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High-throughput classification of yeast mutants for functional genomics using metabolic footprinting

Nature Biotechnology volume 21, pages 692–696 (2003)Cite this article

Abstract

Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes1. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming2,3,4,5,6,7,8, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect.

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Figure 1: Metabolic footprinting of Saccharomyces cerevisiae.
Figure 2: PCA plots of metabolic footprint data to illustrate the robustness of the method with respect to variations that may be expected.
Figure 3: Metabolic footprinting may be used to classify strains on the basis of the deletion they carry.

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Acknowledgements

This work was supported by a grant from the Biotechnology and Biological Sciences Research Council, UK, to D.B.K. and S.G.O., and by a grant from the Wellcome Trust to S.G.O. J.A. was the recipient of a BBSRC CASE studentship with Bayer CropScience (formerly Aventis CropScience). We thank John Pillmoor, Steve Dunn and Jane Dancer for their careful supervision, Bharat Rash and Nicola Burton of the Manchester laboratory for technical assistance and Roy Goodacre (Aberystwyth/UMIST) for useful discussions.

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  1. Present address (from 1 September 2003): Department of Chemistry, UMIST, Sackville St., P.O. Box 88, Manchester M60 1QD, UK.

Authors and Affiliations

  1. Institute of Biological Sciences, Cledwyn Building, University of Wales, Aberystwyth, SY23 3DD, Aberystwyth, UK

    Jess Allen, Hazel M Davey, David Broadhurst, Jim K Heald & Douglas B Kell

  2. Department of Computer Science, University of Wales, Aberystwyth, SY23 3DB, Aberystwyth, UK

    Jem J Rowland

  3. School of Biological Sciences, University of Manchester, 2.205 Stopford Building, Oxford Rd., Manchester, M13 9PT, UK

    Stephen G Oliver

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  1. Jess Allen
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Correspondence to Douglas B Kell.

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D.B.K. and J.J.R. are directors of Aber Genomic Computing, whose software was used for one of the experiments described in the article.

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Allen, J., Davey, H., Broadhurst, D. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat Biotechnol 21, 692–696 (2003). https://doi.org/10.1038/nbt823

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