A BRIEF OVERVIEW OF METABOLOMICS: WHAT IT MEANS, HOW IT IS MEASURED, AND ITS UTILIZATION

What is metabolomics?

Metabolomics is a relatively new member to the ‘-omics’ family of systems biology technologies (Bino et.al. 2004). The term ‘metabolome’ was coined in 1998 and was used to describe the metabolite complement of living tissues (Oliver et.al. 1998). Despite its relative youth (in comparison to genomics and proteomics), metabolomics as a field of study is now firmly established as a functional genetics approach to understanding the molecular complexity of life (Wagner et.al. 2003). Today, it even has a journal with its namesake, Metabolomics, dedicated to scribing its tribulations and advances (available at www.springeronline.com). This paper will briefly describe some important aspects of the innovative field of metabolomics, namely, some working definitions for metabolomics in a scientific setting, measurement methods of the metabolome, and some current applications of metabolomic research.

What is metabolomics and why is it an important addition to the study of biological systems? Metabolomics is the comprehensive, qualitative, and quantitative study of all the small molecules in an organism (Oliver et.al. 1998). By small, I mean molecules that are less than or equal to about 1500 daltons (Da). Recall that a dalton (or, equivalently, an atomic mass unit, u) has a mass approximately equal to one hydrogen atom (Lodish et.al. 2004). The study of metabolomics therefore excludes polymers of amino acids and sugars. The focus is instead on intermediary metabolites used to form the macromolecular structures and other small molecules participating in important metabolic functions and fulfilling critical roles such as signaling molecules or secondary metabolites.

Because metabolomics encompasses such an extensive network of biochemical interactions, many of which have not yet been fully characterized in terms of participating reactants, different approaches to metabolome analyses have arisen. Depending on the goal of one’s experiment, the approach used will differ. The three principal approaches for the analysis of the metabolome are metabolite profiling, metabolic fingerprinting, and metabonomics (Hall 2006).

Metabolite profiling is an approach that aims to identify and quantify metabolites, but does so on a biased scale due to methodological limitations and differences in analytical platforms (Dunn and Ellis 2005, Hall 2006). Much of the bias in this technique is introduced when extracting the sample from the organism or tissue of interest, and is due to differential affinities for extraction solvents. Despite the introduced bias, this technique is, at present, the closest one can get to a true and complete visualization of the metabolome (Bino et.al. 2004).

Another approach in metabolomic technology is metabolic fingerprinting. This high-throughput approach is normally utilized in tissue comparison or discrimination analysis, and so is simpler and coarser in its technique (sample preparation, separation, and detection) in comparison to metabolic profiling.

Metabonomics is yet another aspect of metabolomics, which focuses on the metabolic response of organisms to pathophysiological stimuli or genetic modification. This approach is generally restricted to microbiological and other non-botanical studies.

Though these three different approaches to metabolomic analysis all generate copious amounts of useful information, they do not compensate for the ultimate goal: the comprehensive analysis of the metabolome. However, they do enable scientists to obtain a snap-shot of the metabolic state of an organism at a given time. When used in combination with physiological assays and/or genomics, proteomics, or transcriptomics, this technology could lead to a deeper understanding about an organism’s organization and function.

How is the metabolome measured?

Achieving the ability to visualize the metabolome, of any organism, is currently still a dream because the metabolome is vastly complex. Just imagine all the metabolic pathways that need to be mapped, every intermediate to be accounted for, the amounts of chemical compounds to be determined, and the method or mechanism by which they flow through their various cycles on their way to biomass accumulation, excretion, exudation, etc. This picture is enormous and is difficult to envision in its entirety. The complexity of the whole picture increases particularly when considering the cooperative nature of the different levels of organization of biological systems and the effect that the environment can produce on the metabolism of an organism.

The method by which metabolic complexity is sorted is usually through hyphenated analysis platforms such as chromatography-mass spectrometry. In fact, gas chromatography-mass spectrometry (GC/MS) is probably the most popular analytical platform used in metabolic analyses and will be used as the paradigm method of analysis in this paper (Dixon et.al. 2006). Biological extracts to be analyzed via GC/MS must first be chemically derivatized with agents that make the chemical constituents present in the sample more volatile (Seger and Sturm 2006, Wagner et.al. 2003). Once the sample is injected into the gas chromatograph, there is two-fold separation of sample components based on differences in volatility and size of molecules. Larger molecules take a longer time to move through the column than do small molecules and amongst molecules of similar size, different molecular species display different volatilities.

Upon outflow from the chromatograph column, individual volatilized chemicals are funneled into the mass spectrometer where identification and quantification of individual chemical compounds are determined. Data obtained by the GC/MS is deconvoluted by special software to produce two graphs corresponding to the chromatogram and mass spectra of the sample. Graphs from different samples can be overlaid to aid in comparison and detection. Individual chemicals can be identified based on the retention time (the time it takes for the compound to become vaporized and to flow through the chromatographic column) and the mass spectrum.

One problem that persists in metabolomic analyses is the dearth of comprehensive identification of metabolic components, particularly in pathways outside of primary carbon metabolism (Wagner et.al. 2003). Therefore, there is movement in the scientific community towards a cooperative approach for creating open-access libraries of compounds based on standardized analytical procedures (Bino et.al. 2004, Dixon et.al. 2006). Several libraries already exist and are immensely helpful in chromatogram analysis, though they are far from being comprehensive.

Mass spectroscopy systems coupled with nuclear magnetic resonance systems are ideally the best platforms for identification of unknown chemical compounds, but are prohibitively expensive for most scientific laboratories (Dixon et.al. 2006).

What is metabolomic technology used for?

Metabolomics is said to be a critically important technique in a systems biology framework. Systems biology is a term that envelopes the various ‘-omics’ technologies, of which metabolomics is just one. Studies in systems biology aspire to integrate the structures and functions of various levels of encoded information of the organism (Kitano 2002). If one considers that many proteins, encoded by genes, are in fact enzymes that catalyze chemical reactions, then by extension, metabolomics can be seen as another level of information encoded by the organism but more subject to manipulation by its environment (Seger and Sturm 2006). In a sense, metabolomics represents the meeting of phylogenetics and phenetics.

Currently, metabolomics research is being applied to myriad different uses, from plant science to medicine. In the plant science community, for instance, metabolomic research is utilized in studies relating to biomass accumulation, stress resistance, and secondary metabolite production (Hall 2006, Meyer et.al. 2007). Biomass accumulation and resistance to certain environmental stressors are important in plant science as plants are sought as a potential source of alternative energy production such as biofuel (Meyer et.al. 2007). Breeding resistant or genetically modified plants can be a long and arduous task, and metabolite analyses promise to provide early indication for increased utility in the field via the presence of metabolic biomarkers (Dixon et.al. 2006).

Additionally, plants are an exceedingly rich source of nutrients and secondary metabolites useful in nutritional and medicinal research. Recently, there has been resurgence in the interest for plant-based pharmaceutical compounds (Hall 2006). A conservative estimate places the number of plant metabolic compounds at 200,000, which provides for an extensive searching ground for new and improved medicinal and nutritional products (Bino et.al. 2004, Dunn and Ellis 2005). On a related note, comparative metabolomics in humans may provide for enhanced diagnostic power and individualized treatment for illness and disease (Bino et.al. 2004).

In conclusion, metabolomics represents the interface between genetic pre-disposition and environmental influence. It is because of this unique position in the systems biology hierarchy that metabolomics could prove invaluable in our quest to understand the function of genes, to be able to control and/or design novel organisms that may benefit our health or lifestyles, and to understand more fully the molecular physiology of ourselves and that of other organisms.

References

Bino RJ, Hall RD, Fiehn O, Kopka J, Saito K, Draper J, Nikolau BJ, Mendes P, Roessner-Tunali U, Beale MH, Trethewey RN, Lange BM, Wurtele ES, Sumner LW. 2004. Potential of metabolomics as a functional genomics tool. Trends in Plant Science 9: 418-425.

Dixon RA, Gang DR, Charlton AJ, Fiehn O, Kuiper HA, Reynolds TL, Tjeerdema RS, Jeffery EH, German JB, Ridley WP, Seiber JN. 2006. Applications of metabolomics in agriculture. Journal of Agriculture and Food Chemistry 54: 8984-8994.

Dunn WB, Ellis DI. 2005. Metabolomics: current analytical platforms and methodologies. Trends in Analytical Chemistry 24: 285-294.

Hall RD. 2006. Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytologist 169: 453-468.

Kitano H. 2002. Systems biology: a brief overview. Science 295: 1662-1664.

Lodish H, Berk A, Matsudaira P, Kaiser C, Krieger M, Scott MP, Zipursky SL, Darnell J. 2004. Molecular Cell Biology, 5th ed. WH Freeman and Company, New York, pg. 61.

Oliver SG, Winson MK, Kell DB, Baganz F. 1998. Systematic functional analysis of the yeast genome. Trends in Biotechnology 16: 373-378.

Seger C, Sturm S. 2006. Analytical aspects of plant metabolite profiling platforms: current standings and future aims. Journal of Proteome Research 6: 480-497.

Wagner C, Sefkow M, Kopka J. 2003. Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles. Phytochemistry 62: 887-900.