Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations

Abstract

A large proportion of the 6,000 genes present in the genome of Saccharomyces cerevisiae, and of those sequenced in other organisms, encode proteins of unknown function. Many of these genes are “silent,” that is, they show no overt phenotype, in terms of growth rate or other fluxes, when they are deleted from the genome. We demonstrate how the intracellular concentrations of metabolites can reveal phenotypes for proteins active in metabolic regulation. Quantification of the change of several metabolite concentrations relative to the concentration change of one selected metabolite can reveal the site of action, in the metabolic network, of a silent gene. In the same way, comprehensive analyses of metabolite concentrations in mutants, providing “metabolic snapshots,” can reveal functions when snapshots from strains deleted for unstudied genes are compared to those deleted for known genes. This approach to functional analysis, using comparative metabolomics, we call FANCY—an abbreviation for functional analysis by co-responses in yeast.

This is a preview of subscription content

Access options

Figure 1: Competition between FY23pfk27Δ and its wild-type parent.
Figure 2: Cluster analysis of NMR spectra from cell extracts.

References

  1. Hieter, P. & Boguski, M. Functional genomics: It's all how you read it. Science 278, 601 –602 (1997).

    CAS  Article  PubMed  Google Scholar 

  2. Brent R. Genomic biology. Cell 10, 169–183 (2000).

    Article  Google Scholar 

  3. Oliver, S.G. From DNA sequence to biological function. Nature 379 , 597–600 (1996).

    CAS  Article  PubMed  Google Scholar 

  4. Oliver, S. Guilt-by-association goes global. Nature 403, 601–603 (2000).

    CAS  Article  PubMed  Google Scholar 

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

    CAS  Article  PubMed  Google Scholar 

  6. Goffeau, A. et al. Life with 6000 genes. Science 274, 546, 563–7 (1996).

    CAS  Article  PubMed  Google Scholar 

  7. Teusink, B., Baganz, F., Westerhoff, H.V. & Oliver, S.G. Metabolic control analysis as a tool in the elucidation of the function of novel genes. Methods Microbiol. 26, 297– 336 (1998).

    CAS  Article  Google Scholar 

  8. Smith, V., Chou, K.N., Lashkari, D., Botstein, D. & Brown, P.O. Functional analysis of the genes of yeast chromosome V by genetic footprinting. Science 274, 2069–2074 (1996).

    CAS  Article  PubMed  Google Scholar 

  9. Baganz F. et al. Quantitative analysis of yeast gene function using competition experiments in continuous culture. Yeast 14, 1417–1427 (1998).

    CAS  Article  PubMed  Google Scholar 

  10. Giaever, G. et al. Genomic profiling of drug sensitivities via induced haploinsufficiency . Nat. Genet. 21, 278–283 (1999).

    CAS  Article  PubMed  Google Scholar 

  11. Hofmeyr, J.H., Cornish-Bowden, A. & Rohwer, J.M. Taking enzyme kinetics out of control; putting control into regulation. Eur. J. Biochem. 212 , 833–837 (1993).

    CAS  Article  PubMed  Google Scholar 

  12. Hofmeyr, J.H. & Cornish-Bowden, A. Co-response analysis: a new experimental strategy for metabolic control analysis. J. Theoret. Biol. 182, 371–380 (1996).

    CAS  Article  Google Scholar 

  13. Kholodenko, B.N., Schuster, S., Rohwer, J.M., Cascante, M. & Westerhoff, H.V. Composite control of cell function: metabolic pathways behaving as single control units. FEBS Lett. 368, 1–4 ( 1995).

    CAS  Article  PubMed  Google Scholar 

  14. Rohwer, J.M. Interaction of functional units in metabolism (Ph.D. Thesis). (University of Amsterdam, Amsterdam; 1997).

    Google Scholar 

  15. Rohwer, J.M., Schuster, S. & Westerhoff, H.V. How to recognize monofunctional units in a metabolic system. J. Theoret. Biol. 179, 213– 228 (1996).

    CAS  Article  Google Scholar 

  16. Oliver, S.G. Yeast as a navigational aid in genome analysis. Microbiology 143, 1483–1487 (1997).

    CAS  Article  PubMed  Google Scholar 

  17. Kell, D.B. & Mendes, P. Snapshots of systems: metabolic control analysis and biotechnology in the post-genomic era. In Technological and medical implications of metabolic control analysis . (eds Cornish-Bowden, A. & Cárdenas, M.L.) 3– 25 (Kluwer Academic Publishers, Dordrecht; 2000).

    Chapter  Google Scholar 

  18. Kretschmer, M. & Fraenkel, D.G. Yeast 6-phosphofructo-2-kinase: sequence and mutant. Biochemistry 30, 10663 –10672 (1991).

    CAS  Article  PubMed  Google Scholar 

  19. Kretschmer, M., Tempst, P. & Fraenkel, D.G. Identification and cloning of yeast phosphofructokinase 2. Eur. J. Biochem. 197, 367– 372 (1991).

    CAS  Article  PubMed  Google Scholar 

  20. Paravicini, G. & Kretschmer, M. The yeast FBP26 gene codes for a fructose-2,6-bisphosphatase. Biochemistry 31, 7126–7133 ( 1992).

    CAS  Article  PubMed  Google Scholar 

  21. Boles, E., Goehlmann, W.H. & Zimmermann, F.K. Cloning of a second gene encoding 6-phosphofructo-2-kinase in yeast, and its characterization of mutant strains without fructose-2,6-bisphosphate . Mol. Microbiol. 20, 65– 76 (1996).

    CAS  Article  PubMed  Google Scholar 

  22. Rousseau, G.G. & Hue, L. Mammalian 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase: a bifunctional enzyme that controls glycolysis. Prog. Nucleic Acids Res. Mol. Biol. 45, 99–127 (1993).

    CAS  Article  Google Scholar 

  23. Van Schaftingen, E. Fructose 2,6-bisphosphate. Adv. Enzymol. Rel. Areas Mol. Biol. 59, 315–395 ( 1987).

    CAS  Google Scholar 

  24. Van Schaftingen, E., Lederer, B., Bartrons, R. & Hers, H.G. A kinetic study of pyrophosphate: fructose-6-phosphate phosphotransferase from potato tubers. Application to a microassay of fructose 2,6-bisphosphate . Eur. J. Biochem. 129, 191– 195 (1982).

    CAS  Article  PubMed  Google Scholar 

  25. 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).

    CAS  Article  PubMed  Google Scholar 

  26. Wach, A., Brachat, A., Pöhlmann, R. & Philippsen, P. New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae. Yeast 10, 1793–1808 (1994).

    CAS  Article  PubMed  Google Scholar 

  27. Yocum, R. Genetic engineering of industrial yeasts. Proc. Bio Expo 86, 17 (1986).

    Google Scholar 

  28. Hutter, A. & Oliver, S.G. Ethanol production using nuclear petite yeast mutants. Appl. Microbiol. Biotechnol. 49, 511–516 ( 1998).

    CAS  Article  PubMed  Google Scholar 

  29. Griffiths, P.R. & de Haseth, J.A. Fourier transform infrared spectrometry. (Wiley, New York; 1986).

    Google Scholar 

  30. Winson, M.K. et al. Diffuse reflectance absorbance spectroscopy taking in chemometrics (DRASTIC). A hyperspectral FT-IR-based approach to rapid screening for metabolite overproduction. Analyt. Chim. Acta 348, 273–282 (1997).

    CAS  Article  Google Scholar 

  31. Cole, R.B. Electrospray ionization mass spectrometry: fundamentals, instrumentation and applications. (Wiley, New York; 1997)

    Google Scholar 

  32. Gaskell, S.J. Electrospray: principles and practice. J. Mass Spec. 32, 677–688 (1997).

    CAS  Article  Google Scholar 

  33. Lindon, J.C., Nicholson, J.K. & Wilson, I.D. Direct coupling of chromatographic separations to NMR spectroscopy. Prog. Nucl. Magn. Reson. Spectrosc. 29, 1–49 (1996).

    CAS  Article  Google Scholar 

  34. Kacser, H. & Burns, J.A. The control of flux . Symp. Soc. Exp. Biol. 27, 65– 104 (1973).

    CAS  PubMed  Google Scholar 

  35. Burns, J.A. et al. Control analysis of metabolic systems. Trends Biochem. Sci. 10, 16 (1985).

  36. Kell, D.B. & Westerhoff, H.V. Metabolic control theory—its role in microbiology and biotechnology. FEMS Microbiol. Rev. 39, 305–320 ( 1986).

    CAS  Article  Google Scholar 

  37. Fell, D.A. Understanding the control of metabolism. (Portland Press, London; 1996)

    Google Scholar 

  38. Cornish-Bowden, A. & Cardenas, M.L. From genome to cellular phenotype—a role for metabolic flux analysis? Nat. Biotechnol. 18, 267–268 (2000).

    CAS  Article  PubMed  Google Scholar 

  39. Heinrich, R. & Rapoport, T.A. Linear theory of enzymatic chains; its application for the analysis of the crossover theorem and of the glycolysis of human erythrocytes. Acta Biol. Med. Ger. 31, 479–494 ( 1973).

    CAS  PubMed  Google Scholar 

  40. Oliver, S.G. Redundancy reveals drugs in action. Nat. Genet. 21, 245–246 (1999).

    CAS  Article  PubMed  Google Scholar 

  41. Arkin, A., Shen, P.D. & Ross, J. A test case of correlation metric construction of a reaction pathway from measurements. Science 277, 1275–1279 (1997).

    CAS  Article  Google Scholar 

  42. Winzeler, E. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285 , 901–906 (1999).

    CAS  Article  PubMed  Google Scholar 

  43. Slonimski, P.P., Perrodin, G. & Croft, J.H. Ethidium bromide induced mutation of yeast mitochondria: complete transformation of cells into respiratory deficient non-chromosomal petites. Biochem. Biophys. Res. Commun. 30, 232–239 (1968).

    CAS  Article  PubMed  Google Scholar 

  44. Gonzalez, B., Francois, J. & Renaud, M. A rapid and reliable method for metabolite extraction in yeast using boiling buffered ethanol. Yeast 13, 1347–1355 (1997).

    CAS  Article  PubMed  Google Scholar 

  45. Bergmeyer, H.U. Methods of enzymatic analysis. (Verlag Chemiel, Basel; 1974)

    Google Scholar 

  46. Cornish-Bowden, A. & Hofmeyr, J.H. Determination of control coefficients in intact metabolic systems. Biochem. J. 298, 367–375 ( 1994).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  47. Goodacre, R. et al. On mass spectrometer instrument standardization and interlaboratory calibration transfer using neural networks. Analyt. Chim. Acta 348, 511–532 ( 1997).

    CAS  Article  Google Scholar 

  48. 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).

    CAS  Article  PubMed  Google Scholar 

  49. Martens, H. & Næs, T. Multivariate calibration . (Wiley, Chichester; 1989).

    Google Scholar 

  50. Causton, D.R. A biologist's advanced mathematics. (London, Allen & Unwin; 1987).

    Google Scholar 

  51. Jolliffe, I.T. Principal components analysis. (Springer-Verlag, New York; 1986)

    Book  Google Scholar 

  52. 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).

    CAS  Article  PubMed  Google Scholar 

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

    CAS  Article  Google Scholar 

  54. Winston, F., Dollard, C. & Ricupero-Hovasse, S.L. Construction of a set of convenient Saccharomyces cerevisiae strains that are isogenic to S288C. Yeast 11, 53–55 (1995).

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by EC contracts, within the frame of the EUROFAN program, to S.G.O., K.v.D., and H.V.W., and by a grant from the UK's Biotechnology and Biological Sciences Research Council to S.G.O. and D.B.K. We would like to thank Cathy Day for her superb technical assistance, and Barbara Bakker and Johann Rohwer for stimulating discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephen G. Oliver.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Raamsdonk, L., Teusink, B., Broadhurst, D. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat Biotechnol 19, 45–50 (2001). https://doi.org/10.1038/83496

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/83496

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing