Metabolomics

Introduction

Whilst there is much current interest in the genome-wide analysis of cells at the level of transcription (to define the 'transcriptome') and translation (to define the 'proteome'), the third level of analysis, that of the 'metabolome', has been curiously unexplored to date. The term 'metabolome' refers to the entire complement of all the small molecular weight metabolites inside a cell suspension (or other sample) of interest. It is likely that measurement of the metabolome in different physiological states will in fact be much more discriminating for the purposes of functional genomics. Why should this be so?

As proven in the summation theorem of Metabolic Control Analysis (MCA), changes in the concentrations of individual enzymes tend to have little effect on particular metabolic fluxes (nor, indeed, on the phenotype under most laboratory conditions). However, in part because of the so-called connectivities of MCA, changes in individual enzyme concentrations can and do have substantial effects on metabolite concentrations, even when the changes in flux are negligible.

We have long pioneered the rapid analysis of metabolites at the whole-cell level, using methods such as Pyrolysis mass spectrometry, Fourier-Transfrom Infrared Spectrometry, Raman spectrometry, GC-MS, and most recently, LC-Electrospray and cap-LC-tandem-electrospray mass spectrometries. In combination with our established chemometrics methods, including artificial neural networks and genetic programming, these methods have been shown, in many publications, to be highly capable of discriminating closely related samples. Current work is directed to the development and exploitation of these and related methods for the purposes of functional genomics.

Links

Here are some metabolome-related links:

• Armet
• Biochemical pathways (ExPASy) – very useful on-line version of the classic maps
• Binding database with binding and inhibition constants
• The Biopathways consortium – developing formalisms for biological pathway analysis
• Brenda – 'the comprehensive enzyme system'
• COMPANIES –  Beyond Genomics – Lipomics – Metabolon – Metabometrix – metanomics – Nutriogenomics – Paradigm Genetics – Phenomenome – Surromed – Target Discovery –
• Ecocyc and metacyc – encyclopedia of E. coli genes and metabolism
• Enzyme nomenclature database at ExPASy.
• The E-cell project – integrating genomics and metabolism
• GeneCards – for human genes, proteins and diseases
• Human metabolome project (Genome Canada)
• Husermet metabolomics LINK scheme
• IntEnz enzyme nomenclature –
• IUBMB metabolic mini-maps
• KEGG – Kyoto encyclopedia of genes and genomes – and the LIGAND enzyme database
• Main metabolic pathways on Internet – what it says, plus a nice (and searchable) downloadable version
• Metabolic Control Analysis (MCA), Gepasi, and an on-line review of MCA in the postgenomic era
• Metabolomics.net portal – Metabolite Profiling Forum –
• Metabolomics papers
• Metabolomics portal at Scripps including metabolite databases
• Metabolic pathways portal at NAR
• Metabolomics Society and Metabolomics 2007 meeting
• Met-Ro project
• MPI for Molecular Plant Physiology – Plant Metabolic profiling
• Minnesota Biocatalysis/Biodegradation Database – pathways primarily for xenobiotic, chemical compounds
• NuGO nutritional metabolomics
• Parasitology metabolomics (Aberystwyth)
• PathBinderH – useful literature tool for metabolomics
• PathDB – [Dead link: This package is no nonger being maintained] a metabolic pathway database which will conceivably link with Gepasi, a metabolic pathway and bio/chemical kinetics simulator written by Pedro Mendes in Aberystwyth; Pedro has now moved to the VBI and since to Manchester.
• Pathguide pathway databases portal
• Pathway commons
• Platform Plant Metabolomics
• Protein Function and Biochemical Pathways (PFBP) at the EBI
• Proteomic Pathway Project (P3)
• The metabolic part of Soybase.
• Reactome – 'a knowledgebase of biological processes'
• SMRS (Standard metabolite reporting structure)
• T1dBase
• WIT – a server linking genes and metabolism, and including metabolic pathways based on the Enzymes and Metabolic Pathways database; See also DBSolve
• Comprehensive Yeast Genome Database, including metabolic pathways
• IBIS – King's portal – Mavens –

CHEMICAL STRUCTURES AND CHEMICAL GENOMICS

• Biocyc and BOCD – BMRB – ChEBI – CML – Drugbank – HMDB Canada – InChI – KEGG – Ligand Depot – METLIN – NIST species – PubChem – TTD –

Publications

Here are some of our recent metabolome publications:

• Underwood, B.R., Broadhurst, D., Dunn, W.B., Ellis, D.I., Michell, A.W., Vacher, C., Mosedale, D.E., Kell, D.B., Barker, R., Grainger, D.J. & Rubinsztein, D.C. (2006) Huntington disease patients and transgenic mice have similar pro-catabolic serum metabolite profiles. Brain 129(4), 877-8
• Kell, D.B. (2006) Metabolomics, modelling and machine learning in systems biology – towards an understanding of the languages of cells. The FEBS Journal 273, 873–894.
• Kenny, L.C., Dunn, W.B., Ellis, D.I., Myers, J., Baker, P.N., The GOPEC Consortium & Kell, D.B. (2005). Novel biomarkers for pre-eclampsia detected using metabolomics and machine learning. Metabolomics, 1(3), 227-234. DOI: 10.1007/s11306-005-0003-1.
• Catchpole, G. S., Beckmann, M., Enot, D. P., Mondhe, M., Zywicki, B., Taylor, J., Hardy, N., Smith, A., King, R. D., Kell, D. B., Fiehn, O. & Draper, J. (2005). Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci U S A 102, 14458-62.
• Kell, D.B., Brown, M., Davey, H.M., Dunn, W.B., Spasic, I. & Oliver, S.G. (2005) Metabolic footprinting and systems biology: the medium is the message. Nat Rev Microbiol, 3, 557-565.
• Kell, D.B. (2005) Metabolomics, machine learning and modelling: towards an understanding of the language of cells. Biochem Soc Trans 33, 520-524.
• Brown, M., Dunn, W.B., Ellis, D.I., Goodacre, R., Handl, J., Knowles, J.D., O'Hagan, S., Spasic, I. & Kell, D.B. (2005) A metabolome pipeline: from concept to data to knowledge. Metabolomics 1(1), 39-51.
• O’Hagan, S., Dunn, W.B., Brown, M., Knowles, J.D. & Kell, D.B. (2005) Closed-loop, multiobjective optimization of analytical instrumentation: gas chromatography/time-of-flight mass spectrometry of the metabolomes of human serum and of yeast fermentations. Anal. Chem 77, 290-303.
• Jenkins, H., Hardy, N., Beckmann, M., Draper, J., Smith, A.R., Taylor, J., Fiehn, O., Goodacre, R., Bino, R.J., Hall, R., Kopka, J., Lane, G.A., Lange, B.M., Liu, J.R., Mendes, P., Nikolau, B.J., Oliver, S.G., Paton, N.W., Rhee, S., Roessner-Tunali, U., Saito, K., Smedsgaard, J., Sumner, L.W., Wang, T., Walsh, S., Syrkin Wurtele, E. & Kell, D.B. (2004) A proposed framework for the description of plant metabolomics experiments and their results. Nature Biotechnol. 22(12), 1601-1606.
• Allen, J., Davey, H.M., Broadhurst, D., Rowland, J.J., Oliver, S.G. & Kell, D.B. (2004) Discrimination of modes of action of antifungal substances by use of metabolic footprinting. Applied and Environmental Microbiology 70(10), 6157–6165.
• Kell, D. B. (2004) Metabolomics and systems biology: making sense of the soup. Current Opinion in Microbiology 7(3), 296-307.
• Goodacre, R., Vaidyanathan, S., Dunn, W.B., Harrigan, G.G. & Kell, D.B. (2004) Metabolomics by numbers: acquiring and understanding global metabolite data. Trends. Biotechnol. 22, 245-252.
• Johnson, H.E., Broadhurst, D., Kell, D.B., Theodorou, M.K., Merry, R.J. & Griffith, G.W. (2004) High-Throughput Metabolic Fingerprinting of Legume Silage Fermentations via Fourier Transform Infrared Spectroscopy and Chemometrics. Appl. Env.Microbiol. 70, 1583–1592.
• Allen, J. K., Davey, H. M., Broadhurst, D., Heald, J. K., Rowland, J. J., Oliver, S. G. & Kell, D. B. (2003) High-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting. Nature Biotechnol. 21, 692-696.
• Goodacre, R. & Kell, D. B. (2003). Evolutionary computation for the interpretation of metabolome data. In Metabolic profiling: its role in biomarker discovery and gene function analysis (ed. G. G. Harrigan and R. Goodacre), pp. 239-256. Kluwer Academic Publishers, Boston.
• Kaderbhai, N. N., Broadhurst D. I., Ellis D. I., Goodacre, R. & Kell, D. B. (2003). Functional genomics via metabolic footprinting: Monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comp. Func. Genomics
• Allen, J. K., Davey, H. M., Broadhurst, D., Heald, J. K., Rowland, J. J., Oliver, S. G. & Kell, D. B. (2003). High-throughput characterisation of yeast mutants for functional genomics using metabolic footprinting. Nature Biotechnol. 21, 692-696.
• Goodacre, R., Vaidyanathan, S., Bianchi, G. & Kell, D. B. (2002). Metabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils. Analyst 127, 1457-1462. Abstract
• Kell, D.B. (2002) Metabolomics and machine learning: explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules. Mol. Biol. Rep. 29, 237-242. Full paper
• Kell, D.B., Darby, R.M. & Draper, J. (2001) Genomic computing: explanatory analysis of plant expression profiling data using machine learning. Plant Physiology 126, 943.951. Abstract – Full paper at Plant Physiology
• Raamsdonk, L. M., Teusink, B., Broadhurst, D., Zhang, N., Hayes, A., Walsh, M., Berden, J. A., Brindle, K. M., Kell, D. B., Rowland, J. J., Westerhoff, H. V., van Dam, K. & Oliver, S. G. (2001). A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnology 19, 45-50. Abstract
• Johnson, H.E., Gilbert, R.J., Winson, M.K., Goodacre, R., Smith, A.R., Rowland, J.J., Hall, M.A. & Kell, D.B. (2000) Explanatory analysis of the metabolome using genetic programming of simple, interpretable rules. Genetic Programming and Evolvable Machines 1, 243-258.
• Oliver, S.G., Winson, M.K., Kell, D.B. & Baganz, F. (1998) Systematic functional analysis of the yeast genome.Trends Biotechnol. 16, 373-378. Abstract

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