Time-dependent profiling of metabolites from Snf1 mutant and wild type yeast cells.

Abstract

The effect of sampling time in the context of growth conditions on a dynamic metabolic system was investigated in order to assess to what extent a single sampling time may be sufficient for general application, as well as to determine if useful kinetic information could be obtained. A wild type yeast strain (W) was compared to a snf1Delta mutant yeast strain (S) grown in high-glucose medium (R) and in low-glucose medium containing ethanol (DR). Under these growth conditions, different metabolic pathways for utilizing the different carbon sources are expected to be active. Thus, changes in metabolite levels relating to the carbon source in the growth medium were anticipated. Furthermore, the Snf1 protein kinase complex is required to adapt cellular metabolism from fermentative R conditions to oxidative DR conditions. So, differences in intracellular metabolite levels between the W and S yeast strains were also anticipated. Cell extracts were collected at four time points (0.5, 2, 4, 6 h) after shifting half of the cells from R to DR conditions, resulting in 16 sample classes (WR, WDR, SR, SDR) x (0.5, 2, 4, 6 h). The experimental design provided time course data, so temporal dependencies could be monitored in addition to carbon source and strain dependencies. Comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC x GC-TOFMS) was used with discovery-based data mining algorithms ( Anal. Chem. 2006, 78, 5068-5075 (ref 1); J. Chromatogr., A 2008, 1186, 401-411 (ref 2)) to locate regions within the 2D chromatograms (i.e., metabolites) that provided chemical selectivity between the 16 sample classes. These regions were mathematically resolved using parallel factor analysis to positively identify the metabolites and to acquire quantitative results. With these tools, 51 unique metabolites were identified and quantified. Various time course patterns emerged from these data, and principal component analysis (PCA) was utilized as a comparison tool to determine the sources of variance between these 51 metabolites. The effect of sampling time was investigated with separate PCA analyses using various subsets of the data. PCA utilizing all of the time course data, averaged time course data, and each individual time point data set independently were performed to discern the differences. For the yeast strains examined in the current study, data collection at either 4 or 6 h provided information comparable to averaged time course data, albeit with a few metabolites missing using a single sampling time point.