New techniques, such as metabolite imaging, and improved analytical technologies are making metabolomics increasingly useful for a wider range of biomedical and pharmaceutical applications.
Metabolomics has already entered the clinic, with applications in newborn screening. Many other metabolomic-based clinical applications and tests are now emerging.
Metabolomics is revealing surprising metabolic causes and metabolite biomarkers for several prominant diseases such as diabetes, Alzheimer disease, atherosclerosis and cancer. These findings are identifying previously unsuspected therapeutic targets and novel potential therapeutic strategies.
Metabolomics is reducing the cost of toxicological screening, enabling improved clinical trial design, allowing better patient selection and monitoring and shortening the time needed for drugs to move through the development pipeline.
Metabolomics is beginning to play a part in precision medicine through the development of personalized phenotyping and individualized drug-response monitoring.
The use of metabolomics to phenotype tumours and to design custom cancer therapies represents the most 'cutting-edge' example of metabolomics enabling precision medicine.
Metabolomics is an emerging 'omics' science involving the comprehensive characterization of metabolites and metabolism in biological systems. Recent advances in metabolomics technologies are leading to a growing number of mainstream biomedical applications. In particular, metabolomics is increasingly being used to diagnose disease, understand disease mechanisms, identify novel drug targets, customize drug treatments and monitor therapeutic outcomes. This Review discusses some of the latest technological advances in metabolomics, focusing on the application of metabolomics towards uncovering the underlying causes of complex diseases (such as atherosclerosis, cancer and diabetes), the growing role of metabolomics in drug discovery and its potential effect on precision medicine.
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Wild, C. P., Scalbert, A. & Herceg, Z. Measuring the exposome: a powerful basis for evaluating environmental exposures and cancer risk. Environ. Mol. Mutag. 54, 480–499 (2013).
Houten, S. M. Metabolomics: unraveling the chemical individuality of common human diseases. Ann. Med. 41, 402–407 (2009).
Wishart, D. S. Applications of metabolomics in drug discovery and development. Drugs R. D. 9, 307–322 (2008).
Kaddurah-Daouk, R., Kristal, B. S. & Weinshilboum, R. M. Metabolomics: a global biochemical approach to drug response and disease. Annu. Rev. Pharmacol. Toxicol. 48, 653–683 (2008). One of the first comprehensive reviews highlighting the many roles that metabolomics can have in drug discovery and personalized medicine.
Everett, J. R. Pharmacometabonomics in humans: a new tool for personalized medicine. Pharmacogenomics 16, 737–754 (2015).
Kricka, L. J. & Savory, J. International year of Chemistry 2011. A guide to the history of clinical chemistry. Clin. Chem. 57, 1118–1126 (2011).
Carpenter, K. J. A short history of nutritional science: part 3 (1912–1944). J. Nutr. 133, 3023–3032 (2003).
Nicholson, J. K. & Lindon, J. C. & Holmes, E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29, 1181–1189 (1999). The paper that unofficially launched the field of metabolomics (here referred to as metabonomics).
Verdonk, J. C. et al. Regulation of floral scent production in petunia revealed by targeted metabolomics. Phytochemistry 62, 997–1008 (2003).
Lommen, A. et al. An untargeted metabolomics approach to contaminant analysis: pinpointing potential unknown compounds. Anal. Chim. Acta 584, 43–49 (2007).
Allen, J. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 (2003).
Choi, H. K. et al. Metabolic fingerprinting of wild type and transgenic tobacco plants by 1H NMR and multivariate analysis technique. Phytochemistry 65, 857–864 (2004).
Sanford, K., Soucaille, P., Whited, G. & Chotani, G. Genomics to fluxomics and physiomics — pathway engineering. Curr. Opin. Microbiol. 5, 318–322 (2002).
Han, X. & Gross, R. W. Global analyses of cellular lipidomes directly from crude extracts of biological samples by ESI mass spectrometry: a bridge to lipidomics. J. Lipid Res. 44, 1071–1079 (2003).
Szpunar, J. Metallomics: a new frontier in analytical chemistry. Anal. Bioanal. Chem. 378, 54–56 (2004).
Wild, C. P. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol. Biomarkers Prev. 14, 1847–1850 (2005). A seminal paper that links metabolomics with exposure science and molecular epidemiology.
Wishart, D. S. Advances in metabolite identification. Bioanalysis 3, 1769–1782 (2011).
Wishart, D. S. Quantitative metabolomics using NMR. TrAC Trends Anal. Chem. 27, 228–237 (2008).
Zhang, A., Sun, H., Wang, P., Han, Y. & Wang, X. Modern analytical techniques in metabolomics analysis. Analyst 137, 293–300 (2012).
Dunn, W. B., Bailey, N. J. & Johnson, H. E. Measuring the metabolome: current analytical technologies. Analyst 130, 606–625 (2005).
Psychogios, N. et al. The human serum metabolome. PLoS ONE 6, e16957 (2011).
Bouatra, S. et al. The human urine metabolome. PLoS ONE 8, e73076 (2013). A nice example of the power and potential of comprehensive, quantitative metabolomics.
Lindon, J. C. & Nicholson, J. K. Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics. Annu. Rev. Anal. Chem. 1, 45–69 (2008).
Brown, M. et al. Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics 27, 1108–1112 (2011).
Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).
Grebe, S. K. & Singh, R. J. LC-MS/MS in the clinical laboratory — where to from here? Clin. Biochem. Rev. 32, 5–31 (2011).
Lehotay, D. C. et al. LC-MS/MS progress in newborn screening. Clin. Biochem. 44, 21–31 (2011).
Chace, D. H. & Spitzer, A. R. Altered metabolism and newborn screening using tandem mass spectrometry: lessons learned from the bench to bedside. Curr. Pharm. Biotechnol. 12, 965–975 (2011).
Hao, J. et al. Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN. Nat. Protoc. 9, 1416–1427 (2014).
Ravanbakhsh, S. et al. Accurate, fully-automated NMR spectral profiling for metabolomics. PLoS ONE 10, e0124219 (2015).
Aggio, R., Villas-Boas, S. G. & Ruggiero, K. Metab: an R package for high-throughput analysis of metabolomics data generated by GC-MS. Bioinformatics 27, 2316–2318 (2011).
Ni, Y. et al. ADAP-GC 2.0: deconvolution of coeluting metabolites from GC/TOF-MS data for metabolomics studies. Anal. Chem. 84, 6619–6629 (2012).
Weaver, E. M. & Hummon, A. B. Imaging mass spectrometry: from tissue sections to cell cultures. Adv. Drug Deliv. Rev. 65, 1039–1055 (2013).
Lin, A. P. et al. Metabolic imaging of mild traumatic brain injury. Brain Imag. Behav. 6, 208–223 (2012).
Tkac, I., Oz, G., Adriany, G., Ugurbil, K. & Gruetter, R. In vivo1H NMR spectroscopy of the human brain at high magnetic fields: metabolite quantification at 4T versus 7T. Magn. Reson. Med. 62, 868–879 (2009).
Tu, Z. & Mach, R. H. C-11 radiochemistry in cancer imaging applications. Curr. Top. Med. Chem. 10, 1060–1095 (2010).
Qu, W. et al. Preparation and characterization of L-[5-11C]-glutamine for metabolic imaging of tumors. J. Nucl. Med. 53, 98–105 (2012).
Balog, J. et al. Intraoperative tissue identification using rapid evaporative ionization mass spectrometry. Sci. Transl. Med. 5, 194ra93 (2013).
Sekula, J., Niziol, J., Rode, W. & Ruman, T. Gold nanoparticle-enhanced target (AuNPET) as universal solution for laser desorption/ionization mass spectrometry analysis and imaging of low molecular weight compounds. Anal. Chim. Acta 875, 61–72 (2015).
Gessel, M. M., Norris, J. L. & Caprioli, R. M. MALDI imaging mass spectrometry: spatial molecular analysis to enable a new age of discovery. J. Proteom. 107, 71–82 (2014). A nice review of the promise and potential of MS-based imaging.
Botstein, D. & Risch, N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat. Genet. 33, 228–237 (2003).
Stranger, B. E., Stahl, E. A. & Raj, T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 187, 367–383 (2011).
Hall, S. S. Revolution postponed. Sci. Am. 303, 60–67 (2010).
Maher, B. Personal genomes: the case of the missing heritability. Nature 456, 18–21 (2008).
Cuatrecasas, P. Drug discovery in jeopardy. J. Clin. Invest. 116, 2837–2842 (2006).
Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006).
Rappaport, S. M., Barupal, D. K., Wishart, D., Vineis, P. & Scalbert, A. The blood exposome and its role in discovering causes of disease. Environ. Health Perspect. 122, 769–774 (2014).
Mokdad, A. H., Marks, J. S., Stroup, D. F. & Gerberding, J. L. Actual causes of death in the United States, 2000. JAMA 291, 1238–1245 (2004).
Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).
Feil, R. & Fraga, M. F. Epigenetics and the environment: emerging patterns and implications. Nat. Rev. Genet. 13, 97–109 (2011).
Scalbert, A. et al. The food metabolome: a window over dietary exposure. Am. J. Clin. Nutr. 99, 1286–1308 (2014).
Wikoff, W. R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl Acad. Sci. USA 106, 3698–3703 (2009).
Joice, R. et al. Determining microbial products and identifying molecular targets in the human microbiome. Cell. Metab. 20, 731–741 (2014).
Lusis, A. J., Mar, R. & Pajukanta, P. Genetics of atherosclerosis. Annu. Rev. Genom. Hum. Genet. 5, 189–218 (2004).
Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011). One of the first in a series of superb papers produced by Stanley Hazen's laboratory that link diet, gut microflora and metabolites to cardiovascular disease.
Koeth, R. A. et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).
Wang, Z. et al. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide. Eur. Heart J. 35, 904–910 (2014).
Gregory, J. C. et al. Transmission of atherosclerosis susceptibility with gut microbial transplantation. J. Biol. Chem. 290, 5647–5460 (2015).
Warrier, M. et al. The TMAO-generating enzyme flavin monooxygenase 3 is a central regulator of cholesterol balance. Cell Rep. 10, 326–338 (2015).
Seyfried, T. N., Flores, R. E., Poff, A. M. & D'Agostino, D. P. Cancer as a metabolic disease: implications for novel therapeutics. Carcinogenesis 35, 515–527 (2014).
Levine, A. J. & Puzio-Kuter, A. M. The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes. Science 330, 1340–1344 (2010).
Ward, P. S. et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting α-ketoglutarate to 2-hydroxyglutarate. Cancer Cell. 17, 225–234 (2010).
Yang, M., Soga, T. & Pollard, P. J. Oncometabolites: linking altered metabolism with cancer. J. Clin. Invest. 123, 3652–3658 (2013).
Wishart, D. S. Is cancer a genetic disease or a metabolic disease? EBioMedicine 2, 478–479 (2015).
Fu, X. et al. 2-Hydroxyglutarate inhibits ATP synthase and mTOR signaling. Cell. Metab. 22, 508–515 (2015).
Shanmugasundraram, K. et al. The oncometabolite fumarate promotes pseudohypoxia through noncanonical activation of NF-κB signaling. J. Biol. Chem. 289, 24691–24699 (2014).
Hu, F. B. et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N. Engl. J. Med. 345, 790–797 (2001).
Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011). One of thefirst in a series of excellent papers produced by Robert Gerszten's laboratory that use metabolomics to identify predictive metabolite biomarkers for developing type 2 diabetes.
Wang, T. J. et al. 2-Aminoadipic acid is a biomarker for diabetes risk. J. Clin. Invest. 123, 4309–4317 (2013).
Palmer, N. D. et al. Metabolomic profile associated with insulin resistance and conversion to diabetes in the Insulin Resistance Atherosclerosis Study. J. Clin. Endocrinol. Metab. 100, E463–E468 (2015).
Wurtz, P. et al. Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 36, 648–655 (2013).
Neis, E. P., Dejong, C. H. & Rensen, S. S. The role of microbial amino acid metabolism in host metabolism. Nutrients 7, 2930–2946 (2015).
Li, X. et al. Chronic leucine supplementation increases body weight and insulin sensitivity in rats on high-fat diet likely by promoting insulin signaling in insulin-target tissues. Mol. Nutr. Food Res. 57, 1067–1079 (2013).
Wishart, D. S. et al. HMDB 3.0 — The Human Metabolome Database in 2013. Nucleic Acids Res. 41, D801–D807 (2013).
Shaw, W. Increased urinary excretion of a 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA), an abnormal phenylalanine metabolite of Clostridia spp. in the gastrointestinal tract, in urine samples from patients with autism and schizophrenia. Nutr. Neurosci. 13, 135–143 (2010).
Mapstone, M. et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat. Med. 20, 415–418 (2014). An interesting paper that suggests a new metabolomic approach to non-invasively identify early Alzheimer disease.
Steinmeyer, S., Lee, K., Jayaraman, A. & Alaniz, R. C. Microbiota metabolite regulation of host immune homeostasis: a mechanistic missing link. Curr. Allergy Asthma Rep. 15, 524 (2015).
Hughes, J. P., Rees, S., Kalindjian, S. B. & Philpott, K. L. Principles of early drug discovery. Br. J. Pharmacol. 162, 1239–1249 (2011).
Bains, W. Failure reates in drug discovery and development: will we ever get any better? Drug Discovery World 5, 9–18 (2004).
Mullard, A. New drug costs US $2.6 billion to develop. Nat. Rev. Drug. Discov. 13, 877 (2014).
Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).
Kim, J. W. & Dang, C. V. Cancer's molecular sweet tooth and the Warburg effect. Cancer Res. 66, 8927–8930 (2006).
Hanahan, D. & Weinberg, R. A. The hallmarks of cancer: the next generation. Cell 144, 648–674 (2011).
Cai, H. et al. Metabolic dysfunction in Alzheimer's disease and related neurodegenerative disorders. Curr. Alzheimer Res. 9, 5–17 (2012).
de la Monte, S. M. & Wands, J. R. Alzheimer's disease is type 3 diabetes — evidence reviewed. J. Diabetes Sci. Technol. 2, 1101–1113.
Brown, J. M. & Hazen, S. L. The gut microbial endocrine organ: bacterially derived signals driving cardiometabolic diseases. Annu. Rev. Med. 66, 343–359 (2015).
Cracuin, S. & Balskus, E. P. Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme. Proc. Natl Acad. Sci. USA 109, 21307–21312 (2014).
Baker, J. R. & Chaykin, S. The biosynthesis of trimethylamin-N-oxide. J. Biol. Chem. 237, 1309–1313 (1962).
Copeland, R. A., Harpel, M. R. & Tummino, P. J. Targeting enzyme inhibitors in drug discovery. Expert Opin. Ther. Targets 11, 967–978 (2007).
Morgan, S. L. & Baggott, J. E. Medical foods: products for the management of chronic diseases. Nutr. Rev. 64, 495–501 (2006).
Semba, R. D. The historical evolution of thought regarding multiple micronutrient nutrition. J. Nutr. 142, 143S–156S (2012).
Baranano, K. W. & Hartman, A. L. The ketogenic diet: uses in epilepsy and other neurologic illnesses. Curr. Treat. Opt. Neurol. 10, 410–409 (2008).
Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 (2002).
Lindon, J. C., Holmes, E. & Nicholson, J. K. Metabonomics in pharmaceutical R&D. FEBS J. 274, 1140–1151 (2007). A comprehensive review of how metabolomics (here referred to as metabonomics) could be used in drug research and development.
Lindon, J. C. et al. The consortium for metabonomic toxicology (COMET): aims, activities and achievements. Pharmacogenomics 6, 691–699 (2005).
Chen, C., Gonzalez, F. J. & Idle, J. R. LC-MS-based metabolomics in drug metabolism. Drug Metab. Rev. 39, 581–597 (2007).
Walker, G. S. et al. Biosynthesis of drug metabolites and quantitation using NMR spectroscopy for use in pharmacologic and drug metabolism studies. Drug Metab. Dispos. 42, 1627–1639 (2014).
Tomaszewski, M. et al. High rates of non-adherence to antihypertensive treatment revealed by high-performance liquid chromatography-tandem mass spectrometry (HP LC-MS/MS) urine analysis. Heart 100, 855–861 (2014).
Koster, R. A., Alffenaar, J. W., Greijdanus, B., VanDernagel, J. E. & Uges, D. R. Fast and highly selective LC-MS/MS screening for THC and16 other abused drugs and metabolites in human hair to monitor patients for drug abuse. Ther. Drug Monit. 24, 234–243 (2014).
Guo, A. Y., Ma, J. D., Best, B. M. & Atayee, R. S. Urine specimen detection of concurrent nonprescribed medicinal and illicit drug use in patients prescribed buprenorphine. J. Anal. Toxicol. 32, 636–641 (2013).
Andersen, M. B. et al. Untargeted metabolomics as a screening tool for estimating compliance to a dietary pattern. J. Proteome Res. 13, 1405–1418 (2014).
Couchman, L., Belsey, S. L., Handley, S. A. & Flanagan, R. J. A novel approach to quantitative LC-MS/MS: therapeutic drug monitoring of clozapine and norclozapine using isotopic internal calibration. Anal. Bioanal. Chem. 405, 9455–9466 (2013).
Coen, M. Metabolic phenotyping applied to pre-clinical and clinical studies of acetaminophen metabolism and hepatotoxicity. Drug Metab. Rev. 47, 29–44 (2015).
Navarrrete, A. et al. Simultaneous online SPE-HPLC-MS/MS analysis of docetaxel, temsirolimus and sirolimus in whole blood and human plasma. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 15, 922–935 (2013).
Pickering, M. & Brown, S. Quantification and validation of HPLC-UV and LC-MS assays for therapeutic drug monitoring of ertapenem in human plasma. Biomed. Chromatogr. 27, 568–575 (2013).
Ubhi, B. K. et al. Targeted metabolomics identifies perturbations in amino acid metabolism that sub-classify patients with COPD. Mol. Biosyst. 8, 3125–3133 (2013).
Puskarich, M. A. et al. Pharmcometabolomics of l-carnitine treatment response phenotypes in patients with septic shock. Ann. Am. Thorac. Soc. 12, 46–56 (2015).
Hou, Y. et al. A metabolomics approach for predicting the response to neoadjuvant chemotherapy in cervical cancer patients. Mol. Biosys. 10, 2126–2133 (2014).
Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293–1307 (2012). A fascinating paper describing an 'accidental' case study of disease discovery, monitoring and treatment using multiple omics techniques.
Flores, M., Glusman, G., Brogaard, K., Price, N. D. & Hood, L. P4 medicine: how systems medicine will transform the healthcare sector and society. Per. Med. 10, 565–576 (2013).
Hood, L., Lovejoy, J. C. & Price, N. D. Integrating big data and actionable health coaching to optimize wellness. BMC Med. 13, 4 (2015).
Xia, J., Broadhurst, D. I., Wilson, M. & Wishart, D. S. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 9, 280–299 (2013).
la Marca, G. Mass spectrometry in clinical chemistry: the case of newborn screening. J. Pharm. Biomed. Anal. 101, 174–182 (2014).
Diamandis, E. P. The failure of protein cancer biomarkers to reach the clinic: why, and what can be done to address the problem? BMC Med. 10, 87 (2012).
Castaldi, P. J., Dahabreh, I. J. & Ioannidis, J. P. An empirical assessment of validation practices for molecular classifiers. Brief. Bioinform. 12, 189–202 (2011).
Turner, R. M. From the lab to the prescription pad: genetics, CYP450 analysis, and medication response. J. Child Adolesc. Psychiatr. Nurs. 26, 119–123 (2013).
Kaddurah-Daouk, R., Weinshilboum, R. & Pharmacometabolomics Research Network. Metabolomic signatures for drug response phenotypes: pharmacometabolomics enables precision medicine. Clin. Pharmacol. Ther. 98, 71–75 (2015).
Gamazon, E. R., Skol, A. D. & Perera, M. A. The limits of genome-wide methods for pharmacogenomic testing. Pharmacogenet. Genom. 22, 261–272 (2012).
Sallustio, B. C. LC-MS/MS for immunosuppressant therapeutic drug monitoring. Bioanalysis 2, 1141–1153 (2010).
Brozmanová, H., Perinová, I., Halvová, P. & Grundmann, M. Liquid chromatography–tandem mass spectrometry method for simultaneous determination of cyclosporine A and its three metabolites AM1, AM9 and AM4N in whole blood and isolated lymphocytes in renal transplant patients. J. Sep. Sci. 33, 2287–2293 (2010).
Shen, B. et al. Determination of total, free and saliva mycophenolic acid with a LC-MS/MS method: application to pharmacokinetic study in healthy volunteers and renal transplant patients. J. Pharm. Biomed. Anal. 50, 515–521 (2009).
Holt, D. W. et al. Long-term evaluation of analytical methods used in sirolimus therapeutic drug monitoring. Clin. Translplant. 28, 243–251 (2014).
Moes, D. J., Press, R. R., de Fijter, J. W., Guchelaar, H. J. & den Hartigh, J. Liquid chromatography–tandem mass spectrometry outperforms fluorescence polarization immunoassay in monitoring everolimus therapy in renal transplantation. Ther. Drug Monit. 32, 413–419 (2010).
Ponnayyan Sulochana, S., Sharma, K., Mullangi, R. & Sukumaran, S. K. Review of the validated HPLC and LC-MS/MS methods for determination of drugs used in clinical practice for Alzheimer's disease. Biomed. Chromatogr. 28, 1431–1490 (2014).
Zgheib, N. K., Frye, R. F., Tracy, T. S., Romkes, M. & Branch, R. A. Validation of incorporating flurbiprofen into the Pittsburgh cocktail. Clin. Pharmacol. Ther. 80, 257–263 (2006).
Stewart, N. A., Buch, S. C., Conrads, T. P. & Branch, R. A. A. UPLC-MS/MS assay of the “Pittsburgh cocktail”: six CYP probe-drug/metabolites from human plasma and urine using stable isotope dilution. Analyst 136, 605–612 (2011).
Krauss, R. M., Zhu, H. & Kaddurah-Daouk, R. Pharmacometabolomics of statin response. Clin. Pharmacol. Ther. 94, 562–565 (2013).
Zhu, H. et al. Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder — possible role for methoxyindole pathway. PLoS ONE 8, e68283 (2013).
Ellero-Simatos, S. et al. Pharmacometabolomics reveals that serotonin is implicated in aspirin response variability. CPT Pharmacometr. Syst. Pharmacol. 3, e125 (2014).
Yerges-Armstrong, L. M. et al. Purine pathway implicated in mechanism of resistance to aspirin therapy: pharmacometabolomics-informed pharmacogenomics. Clin. Pharmacol. Ther. 94, 525–532 (2013).
Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011). An important paper that shows the influence of genetics on individual metabotypes.
Shin, S. Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).
Wikoff, W. R. et al. Pharmacometabolomics reveals racial differences in response to atenolol treatment. PLoS ONE 8, e57639 (2013).
Walther, Z. & Sklar, J. Molecular tumor profiling for prediction of response to anticancer therapies. Cancer J. 17, 71–79 (2011).
Forbes, S. A. et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–811 (2015).
Hipp, S. J. et al. Molecular imaging of pediatric brain tumors: comparison of tumor metabolism using 18F-FDG-PET and MRSI. J. Neurooncol. 109, 521–527 (2012).
Zhan, H., Ciano, K., Dong, K. & Zucker, S. Targeting glutamine metabolism in myeloproliferative neoplasms. Blood Cells Mol. Dis. 55, 241–247 (2015).
Sutinen, E. et al. Kinetics of [11C]choline uptake in prostate cancer: a PET study. Eur. J. Nucl. Med. Mol. Imag. 31, 317–324 (2004).
Choi, C. et al. A comparative study of short- and long-TE 1H MRS at 3 T for in vivo detection of 2-hydroxyglutarate in brain tumors. NMR Biomed. 26, 1242–1250 (2013).
Zhu, Z. J. et al. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat. Protoc. 8, 451–460 (2013).
Haug, K. et al. MetaboLights — an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res. 41, D781–D786 (2013).
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3, 211–221 (2007).
Xia, J., Sinelnikov, I. V., Han, B. & Wishart, D. S. MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res. 43, W251–W257 (2015).
Yang, M., Soga, T. & Pollard, P. J. Oncometabolites: linking altered metabolism with cancer. J. Clin. Invest. 123, 3652–3659 (2013). A well-written review regarding the discovery and emerging importance of oncometabolites in cancer and cancer treatment.
Yang, M., Soga, T., Pollard, P. J. & Adam, J. The emerging role of fumarate as an oncometabolite. Front. Oncol. 2, 85 (2012).
Morin, A., Letouze, E., Gimenez-Roqeuplo, A. P. & Favier, J. Oncometabolites-driven tumorigenesis: From genetics to targeted therapy. Int. J. Cancer 135, 2237–2248 (2014).
Khan, A. P. et al. The role of sarcosine metabolism in prostate cancer progression. Neooplasia 15, 491–501 (2013).
Chen, K. T. et al. AMPA receptor–mTOR activation is required for the antidepressant-like effects of sarcosine during swim tests in rats: insertion of AMPA receptor may play a role. Front. Behav. Neurosci. 9, 162 (2015).
Lee, A. S. Glucose-regulated proteins in cancer: molecular mechanisms and therapeutic potential. Nat. Rev. Cancer 14, 263–276 (2014).
Wolf, A. et al. Hexokinase 2 is a key mediator of aerobic glycolysis and promotes tumor growth in human glioblastoma multiforme. J. Exp. Med. 208, 313–326 (2011).
Wise, D. R. & Thompson, C. B. Glutamine addiction: a new therapeutic target in cancer. Trends Biochem. Sci. 35, 427–433 (2010).
Miller, D. M., Thomas, S. D., Islam, A., Muench, D. & Sedoris, K. c-Myc and cancer metabolism. Clin. Cancer Res. 18, 5546–5553 (2012).
Zhang, J. et al. Asparagine plays a critical role in regulating cellular adaptation to glutamine depletion. Mol. Cell 56, 205–218 (2014).
Awwad, H. M., Geisel, J. & Obeid, R. The role of choline in prostate cancer. Clin. Biochem. 45, 1548–1553 (2012).
Choi, S. Y., Collins, C. C., Gout, P. W. & Wang, Y. Cancer-generated lactic acid: a regulatory, immunosuppressive metabolite? J. Pathol. 230, 350–355 (2013).
Gillies, R. J. & Gatenby, R. A. Metabolism and its sequelae in cancer evolution and therapy. Cancer J. 21, 88–96 (2015).
The author wishes to thank Genome Canada, the Canadian Institutes for Health Research (CIHR) and Alberta Innovates for financial support.
The author declares no competing financial interests.
- Metabolic phenotyping
The characterization of a cell, organism or biological system using metabolomics or metabolic profiling. Metabolic phenotyping is a method of describing the phenotype using chemical or metabolite readouts as a proxy for an organism's observable biochemical traits.
- Endogenous metabolites
Metabolites that are biosynthesized or potentially biosynthesized by the host organism and/or its endogenous microflora. Endogenous metabolites also include xenobiotics that have been metabolically transformed by the host.
- Exogenous metabolites
Xenobiotic metabolites or chemicals that the host (and/or its endogenous microflora) is not capable of biosynthesizing or that have not yet been metabolically transformed.
A branch of omics science that involves the study of the complete collection of environmental exposures (chemicals, foods, pollutants and pathogens) that a human is exposed to from conception onwards, which is referred to as the exposome.
- Coulometric array detectors
Multi-array electrochemical detection systems for detecting redox-active compounds as they elute from a high performance liquid chromatography (HPLC) column. Chemicals or metabolites react with specific electrodes in the detector depending on their redox potential.
- Inductively coupled plasma mass spectrometers
Mass spectrometers that are specifically designed to detect and quantify metals at very low concentrations. Metal ions are ionized by inductive heating to create an electrically conductive plasma that is then sent to a conventional mass spectrometer for detection.
- Evaporative light-scattering detectors
(ELSDs). Instruments that detect compounds eluting from a high-performance liquid chromatography (HPLC) system on the basis of light scattering rather than ultraviolet absorption or fluorescence. ELSDs permit the detection of far more compounds than other optical techniques.
- Secondary ion MS
(SIMS). A mass spectrometry (MS) technique that can be used to analyse and image the composition of thin films. Ions (that is, secondary ions) are generated by sputtering the surface of the sample with an intense ion beam.
- Desorption electrospray ionization MS
(DESI-MS). A mass spectrometry technique (MS) that uses atmospheric pressure ion sources to ionize samples in open air under ambient conditions. It is a combination of both electrospray and desorption ionization techniques wherein ionization occurs by spraying an electrically charged mist onto the sample surface.
The collection of microorganisms that reside in or on a larger organism, a larger organ or within a specific environmental niche.
The collection of chemical compounds that act on DNA as well as the collection of chemical modifications to DNA (and histones) that direct and/or alter the original instructions in the genome.
An agent (specifically, a chemical, protein or pathogen) that damages arteries leading to atherosclerosis or cardiovascular disease.
A metabolic process involving the catabolism of glutamine to generate energy as well as nitrogen and carbon byproducts. It is an important energy pathway for tumour cells.
- Mammalian target of rapamycin
(mTOR). A serine/threonine kinase that acts as a master controller of cell metabolism, cell growth, cell proliferation, cell survival and protein synthesis.
A field of information science that extracts useful information from chemical data using statistical or data-driven techniques.
A branch of omics science that involves the study of the microbiome.
A branch of metabolomics that involves the metabolomic analysis of both pharmaceutical compounds and endogenous metabolites after the administration of a pharmaceutical compound.
- Intronic SNP
A single nucleotide polymorphism (SNP) found in an intron or a non-coding region of a gene.
The metabolic equivalent of phenotypes. A metabotype is a metabolic profile that defines or classifies an individual's biochemical state at a given point in time.
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Wishart, D. Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15, 473–484 (2016). https://doi.org/10.1038/nrd.2016.32
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