Toward a global analysis of metabolites in regulatory mutants of yeast

Abstract

The AMP-activated protein kinase in yeast, Snf1, coordinates expression and activity of numerous intracellular signaling and developmental pathways, including those regulating cellular differentiation, response to stress, meiosis, autophagy, and the diauxic transition. Snf1 phosphorylates metabolic enzymes and transcription factors to change cellular physiology and metabolism. Adr1 and Cat8, transcription factors that activate gene expression after the diauxic transition, are regulated by Snf1; Cat8 through direct phosphorylation and Adr1 by dephosphorylation in a Snf1-dependent manner. Adr1 and Cat8 coordinately regulate numerous genes encoding enzymes of gluconeogenesis, the glyoxylate cycle, β-oxidation of fatty acids, and the utilization of alternative fermentable sugars and nonfermentable substrates. To determine the roles of Adr1, Cat8, and Snf1 in metabolism, two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and liquid chromatography coupled to tandem mass spectrometry were used to identify metabolites whose levels change after the diauxic transition in wild-type-, ADR1-, CAT8-, and SNF1-deficient yeast. A discovery-based approach to data analysis utilized chemometric algorithms to identify, quantify, and compare 63 unique metabolites between wild type, adr1∆, cat8∆, adr1∆cat8∆, and snf1∆ strains. The primary metabolites found to differ were those of gluconeogenesis, the glyoxylate and tricarboxylic acid cycles, and amino acid metabolism. In general, good agreement was observed between the levels of metabolites derived from these pathways and the levels of transcripts from the same strains, suggesting that transcriptional control plays a major role in regulating the levels of metabolites after the diauxic transition.

Fig. 1

Data analysis flow chart for GC×GC data. The discovery-based data analysis approach utilizes ChromaTOF and Matlab to locate, identify, and quantify class distinguishing compounds. The traditional ChromaTOF approach (thin arrows) follows the path on the left while the discovery-based approach (thick arrows) applied herein follows the path on the right incorporating some elements of ChromaTOF data processing. The discovery-based approach could also be done in a nontargeted way as is demonstrated with the dotted arrow

Fig. 2

GC × GC–TOFMS chromatographic data for a wild-type strain. Metabolites from five yeast strains (wild type, snf1Δ, adr1Δ, cat8Δ, and adr1Δcat8Δ) were extracted and derivatized for GC analysis as described in “Materials and methods”. The derivatized metabolites were analyzed via GC×GC–TOFMS, with a representative wild-type chromatogram provided here. The complex samples benefit from 2D–GC as the peaks spread out in both GC separation dimensions

Fig. 3

PCA of metabolites and strains. PCA was employed as a data classification tool. When mean-centered metabolites are loaded as samples, the loadings (a) provide information on which strains are most similar to each other, in the context of the analyzed metabolites. The scores (b) provide information on which metabolites are similar to each other, in the context of these strains. The metabolite levels spread out in the PC space based on the various trends, which can be seen in Table 2

Fig. 4

TCA cycle intermediates. Five of the TCA cycle intermediates were detected with this methodology and were quantified by PARAFAC analysis. The signal value for each metabolite was averaged for each strain, incorporating injection variation and biological variation, and then normalized to wild type. Stearic acid is also provided as a reference as it has been observed to be relatively constant in different growth conditions and strains in a previous study [8]. By ANOVA, α-ketoglutarate, succinate, fumarate, and malate all have statistically significant differences

Fig. 5

Glucose consumption per strain. Glucose levels in the media were measured using a PGO Enzymes kit for all five strains over the course of the experiment. Glucose was essentially depleted in all strains by 2 h, except for snf1Δ in which glucose remained at low levels throughout the experiment