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

• Published in: Nature biotechnology 2001-01, Vol.19 (1), p.45-50
• Main Authors: Oliver, Stephen G, Raamsdonk, Léonie M, Teusink, Bas, Broadhurst, David, Zhang, Nianshu, Hayes, Andrew, Walsh, Michael C, Berden, Jan A, Brindle, Kevin M, Kell, Douglas B, Rowland, Jem J, Westerhoff, Hans V, van Dam, Karel
• Format: Article
• Language: English
• Subjects: Adenine Nucleotides - metabolism; Agriculture; Bioinformatics; Biological and medical sciences; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Cluster Analysis; Energy Metabolism - genetics; Fundamental and applied biological sciences. Psychology; Genome, Fungal; Genomics; Genomics - methods; Genotype; Growth, nutrition, metabolism, transports, enzymes. Molecular biology; Hexosephosphates - metabolism; Life Sciences; Metabolites; Microbiology; Mutation; Mycology; Phenotype; Proteins; Pyruvates - metabolism; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - growth & development; Saccharomyces cerevisiae - metabolism; Yeast; Yeasts
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/83496

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.