Metabolomics as a tool for drug discovery and personalised medicine. A review

Current Topics in Medicinal Chemistry

Volumn 14, Issue 23, 2014, Pages 2627-2636

  Mastrangelo, Annalaura ^a   Armitage, Emily G ^a   García, Antonia ^a   Barbas, Coral ^a  

^a UNIVERSIDAD SAN PABLO CEU   (Spain)

Author keywords

Biomarker; Drug discovery and development; Metabolomics; Personalised medicine; Pharmacometabolomics

Indexed keywords

ACETYLSALICYLIC ACID; ATORVASTATIN; CLOFIBRATE; DABIGATRAN; DOXORUBICIN; ETOPOSIDE; FENOFIBRATE; FLUOROURACIL; GENIPOSIDE; GLYCINE DEHYDROGENASE; KETAMINE; PARACETAMOL; PRAVASTATIN; TUMOR NECROSIS FACTOR INHIBITOR; BIOLOGICAL MARKER; NEW DRUG;

APOPTOSIS; CAPILLARY ELECTROPHORESIS; CLINICAL TRIAL (TOPIC); DNA FINGERPRINTING; DRUG ABSORPTION; DRUG ACCUMULATION; DRUG DESIGN; DRUG DISTRIBUTION; DRUG EFFICACY; DRUG EXCRETION; DRUG METABOLISM; DRUG RESPONSE; DRUG SAFETY; DRUG SURVEILLANCE PROGRAM; GAS CHROMATOGRAPHY; HUMAN; HUMAN CYTOMEGALOVIRUS; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; METABOLOMICS; NONHUMAN; NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY; PERSONALIZED MEDICINE; PHARMACOMETABOLOMICS; REVIEW; SINGLE NUCLEOTIDE POLYMORPHISM; TOXICITY; TREATMENT OUTCOME; ANIMAL; CHEMISTRY; DEVICES; DISEASE MODEL; DRUG DEVELOPMENT; GENETICS; METABOLIC DISEASES; METABOLISM; METABOLOME; NEOPLASMS; NEURODEGENERATIVE DISEASES; PATHOLOGY; PHARMACEUTICS; PHARMACOGENETICS; PROCEDURES;

ANIMALS; BIOLOGICAL MARKERS; BIOPHARMACEUTICS; DISEASE MODELS, ANIMAL; DRUG DISCOVERY; DRUGS, INVESTIGATIONAL; HUMANS; INDIVIDUALIZED MEDICINE; METABOLIC DISEASES; METABOLOME; METABOLOMICS; NEOPLASMS; NEURODEGENERATIVE DISEASES; PHARMACOGENETICS;

EID: 84926347366     PISSN: 15680266     EISSN: 18734294     Source Type: Journal    
DOI: 10.2174/1568026614666141215124956     Document Type: Review

References (94)

1. [1] 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, 1999, 29 (11), 1181-9.
2. [2] Fiehn, O. Metabolomics--the link between genotypes and phenotypes. Plant Mol. Biol., 2002, 48 (1-2), 155-71.
3. [3] Holmes, E.; Wilson, I.D.; Nicholson, J.K. Metabolic phenotyping in health and disease. Cell, 2008, 134 (5), 714-7.
4. [4] Fiehn, O. Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp. Funct. Genom., 2001, 2 (3), 155-68.
5. [5] Basanta, M.; Jarvis, R.M.; Xu, Y.; Blackburn, G.; Tal-Singer, R.; Woodcock, A.; Singh, D.; Goodacre, R.; Paul Thomas, C.L.; Fowler, S.J. Non-invasive metabolomic analysis of breath using differential mobility spectrometry in patients with chronic obstructive pulmonary disease and healthy smokers. Analyst, 2010, 135 (2), 315-320.
6. [6] Zhang, A.; Sun, H.; Wang, P.; Han, Y.; Wang, X. Modern analytical techniques in metabolomics analysis. Analyst, 2012, 137 (2), 293-300.
7. [7] Wang, J.H.; Byun, J.; Pennathur, S. Analytical approaches to metabolomics and applications to systems biology. Semin. Nephrol., 2010, 30 (5), 500-11.
8. [8] Godzien, J.; Ciborowski, M.; Angulo, S.; Barbas, C. From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS. Electrophoresis, 2013, 34 (19), 2812-26.
9. [9] Tulpan, D.; Léger, S.; Belliveau, L.; Culf, A.; Čuperlovi_ćCulf, M. MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures. BMC Bioinformatics, 2011, 12 (1), 400.
10. [10] 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., 2006, 78 (3), 779-787.
11. [11] Brown, M.; Wedge, D.C.; Goodacre, R.; Kell, D.B.; Baker, P.N.; Kenny, L.C.; Mamas, M.A.; Neyses, L.; Dunn, W.B. Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics, 2011, 27 (8), 1108-1112.
12. [12] Stein, S.E. An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J. Am. Soc. Mass Spectrom., 1999, 10 (8), 770-781.
13. Metabololomics Society. Metabolomics Software and Servers. http://www.metabolomicssociety.org/software (accessed January 29, 2014).
14. [14] Refoios Camejo, R.; McGrath, C.; Herings, R. A dynamic perspective on pharmaceutical competition, drug development and cost effectiveness. Health Policy, 2011, 100 (1), 18-24.
15. [15] Morgan, S.; Grootendorst, P.; Lexchin, J.; Cunningham, C.; Greyson, D. The cost of drug development: a systematic review. Health Policy, 2011, 100 (1), 4-17.
16. [16] Watkins, S.M.; German, J.B. Metabolomics and biochemical profiling in drug discovery and development. Curr. Opin. Mol. Ther., 2002, 4 (3), 224-8.
17. [17] Wishart, D.S. Applications of metabolomics in drug discovery and development. Drugs R. D., 2008, 9 (5), 307-22.
18. [18] Robertson, D.G.; Frevert, U. Metabolomics in drug discovery and development. Clin. Pharmacol. Ther., 2013, 94 (5), 559-61.
19. [19] Russell, C.; Rahman, A.; Mohammed, A.R. Application of genomics, proteomics and metabolomics in drug discovery, development and clinic. Ther. Deliv., 2013, 4 (3), 395-413.
20. [20] Kaddurah-Daouk, R.; Kristal, B.S.; Weinshilboum, R.M. Metabolomics: a global biochemical approach to drug response and disease. Annu. Rev. Pharmacol. Toxicol., 2008, 48, 653-83.
21. [21] Newman, D.J.; Cragg, G.M. Natural products as sources of new drugs over the last 25 years. J. Nat. Prod., 2007, 70 (3), 461-77.
22. [22] Newman, D.J.; Cragg, G.M. Natural products as sources of new drugs over the 30 years from 1981 to 2010. J. Nat. Prod., 2012, 75 (3), 311-35.
23. [23] Hefti, F.F. Requirements for a lead compound to become a clinical candidate. BMC Neurosci., 2008, 9 Suppl 3, S7.
24. [24] Vandamme, D.; Minke, B.A.; Fitzmaurice, W.; Kholodenko, B.N.; Kolch, W. Systems biology-embedded target validation: improving efficacy in drug discovery. Wiley. Interdiscip. Rev. Syst. Biol. Med., 2014, 6(1):1-11.
25. [25] Yan, L.; Nie, W.; Parker, T.; Upton, Z.; Lu, H. MS-based metabolomics facilitates the discovery of in vivo functional small molecules with a diversity of biological contexts. Future Med. Chem., 2013, 5 (16), 1953-65.
26. [26] Kell, D.B.; Goodacre, R. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov. Today, 2014, 19(2):171-82.
27. [27] Anderson, D.C.; Kodukula, K. Biomarkers in pharmacology and drug discovery. Biochem. Pharmacol., 2014,87(1):172-88.
28. [28] Kang, J.J.; Wang, H.W.; Liu, T.Y.; Chen, Y.C.; Ueng, T.H. Modulation of cytochrome P-450-dependent monooxygenases, glutathione and glutathione S-transferase in rat liver by geniposide from Gardenia jasminoides. Food Chem. Toxicol., 1997, 35 (10-11), 957-65.
29. [29] Koo, H.J.; Lim, K.H.; Jung, H.J.; Park, E.H. Anti-inflammatory evaluation of gardenia extract, geniposide and genipin. J. Ethnopharmacol., 2006, 103 (3), 496-500.
30. [30] Zhang, A.; Sun, H.; Yuan, Y.; Sun, W.; Jiao, G.; Wang, X. An in vivo analysis of the therapeutic and synergistic properties of Chinese medicinal formula Yin-Chen-Hao-Tang based on its active constituents. Fitoterapia., 2011, 82 (8), 1160-8.
31. [31] Wang, X.; Zhang, A.; Yan, G.; Sun, W.; Han, Y.; Sun, H. Metabolomics and proteomics annotate therapeutic properties of geniposide: targeting and regulating multiple perturbed pathways. PLoS One, 2013, 8 (8), e71403.
32. [32] Rabinowitz, J.D.; Purdy, J.G.; Vastag, L.; Shenk, T.; Koyuncu, E. Metabolomics in drug target discovery. Cold Spring Harb. Symp. Quant. Biol., 2011, 76, 235-46.
33. [33] Mutlib, A.E. Application of stable isotope-labeled compounds in metabolism and in metabolism-mediated toxicity studies. Chem. Res. Toxicol., 2008, 21 (9), 1672-89.
34. [34] Fan, T.W.; Lorkiewicz, P.K.; Sellers, K.; Moseley, H.N.; Higashi, R.M.; Lane, A.N. Stable isotope-resolved metabolomics and applications for drug development. Pharmacol. Ther., 2012, 133 (3), 366-91.
35. International Commission on Radiological Protection. Recommendations of the International Commission on Radiological Protection. http://www.icrp.org/publication.asp?id=ICRP%20Publication%20103 (accessed January 26, 2014).
36. [36] Krauser, J.; Walles, M.; Wolf, T.; Graf, D.; Swart, P. A unique automation platform for measuring low level radioactivity in metabolite identification studies. PLoS One, 2012, 7 (6), e39070.
37. [37] Blech, S.; Ebner, T.; Ludwig-Schwellinger, E.; Stangier, J.; Roth, W. The metabolism and disposition of the oral direct thrombin inhibitor, dabigatran, in humans. Drug Metab. Dispos., 2008, 36 (2), 386-99.
38. [38] Nicholson, J.K.; Wilson, I.D. High resolution proton magnetic resonance spectroscopy of biological fluids. Progress in Nuclear Magnet. Reson. Spectro., 1989, 21 (4-5), 449-501.
39. [39] Lindon, J.C.; Nicholson, J.K.; Holmes, E.; Antti, H.; Bollard, M.E.; Keun, H.; Beckonert, O.; Ebbels, T.M.; Reily, M.D.; Robertson, D.; Stevens, G.J.; Luke, P.; Breau, A.P.; Cantor, G.H.; Bible, R.H.; Niederhauser, U.; Senn, H.; Schlotterbeck, G.; Sidelmann, U.G.; Laursen, S.M.; Tymiak, A.; Car, B.D.; Lehman-McKeeman, L.; Colet, J.M.; Loukaci, A.; Thomas, C. Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl. Pharmacol., 2003, 187 (3), 137-46.
40. [40] Ebbels, T.M.; Keun, H.C.; Beckonert, O.P.; Bollard, M.E.; Lindon, J.C.; Holmes, E.; Nicholson, J.K. Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach. J. Proteome Res., 2007, 6 (11), 4407-22.
41. [41] Ramirez, T.; Daneshian, M.; Kamp, H.; Bois, F.Y.; Clench, M.R.; Coen, M.; Donley, B.; Fischer, S.M.; Ekman, D.R.; Fabian, E.; Guillou, C.; Heuer, J.; Hogberg, H.T.; Jungnickel, H.; Keun, H.C.; Krennrich, G.; Krupp, E.; Luch, A.; Noor, F.; Peter, E.; Riefke, B.; Seymour, M.; Skinner, N.; Smirnova, L.; Verheij, E.; Wagner, S.; Hartung, T.; van Ravenzwaay, B.; Leist, M. Metabolomics in toxicology and preclinical research. Altex, 2013, 30 (2), 209-25.
42. [42] Suter, L.; Schroeder, S.; Meyer, K.; Gautier, J.C.; Amberg, A.; Wendt, M.; Gmuender, H.; Mally, A.; Boitier, E.; Ellinger-Ziegelbauer, H.; Matheis, K.; Pfannkuch, F. EU framework 6 project: predictive toxicology (PredTox)--overview and outcome. Toxicol. Appl. Pharmacol., 2011, 252 (2), 73-84.
43. [43] Bai, J.P.; Abernethy, D.R. Systems pharmacology to predict drug toxicity: integration across levels of biological organization. Annu. Rev. Pharmacol. Toxicol., 2013, 53, 451-73.
44. [44] Strauss, V.; Mellert, W.; Wiemer, J.; Leibold, E.; Kamp, H.; Walk, T.; Looser, R.; Prokoudine, A.; Fabian, E.; Krennrich, G.; Herold, M.; van Ravenzwaay, B. Increased toxicity when fibrates and statins are administered in combination--a metabolomics approach with rats. Toxicol. Lett., 2012, 211 (2), 187-200.
45. [45] Schnackenberg, L.K.; Sun, J.; Espandiari, P.; Holland, R.D.; Hanig, J.; Beger, R.D. Metabonomics evaluations of age-related changes in the urinary compositions of male Sprague Dawley rats and effects of data normalization methods on statistical and quantitative analysis. BMC Bioinformatics, 2007, 8 Suppl., 7, S3.
46. [46] Williams, R.E.; Lenz, E.M.; Rantalainen, M.; Wilson, I.D. The comparative metabonomics of age-related changes in the urinary composition of male Wistar-derived and Zucker (fa/fa) obese rats. Mol. Biosyst., 2006, 2 (3-4), 193-202.
47. [47] Canuto, G.A.; Castilho-Martins, E.A.; Tavares, M.; Lopez-Gonzalvez, A.; Rivas, L.; Barbas, C. CE-ESI-MS metabolic fingerprinting of Leishmania resistance to antimony treatment. Electrophoresis, 2012, 33 (12), 1901-10.
48. [48] Brotherton, M.C.; Bourassa, S.; Leprohon, P.; Legare, D.; Poirier, G.G.; Droit, A.; Ouellette, M. Proteomic and Genomic Analyses of Antimony Resistant Leishmania infantum Mutant. PLoS One, 2013, 8 (11), e81899.
49. World Health Organization. Neglected tropical diseases: Sustaining the drive to overcome the global impact of neglected tropical diseases, second WHO report on neglected tropical diseases. http://www.who.int/neglected_diseases/9789241564540/en/ (accessed January 29, 2014).
50. [50] Kotze, H.L.; Armitage, E.G.; Fletcher, J.S.; Henderson, A.; Williams, K.J.; Lockyer, N.P.; Vickerman, J.C. ToF-SIMS as a tool for metabolic profiling small biomolecules in cancer systems. Surf. Interface Anal., 2013, 45 (1), 277-281.
51. [51] Halama, A.; Riesen, N.; Moller, G.; Hrabe de Angelis, M.; Adamski, J., Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine. J. Intern. Med., 2013, 274 (5), 425-39.
52. [52] Fan, M.; Sidhu, R.; Fujiwara, H.; Tortelli, B.; Zhang, J.; Davidson, C.; Walkley, S.U.; Bagel, J.H.; Vite, C.; Yanjanin, N.M.; Porter, F.D.; Schaffer, J.E.; Ory, D.S. Identification of Niemann-Pick C1 disease biomarkers through sphingolipid profiling. J. Lipid. Res., 2013, 54 (10), 2800-14.
53. [53] Rawlins, M.D. Clinical pharmacology. Adverse reactions to drugs. Br. Med. J. (Clin Res Ed), 1981, 282 (6268), 974-6.
54. [54] Davies, D.M.; Ferner, R.E.; Glanville, H.D. Davies' textbook of adverse drug reactions. 5th ed.; Oxford University Press: Oxford, 1977.
55. [55] Wilson, I.D. Drugs, bugs, and personalized medicine: pharmacometabonomics enters the ring. Proc. Natl. Acad Sci. U S A, 2009, 106 (34), 14187-8.
56. [56] Martinez-Outschoorn, U.E.; Prisco, M.; Ertel, A.; Tsirigos, A.; Lin, Z.; Pavlides, S.; Wang, C.; Flomenberg, N.; Knudsen, E.S.; Howell, A.; Pestell, R.G.; Sotgia, F.; Lisanti, M.P. Ketones and lactate increase cancer cell "stemness," driving recurrence, metastasis and poor clinical outcome in breast cancer: achieving personalized medicine via Metabolo-Genomics. Cell Cycle, 2011, 10 (8), 1271-86.
57. [57] Kapoor, S.R.; Filer, A.; Fitzpatrick, M.A.; Fisher, B.A.; Taylor, P.C.; Buckley, C.D.; McInnes, I.B.; Raza, K.; Young, S.P. Metabolic profiling predicts response to anti-tumor necrosis factor alpha therapy in patients with rheumatoid arthritis. Arthritis Rheum., 2013, 65 (6), 1448-56.
58. [58] Kaddurah-Daouk, R.; Boyle, S.H.; Matson, W.; Sharma, S.; Matson, S.; Zhu, H.; Bogdanov, M.B.; Churchill, E.; Krishnan, R.R.; Rush, A.J.; Pickering, E.; Delnomdedieu, M. Pretreatment metabotype as a predictor of response to sertraline or placebo in depressed outpatients: a proof of concept. Transl. Psychiat., 2011, 1, e26.
59. [59] Leuchter, A.F.; Cook, I.A.; Hamilton, S.P.; Narr, K.L.; Toga, A.; Hunter, A.M.; Faull, K.; Whitelegge, J.; Andrews, A.M.; Loo, J.; Way, B.; Nelson, S.F.; Horvath, S.; Lebowitz, B.D. Biomarkers to predict antidepressant response. Curr. Psychiat. Rep., 2010, 12 (6), 553-62.
60. [60] Villaseñor, A.; Ramamoorthy, A.; Santos, M.S.; Lorenzo, M.; Laje, G.; Zarate, C.; Barbas, C.; Wainer, I. A pilot study of plasma metabolomic patterns from patients treated with ketamine for bipolar depression: Evidence for a response-related difference in mitochondrial networks. Br. J. Pharmacol., 2014, 171, 2230-42.
61. [61] Clayton, T.A.; Lindon, J.C.; Cloarec, O.; Antti, H.; Charuel, C.; Hanton, G.; Provost, J.P.; Le Net, J.L.; Baker, D.; Walley, R.J.; Everett, J.R.; Nicholson, J.K. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature, 2006, 440 (7087), 1073-7.
62. [62] Li, H.; Ni, Y.; Su, M.; Qiu, Y.; Zhou, M.; Qiu, M.; Zhao, A.; Zhao, L.; Jia, W. Pharmacometabonomic phenotyping reveals different responses to xenobiotic intervention in rats. J. Proteome Res., 2007, 6 (4), 1364-70.
63. Cavill, R.; Kamburov, A.; Ellis, J.K.; Athersuch, T.J.; Blagrove, M.S.; Herwig, R.; Ebbels, T.M.; Keun, H.C. Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells. PLoS Comput. Biol., 2011, 7 (3), e1001113.
64. [64] Coen, M.; Goldfain-Blanc, F.; Rolland-Valognes, G.; Walther, B.; Robertson, D.G.; Holmes, E.; Lindon, J.C.; Nicholson, J.K. Pharmacometabonomic investigation of dynamic metabolic phenotypes associated with variability in response to galactosamine hepatotoxicity. J. Proteome Res., 2012, 11 (4), 2427-40.
65. [65] Li, F.; Lu, X.; Liu, H.; Liu, M.; Xiong, Z. A pharmacometabonomic study on the therapeutic basis and metabolic effects of Epimedium brevicornum Maxim. on hydrocortisone-induced rat using UPLC-MS. Biomed. Chromatogr., 2007, 21 (4), 397-405.
66. [66] Keun, H.C.; Sidhu, J.; Pchejetski, D.; Lewis, J.S.; Marconell, H.; Patterson, M.; Bloom, S.R.; Amber, V.; Coombes, R.C.; Stebbing, J. Serum molecular signatures of weight change during early breast cancer chemotherapy. Clin. Cancer Res., 2009, 15 (21), 6716-23.
67. [67] Kwon, H.N.; Kim, M.; Wen, H.; Kang, S.; Yang, H.J.; Choi, M.J.; Lee, H.S.; Choi, D.; Park, I.S.; Suh, Y.J.; Hong, S.S.; Park, S. Predicting idiopathic toxicity of cisplatin by a pharmacometabonomic approach. Kidney Int., 2011, 79 (5), 529-37.
68. [68] Backshall, A.; Sharma, R.; Clarke, S.J.; Keun, H.C. Pharmacometabonomic profiling as a predictor of toxicity in patients with inoperable colorectal cancer treated with capecitabine. Clin. Cancer Res., 2011, 17 (9), 3019-28.
69. [69] Wikoff, W.R.; Frye, R.F.; Zhu, H.; Gong, Y.; Boyle, S.; Churchill, E.; Cooper-Dehoff, R.M.; Beitelshees, A.L.; Chapman, A.B.; Fiehn, O.; Johnson, J.A.; Kaddurah-Daouk, R. Pharmacometabolomics reveals racial differences in response to atenolol treatment. PLoS One, 2013, 8 (3), e57639.
70. [70] Clayton, T.A.; Baker, D.; Lindon, J.C.; Everett, J.R.; Nicholson, J.K. Pharmacometabonomic identification of a significant hostmicrobiome metabolic interaction affecting human drug metabolism. Proc. Natl. Acad. Sci. U S A, 2009, 106 (34), 14728-33.
71. [71] Swann, J.; Wang, Y.; Abecia, L.; Costabile, A.; Tuohy, K.; Gibson, G.; Roberts, D.; Sidaway, J.; Jones, H.; Wilson, I.D.; Nicholson, J.; Holmes, E. Gut microbiome modulates the toxicity of hydrazine: a metabonomic study. Mol. Biosyst., 2009, 5 (4), 351-5.
72. [72] Kaddurah-Daouk, R.; Baillie, R.A.; Zhu, H.; Zeng, Z.B.; Wiest, M.M.; Nguyen, U.T.; Wojnoonski, K.; Watkins, S.M.; Trupp, M.; Krauss, R.M. Enteric microbiome metabolites correlate with response to simvastatin treatment. PLoS One, 2011, 6 (10), e25482.
73. [73] Sun, J.; Schnackenberg, L.K.; Khare, S.; Yang, X.; Greenhaw, J.; Salminen, W.; Mendrick, D.L.; Beger, R.D. Evaluating effects of penicillin treatment on the metabolome of rats. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci., 2013, 932, 134-43.
74. Zheng, X.; Zhao, A.; Xie, G.; Chi, Y.; Zhao, L.; Li, H.; Wang, C.; Bao, Y.; Jia, W.; Luther, M.; Su, M.; Nicholson, J.K. Melamineinduced renal toxicity is mediated by the gut microbiota. Sci. Transl. Med., 2013, 5 (172), 172ra22.
75. [75] Liu, G.; Yan, T.; Wang, J.; Huang, Z.; Chen, X.; Jia, G.; Wu, C.; Zhao, H.; Xue, B.; Xiao, L.; Tang, J. Biological system responses to zearalenone mycotoxin exposure by integrated metabolomic studies. J. Agric. Food Chem., 2013, 61 (46), 11212-21.
76. [76] Perez-Cobas, A.E.; Gosalbes, M.J.; Friedrichs, A.; Knecht, H.; Artacho, A.; Eismann, K.; Otto, W.; Rojo, D.; Bargiela, R.; von Bergen, M.; Neulinger, S.C.; Daumer, C.; Heinsen, F.A.; Latorre, A.; Barbas, C.; Seifert, J.; dos Santos, V.M.; Ott, S.J.; Ferrer, M.; Moya, A. Gut microbiota disturbance during antibiotic therapy: a multi-omic approach. Gut., 2013, 62 (11), 1591-601.
77. [77] van Duynhoven, J.; Vaughan, E.E.; van Dorsten, F.; Gomez-Roldan, V.; de Vos, R.; Vervoort, J.; van der Hooft, J.J.; Roger, L.; Draijer, R.; Jacobs, D.M. Interactions of black tea polyphenols with human gut microbiota: implications for gut and cardiovascular health. Am. J. Clin. Nutr., 2013, 98 (6), 1631s-41s.
78. [78] Godzien, J.; Ciborowski, M.; Angulo, S.; Ruperez, F.J.; Martinez, M.P.; Senorans, F.J.; Cifuentes, A.; Ibanez, E.; Barbas, C. Metabolomic approach with LC-QTOF to study the effect of a nutraceutical treatment on urine of diabetic rats. J. Proteome Res., 2011, 10 (2), 837-44.
79. [79] Tulipani, S.; Urpi-Sarda, M.; Garcia-Villalba, R.; Rabassa, M.; Lopez-Uriarte, P.; Bullo, M.; Jauregui, O.; Tomas-Barberan, F.; Salas-Salvado, J.; Espin, J.C.; Andres-Lacueva, C. Urolithins are the main urinary microbial-derived phenolic metabolites discriminating a moderate consumption of nuts in free-living subjects with diagnosed metabolic syndrome. J. Agric. Food Chem., 2012, 60 (36), 8930-40.
80. [80] Collino, S.; Montoliu, I.; Martin, F.P.; Scherer, M.; Mari, D.; Salvioli, S.; Bucci, L.; Ostan, R.; Monti, D.; Biagi, E.; Brigidi, P.; Franceschi, C.; Rezzi, S. Metabolic signatures of extreme longevity in northern Italian centenarians reveal a complex remodeling of lipids, amino acids, and gut microbiota metabolism. PLoS One, 2013, 8 (3), e56564.
81. [81] Waldram, A.; Holmes, E.; Wang, Y.; Rantalainen, M.; Wilson, I.D.; Tuohy, K. M.; McCartney, A. L.; Gibson, G. R.; Nicholson, J. K., Top-down systems biology modeling of host metabotypemicrobiome associations in obese rodents. J Proteome Res 2009, 8 (5), 2361-75.
82. [82] Le Gall, G.; Noor, S.O.; Ridgway, K.; Scovell, L.; Jamieson, C.; Johnson, I.T.; Colquhoun, I.J.; Kemsley, E.K.; Narbad, A. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res., 2011, 10 (9), 4208-18.
83. [83] Calvani, R.; Miccheli, A.; Capuani, G.; Tomassini Miccheli, A.; Puccetti, C.; Delfini, M.; Iaconelli, A.; Nanni, G.; Mingrone, G. Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype. Int. J. Obes. (Lond), 2010, 34 (6), 1095-8.
84. [84] Hong, Y.S.; Ahn, Y.T.; Park, J.C.; Lee, J.H.; Lee, H.; Huh, C.S.; Kim, D. H.; Ryu do, H.; Hwang, G.S. 1H NMR-based metabonomic assessment of probiotic effects in a colitis mouse model. Arch. Pharm. Res., 2010, 33 (7), 1091-101.
85. [85] Sellitto, M.; Bai, G.; Serena, G.; Fricke, W.F.; Sturgeon, C.; Gajer, P.; White, J.R.; Koenig, S.S.; Sakamoto, J.; Boothe, D.; Gicquelais, R.; Kryszak, D.; Puppa, E.; Catassi, C.; Ravel, J.; Fasano, A. Proof of concept of microbiome-metabolome analysis and delayed gluten exposure on celiac disease autoimmunity in genetically at-risk infants. PLoS One, 2012, 7 (3), e33387.
86. [86] Lu, K.; Knutson, C.G.; Wishnok, J.S.; Fox, J.G.; Tannenbaum, S.R. Serum metabolomics in a Helicobacter hepaticus mouse model of inflammatory bowel disease reveal important changes in the microbiome, serum peptides, and intermediary metabolism. J. Proteome Res., 2012, 11 (10), 4916-26.
87. [87] Ming, X.; Stein, T.P.; Barnes, V.; Rhodes, N.; Guo, L. Metabolic perturbance in autism spectrum disorders: a metabolomics study. J. Proteome Res., 2012, 11 (12), 5856-62.
88. [88] He, Q.; Ren, P.; Kong, X.; Wu, Y.; Wu, G.; Li, P.; Hao, F.; Tang, H.; Blachier, F.; Yin, Y. Comparison of serum metabolite compositions between obese and lean growing pigs using an NMRbased metabonomic approach. J. Nutr. Biochem., 2012, 23 (2), 133-9.
89. [89] Yap, I.K.; Angley, M.; Veselkov, K.A.; Holmes, E.; Lindon, J.C.; Nicholson, J. K., Urinary metabolic phenotyping differentiates children with autism from their unaffected siblings and agematched controls. J. Proteome Res., 2010, 9 (6), 2996-3004.
90. [90] Jansson, J.; Willing, B.; Lucio, M.; Fekete, A.; Dicksved, J.; Halfvarson, J.; Tysk, C.; Schmitt-Kopplin, P. Metabolomics reveals metabolic biomarkers of Crohn's disease. PLoS One, 2009, 4 (7), e6386.
91. [91] Bertini, I.; Calabro, A.; De Carli, V.; Luchinat, C.; Nepi, S.; Porfirio, B.; Renzi, D.; Saccenti, E.; Tenori, L. The metabonomic signature of celiac disease. J. Proteome Res., 2009, 8 (1), 170-7.
92. [92] Ji, Y.; Hebbring, S.; Zhu, H.; Jenkins, G.D.; Biernacka, J.; Snyder, K.; Drews, M.; Fiehn, O.; Zeng, Z.; Schaid, D.; Mrazek, D.A.; Kaddurah-Daouk, R.; Weinshilboum, R.M. Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics. Clin. Pharmacol. Ther., 2011, 89 (1), 97-104.
93. [93] Abo, R.; Hebbring, S.; Ji, Y.; Zhu, H.; Zeng, Z.B.; Batzler, A.; Jenkins, G.D.; Biernacka, J.; Snyder, K.; Drews, M.; Fiehn, O.; Fridley, B.; Schaid, D.; Kamatani, N.; Nakamura, Y.; Kubo, M.; Mushiroda, T.; Kaddurah-Daouk, R.; Mrazek, D.A.; Weinshilboum, R.M. Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation: selective serotonin reuptake inhibitor response pharmacogenomics. Pharmaco. Genom., 2012, 22 (4), 247-53.
94. [94] Yerges-Armstrong, L.M.; Ellero-Simatos, S.; Georgiades, A.; Zhu, H.; Lewis, J.P.; Horenstein, R.B.; Beitelshees, A.L.; Dane, A.; Reijmers, T.; Hankemeier, T.; Fiehn, O.; Shuldiner, A.R.; Kaddurah-Daouk, R., Purine pathway implicated in mechanism of resistance to aspirin therapy: pharmacometabolomics-informed pharmacogenomics. Clin. Pharmacol. Ther., 2013, 94 (4), 525-32