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Metabolomics, a New Promising Technology for Toxicological Research

  • Kim, Kyu-Bong (National Institute of Toxicological Research, Korea Food and Drug Administration) ;
  • Lee, Byung-Mu (Division of Toxicology, School of Pharmacy, Sungkyunkwan University)
  • Published : 2009.06.01

Abstract

Metabolomics which deals with the biological metabolite profile produced in the body and its relation to disease state is a relatively recent research area for drug discovery and biological sciences including toxicology and pharmacology. Metabolomics, based on analytical method and multivariate analysis, has been considered a promising technology because of its advantage over other toxicogenomic and toxicoproteomic approaches. The application of metabolomics includes the development of biomarkers associated with the pathogenesis of various diseases, alternative toxicity tests, high-throughput screening (HTS), and risk assessment, allowing the simultaneous acquisition of multiple biochemical parameters in biological samples. The metabolic profile of urine, in particular, often shows changes in response to exposure to xenobiotics or disease-induced stress, because of the biological system's attempt to maintain homeostasis. In this review, we focus on the most recent advances and applications of metabolomics in toxicological research.

Keywords

References

  1. Allen, J., Davey, H.M., Broadhurst, D., Heald, J.K., Rowland, J.J., Oliver, S.G. and Kell, D.B. (2003). High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol., 21, 692-696 https://doi.org/10.1038/nbt823
  2. Bathen, T.F., Engan, T., Krane, J. and Axelson, D. (2000). Analysis and classification of proton NMR spectra of lipoprotein fractions from healthy volunteers and patients with cancer or CHD. Anticancer Res., 20, 2393-2408
  3. Beckwith-Hall, B.M., Nicholson, J.K., Nicholls, A.W., Foxall, P.J., Lindon, J.C., Connor, S.C., Abdi, M., Connelly, J. and Holmes, E. (1998). Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chem. Res. Toxicol., 11, 260-272 https://doi.org/10.1021/tx9700679
  4. Bino, R.J., Hall, R.D., Fiehn, O., Kopka, J., Saito, K., Draper, J., Nikolau, B.J., Mendes, P., Roessner-Tunali, U., Beale, M.H., Trethewey, R.N., Lange, B.M., Wurtele, E.S. and Sumner, L.W. (2004). Potential of metabolomics as a functional genomics tool. Trends Plant Sci., 9, 418-425 https://doi.org/10.1016/j.tplants.2004.07.004
  5. Bollard, M.E., Keun, H.C., Beckonert, O., Ebbels, T.M., Antti, H., Nicholls, A.W., Shockcor, J.P., Cantor, G.H., Stevens, G., Lindon, J.C., Holmes, E. and Nicholson, J.K. (2005). Comparative metabonomics of differential hydrazine toxicity in the rat and mouse. Toxicol. Appl. Pharmacol., 204, 135-151 https://doi.org/10.1016/j.taap.2004.06.031
  6. Brindle, J.T., Antti, H., Holmes, E., Tranter, G., Nicholson, J.K., Bethell, H.W., Clarke, S., Schofield, P.M., McKilligin, E., Mosedale, D.E. and Grainger, D.J. (2002). Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat. Med., 8, 1439-1444 https://doi.org/10.1038/nm802
  7. Brown, S.C., Kruppa, G. and Dasseux, J.L. (2005). Metabolomics applications of FT-ICR mass spectrometry. Mass Spectrom. Rev., 24, 223-231 https://doi.org/10.1002/mas.20011
  8. Buchholz, A., Takors, R. and Wandrey, C. (2001). Quantification of intracellular metabolites in Escherichia coli K12 using liquid chromatographic-electrospray ionization tandem mass spectrometric techniques. Anal. Biochem., 295, 129-137 https://doi.org/10.1006/abio.2001.5183
  9. Bundy, J.G., Willey, T.L., Castell, R.S., Ellar, D.J. and Brindle, K.M. (2005). Discrimination of pathogenic clinical isolates and laboratory strains of Bacillus cereus by NMR-based metabolomic profiling. FEMS Microbiol. Lett., 242, 127-136 https://doi.org/10.1016/j.femsle.2004.10.048
  10. Celia, M.H. (2002). The "metabonome" provides real-world information about drug toxicity, gene function. Chem. Eng. News, 80, 66-70
  11. Choi, M.H., Yoo, Y.S. and Chung, B.C. (2001). Biochemical roles of testosterone and epitestosterone to 5 alpha-reductase as indicators of male-pattern baldness. J. Invest. Dermatol., 116, 57-61 https://doi.org/10.1046/j.1523-1747.2001.00188.x
  12. 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. and Nicholson, J.K. (2006). Pharmacometabonomic phenotyping and personalized drug treatment. Nature, 440, 1073-1077 https://doi.org/10.1038/nature04648
  13. Coen, M., O'Sullivan, M., Bubb, W.A., Kuchel, P.W. and Sorrell, T. (2005). Proton nuclear magnetic resonance-based metabonomics for rapid diagnosis of meningitis and ventriculitis. Clin. Infect. Dis., 41, 1582-1590 https://doi.org/10.1086/497836
  14. Constantinou, M.A., Theocharis, S.E. and Mikros, E. (2007). Application of metabonomics on an experimental model of fibrosis and cirrhosis induced by thioacetamide in rats. Toxicol. Appl. Pharmacol., 218, 11-19 https://doi.org/10.1016/j.taap.2006.10.007
  15. Craig, A., Sidaway, J., Holmes, E., Orton, T., Jackson, D., Rowlinson, R., Nickson, J., Tonge, R., Wilson, I. and Nicholson, J. (2006). Systems toxicology: integrated genomic, proteomic and metabonomic analysis of methapyrilene induced hepatotoxicity in the rat. J. Proteome Res., 5, 1586-1601 https://doi.org/10.1021/pr0503376
  16. Denkert, C., Budczies, J., Weichert, W., Wohlgemuth, G., Scholz, M., Kind, T., Niesporek, S., Noske, A., Buckendahl, A., Dietel, M. and Fiehn, O. (2008). Metabolite profiling of human colon carcinoma--deregulation of TCA cycle and amino acid turnover. Mol. Cancer., 7, 72 https://doi.org/10.1186/1476-4598-7-72
  17. Dunn, W.B., Bailey, N.J.C. and Johnson, H.E. (2005). Measuring the metabolome: current analytical technologies. Analyst, 130, 606-625 https://doi.org/10.1039/b418288j
  18. Dunn, W.B. and Ellis, D.I. (2005). Metabolomics: current analytical platforms and methodologies. Trac-trends In Analytical Chemistry, 24, 285-294 https://doi.org/10.1016/j.trac.2004.11.021
  19. Ebbels, T.M., Keun, H.C., Beckonert, O.P., Bollard, M.E., Lindon, J.C., Holmes, E. and Nicholson, J.K. (2007). Prediction and classification of drug toxicity using probabilistic modeling of temporal metabolic data: the consortium on metabonomic toxicology screening approach. J. Proteome Res., 6, 4407-4422 https://doi.org/10.1021/pr0703021
  20. Exarchou, V., Krucker, M., van Beek, T.A., Vervoort, J., Gerothanassis, I.P. and Albert, K. (2005). LC-NMR coupling technology: recent advancements and applications in natural products analysis. Magn. Reson. Chem., 43, 681-687 https://doi.org/10.1002/mrc.1632
  21. Fan, X., Bai, J. and Shen, P. (2005). Diagnosis of breast cancer using HPLC metabonomics fingerprints coupled with computational methods. Conf. Proc. IEEE Eng. Med. Biol. Soc., 6, 6081-6084
  22. Fiehn, O. (2002). Metabolomics--the link between genotypes and phenotypes. Plant Mol. Biol., 48, 155-171 https://doi.org/10.1023/A:1013713905833
  23. Fiehn, O., Kopka, J., Dormann, P., Altmann, T., Trethewey, R. N. and Willmitzer, L. (2000). Metabolite profiling for plant functional genomics. Nat. Biotechnol., 18, 1157-1161 https://doi.org/10.1038/81137
  24. Gidley, M., Wahlqvist, M., Okada, A., Samman, S. and Sullivan, D. (2004). Naturally functional foods-challenges and opportunities. Proc. Nutri, Soc. Australia, 13, 531
  25. Gieger, C., Geistlinger, L., Altmaier, E., Hrabe de Angelis, M., Kronenberg, F., Meitinger, T., Mewes, H.W., Wichmann, H.E., Weinberger, K.M., Adamski, J., Illig, T. and Suhre, K. (2008). Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet., 4, e1000282 https://doi.org/10.1371/journal.pgen.1000282
  26. Goodacre, R. (2005). Making sense of the metabolome using evolutionary computation: seeing the wood with the trees. J. Exp. Bot., 56, 245-254 https://doi.org/10.1093/jxb/eri043
  27. Griffin, J.L. (2003). Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. Curr. Opin. Chem. Biol., 7, 648-654 https://doi.org/10.1016/j.cbpa.2003.08.008
  28. Holmes, E. and Antti, H. (2002). Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra. Analyst, 127, 1549-1557 https://doi.org/10.1039/b208254n
  29. Holmes, E., Nicholls, A.W., Lindon, J.C., Ramos, S., Spraul, M., Neidig, P., Connor, S.C., Connelly, J., Damment, S.J., Haselden, J. and Nicholson, J.K. (1998). Development of a model for classification of toxin-induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition. NMR Biomed., 11, 235-244 https://doi.org/10.1002/(SICI)1099-1492(199806/08)11:4/5<235::AID-NBM507>3.0.CO;2-V
  30. Hopfield, J.J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A., 79, 2554-2558 https://doi.org/10.1073/pnas.79.8.2554
  31. Hrmova, M. and Fincher, G.B. (2009). Functional genomics and structural biology in the definition of gene function. Methods Mol. Biol., 513, 199-227 https://doi.org/10.1007/978-1-59745-427-8_11
  32. Jonsson, P., Gullberg, J., Nordstrom, A., Kusano, M., Kowalczyk, M., Sjostrom, M. and Moritz, T. (2004). A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS. Anal. Chem., 76, 1738-1745 https://doi.org/10.1021/ac0352427
  33. Kell, D.B. (2004). Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol., 7, 296-307 https://doi.org/10.1016/j.mib.2004.04.012
  34. Kenney, B. and Shockcor, J.P. (2003). Complementary NMR and LC-MS technologies for metabonomic studies. PharmaGenomics, 56-63
  35. Khandelwal, P., Beyer, C.E., Lin, Q., Schechter, L.E. and Bach, A.C., 2nd. (2004). Studying rat brain neurochemistry using nanoprobe NMR spectroscopy: a metabonomics approach. Anal. Chem., 76, 4123-4127 https://doi.org/10.1021/ac049812u
  36. Kim, K.B., Chung, M.W., Um, S.Y., Oh, J.S., Kim, S.H., Na, M.A., Oh, H.Y., Cho, W.S. and Choi, K.H. (2008). Metabolomics and biomarker discovery: NMR spectral data of urine and hepatotoxicity by carbon tetrachloride, acetaminophen, and D-galactosamine in rats. Metabolomics, 4, 377-392 https://doi.org/10.1007/s11306-008-0131-5
  37. Kim, K.B., Kim, S.H., Um, S.Y., Chung, M.W., Oh, J.S., Jung, S.C., Kim, T.S., Moon, H.J., Han, S.Y., Oh, H.Y., Lee, B.M. and Choi, K.H. (2009). Metabolomics approach to risk assessment: methoxyclor exposure to rats. J. Toxicol. Environ. Health A (in print)
  38. Lee, S.H., Woo, H.M., Jung, B.H., Lee, J., Kwon, O.S., Pyo, H.S., Choi, M.H. and Chung, B.C. (2007). Metabolomic approach to evaluate the toxicological effects of nonylphenol with rat urine. Anal. Chem., 79, 6102-6110 https://doi.org/10.1021/ac070237e
  39. Lee, S.H., Yang, Y.J., Kim, K.M. and Chung, B.C. (2003). Altered urinary profiles of polyamines and endogenous steroids in patients with benign cervical disease and cervical cancer. Cancer Lett., 201, 121-131 https://doi.org/10.1016/S0304-3835(03)00014-4
  40. Lenz, E.M., Bright, J., Knight, R., Westwood, F.R., Davies, D., Major, H. and Wilson, I.D. 2005. Metabonomics with 1HNMR spectroscopy and liquid chromatography-mass spectrometry applied to the investigation of metabolic changes caused by gentamicin-induced nephrotoxicity in the rat. Biomarkers, 10, 173-187 https://doi.org/10.1080/13547500500094034
  41. Lenz, E.M., Bright, J., Knight, R., Westwood, F.R., Davies, D., Major, H. and Wilson, I.D. 2005. Metabonomics with 1HNMR spectroscopy and liquid chromatography-mass spectrometry applied to the investigation of metabolic changes caused by gentamicin-induced nephrotoxicity in the rat. Biomarkers, 10, 173-187 https://doi.org/10.1080/13547500500094034
  42. Lenz, E.M., Bright, J., Wilson, I.D., Hughes, A., Morrisson, J., Lindberg, H. and Lockton, A. (2004). Metabonomics, dietary influences and cultural differences: a 1H NMRbased study of urine samples obtained from healthy British and Swedish subjects. J. Pharm. Biomed. Anal., 36, 841-849 https://doi.org/10.1016/j.jpba.2004.08.002
  43. Leo, G.C., Caldwell, G.W., Crooke, J., Malatynska, E., Cotto, C., Hastings, B., Scowcroft, J., Hall, J., Browne, K. and Hageman, W. (2005). The application of nuclear magnetic resonance-based metabonomics to the dominant-submissive rat behavioral model. Anal. Biochem., 339, 174-178 https://doi.org/10.1016/j.ab.2005.01.029
  44. Lindon, J.C., Holmes, E., Bollard, M.E., Stanley, E.G. and Nicholson, J.K. (2004). Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers, 9, 1-31 https://doi.org/10.1080/13547500410001668379
  45. 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. and Thomas, C. (2003). Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl. Pharmacol., 187, 137-146 https://doi.org/10.1016/S0041-008X(02)00079-0
  46. Mao, J., Jain, A.K., Center, I.B.M.A.R. and San Jose, C. (1995). Artificial neural networks for feature extraction and multivariatedata projection. IEEE Trans. Neural Networks, 6, 296-317 https://doi.org/10.1109/72.363467
  47. Nealson, K.H. and Conrad, P.G. (1999). Life: past, present and future. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 354, 1923-1939 https://doi.org/10.1098/rstb.1999.0532
  48. Nicholson, J.K. and Wilson, I.D. (1989). High resolution proton magnetic resonance spectroscopy of biological fluids. Prog. NMR Spectrosc., 21, 449-501 https://doi.org/10.1016/0079-6565(89)80008-1
  49. Nicholson, J.K., Lindon, J.C. and 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 https://doi.org/10.1080/004982599238047
  50. Nicholson, J.K., Timbrell, J.A. and Sadler, P.J. (1985). Proton NMR spectra of urine as indicators of renal damage. Mercury-induced nephrotoxicity in rats. Mol. Pharmacol., 27, 644-651
  51. Nordstrom, A., Want, E., Northen, T., Lehtio, J. and Siuzdak, G. (2008). Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics. Anal. Chem., 80, 421-429 https://doi.org/10.1021/ac701982e
  52. Papassotiropoulos, A., Wollmer, M.A., Tsolaki, M., Brunner, F., Molyva, D., Lutjohann, D., Nitsch, R.M. and Hock, C. (2005). A cluster of cholesterol-related genes confers susceptibility for Alzheimer's disease. J. Clin. Psychiatry, 66, 940-947 https://doi.org/10.4088/JCP.v66n0720
  53. Park, J.C., Hong, Y.S., Kim, Y.J., Yang, J.Y., Kim, E.Y., Kwack, S.J., Ryu do, H., Hwang, G.S. and Lee, B.M. (2009). A metabonomic study on the biochemical effects of doxorubicin in rats using (1)H-NMR spectroscopy. J. Toxicol. Environ. Health A, 72, 374-384 https://doi.org/10.1080/15287390802647195
  54. Pitt, J.J., Eggington, M. and Kahler, S.G. (2002). Comprehensive screening of urine samples for inborn errors of metabolism by electrospray tandem mass spectrometry. Clin. Chem., 48, 1970-1980
  55. Plumb, R.S., Stumpf, C.L., Gorenstein, M.V., Castro-Perez, J.M., Dear, G.J., Anthony, M., Sweatman, B.C., Connor, S.C. and Haselden, J.N. (2002). Metabonomics: the use of electrospray mass spectrometry coupled to reversedphase liquid chromatography shows potential for the screening of rat urine in drug development. Rapid Commun. Mass Spectrom., 16, 1991-1996 https://doi.org/10.1002/rcm.813
  56. Price, K.E., Vandaveer, S.S., Lunte, C.E. and Larive, C.K. (2005). Tissue targeted metabonomics: metabolic profiling by microdialysis sampling and microcoil NMR. J. Pharm. Biomed. Anal., 38, 904-909 https://doi.org/10.1016/j.jpba.2005.02.034
  57. Quinones, M.P. and Kaddurah-Daouk, R. (2009). Metabolomics tools for identifying biomarkers for neuropsychiatric diseases. Neurobiol. Dis. (Epub ahead of print) doi:10.1016/j.nbd.2009.02.019
  58. Raamsdonk, L.M., Teusink, B., Broadhurst, D., Zhang, N., Hayes, A., Walsh, M.C., Berden, J.A., Brindle, K.M., Kell, D.B., Rowland, J.J., Westerhoff, H.V., van Dam, K. and Oliver, S.G. (2001). A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol., 19, 45-50 https://doi.org/10.1038/83496
  59. Raman, R., Raguram, S., Venkataraman, G., Paulson, J.C. and Sasisekharan, R. (2005). Glycomics: an integrated systems approach to structure-function relationships of glycans. Nat. Methods, 2, 817-824 https://doi.org/10.1038/nmeth807
  60. Rampitsch, C. and Bykova, N.V. (2009). Methods for functional proteomic analyses. Methods Mol. Biol., 513, 93-110 https://doi.org/10.1007/978-1-59745-427-8_6
  61. Reo, N.V. (2002). NMR-based metabolomics. Drug Chem. Toxicol., 25, 375-382 https://doi.org/10.1081/DCT-120014789
  62. Ripley, B.D. (1996). Pattern recognition and neural networks. $1^{st}$ Ed. Cambridge university press, UK
  63. Roessner, U., Willmitzer, L. and Fernie, A.R. (2001). High-resolution metabolic phenotyping of genetically and environmentally diverse potato tuber systems. Identification of phenocopies. Plant Physiol., 127, 749-764 https://doi.org/10.1104/pp.010316
  64. Saito, K. and Matsuda, F. (2008). Metabolomics for Functional Genomics, Systems Biology, and Biotechnology. Annu. Rev. Plant. Biol. (Epub ahead of print)
  65. Schoonen, W.G., Kloks, C.P., Ploemen, J.P., Horbach, G.J., Smit, M.J., Zandberg, P., Mellema, J.R., Zuylen, C.T., Tas, A.C., van Nesselrooij, J.H. and Vogels, J.T. (2007). Sensitivity of (1)H NMR analysis of rat urine in relation to toxicometabonomics. Part I: dose-dependent toxic effects of bromobenzene and paracetamol. Toxicol. Sci., 98, 271-285 https://doi.org/10.1093/toxsci/kfm076
  66. Stylianou, I.M., Affourtit, J.P., Shockley, K.R., Wilpan, R.Y., Abdi, F.A., Bhardwaj, S., Rollins, J., Churchill, G.A. and Paigen, B. (2008). Applying gene expression, proteomics and single-nucleotide polymorphism analysis for complex trait gene identification. Genetics, 178, 1795-1805 https://doi.org/10.1534/genetics.107.081216
  67. Testa, B. and Kramer, S.D. 2006. The biochemistry of drug metabolism--an introduction: part 1. Principles and overview. Chem. Biodivers, 3, 1053-1101 https://doi.org/10.1002/cbdv.200690111
  68. Tiziani, S., Lopes, V. and Gunther, U.L. (2009). Early stage diagnosis of oral cancer using 1H NMR-based metabolomics. Neoplasia, 11, 269-276 https://doi.org/10.1593/neo.81396
  69. Um, S.Y., Chung, M.W., Kim, K.B., Kim, S.H., Oh, J.S., Oh, H.Y., Lee, H.J. and Choi, K.H. (2009). Pattern recognition analysis for the prediction of adverse effects by NSAIDs using 1H-NMR based metabolomics in rats. Anal. Chem. (in print)
  70. van Doorn, M., Vogels, J., Tas, A., van Hoogdalem, E.J., Burggraaf, J., Cohen, A. and van der Greef, J. (2007). Evaluation of metabolite profiles as biomarkers for the pharmacological effects of thiazolidinediones in Type 2 diabetes mellitus patients and healthy volunteers. Br. J. Clin. Pharmacol., 63, 562-574 https://doi.org/10.1111/j.1365-2125.2006.02816.x
  71. Wang, C., Yang, J. and Nie, J. (2009). Plasma phospholipid metabolic profiling and biomarkers of rats following radiation exposure based on liquid chromatography-mass spectrometry technique. Biomed. Chromatogr. (Epub ahead of print) 10.1002/bmc.1226
  72. Waters, N.J., Holmes, E., Williams, A., Waterfield, C.J., Farrant, R.D. and Nicholson, J.K. (2001). NMR and pattern recognition studies on the time-related metabolic effects of alpha-naphthylisothiocyanate on liver, urine, and plasma in the rat: an integrative metabonomic approach. Chem. Res. Toxicol., 14, 1401-1412 https://doi.org/10.1021/tx010067f
  73. Waters, N.J., Waterfield, C.J., Farrant, R.D., Holmes, E. and Nicholson, J.K. (2006). Integrated metabonomic analysis of bromobenzene-induced hepatotoxicity: novel induction of 5-oxoprolinosis. J. Proteome Res., 5, 1448-1459 https://doi.org/10.1021/pr060024q
  74. Watkins, S.M. and German, J.B. (2002). Metabolomics and biochemical profiling in drug discovery and development. Curr. Opin. Mol. Ther., 4, 224-228
  75. Weckwerth, W. and Morgenthal, K. (2005). Metabolomics: from pattern recognition to biological interpretation. Drug Discov. Today, 10, 1551-1558 https://doi.org/10.1016/S1359-6446(05)03609-3
  76. Wei, L., Liao, P., Wu, H., Li, X., Pei, F., Li, W. and Wu, Y. (2009). Metabolic profiling studies on the toxicological effects of realgar in rats by (1)H NMR spectroscopy. Toxicol. Appl. Pharmacol., 234, 314-325 https://doi.org/10.1016/j.taap.2008.11.010
  77. Wilson, I.D., Plumb, R., Granger, J., Major, H., Williams, R. and Lenz, E.M. (2005). HPLC-MS-based methods for the study of metabonomics. J. Chromatogr. B. Analyt. Technol. Biomed. Life Sci., 817, 67-76 https://doi.org/10.1016/j.jchromb.2004.07.045
  78. Wu, L., van Winden, W.A., van Gulik, W.M. and Heijnen, J.J. (2005). Application of metabolome data in functional genomics: a conceptual strategy. Metab. Eng., 7, 302-310 https://doi.org/10.1016/j.ymben.2005.05.003
  79. Yang, J., Xu, G., Hong, Q., Liebich, H.M., Lutz, K., Schmulling, R.M. and Wahl, H.G. (2004). Discrimination of Type 2 diabetic patients from healthy controls by using metabonomics method based on their serum fatty acid profiles. J. Chromatogr. B. Analyt Technol. Biomed. Life. Sci., 813, 53-58 https://doi.org/10.1016/j.jchromb.2004.09.023
  80. Yang, J., Xu, G., Zheng, Y., Kong, H., Pang, T., Lv, S. and Yang, Q. (2004). Diagnosis of liver cancer using HPLCbased metabonomics avoiding false-positive result from hepatitis and hepatocirrhosis diseases. J. Chromatogr. B. Analyt Technol. Biomed. Life. Sci., 813, 59-65 https://doi.org/10.1016/j.jchromb.2004.09.032
  81. Yang, Y., Adelstein, S.J. and Kassis, A.I. (2009). Target discovery from data mining approaches. Drug Discov. Today, 14, 147-154 https://doi.org/10.1016/j.drudis.2008.12.005
  82. Zhang, J., McCombie, G., Guenat, C. and Knochenmuss, R. (2005). FT-ICR mass spectrometry in the drug discovery process. Drug Discov. Today, 10, 635-642 https://doi.org/10.1016/S1359-6446(05)03438-0
  83. Zhu, J., Wiener, M.C., Zhang, C., Fridman, A., Minch, E., Lum, P.Y., Sachs, J.R. and Schadt, E.E. (2007). Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations. PLoS Comput. Biol., 3, e69 https://doi.org/10.1371/journal.pcbi.0030069

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