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.

Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.For scientific reasons and because of the needs of the pharmaceutical and agrochemical industries, there is much current interest in detecting the site or mode of interaction between an exogenous ligand and a cell or organism, especially in identifying new ones (6,44,45). Such studies are nowadays typically carried out by using high-throughput methods, but there is often an inverse relation between the speed of an assay (involving, e.g., a cloned receptor with a fluorescence readout) and the amount of information it contains (which in this case is restricted to the target of interest). However, array-based methods are showing promise in this regard (3,20,34,37,46). Genome-wide screens can also provide such information (9, 10, 19) but require numerous strains or cell lines to be studied in parallel. Methods with high information content would combine the virtues of screening just a small number of strains with a high-dimensional readout similar to that provided by the array-based methods.Metabolic control analysis (see, e.g., references 7, 15, 16, 21, and 29) tells us that while changes in the activity of a target enzyme tend to have only small effects on the flux through metabolic pathways, they can and do have substantial effects on the concentrations of metabolic intermediates. Since the use of metabolomics, especially in the form of "metabolic fingerprinting," has a much higher throughput and is much cheaper than are, say, transcriptomics and proteomics (8,14,26,42), it makes an attractive candidate for mode-of-action studies (18) and has been used to advantage by Ott and colleagues (2, 38), where the metabolic fingerprints of cells or tissues exposed to specific substances with known targets were analyzed for their modes of binding or action by using pattern recognition techniques.Normally, metabolomics measures (or seeks to measure) the concentrations of all the small molecules within a cell or tissue; and for purposes of functional genomics, we and others have exploited such strategies in the analysis of ge...