In the post-genomic era, increasing our understanding of genotype-phenotypic correlation and its changes in diseases is of the highest priority in drug development. The phenotype of an organism consists of its physical/chemical attributes and its functional attributes. Metabolomics is a promising research tool for the phenotypic characterisation of an organism providing physical and functional assessment of a cellular metabolic network. Metabolomics has evolved from analytical biochemistry.
With advances in nuclear magnetic resonance spectroscopy and mass spectrometry, metabolomics provides comprehensive analyses that detect and measure a wide range of small molecules (metabolites) in bodily fluid or tissue extracts. With automation, metabolomics analysis has the potential of a high throughput screening tool for measuring the effects of drugs in cells or in whole organisms, including humans. There are two major forms of metabolomics: metabolite profiling, which includes ‘fingerprinting’, and metabolic profiling, which includes tracer-based metabolomics. In this review, techniques and concepts for each of these modalities is reviewed. Examples of the applications of metabolomics in the characterisation of phenotype, determining the mode of action of compounds and detection of drug toxicity are presented.
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.
The authors wish to thank their colleagues at the UCLA School of Medicine’s Stable Isotope Laboratory at LA BioMed, and at the SiDMAP Research Laboratories for their help with the tracer-based metabolomic experiments described in this paper. We especially thank Istvan Szegeti, MD at the SiDMAP Research Laboratories for his invaluable analytical insights, and Mr I. Szegeti of Gyor, Hungary, for his inspiration. Drs Maguire and Boros are employees of SiDMAP and own stock options in the company. The authors currently have a patent application under review for SiDMAP technologies.
Harrigan GG, Goodacre, R, editors. Metabolic profiling: its role in biomarker discovery and gene function analysis. Boston (PA): Kluwer Academic Publish- ers, 2003Google Scholar
Aardema MJ, MacGregor JT. Toxicology and genetic toxicology in the new era of “toxicogenomics”: impact of “-omics” technologies. Mutat Res 2002 Jan 29; 499 (1): 13–25PubMedCrossRefGoogle Scholar
Brown M, Dunn WB, Ellis DI, et al. A metabolome pipeline: from concept to data to knowledge. Metabolomics 2006; 1: 39–51CrossRefGoogle Scholar
Boros LG, Casacante M, Lee WN. Metabolic profiling of cell growth and death in cancer; applications in drug discovery. Drug Discovery Today 2002; 7: 364–72PubMedCrossRefGoogle Scholar
Lee WN. Mass isotopomer study of the non-oxidative pathways of the pentose cycle with [1,2-13C2] glucose. Am J Physiol 1998; 274: E843–51PubMedGoogle Scholar
Lee WNP. Characterizing phenotype with tracer based metabolomics. Metabolomics 2006; 2: 31–9CrossRefGoogle Scholar
Maguire G, Lee P, Manheim D, et al. SiDMAP: a metabolomics approach to assess the effects of drug candidates on the dynamic properties of biochemical pathways. Expert Opin Drug Disc 2006; 4: 351–9CrossRefGoogle Scholar
Lindon JC, Holmes E, Nicholson JK. Metabonomics techniques and applications to pharmaceutical research and development. Pharm Res 2006; 23: 1075–88PubMedCrossRefGoogle Scholar
Ellis DI, Goodacre R. Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy. Analyst 2006 Aug; 131 (8): 875–885. Epub 2006 Apr 25PubMedCrossRefGoogle Scholar
Hollywood K, Briston DR, Goodacre R. Metabolomics: current technologies and future trends. Proteomics 2006; 6: 4716–23PubMedCrossRefGoogle Scholar
Causton DR. A biologist’s advanced mathematics. London: Allen & Unwin, 1987Google Scholar
Manly BFJ. Multivariate statistical methods: a primer. London: Chapman and Hall, 1994Google Scholar
Wang Y, Holmes E, Tang H, et al. Experimental metabonomic model of dietary variation and stress interactions. J Proteome Res 2006 Jul; 5 (7): 1535–42PubMedCrossRefGoogle Scholar
Rossell S, van der Weijden CC, Lindenbergh A, et al. Unraveling the complexity of flux regulation: a new method demonstrated for nutrient starvation in Saccharomyces cerevisiae. Proc Natl Acad Sci USA 2006 Feb 14; 103 (7): 2166–71. Epub 2006 Feb 7PubMedCrossRefGoogle Scholar
Fell DA. Understanding the control of metabolism. London: Portland Press, 1996Google Scholar
Kell DB, Westerhoff HV. Metabolic control theory: its role in microbiology and biotechnology. FEMS Microbiol Rev 1986; 39: 305–20CrossRefGoogle Scholar
Bolling C, Fiehn O. Metabolite profiling of Chlamydomonas reinhardtii under nutrient deprivation. Plant Physiol 2005 Dec; 139 (4): 1995–2005. Epub 2005 Nov 23PubMedCrossRefGoogle Scholar
Marin S, Lee WN, Bassilian S, et al. Dynamic profiling of the glucose metabolic network in fasted rat hepatocytes using [1,2-13C2]glucose. Biochem J 2004 Jul 1; 381 (Pt 1): 287–94PubMedGoogle Scholar
Famili I, Palsson BO. Systemic metabolic reactions are obtained by singular value decomposition of genome-scale stoichiometric matrices. J Theor Biol 2003 Sep 7; 224 (1): 87–96PubMedCrossRefGoogle Scholar
Papin S, Cazeneuve C, Duquesnoy P, et al. The tumor necrosis factor alpha-dependent activation of the human Mediterranean fever (MEFV) promoter is mediated by a synergistic interaction between C/EBP beta and NF kappaB p65. J Biol Chem 2003 Dec 5; 278 (49): 48839–47. Epub 2003 Sep 26PubMedCrossRefGoogle Scholar
Reed JL, Palsson BO. Thirteen years of building constraint-based in silico models of Escherichia coli. J Bacteriol 2003 May; 185 (9): 2692–9PubMedCrossRefGoogle Scholar
Boren J, Lee WN, Bassilian S, et al. The stable isotope-based dynamic metabolic profile of butyrate-induced HT29 cell differentiation. J Biol Chem 2003; 278: 395–402CrossRefGoogle Scholar
Boros LG, Bassilian S, Lim S, et al. Genistein inhibits nonoxidative ribose synthesis in MIA pancreatic adenocarcinoma cells: a new mechanism of controlling tumor growth. Pancreas 2001 Jan; 22 (1): 1–7PubMedCrossRefGoogle Scholar
Boros LG, Serkova NJ, Cascante MS, et al. Use of metabolic pathway flux information in targeted cancer drug design. Drug Disc Today Ther Strat 2004; 4: 435–43CrossRefGoogle Scholar
Harrigan GG. Metabolomics: a systems contribution to pharmaceutical discovery and drug development. Drug Disc World 2006 Spring: 39–46Google Scholar
Michael LF. Targeting endocrine function of bile acids. IBC Fifth Annual Targeting Metabolic Syndrome; 2007 Feb 26–28; Boston (MA).Google Scholar
Harrigan GG, Colca J, Szalma S, et al. PNU-91325 increases fatty acid synthesis from glucose and mitochondrial long chain fatty acid degradation: a comparative tracer-based metabolomics study with rosiglitazone and pioglitazone in HepG2 cells. Metabolomics 2006; 2 (1): 21–9CrossRefGoogle Scholar
Raamsdonk LM, Teusink B, Broadhurst D, et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnol 2001 Jan; 19 (1): 45–50CrossRefGoogle Scholar