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Scientific
Publications - Work Done by Microbiology Reader
Jess Allen, Hazel M. Davey, David Broadhurst, Jim K. Heald, Jem J. Rowland, Stephen G.Oliver and Douglas B. Kell, Metabolic footprinting: a high-throughput, high-information approach to cellular characterisation and functional genomics, Report, 2002, University of Wales, Aberystwyth ABSTRACT A number of technologies have been developed to aid in
elucidating the function of novel genes discovered by systematic genome
sequencing. While transcriptome and proteome studies currently dominate
large-scale functional analysis strategies, the metabolome is ‘downstream’
of these. The effects of genetic or physiological changes should thereby be
amplified and the metabolome, in consequence, is much closer to the phenotype of
the organism. In a previous paper 1, we presented a strategy (termed ‘FANCY’)
that exploited metabolic fingerprinting to reveal the phenotype of silent
mutations of yeast genes. While useful, FANCY is difficult to scale up as a
high-throughput screening technique. Here, we present a radical alternative that
has the required throughput (2 min per sample). This new approach, which we call
‘metabolic footprinting’, focuses not on the measurement of intracellular
metabolites (a time-consuming procedure often beset by technical difficulties)
but on the direct, mass-spectrometric monitoring of extracellular metabolites
present in spent culture medium. Metabolic footprinting may be used to
distinguish between different physiological states of wild-type yeast, and
between yeast single-gene deletion mutants from even nominally closely related
areas of metabolism. By using appropriate clustering and machine learning
techniques, the latter based on Genetic Programming, we show that footprinting
represents a highly effective method to classify ‘unknown’ mutants according
to their genetic defect.
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