Metabolomics - A Rapidly Evolving Field

Metabolomics is the study of metabolism at the global, or -omics, level. This discipline involves the study of the metabolome, the total repertoire of small molecules present in cells, tissues, organs, and biological fluids and involves the collection of quantitative data on a broad series of metabolites in an attempt to gain an overall understanding of metabolism and/or metabolic dynamics associated with conditions of interest, including drug exposure. The overall size of the metabolome remains a subject of debate and depends on the definition of exactly what components should be included and on the analytical platform used to perform the assay. Numbers that range from a few thousand to tens of thousands of small molecules have been proposed. As outlined earlier, metabolomic information complements data obtained from other fields that comprise the new biology—genomics, transcriptomics, and proteomics – adding a final piece to a systems approach to the study of drug action, individual variation in drug response, and disease pathophysiology. Ideally, metabolomics will ultimately be able to contribute a detailed map of the regulation of metabolic pathways and, therefore, of the interaction of proteins encoded by the genome with environmental factors, environmental factors that include drug exposure. Therefore, the metabolome represents a state function for an individual at a particular point in time or after exposure to a specific environmental stimulus, e.g., a specific drug or xenobiotic. The “microbiome” also contributes to the metabolome, and interactions between the two remain to be defined and understood.

Metabolomics utilizes instruments that can simultaneously quantitate thousands of small molecules in a biological sample. This analytical capability must then be joined to sophisticated mathematical tools that can identify a molecular signal among millions of pieces of data.

Metabolomics graphicMetabolomics process: A typical metabolomics study is depicted schematically in this figure to the right. Samples of interest (e.g., plasma, cerebral spinal fluid, or tissue biopsies) are collected. Small molecules are extracted from the sample and are analyzed using techniques that separate and quantitate the molecules of interest. Those analytical techniques include, as outlined earlier, liquid and gas chromatography, mass and nuclear magnetic resonance (NMR) spectroscopy, and liquid chromatography with electrochemical detection. Combinations of these techniques can also be used to augment separations and/or to expand the analyte information collected. These datasets must then be collected and curated, a process that can take significant time. After curation, the data are analyzed by one or more software packages designed for use with large datasets. A database is then generated for the same patient before and after drug therapy or for diseased patients and control subjects. These databases include levels of detectable metabolites and the identity or a description of the properties of the metabolites, i.e., oxidation reduction potential, mass/charge ratio, etc. Software tools can then be used to (a) identify disease or drug exposure signatures, (b) predict class (e.g., pre- or postdrug exposure, disease or control), (c) identify unrecognized groups in the data (e.g., drug response subgroups), (d) identify interactions among variables, and (e) map variables to known biochemical pathways. A critical metabolomics concept is that a biomarker that predicts disease or helps to monitor drug therapy is most often not a single molecule, but rather a pattern of several molecules. That concept determines the need for quantitative precision and the careful avoidance of artifacts during this type of research. Although this can be a difficult analytical task in the early stages of metabolite pattern detection, if the relevant metabolomic species can be defined and identified, appropriate techniques can then be used to develop rapid targeted assays suitable for more routine application, both in the research laboratory and/or in a clinical setting.

The choice of metabolomic analytical instrumentation and software is often goal-specific because each type of instrument has, as discussed subsequently, specific strengths and limitations. For example, liquid chromatography (LC) followed by coulometric array detection is ideal for mapping neurotransmitter pathways. Gas chromatography (GC) in conjunction with mass spectrometry (MS) is often used in the analysis of lipid subsets (lipidomics). Liquid chromatography together with mass spectroscopy (LC-MS) is often used to obtain the largest possible biochemical profile, and NMR has been used successfully to perform toxicology studies. In a similar fashion, different software packages include specific tools designed to address questions distinct to each study.

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