsiek J, Schramm K, Adamski J, Gieger C, Herder C et al (2015) The human blood metabolome-transcriptome interface. Inouye M, editor. PLoS Genet e1005274:11

    Google Scholar 

  • Beckett AH (2008) Metabolic oxidation of aliphatic basic nitrogen atoms and their α-carbon atoms. Xenobiotica 1:365–384

    Article  Google Scholar 

  • Beckonert O, Keun HC, Ebbels TMD, Bundy J, Holmes E, Lindon JC et al (2007) Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts. Nat Protoc 2:2692–2703

    Article  CAS  Google Scholar 

  • Beckonert O, Coen M, Keun HC, Wang Y (2010) High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat Protoc 5(6):1019–1032

    Article  CAS  Google Scholar 

  • Benton HP, Want E, Keun HC, Amberg A, Plumb RS, Goldfain-Blanc F et al (2012) Intra- and interlaboratory reproducibility of ultra performance liquid chromatography-time-of-flight mass spectrometry for urinary metabolic profiling. Anal Chem 84:2424–2432

    Article  CAS  Google Scholar 

  • Blaise BJ, Correia G, Tin A, Young JH, Vergnaud A-C, Lewis M et al (2016) Power analysis and sample size determination in metabolic phenotyping. Anal Chem 88:5179–5188

    Article  CAS  Google Scholar 

  • Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C et al (2013) The human urine metabolome. Dzeja P, editor. PLoS One e73076:8

    Google Scholar 

  • Budde K, Gök Ö-N, Pietzner M, Meisinger C, Leitzmann M, Nauck M et al (2016) Quality assurance in the pre-analytical phase of human urine samples by 1H NMR spectroscopy. Arch Biochem Biophys 589:10–17

    Article  CAS  Google Scholar 

  • Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA et al (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 42:D459–D471

    Article  CAS  Google Scholar 

  • Chan ECY, Pasikanti KK, Nicholson JK (2011) Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry. Nat Protoc 6:1483–1499

    Article  CAS  Google Scholar 

  • Cherney DP, Ekman DR, Dix DJ, Collette TW (2007) Raman spectroscopy-based metabolomics for differentiating exposures to triazole fungicides using rat urine. Anal Chem 79:7324–7332

    Article  CAS  Google Scholar 

  • Contrepois K, Jiang L, Snyder M (2015) Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)-mass spectrometry. Mol Cell Proteomics 14:1684–1695

    Article  CAS  Google Scholar 

  • Creek DJ (2013) Stable isotope labeled metabolomics improves identification of novel metabolites and pathways. Bioanalysis 5:1807–1810. https://doi.org/10.4155/bio.13.131

    Article  CAS  Google Scholar 

  • Creek DJ, Chokkathukalam A, Jankevics A, Burgess KEV, Breitling R, Barrett MP (2012) Stable isotope-assisted metabolomics for network-wide metabolic pathway elucidation. Anal Chem 84:8442–8447

    Article  CAS  Google Scholar 

  • Cui Q, Lewis IA, Hegeman AD, Anderson ME, Li J, Schulte CF et al (2008) Metabolite identification via the Madison Metabolomics Consortium Database. Nat Biotechnol 26:162–164

    Article  CAS  Google Scholar 

  • Cumeras R, Figueras E, Davis CE, Baumbach JI, Gràcia I (2015a) Review on Ion Mobility Spectrometry. Part 1: current instrumentation. Analyst 140:1376–1390

    Article  CAS  Google Scholar 

  • Cumeras R, Figueras E, Davis CE, Baumbach JI, Gràcia I (2015b) Review on Ion Mobility Spectrometry. Part 2: hyphenated methods and effects of experimental parameters. Analyst 140:1391–1410

    Article  CAS  Google Scholar 

  • Damjanovich L, Darzi A, Nicholson JK (2013) Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci Transl Med 5(194):194ra93

    Google Scholar 

  • Damsten MC, Commandeur JNM, Fidder A, Hulst AG, Touw D, Noort D et al (2007) Liquid chromatography/tandem mass spectrometry detection of covalent binding of acetaminophen to human serum albumin. Drug Metab Dispos 35:1408–1417

    Article  CAS  Google Scholar 

  • Davidson RL, Weber RJM, Liu H, Sharma-Oates A, Viant MR (2016) Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. Gigascience 5:10

    Article  CAS  Google Scholar 

  • Davies SK, Ang JE, Revell VL, Holmes B, Mann A, Robertson FP et al (2014) Effect of sleep deprivation on the human metabolome. PNAS 111:10761–10766

    Article  CAS  Google Scholar 

  • Dénes J, Katona M, Hosszú Á, Czuczy N, Takáts Z (2009) Analysis of biological fluids by direct combination of solid phase extraction and desorption electrospray ionization mass spectrometry. Anal Chem 81:1669–1675

    Article  CAS  Google Scholar 

  • Dénes J, Szabó E, Robinette SL, Szatmári I, Szőnyi L, Kreuder JG et al (2012) Metabonomics of newborn screening dried blood spot samples: a novel approach in the screening and diagnostics of inborn errors of metabolism. Anal Chem 84:10113–10120

    Article  CAS  Google Scholar 

  • Dona AC, Jiménez B, Schäfer H, Humpfer E, Spraul M, Lewis MR et al (2014) Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping. Anal Chem 86:9887–9894

    Article  CAS  Google Scholar 

  • Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. PNAS 104:1777–1782

    Article  CAS  Google Scholar 

  • Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N et al (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6:1060–1083

    Article  CAS  Google Scholar 

  • Dunn WB, Lin W, Broadhurst D, Begley P, Brown M, Zelena E et al (2014) Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics 11:9–26

    Article  CAS  Google Scholar 

  • Ellis DI, Goodacre R (2006) Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy. Analyst 131:875–885

    Article  CAS  Google Scholar 

  • Ellis JK, Athersuch TJ, Thomas LD, Teichert F, Pérez-Trujillo M, Svendsen C et al (2012) Metabolic profiling detects early effects of environmental and lifestyle exposure to cadmium in a human population. BMC Med 10:61

    Article  CAS  Google Scholar 

  • Fiehn O (2001) Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp Funct Genomics 2:155–168

    Article  CAS  Google Scholar 

  • Fiehn O, Robertson D, Griffin J, van der Werf M, Nikolau B, Morrison N et al (2007) The metabolomics standards initiative (MSI). Metabolomics 3:175–178

    Article  CAS  Google Scholar 

  • Fink T, Reymond J-L (2007) Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J Chem Inf Model 47:342–353

    Article  CAS  Google Scholar 

  • García A, Barbas C (2010) Gas chromatography-mass spectrometry (GC-MS)-based metabolomics. In: Fan J-B (ed) Metabolic profiling. Humana Press, Totowa, NJ, pp 191–204

    Google Scholar 

  • Ghaste M, Mistrik R, Shulaev V (2016) Applications of fourier transform ion cyclotron resonance (FT-ICR) and orbitrap based high resolution mass spectrometry in metabolomics and lipidomics. Int J Mol Sci 17(6):pii: E816

    Article  CAS  Google Scholar 

  • Gieger C, Geistlinger L, Altmaier E, Hrabé de Angelis M, Kronenberg F, Meitinger T et al (2008) Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4:e1000282

    Article  CAS  Google Scholar 

  • Gika HG, Theodoridis GA, Wilson ID (2008) Liquid chromatography and ultra-performance liquid chromatography–mass spectrometry fingerprinting of human urine. J Chromatogr A 1189:314–322

    Article  CAS  Google Scholar 

  • Gika HG, Theodoridis GA, Earll M, Wilson ID (2012) A QC approach to the determination of day-to-day reproducibility and robustness of LC–MS methods for global metabolite profiling in metabonomics/metabolomics. Bioanalysis 4:2239–2247. https://doi.org/10.4155/bio.12.212

    Article  CAS  Google Scholar 

  • Giskeødegård GF, Davies SK, Revell VL, Keun H, Skene DJ (2015) Diurnal rhythms in the human urine metabolome during sleep and total sleep deprivation. Sci Rep 5:14843

    Article  CAS  Google Scholar 

  • Gray N, Zia R, King A, Patel VC, Wendon J, McPhail MJW et al (2017) High-speed quantitative UPLC-MS analysis of multiple amines in human plasma and serum via precolumn derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate: application to acetaminophen-induced liver failure. Anal Chem 89:2478–2487

    Article  CAS  Google Scholar 

  • Guthrie R, Susi A (1963) A simple phenylalanine method for detecting phenylketonuria in large populations of newborn infants. Pediatrics 32:338–343

    CAS  Google Scholar 

  • Hastings J, de Matos P, Dekker A, Ennis M, Harsha B, Kale N et al (2013) The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013. Nucleic Acids Res 41:D456–D463

    Article  CAS  Google Scholar 

  • Haug K, Salek RM, Conesa P, Hastings J (2013) MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res 41:D781–D786

    Article  CAS  Google Scholar 

  • Haug K, Salek RM, Steinbeck C (2017) Global open data management in metabolomics. Curr Opin Chem Biol 36:58–63

    Article  CAS  Google Scholar 

  • Hebels DGAJ, Georgiadis P, Keun HC, Athersuch TJ, Vineis P, Vermeulen R et al (2013) Performance in omics analyses of blood samples in long-term storage: opportunities for the exploitation of existing biobanks in environmental health research. Environ Health Perspect 121(4):480–487

    Article  CAS  Google Scholar 

  • Hernandes VV, Barbas C, Dudzik D (2017) A review of blood sample handling and pre-processing for metabolomics studies. Electrophoresis 38(18):2232–2241

    Article  CAS  Google Scholar 

  • Hicks AA, Pramstaller PP, Johansson Å, Vitart V, Rudan I, Ugocsai P et al (2009) Genetic determinants of circulating sphingolipid concentrations in European populations. Gibson G, editor. PLoS Genet e1000672:5

    Google Scholar 

  • Holmes E, Nicholson JK (2008) Human metabolic phenotyping and metabolome wide association studies. Oncogenes meet metabolism. Springer, Berlin, Heidelberg, pp 227–249

    Google Scholar 

  • Holmes E, Loo RL, Cloarec O, Coen M, Tang H, Maibaum E et al (2007) Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. Anal Chem 79:2629–2640

    Article  CAS  Google Scholar 

  • Holmes E, Wilson ID, Nicholson JK (2008) Metabolic phenotyping in health and disease. Cell 134:714–717

    Article  CAS  Google Scholar 

  • Holmes E, Stamler J, Nicholson JK (2010) Opening up the “Black Box”: metabolic phenotyping and metabolome-wide association studies in epidemiology. J Clin Epidemiol 63(9):970–979

    Article  Google Scholar 

  • Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K et al (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45:703–714

    Article  CAS  Google Scholar 

  • Hu Q, Noll RJ, Li H, Makarov A, Hardman M, Graham CR (2005) The Orbitrap: a new mass spectrometer. J Mass Spectrom 40:430–443

    Article  CAS  Google Scholar 

  • Huan T, Forsberg EM, Rinehart D, Johnson CH (2017) Systems biology guided by XCMS Online metabolomics. Nat Methods 14(5):461–462

    Article  CAS  Google Scholar 

  • IARC (2012) Chemical agents and related occupations. Benzo[α]pyrene. IARC Monogr Eval Carcinog Risk Chem Man 100F:111–144

    Google Scholar 

  • Illig T, Gieger C, Zhai G, Römisch-Margl W, Wang Sattler R, Prehn C et al (2010) A genome-wide perspective of genetic variation in human metabolism. Nat Genet 42:137–141

    Article  CAS  Google Scholar 

  • Jansen RJ, Argos M, Tong L, Li J, Rakibuz-Zaman M, Islam MT et al (2016) Determinants and consequences of arsenic metabolism efficiency among 4,794 individuals: demographics, lifestyle, genetics, and toxicity. Cancer Epidemiol Biomark Prev 25(2):381–390

    Article  CAS  Google Scholar 

  • Jenkins H, Hardy N, Beckmann M, Draper J, Smith AR, Taylor J et al (2004) A proposed framework for the description of plant metabolomics experiments and their results. Nat Biotechnol 22:1601–1606

    Article  CAS  Google Scholar 

  • Jones DP (2016) Sequencing the exposome: a call to action. Toxicol Rep 3:29–45

    Article  CAS  Google Scholar 

  • Jones DR, Wu Z, Chauhan D, Anderson KC, Peng J (2014) A nano ultra-performance liquid chromatography–high resolution mass spectrometry approach for global metabolomic profiling and case study on drug-resistant multiple myeloma. Anal Chem 86:3667–3675

    Article  CAS  Google Scholar 

  • Kamlage B, Maldonado SG, Bethan B, Peter E, Schmitz O, Liebenberg V et al (2014) Quality markers addressing preanalytical variations of blood and plasma processing identified by broad and targeted metabolite profiling. Clin Chem 60:399–412

    Article  CAS  Google Scholar 

  • Karagas MR, Gossai A, Pierce B, Ahsan H (2015) Drinking water arsenic contamination, skin lesions, and malignancies: a systematic review of the global evidence. Curr Envir Health Rep 2:52–68

    Article  CAS  Google Scholar 

  • Kassner MK, Charney R, Fernandez FM (2008) Novel approach to employing SFC for metabolomics research. In: Aiche annual meeting, conference proceeding

    Google Scholar 

  • Kastenmüller G, Raffler J, Gieger C, Suhre K (2015) Genetics of human metabolism: an update. Hum Mol Genet 24(R1):R93–R101

    Article  CAS  Google Scholar 

  • Keun HC, Athersuch TJ (2010) Nuclear magnetic resonance (NMR)-based metabolomics. In: Fan J-B (ed) Metabolic profiling. Humana Press, Totowa, NJ, pp 321–334

    Google Scholar 

  • Keun HC, Beckonert O, Griffin JL, Richter C, Moskau D, Lindon JC, Nicholson JK et al (2002) Cryogenic probe 13C NMR spectroscopy of urine for metabonomic studies. Anal Chem 74:4588–4593

    Article  CAS  Google Scholar 

  • Kim K, Mall C, Taylor SL, Hitchcock S, Zhang C, Wettersten HI et al (2014) Mealtime, temporal, and daily variability of the human urinary and plasma metabolomes in a tightly controlled environment. Brennan L, editor. PLoS One e86223:9

    Google Scholar 

  • Kim S, Thiessen PA, Bolton EE, Chen J (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213

    Article  CAS  Google Scholar 

  • Kind T, Wohlgemuth G, Lee DY, Lu Y, Palazoglu M, Shahbaz S et al (2009) FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal Chem 81:10038–10048

    Article  CAS  Google Scholar 

  • Lewis MR, Pearce JTM, Spagou K, Green M, Dona AC, Yuen AHY et al (2016) Development and application of ultra-performance liquid chromatography-TOF MS for precision large scale urinary metabolic phenotyping. Anal Chem 88:9004–9013

    Article  CAS  Google Scholar 

  • Li N, Song YP, Tang H, Wang Y (2016) Recent developments in sample preparation and data pre-treatment in metabonomics research. Arch Biochem Biophys 589:4–9

    Article  CAS  Google Scholar 

  • Lindon JC, Nicholson JK, Holmes E, Everett JR (2000) Metabonomics: metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn Reson 12:289–320

    Article  CAS  Google Scholar 

  • Lindon JC, Nicholson JK, Holmes E, Keun HC (2005) Summary recommendations for standardization and reporting of metabolic analyses. Nat Biotechnol 23(7):833–838

    Article  CAS  Google Scholar 

  • Loo RL, Coen M, Ebbels T, Cloarec O, Maibaum E, Bictash M et al (2009) Metabolic profiling and population screening of analgesic usage in nuclear magnetic resonance spectroscopy-based large-scale epidemiologic studies. Anal Chem 81:5119–5129

    Article  CAS  Google Scholar 

  • Ma H, Sorokin A, Mazein A, Selkov A, Selkov E, Demin O et al (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Molecular Systems Biology. EMBO Press 3:135

    Google Scholar 

  • Maier TV, Schmitt-Kopplin P (2016) Capillary electrophoresis in metabolomics. Methods Mol Biol 1483:437–470

    Article  CAS  Google Scholar 

  • Maitre L, Lau C-HE, Vizcaino E, Robinson O, Casas M, Siskos AP et al (2017) Assessment of metabolic phenotypic variability in children’s urine using (1)H NMR spectroscopy. Sci Rep 7:46082

    Article  CAS  Google Scholar 

  • Marchand J, Martineau E, Guitton Y, Dervilly-Pinel G, Giraudeau P (2017) Multidimensional NMR approaches towards highly resolved, sensitive and high-throughput quantitative metabolomics. Curr Opin Biotechnol 43:49–55

    Article  CAS  Google Scholar 

  • Martin J-C, Maillot M, Mazerolles G, Verdu A, Lyan B, Migné C et al (2015) Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study. Metabolomics 11:807–821

    Article  CAS  Google Scholar 

  • McShane LM, Cavenagh MM, Lively TG, Eberhard DA, Bigbee WL, Williams PM et al (2013) Criteria for the use of omics-based predictors in clinical trials. Nature 502:317–320

    Article  CAS  Google Scholar 

  • Melkonian S, Argos M, Hall MN, Chen Y, Parvez F, Pierce B et al (2013) Urinary and dietary analysis of 18,470 bangladeshis reveal a correlation of rice consumption with arsenic exposure and toxicity. States JC, editor. PLoS One e80691:8

    Google Scholar 

  • Miller GW, Jones DP (2014) The nature of nurture: refining the definition of the exposome. Toxicol Sci 137(1):1–2

    Article  CAS  Google Scholar 

  • Moros G, Chatziioannou AC, Gika HG, Raikos N, Theodoridis G (2017) Investigation of the derivatization conditions for GC-MS metabolomics of biological samples. Bioanalysis 9:53–65

    Article  CAS  Google Scholar 

  • Nakamura Y, Afendi FM, Parvin AK, Ono N, Tanaka K, Hirai Morita A et al (2014) KNApSAcK Metabolite Activity Database for retrieving the relationships between metabolites and biological activities. Plant Cell Physiol 55:e7

    Article  CAS  Google Scholar 

  • Nicholson G, Rantalainen M, Maher AD, Li JV, Malmodin D, Ahmadi KR et al (2011) Human metabolic profiles are stably controlled by genetic and environmental variation. Mol Syst Biol 7:525–525

    Article  CAS  Google Scholar 

  • Nicholson JK, Wilson ID (2003) Understanding “global” systems biology: metabonomics and the continuum of metabolism. Nat Rev Drug Discov 2:668–676

    Article  CAS  Google Scholar 

  • Nicholson JK, Lindon JC, Holmes E (1999) “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181–1189

    Article  CAS  Google Scholar 

  • Oliver S (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol 16:373–378

    Article  CAS  Google Scholar 

  • Paglia G, Angel P, Williams JP, Richardson K, Olivos HJ, Thompson JW et al (2014a) Ion mobility-derived collision cross section as an additional measure for lipid fingerprinting and identification. Anal Chem 87:1137–1144

    Article  CAS  Google Scholar 

  • Paglia G, Williams JP, Menikarachchi L, Thompson JW, Tyldesley-Worster R, Halldórsson S et al (2014b) Ion mobility derived collision cross sections to support metabolomics applications. Anal Chem 86:3985–3993

    Article  CAS  Google Scholar 

  • Peter Guengerich F, Shimada T (1998) Activation of procarcinogens by human cytochrome P450 enzymes. Mutat Res 400:201–213

    Article  Google Scholar 

  • Petersen A-K, Zeilinger S, Kastenmüller G, Römisch-Margl W, Brugger M, Peters A et al (2014) Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet 23:534–545

    Article  CAS  Google Scholar 

  • Pierce BL, Tong L, Argos M, Gao J, Jasmine F (2013) Arsenic metabolism efficiency has a causal role in arsenic toxicity: Mendelian randomization and gene-environment interaction. Int J Epidemiol 42(6):1862–1871

    Article  Google Scholar 

  • Pitt JJ (2010) Newborn screening. Clin Biochem Rev 31(2):57–68

    Google Scholar 

  • Plumb RS, Shockcor J, Holmes E, Nicholson JK (2010) Global metabolic profiling procedures for urine using UPLC–MS. Nat Protoc 5(6):1005–1018

    Article  CAS  Google Scholar 

  • Posma JM, Robinette SL, Holmes E, Nicholson JK (2014) MetaboNetworks, an interactive Matlab-based toolbox for creating, customizing and exploring sub-networks from KEGG. Bioinformatics 30:893–895

    Article  CAS  Google Scholar 

  • Posma JM, Garcia-Perez I, Heaton JC, Burdisso P, Mathers JC, Draper J et al (2017) Integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: application to dietary biomarkers. Anal Chem 89:3300–3309

    Article  CAS  Google Scholar 

  • Psychogios N, Hau DD, Peng J, Guo AC, Mandal R (2011) The human serum metabolome. PLoS One 6(2):e16957

    Article  CAS  Google Scholar 

  • Qi Y, Song Y, Gu H, Fan G, Chai Y (2014) Global metabolic profiling using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry. Methods Mol Biol 1198:15–27

    Article  CAS  Google Scholar 

  • Ramautar R, Somsen GW, de Jong GJ (2017) CE-MS for metabolomics: developments and applications in the period 2014-2016. Rassi El Z, editor. Electrophoresis 38:190–202

    Article  CAS  Google Scholar 

  • Rappaport SM, Barupal DK, Wishart D, Vineis P, Scalbert A (2014) The blood exposome and its role in discovering causes of disease. Environ Health Perspect 122:769

    Article  CAS  Google Scholar 

  • Reymond J-L, van Deursen R, Blum LC, Ruddigkeit L (2010) Chemical space as a source for new drugs. Med Chem Commun 1:30–38

    Article  CAS  Google Scholar 

  • Reymond J-L, Ruddigkeit L, Blum L, van Deursen R (2012) The enumeration of chemical space. Wiley Interdiscip Rev Comput Mol Sci 2:717–733

    Article  CAS  Google Scholar 

  • Robinette SL, Lindon JC, Nicholson JK (2013) Statistical spectroscopic tools for biomarker discovery and systems medicine. Anal Chem 85:5297–5303

    Article  CAS  Google Scholar 

  • Rocca-Serra P, Salek RM, Arita M, Correa E, Dayalan S, Gonzalez-Beltran A et al (2016) Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics 12:14

    Article  CAS  Google Scholar 

  • Ruddigkeit L, van Deursen R, Blum LC, Reymond J-L (2012) Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 52:2864–2875

    Article  CAS  Google Scholar 

  • Salek RM, Neumann S, Schober D, Hummel J, Billiau K, Kopka J et al (2015) COordination of standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access. Metabolomics 11:1587–1597

    Article  CAS  Google Scholar 

  • Sampson JN, Boca SM, Shu XO, Stolzenberg-Solomon RZ, Matthews CE, Hsing AW et al (2013) Metabolomics in epidemiology: sources of variability in metabolite measurements and implications. Cancer Epidemiol Biomark Prev 22:631–640

    Article  CAS  Google Scholar 

  • Sansone S-A, Fan T, Goodacre R, Griffin JL, Hardy NW, Kaddurah-Daouk R et al (2007) The metabolomics standards initiative. Nat Biotechnol 25:846–848

    CAS  Google Scholar 

  • Scalbert A, Brennan L, Manach C, Andres-Lacueva C, Dragsted LO, Draper J et al (2014) The food metabolome: a window over dietary exposure. Am J Clin Nutr 99:1286–1308

    Article  CAS  Google Scholar 

  • Schmelzer K, Fahy E, Subramaniam S, Dennis EA (2007) The lipid maps initiative in lipidomics. Methods Enzymol 432:171–183

    Article  CAS  Google Scholar 

  • Shimada T, Oda Y, Gillam EMJ, Guengerich FP, Inoue K (2001) Metabolic activation of polycyclic aromatic hydrocarbons and other procarcinogens by cytochromes P450 1A1 and P450 1B1 allelic variants and other human cytochromes P450 in Salmonella typhimurium NM2009. Drug Metab Dispos 29:1176–1182

    CAS  Google Scholar 

  • Siskos AP, Jain P, Römisch-Margl W, Bennett M, Achaintre D, Asad Y et al (2017) Interlaboratory reproducibility of a targeted metabolomics platform for analysis of human serum and plasma. Anal Chem 89:656–665

    Article  CAS  Google Scholar 

  • Smith CA, O’Maille G, Want EJ, Qin C, Trauger SA, Brandon TR et al (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27:747–751

    Article  CAS  Google Scholar 

  • Spagou K, Wilson ID, Masson P, Theodoridis G, Raikos N, Coen M et al (2010) HILIC-UPLC-MS for exploratory urinary metabolic profiling in toxicological studies. Anal Chem 83:382–390

    Article  CAS  Google Scholar 

  • Strittmatter N, Rebec M, Jones EA, Golf O, Abdolrasouli A, Balog J et al (2014) Characterization and identification of clinically relevant microorganisms using rapid evaporative ionization mass spectrometry. Anal Chem 86:6555–6562

    Article  CAS  Google Scholar 

  • Sud M, Fahy E, Cotter D, Azam K, Vadivelu I, Burant C et al (2016) Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Res 44:D463–D470

    Article  CAS  Google Scholar 

  • Suhre K, Meisinger C, Döring A, Altmaier E, Belcredi P, Gieger C et al (2010) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. Breant B, editor. PLoS One e13953:5

    Google Scholar 

  • Suhre K, Wallaschofski H, Raffler J, Friedrich N (2011) A genome-wide association study of metabolic traits in human urine. Nature 43(6):565–569

    CAS  Google Scholar 

  • Suhre K, Raffler J, Kastenmüller G (2016) Biochemical insights from population studies with genetics and metabolomics. Arch Biochem Biophys 589:168–176

    Article  CAS  Google Scholar 

  • Swainston N, Smallbone K, Hefzi H, Dobson PD, Brewer J, Hanscho M et al (2016) Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12:109

    Article  CAS  Google Scholar 

  • Takáts Z, Wiseman JM, Gologan B, Cooks RG (2004) Mass spectrometry sampling under ambient conditions with desorption electrospray ionization. Science 306:471–473

    Article  CAS  Google Scholar 

  • Takáts Z, Wiseman JM, Cooks RG (2005) Ambient mass spectrometry using desorption electrospray ionization (DESI): instrumentation, mechanisms and applications in forensics, chemistry, and biology. J Mass Spectrom 40:1261–1275

    Article  CAS  Google Scholar 

  • Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(D1):D353–D361

    Article  CAS  Google Scholar 

  • Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem 84:5035–5039

    Article  CAS  Google Scholar 

  • Teahan O, Gamble S, Holmes E, Waxman J, Nicholson JK, Bevan C et al (2006) Impact of analytical bias in metabonomic studies of human blood serum and plasma. Anal Chem 78:4307–4318

    Article  CAS  Google Scholar 

  • Thévenot EA, Roux A, Xu Y, Ezan E, Junot C (2015) Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J Proteome Res 14(8):3322–3335

    Article  CAS  Google Scholar 

  • Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419–425

    Article  CAS  Google Scholar 

  • Thomas L, Hodgson S, Nieuwenhuijsen M, Jarup L (2007) Early renal damage in a population environmentally exposed to cadmium—the Avonmouth Pilot Study. Epidemiology 18:S123

    Article  Google Scholar 

  • Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J et al (2008) BioMagResBank. Nucleic Acids Res 36:D402–D408

    Article  CAS  Google Scholar 

  • Uppal K, Walker DI, Liu K, Li S, Go Y-M, Jones DP (2016) Computational metabolomics: a framework for the million metabolome. Chem Res Toxicol 29:1956–1975

    Article  CAS  Google Scholar 

  • Van Deemter JJ, Zuiderweg FJ, Klinkenberg A (1956) Longitudinal diffusion and resistance to mass transfer as causes of nonideality in chromatography. Chem Eng Sci 5:271–289

    Article  Google Scholar 

  • Vineis P, Veldhoven K, Chadeau-Hyam M, Athersuch TJ (2013) Advancing the application of omics-based biomarkers in environmental epidemiology. Environ Mol Mutagen 54(7):461

    Article  CAS  Google Scholar 

  • Vineis P, Chadeau-Hyam M, Gmuender H, Gulliver J, Herceg Z, KLEINJANS J et al (2017) The exposome in practice: design of the EXPOsOMICS project. Int J Hyg Environ Health 220:142–151

    Article  CAS  Google Scholar 

  • Vlaanderen JJ, Janssen NA, Hoek G, Keski-Rahkonen P, Barupal DK, Cassee FR et al (2017) The impact of ambient air pollution on the human blood metabolome. Environ Res 156:341–348

    Article  CAS  Google Scholar 

  • Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P et al (2014) The human early-life exposome (HELIX): project rationale and design. Environ Health Perspect 122(6):535–544

    Article  Google Scholar 

  • Want EJ, Wilson ID, Gika H, Theodoridis G, Plumb RS, Shockcor J et al (2010) Global metabolic profiling procedures for urine using UPLC|[ndash]|MS. Nat Protoc 5:1005–1018

    Article  CAS  Google Scholar 

  • Warth B, Levin N, Rinehart D, Teijaro J, Benton HP, Siuzdak G (2017) Metabolizing data in the cloud. Trends Biotechnol 35:481–483

    Article  CAS  Google Scholar 

  • Wild CP (2005) Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev 14:1847–1850

    Article  CAS  Google Scholar 

  • Wild CP (2012) The exposome: from concept to utility. Int J Epidemiol 41:24–32

    Article  Google Scholar 

  • Wild CP, Scalbert A, Herceg Z (2013) Measuring the exposome: a powerful basis for evaluating environmental exposures and cancer risk. Environ Mol Mutagen 54:480–499

    Article  CAS  Google Scholar 

  • WILSON I, Plumb R, Granger J, Major H, WILLIAMS R, LENZ E (2005a) HPLC-MS-based methods for the study of metabonomics. J Chromatogr B 817:67–76

    Article  CAS  Google Scholar 

  • Wilson ID, Nicholson JK, Castro-Perez J, Granger JH, Johnson KA, Smith BW et al (2005b) High resolution “ultra performance” liquid chromatography coupled to oa-tof mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteome Res 4:591–598

    Article  CAS  Google Scholar 

  • Wishart DS, Lewis MJ, Morrissey JA, Flegel MD, Jeroncic K, Xiong Y et al (2008) The human cerebrospinal fluid metabolome. J Chromatogr B 871:164–173

    Article  CAS  Google Scholar 

  • Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y et al (2012) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41:D801–D807

    Article  CAS  Google Scholar 

  • Wist J (2017) Complex mixtures by NMR and complex NMR for mixtures: experimental and publication challenges. A special issue of the associate ed, editor. Magn Reson Chem 55:22–28

    Article  CAS  Google Scholar 

  • Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0—making metabolomics more meaningful. Nucl Acids Res 43(W1):W251–W257

    Article  CAS  Google Scholar 

  • Xiao R, Zhang X, Rong Z, Xiu B, Yang X, Wang C et al (2016) Non-invasive detection of hepatocellular carcinoma serum metabolic profile through surface-enhanced Raman spectroscopy. Nanomedicine 12:2475–2484

    Article  CAS  Google Scholar 

  • Xu X, Veenstra TD, Fox SD, Roman JM, Issaq HJ, Falk R et al (2005) Measuring fifteen endogenous estrogens simultaneously in human urine by high-performance liquid chromatography-mass spectrometry. Anal Chem 77:6646–6654

    Article  CAS  Google Scholar 

  • Yin P, Lehmann R, Xu G (2015) Effects of pre-analytical processes on blood samples used in metabolomics studies. Anal Bioanal Chem 407:4879–4892

    Article  CAS  Google Scholar 

  • Zhao L, Pickering G (2011) Paracetamol metabolism and related genetic differences. Drug Metab Rev 43:41–52

    Article  CAS  Google Scholar 

  • Zheng X, Zhao A, Xie G, Chi Y, Zhao L (2013) Melamine-induced renal toxicity is mediated by the gut microbiota. Sci Transl Med 5(172):172ra22

    Article  CAS  Google Scholar 

  • Zubarev RA, Makarov A (2013) Orbitrap mass spectrometry. Anal Chem 85:5288–5296

    Article  CAS  Google Scholar 

Download references

Conflict of Interest

The author confirms no conflict of interest.

Acknowledgments

TJA is supported by the EU FP7 EXPOsOMICS (grant agreement: 308610) and HELIX (grant agreement: 308333) projects.

Author information

Authors and Affiliations

  1. Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK

    Toby Athersuch

Authors
  1. Toby Athersuch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Toby Athersuch .

Editor information

Editors and Affiliations

  1. Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK

    Sonia Dagnino

  2. The Johns Hopkins School of Medicine, Agilent Technologies, Baltimore, MD, USA

    Anthony Macherone

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Athersuch, T. (2019). Metabonomics. In: Dagnino, S., Macherone, A. (eds) Unraveling the Exposome. Springer, Cham. https://doi.org/10.1007/978-3-319-89321-1_6

Download citation

Publish with us

Policies and ethics

Search

Navigation