Downstream of the genome, transcriptome, and proteome, the metabolome () may in fact be the most accurate indicator of cellular physiology (Idle and Gonzalez 2007
). The metabolome represents the complete set of small molecules found in biofluids (blood, plasma, serum, urine, sweat, saliva) and, unlike the genome, only a small fraction of the metabolome has been annotated. However, efforts as part of the Human Metabolome Database and the Human Serum Metabolome in Health and Disease initiatives have begun to systematically identify and describe metabolites found in various biofluids (Cottingham 2008
). Once thought to be a simple source (Pearson 2007
) of biological information (compared with the genome, transcriptome, and proteome), appreciation for the complexity and richness of the metabolome has grown. In 2007, the initial “draft” of the human metabolome containing 2,500 metabolites was reported (Wishart et al. 2007
); however, in just over 5 years, the Human Metabolome Database (HMDB) now contains nearly 8,000 metabolites. Further, given that discrete chemical exposures in humans are thought to be on the order of 2 to 3 million in a lifetime (Idle and Gonzalez 2007
), metabolomics will also be important for capturing information regarding exposure to xenobiotics. This is particularly relevant for human studies where metabolomics approaches are likely to capture not only endogenous but also xenobiotics and their metabolites (Johnson et al. 2012
; Patterson, Gonzalez, and Idle 2010
Glossary of commonly used terms.
While the precise definition of metabolomics varies throughout the literature, it can be simply defined as the global, unbiased measurement of small molecules found in biofluids, or extracts from cells, tissues, or organisms (Griffin and Nicholls 2006
). In more general terms, metabolomics can be described as the chemical fingerprints left behind by endogenous biological processes or the biological activity on chemicals derived from the diet and/or environment (Daviss 2005
). However defined, metabolomics has already provided unprecedented views of PPAR biology and function yielding important insights and validation of their roles in health and disease.
Metabolomics approaches have been described in great detail elsewhere (Dettmer, Aronov, and Hammock 2007
; Patterson et al. 2010
). Briefly, the process () involves extraction of metabolites from a biofluid (urine, serum, plasma), data acquisition using a variety of platforms including 1
H-NMR, UPLC-ESI-QTOFMS, or GC-MS, followed by peak alignment and normalization using a variety of commercial (Waters MarkerLynx, AB SCIEX MarkerView) and public (XCMS, MZMine) software tools (Smith et al. 2006
; Tautenhahn et al. 2012
; Eliasson et al. 2012
; Katajamaa, Miettinen, and Oresic 2006
; Pluskal et al. 2010
), and ultimately multivariate data analysis (MDA) to identify a metabolite or metabolites that can distinguish one sample group from the other. It is important to note that despite many technological advances in chromatographic separations (GC, LC) and detection platforms (1
H-NMR, MS), there does not yet exist a single platform that can capture all metabolites as say a microarray chip would for gene expression. Therefore, a combination of approaches (1
H-NMR, LC-MS, GC-MS) is necessary to increase coverage of the metabolome. Metabolite identification typically involves structural elucidation using tandem mass spectrometry of the metabolite compared with that from an authentic compound. The last highly recommended steps involve quantitation of the metabolite on platforms such as triplequadrupoles that afford high degrees of linear dynamic range.
Figure 1 An illustration of a typical metabolomics study workflow. Biofluids including but not limited to urine, serum, plasma, whole blood, and tissues extracts are analyzed by a variety of platforms including ultra performance liquid chromatography coupled with (more ...)