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

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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References

  1. Raamsdonk, L.M. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 (2001).

    Article  CAS  Google Scholar 

  2. Cramer, N.L. A representation for the adaptive generation of simple sequential programs. in Proceedings of the First International Conference on Genetic Algorithms and their Applications (ed. Grefenstette, J.J.) 183–187 (Lawrence Erlbaum, Mahwah, New Jersey, 1985).

    Google Scholar 

  3. Koza, J.R. Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, Massachusetts, 1992).

    Google Scholar 

  4. Banzhaf, W., Nordin, P., Keller, R.E. & Francone, F.D. Genetic Programming: An Introduction (Morgan Kaufmann, San Francisco, 1998).

    Book  Google Scholar 

  5. Langdon, W.B. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! (Kluwer, Boston, 1998).

  6. Kell, D.B., Darby, R.M. & Draper, J. Genomic computing: explanatory analysis of plant expression profiling data using machine learning. Plant Physiol. 126, 943–951 (2001).

    Article  CAS  Google Scholar 

  7. Kell, D.B. Genotype:phenotype mapping: genes as computer programs. Trends Genet. 18, 555–559 (2002).

    Article  CAS  Google Scholar 

  8. Langdon, W.B. & Poli, R. Foundations of Genetic Programming (Springer, Berlin, 2002).

    Book  Google Scholar 

  9. Fiehn, O. Metabolomics: the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002).

    Article  CAS  Google Scholar 

  10. Kell, D.B. & King, R.D. On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: the need for machine learning. Trends Biotechnol. 18, 93–98 (2000).

    Article  CAS  Google Scholar 

  11. Baganz, F., Hayes, A., Marren, D., Gardner, D.C.J. & Oliver, S.G. Suitability of replacement markers for functional analysis studies in Saccharomyces cerevisiae. Yeast 13, 1563–1573 (1997).

    Article  CAS  Google Scholar 

  12. Oliver, S.G., Winson, M.K., Kell, D.B. & Baganz, F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373–378 (1998).

    Article  CAS  Google Scholar 

  13. Duda, R.O., Hart, P.E. & Stork, D.E. Pattern Classification, edn. 2 (John Wiley, London, 2001).

    Google Scholar 

  14. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction (Springer, Berlin, 2001).

  15. Oliver, S.G. Proteomics: guilt-by-association goes global. Nature 403, 601–603 (2000).

    Article  CAS  Google Scholar 

  16. Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. Classification and Regression Trees (Wadsworth International, Belmont, California, 1984).

    Google Scholar 

  17. Quinlan, J.R. C4.5: Programs for Machine Learning (Morgan Kaufmann, San Mateo, California, 1993).

    Google Scholar 

  18. Alsberg, B.K., Goodacre, R., Rowland, J.J. & Kell, D.B. Classification of pyrolysis mass spectra by fuzzy multivariate rule induction—comparison with regression, K-nearest neighbour, neural and decision-tree methods. Anal. Chim. Acta 348, 389–407 (1997).

    Article  CAS  Google Scholar 

  19. Aranibar, N., Singh, B.K., Stockton, G.W. & Ott, K.-H. Automated mode-of-action detection by metabolic profiling. Biochem. Biophys. Res. Commun. 286, 150–155 (2001).

    Article  CAS  Google Scholar 

  20. Griffin, J.L. et al. Metabolic profiling of genetic disorders: a multitissue H-1 nuclear magnetic resonance spectroscopic and pattern recognition study into dystrophic tissue. Anal. Biochem. 293, 16–21 (2001).

    Article  CAS  Google Scholar 

  21. Goodacre, R., Vaidyanathan, S., Bianchi, G. & Kell, D.B. Metabolic profiling using direct infusion electrospray ionisation mass spectrometry for the characterisation of olive oils. Analyst 127, 1457–1462 (2002).

    Article  CAS  Google Scholar 

  22. Martens, H. & Næs, T. Multivariate Calibration (John Wiley, Chichester, UK, 1989).

    Google Scholar 

  23. Jolliffe, I.T. Principal Component Analysis (Springer, New York, USA, 1986).

    Book  Google Scholar 

  24. MacFie, H.J.H., Gutteridge, C.S. & Norris, J.R. Use of canonical variates in differentiation of bacteria by pyrolysis gas-liquid chromatography. J. Gen. Microbiol. 104, 67–74 (1978).

    Article  CAS  Google Scholar 

  25. Windig, W., Haverkamp, J. & Kistemaker, P.G. Interpretation of sets of pyrolysis mass spectra by discriminant analysis and graphical rotation. Anal. Chem. 55, 81–88 (1983).

    Article  CAS  Google Scholar 

  26. Manly, B.F.J. Multivariate Statistical Methods: A Primer (Chapman and Hall, London, UK, 1994).

    Google Scholar 

  27. Goodacre, R. et al. Rapid identification of urinary tract infection bacteria using hyperspectral, whole organism fingerprinting and artificial neural networks. Microbiology 144, 1157–1170 (1998).

    Article  CAS  Google Scholar 

  28. Kell, D.B. Defence against the flood: a solution to the data mining and predictive modelling challenges of today. Bioinformat. World 1, 16–18 (http://www.abergc.com/biwpp16-18_as_publ.pdf, 2002).

    Google Scholar 

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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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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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