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Metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind" - specifically, the study of their small-molecule metabolite profiles.[1] The metabolome represents the collection of all metabolites in a biological organism, which are the end products of its gene expression. Thus, while mRNA gene expression data and proteomic analyses do not tell the whole story of what might be happening in a cell, metabolic profiling can give an instantaneous snapshot of the physiology of that cell. One of the challenges of systems biology is to integrate proteomic, transcriptomic, and metabolomic information to give a more complete picture of living organisms.




Metabolome refers to the complete set of small-molecule metabolites (such as metabolic intermediates, hormones and other signalling molecules, and secondary metabolites) to be found within a biological sample, such as a single organism.[2] The word was coined in analogy with transcriptomics and proteomics; like the transcriptome and the proteome, the metabolome is dynamic, changing from second to second. Although the metabolome can be defined readily enough, it is not currently possible to analyse the entire range of metabolites by a single analytical method. In January 2007, scientists at the University of Alberta and the University of Calgary completed the first draft of the human metabolome. They catalogued approximately 2500 metabolites, 1200 drugs and 3500 food components that can be found in the human body, as reported in the literature.[3] This information, available at the Human Metabolome Database (HMDB is available at: and based on analysis of information available in the current the scientific literature, is far from complete. In contrast, much more is known about the metabolomes of other organisms, especially of plants, where over 50,000 metabolites have been characterized from the plant kingdom, and many thousands of metabolites have been identified and/or characterized from single plants. Metabolomics in today's world carries on its shoulders the huge responsibility of providing a detailed description of metabolic pathways and their workings, whether they be in humans, animals, or the plants we both eat and admire.


Metabolites are the intermediates and products of metabolism. The term metabolite is usually restricted to small molecules. A primary metabolite is directly involved in the normal growth, development, and reproduction. A secondary metabolite is not directly involved in those processes, but usually has important ecological function. Examples include antibiotics and pigments.

The metabolome forms a large network of metabolic reactions, where outputs from one enzymatic chemical reaction are inputs to other chemical reactions. Such systems have been described as hypercycles.



Metabonomics is defined as "the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification". This approach originated at Imperial College London and has been used in toxicology, disease diagnosis and a number of other fields.[4]

There has been some disagreement over the exact differences between 'metabolomics' and 'metabonomics', although the term 'metabolomics' is more commonly used. The difference between the two terms is not related to choice of analytical platform: although metabonomics is more associated with NMR spectroscopy and metabolomics with mass spectrometry-based techniques, this is simply because of usages amongst different groups that have popularized the different terms. While there is still no absolute agreement, there is a growing consensus that the difference resides in the fact that 'metabolomics' places a greater emphasis on comprehensive metabolic profiling, regardless of species investigated, while 'metabonomics' is used to describe multiple (but not necessarily comprehensive) metabolic changes caused by a biological perturbation. The term 'metabonomics' is rarely used to describe research not directly related to human disease or nutrition. In practice, within the field of human disease research there is still a large degree of overlap in the way both terms are used, and they are often in effect synonymous.



Metabolic biochemists have arguably been 'doing metabolomics' for decades. The chromatographic separation techniques that made the initial detection of metabolites possible were developed in the late 1960's, which marks the technical origin of the field. [5]

The development of metabolomics began in 1970 by Arthur Robinson investigating Pauling's ideas as to whether biological variability could be explained on the basis of far wider ranges of nutritional requirements than what was generally recognized. In analyzing the "messy" chromatographic patterns of urine from vitamin B6-loaded subjects, Robinson realized that the patterns of hundreds or thousands of chemical constituents in urine contained much useful information.

Although it was not called metabolomics, the first paper devoted to this topic was titled, “Quantitative Analysis of Urine Vapor and Breath by Gas-Liquid Partition Chromatography”, by Robinson and Pauling in 1971 and published in the Proceedings of the National Academy of Sciences. Since then, Robinson has had nineteen more papers published on the quantitative patterns of metabolites in body fluids (see below). Robinson and colleagues have identified several diseases, conditions, and physiological age based on this data. It was his expectation that body fluid analysis can be optimized to make a low cost, information-rich, medically-relevant means of measuring metabolically-driven changes in functional state, even when the chemical constituents are all in the “normal range”.

The core idea that Robinson conceived is that information-rich data that reflects the functional status of a complex biological system resides in the quantitative and qualitative pattern of metabolites in body fluids. Twenty years later, others began to realize the value of this approach, and interest in this has mushroomed. The name metabolomics was coined in the 1990s (the first paper using the word metabolome is Oliver, S. G., Winson, M. K., Kell, D. B. & Baganz, F. (1998). Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373-378), and in 2004 a society was formed to promote its study. Many of the bioanalytical methods used for metabolomics have been adapted (or in some cases simply adopted) from existing biochemical techniques. What sets metabolomics apart from strictly analytical chemistry-based analyses is the scope of the work. Three characteristics common to metabolomic research are:

  1. Effort is made to profile metabolites with as little bias as is possible towards a specific metabolite or group of metabolites. Nevertheless, all profiling approaches require extraction of metabolites from biological tissues, and will therefore be biased due to solvent properties. This holds true, but is reduced, even if multiple solvent systems are used.
  2. Large numbers of metabolites are profiled at the same time, instead of being analyzed one by one.
  3. Relationships between the metabolites are characterized, currently mostly by multivariate methods, although other data analysis tools are being developed.

The field of metabolomics exploded in the early 2000s, particularly as a result of efforts by researchers from the Max Planck Institute for Plant Physiology, in Golm, Germany, under the direction of Prof. Dr. Lothar Willmitzer. Their research, while still more appropriately called 'metabolite profiling' because they analyzed only hundreds of compounds and not the entire complement of the plant cell, set the framework for metabolomics-scale investigations. Their review articles promoting the field and its potential applications to agriculture, medicine, and other fields in the biological sciences, definitely had a strong stimulatory effect on the field as a whole.

On January 23rd, 2007, the Human Metabolome Project, led by Dr. David Wishart of the University of Alberta, Canada, completed the first draft of the human metabolome, consisting of a database of approximately 2500 metabolites, 1200 drugs and 3500 food components. Similar projects have been underway in several plant species, most notably Medicago truncatula and Arabidopsis thaliana for several years.


Analytical technologies

There are four important issues to be addressed for metabolite analysis: 1. Efficient and unbiased extraction of metabolites from biological tissues. 2. Separation of the analytes, usually by chromatography. Electrophoresis, particularly capillary electrophoresis, is also used. 3. Detection of the analytes, following separation by chromatographic or other methods. 4. Identification and quantification of the analytes.


Separation methods

  • Gas chromatography, especially when interfaced with mass spectrometry (GC-MS), is one of the most widely used and powerful methods. It offers very high chromatographic resolution, but requires chemical derivatization for many biomolecules: only volatile chemicals can be analysed without derivatization. (Some modern instruments allow '2D' chromatography, using a short polar column after the main analytical column, which increases the resolution still further.) Some large and polar metabolites cannot be analysed by GC.
  • High performance liquid chromatography (HPLC). Compared to GC, HPLC has lower chromatographic resolution, but it does have the advantage that a much wider range of analytes can potentially be measured.
  • Capillary electrophoresis (CE). So far, there are only a relatively small number of publications on use of CE for metabolite profiling. This will no doubt change, as there are a number of advantages of CE: it has a higher theoretical separation efficiency than HPLC, and is suitable for use with a wider range of metabolite classes than is GC. As for all electrophoretic techniques, it is most appropriate for charged analytes.

Detection methods

  • Mass spectrometry (MS) is used to identify and to quantify metabolites after separation by GC, HPLC, or CE. GC-MS is the most 'natural' combination of the three, and was the first to be developed. In addition, mass spectral fingerprint libraries exist or can be developed that allow identification of a metabolite according to its fragmentation pattern. MS is both sensitive (although, particularly for HPLC-MS, sensitivity is more of an issue as it is affected by the charge on the metabolite, and can be subject to ion suppression artifacts) and can be very specific. There are also a number of studies which use MS as a stand-alone technology: the sample is infused directly into the mass spectrometer with no prior separation, and the MS serves to both separate and to detect metabolites.
  • Nuclear magnetic resonance (NMR) spectroscopy. NMR is the only detection technique which does not rely on separation of the analytes, and the sample can thus be recovered for further analyses. All kinds of small molecule metabolites can be measured simultaneously - in this sense, NMR is close to being a universal detector. Practically, however, it is relatively insensitive compared to mass spectrometry-based techniques; additionally, NMR spectra can be very difficult to interpret for complex mixtures.
  • Other techniques. MS and NMR are by far the two leading technologies for metabolomics. Other methods of detection that have been used include electrochemical detection (coupled to HPLC) and radiolabel (when combined with thin-layer chromatography).


Key applications

  • Toxicity assessment/toxicology. Metabolic profiling (especially of urine or blood plasma samples) can be used to detect the physiological changes caused by toxic insult of a chemical (or mixture of chemicals). In many cases, the observed changes can be related to specific syndromes, e.g. a specific lesion in liver or kidney. This is of particular relevance to pharmaceutical companies wanting to test the toxicity of potential drug candidates: if a compound can be eliminated before it reaches clinical trials on the grounds of adverse toxicity, it saves the enormous expense of the trials.
  • Functional genomics. Metabolomics can be an excellent tool for determining the phenotype caused by a genetic manipulation, such as gene deletion or insertion. Sometimes this can be a sufficient goal in itself -- for instance, to detect any phenotypic changes in a genetically-modified plant intended for human or animal consumption. More exciting is the prospect of predicting the function of unknown genes by comparison with the metabolic perturbations caused by deletion/insertion of known genes. Such advances are most likely to come from model organisms such as Saccharomyces cerevisiae and Arabidopsis thaliana. The Cravatt laboratory at The Scripps Research Institute has recently applied this technology to mammalian systems, identifying the N-acyltaurines as previously uncharacterized endogenous substrates for the enzyme fatty acid amide hydrolase (FAAH) and the monoalkylglycerol ethers as endogenous substrates for the uncharacterized hydrolase KIAA1363. [6][7]
  • Nutrigenomics is a generalised term which links genomics, transcriptomics, proteomics and metabolomics to human nutrition. In general a metabolome in a given body fluid is influenced by endogenous factors such as age, sex, body composition and genetics as well as underlying pathologies. The large bowel microflora are also a very significant potential confounder of metabolic profiles and could be classified as either an endogenous or exogenous factor. The main exogenous factors are diet and drugs. Diet can then be broken down to nutrients and non- nutrients. Metabolomics is one means to determine a biological endpoint, or metabolic fingerprint, which reflects the balance of all these forces on an individual's metabolism.[8]


See also


Sources and notes

  1. ^ B. Daviss, "Growing pains for metabolomics," The Scientist, 19[8]:25-28, April 25, 2005
  2. ^ First use of the term "metabolome" in the literature — Oliver, S. G., Winson, M. K., Kell, D. B. & Baganz, F. (1998). "Systematic functional analysis of the yeast genome". Trends Biotechnol. 16 (10): 373–378. doi:10.1016/S0167-7799(98)01214-1. PMID 9744112. 
  3. ^
    • First book on metabolomics — Harrigan, G. G. & Goodacre, R. (eds) (2003). RMetabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis. Kluwer Academic Publishers (Boston). ISBN 1-4020-7370-4. 
    • Fiehn, O., Kloska, S. & Altmann, T. (2001). "Integrated studies on plant biology using multiparallel techniques". Curr. Opin. Biotechnol. 12 (1): 82–86. doi:10.1016/S0958-1669(00)00165-8. PMID 11167078. .
    • Fiehn, O. (2001). "Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks". Comp. Funct. Genomics 2 (3): 155–168. doi:10.1002/cfg.82.  Publisher abstract link
    • Weckwerth, W. Metabolomics in systems biology. Annu. Rev. Plant Biol. 54, 669–689 (2003).
    • Goodacre, R., Vaidyanathan, S., Dunn, W. B., Harrigan, G. G. & Kell, D. B. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol. 22, 245–252 (2004).
    • Nicholson, J. K., Holmes, E., Lindon, J. C. & Wilson, I. D. The challenges of modeling mammalian biocomplexity. Nature Biotechnol. 22, 1268–1274 (2004). Stresses the role of intestinal microorganisms in contributing to the human metabolome.
    • van der Greef, J., Stroobant, P. & van der Heijden, R. The role of analytical sciences in medical systems biology. Curr. Opin. Chem. Biol. 8, 559–565 (2004).
    • Kell, D. B. Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol. 7, 296–307 (2004).
    • Dunn, W.B. and Ellis, D.I. (2005) Metabolomics: current analytical platforms and methodologies. Trends in Analytical Chemistry 24(4), 285-294.
    • Ellis, D.I. and Goodacre, R. (2006) Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy, Analyst, 131, 875-885. DOI:10.1039/b602376m
    • Wishart, D.S., Tzur, D., Knox, C., Eisner, R., Guo, A.C., Young, N., Cheng, D., Jewell, K., Arndt, D., Sawhney, S., Fung, C., Nikolai, L., Lewis, M., Coutouly, M.-A., Forsythe, I., Tang, P., Shrivastava, S., Jeroncic, K., Stothard, P., Amegbey, G., Block, D., Hau, D.D., Wagner, J., Miniaci, J., Clements, M., Gebremedhin, M., Guo, N., Zhang, Y., Duggan, G.E., Macinnis, G.D., Weljie, A.M., Dowlatabadi, R., Bamforth, F., Clive, D., Greiner, R., Li, L., Marrie, T., Sykes, B.D., Vogel, H.J., Querengesser, L. (2007) HMDB: The Human Metabolome Database. Nucleic Acids Research 35(Database issue), pp D521-6. PMID: 17202168

  4. ^

    • Nicholson, J. K., Lindon, J. C., Holmes, E. (1999). “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 11: p.1181-1189.
    • 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. Vol. 9, No. 1. p.1-/31.
    • Brindle, J.T., , Antti, H., Holmes, E., Tranter, G., Nicholson, J.K., Bethell, H.W.L., Clarke, S., Schofield, P.M., McKilligan, E., Mosedale, D.E., & Grainger, D.J. (2002) Rapid and non-invasive diagnosis of the presence and severity of coronary heart disease using 1H NMR -based metabonomics. Nature Medicine. 8 (12): p.1439-1444.
    • Bollard, M.E., Stanley, E.G., Lindon, J.C., Nicholson, J.K., & Holmes, E. (2005) NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition. NMR Biomed. 2005: p.18:143–162.

  5. ^ Preti, George. "Metabolomics comes of age?" The Scientist, 19[11]:8, June 6, 2005.
  6. ^ Saghatelian A. et al. "Assignment of endogenous substrates to enzymes by global metabolite profiling." Biochemistry. 2004 Nov 16;43(45):14332-9. PMID 15533037
  7. ^ Chiang K. P. et al. "An enzyme that regulates ether lipid signaling pathways in cancer annotated by multidimensional profiling." Chem. Biol. 2006 Oct;13(10):1041-50. PMID 17052608
  8. ^ The European Nutrigenomics Network
  • Pauling, L.C., Robinson, A.B., Teranishi, R., and Cary, P., Quantitative Analysis of Urine Vapor and Breath by Gas-Liquid Partition Chromatography, Proc. Natl. Acad. Sci. (1971) 68, 2374-2376.
  • Teranishi, R. and Mon, T.R. and Robinson, A.B., Cary, P., and Pauling, L.C., Gas Chromatography of Volatiles from Breath and Urine, Analytical Chemistry 44 (1972) pp 18-20.
  • Robinson, A.B. and Pauling, L.C., Quantitative Chromatographic Analysis in Orthomolecular Medicine, Orthomolecular Psychiatry, 1973, ed. D. Hawkins, pp 35-53.
  • Robinson, A.B., Partridge, D., Turner, M., Teranishi, R., and Pauling, L.C., An Apparatus for the Quantitative Analysis of Volatile Compounds in Urine, J. Chromatography (1973) 85, pp 19-29.
  • Matsumoto, K.E., Partridge, D.H., Robinson, A.B., and Pauling, L.C. and Flath, R. A., Mon, T.R., and Teranishi, R., The Identification of Volatile Compounds in Human Urine, J. Chromatography 85 (1973) pp 31-34.
  • Robinson, A.B., Cary, P., Dore, B., Keaveny, I., Brenneman, L., Turner, M. and Pauling, L., Orthomolecular Diagnosis of Mental Retardation and Diurnal Variation in Normal Subjects by Low-Resolution Gas-Liquid Chromatography of Urine, Int. Research Comm. Sys. (1973) 70, p 3.
  • Robinson, A.B. and Westall, F.C., The Use of Urinary Amine Measurement for Orthomolecular Diagnosis of Multiple Sclerosis, J. Orth. Psych. (1974) 3, pp 1-10.
  • Robinson, A.B., Westall, F.C., and Ellison, G.W., Multiple Sclerosis: Urinary Amine Measurement for Orthomolecular Diagnosis, Life Sciences (1974) 14 pp 1747-1753.
  • Robinson, A.B. and Pauling, L.C., Techniques of Orthomolecular Diagnosis, Clinical Chemistry (1974) 20 pp 961-965.
  • Rosenberg, R.N., Robinson, A.B., and Partridge, D., Urine Vapor Pattern for Olivopontocerebellar Degeneration (1975) Clinical Biochemistry 8, pp 365-368.
  • Dirren, H., Robinson, A.B., and Pauling, L.C., Sex-Related Patterns in the profiles of Human Urinary Amino Acids, Clinical Chemistry (1975) 21, pp 1970-1975.
  • Robinson, A.B., Dirren, H., and Sheets, A. and Miquel, J. and Lundgren, P.R., Quantitative Aging Pattern in Mouse Urine Vapor as Measured by Gas-Liquid Chromatography, Experimental Gerontology (1976) 11, pp 11-16.
  • Tomita M., Nishioka T. (2005), Metabolomics: The Frontier of Systems Biology, Springer, ISBN 4-431-25121-9
  • Wolfram Weckwerth W. (2006), Metabolomics: Methods And Protocols (Methods in Molecular Biology), Humana Press, ISBN 1-58829-561-3
  • Dunn, W.B. and Ellis, D.I. (2005), Metabolomics: current analytical platforms and methodologies. Trends in Analytical Chemistry 24(4), 285-294.
  • Ellis, D.I. and Goodacre, R. (2006) Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy. Analyst 131, 875-885. DOI:10.1039/b602376m
  • Wishart, D.S., Tzur, D., Knox, C., Eisner, R., Guo, A.C., Young, N., Cheng, D., Jewell, K., Arndt, D., Sawhney, S., Fung, C., Nikolai, L., Lewis, M., Coutouly, M.-A., Forsythe, I., Tang, P., Shrivastava, S., Jeroncic, K., Stothard, P., Amegbey, G., Block, D., Hau, D.D., Wagner, J., Miniaci, J., Clements, M., Gebremedhin, M., Guo, N., Zhang, Y., Duggan, G.E., Macinnis, G.D., Weljie, A.M., Dowlatabadi, R., Bamforth, F., Clive, D., Greiner, R., Li, L., Marrie, T., Sykes, B.D., Vogel, H.J., Querengesser, L. (2007) HMDB: The Human Metabolome Database. Nucleic Acids Research 35(Database issue), pp D521-6.

PMID: 17202168

  • Claudino, W.M., Quatronne, A., Pestrim, M., Biganzoli, L., Bertini and Di Leo, A.(2007) Metabolomics: Available Results, Current Research Projects in Breast Cancer, and Future *Applications. J Clin Oncol May 14; [Epub ahead of print].

  • Ellis, D.I., Dunn, W.B., Griffin, J.L., Allwood, J.W. and Goodacre, R. (2007) Metabolic Fingerprinting as a Diagnostic Tool. Pharmacogenomics, 8(9), 1243-1266.
  • Gomase VS, Changbhale SS, Patil SA, Kale KV. Metabolomics. Curr. Drug Metab. 2008 Jan; 9(1): 89-98. PMID: 18220576


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