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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2003-06

AUTHORS

Jess Allen, Hazel M Davey, David Broadhurst, Jim K Heald, Jem J Rowland, Stephen G Oliver, Douglas B Kell

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 genes. 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 programming, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect. More... »

PAGES

692-696

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/nbt823

DOI

http://dx.doi.org/10.1038/nbt823

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051888256

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/12740584


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