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Introduction |
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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.
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Links |
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Here are some metabolome-related links:
CHEMICAL STRUCTURES AND CHEMICAL GENOMICS
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Publications |
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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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Last update: 14 November 2005
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