Quantitative comparison of transient growth of Saccharomyces cerevisiae, Saccharomyces kluyveri, and Kluyveromyces lactis

A multitude of metabolic regulations occur in yeast, particularly under dynamic process conditions, such as under sudden glucose excess. However, quantification of regulations and classification of yeast strains under these conditions have yet to be elucidated, which requires high-frequency and consistent quantification of the metabolic response. The present study aimed at quantifying the dynamic regulation of the central metab. of strains Saccharomyces cerevisiae, S. kluyveri, and Kluyveromyces lactis upon sudden glucose excess, accomplished by a shift-up in diln. rate inside of the oxidative region using a small metabolic flux model. It was found that, under transient growth conditions, S. kluyveri behaved like K. lactis, while classification using steady-state conditions would position S. kluyveri close to S. cerevisiae. For transient conditions and based on the observation whether excess glucose is initially used for catabolism (energy) or anabolism (carbon), we propose to classify strains into energy-driven, such as S. cerevisiae, and carbon-driven, such as S. kluyveri and K. lactis, strains. Furthermore, it was found that the delayed onset of fermentative catabolism in carbon-driven strains is a consequence of low catabolic flux and the initial shunt of glucose in non-nitrogen-contg. biomass constituents. The MFA model suggests that energy limitation forced the cell to ultimately increase catabolic flux, while the capacity of oxidative catabolism is not sufficient to process this flux oxidatively. The combination of transient expts. and its exploitation with reconciled intrinsic rates using a small metabolic model could corroborate earlier findings of metabolic regulations, such as tight glucose control in carbon-driven strains and transient changes in biomass compn., as well as explore new regulations, such as assimilation of ethanol before glucose. The benefit from using small metabolic flux models is the richness of information and the enhanced insight into intrinsic metabolic pathways without a priori knowledge of adaptation kinetics. Used in an online context, this approach serves as an efficient tool for strain characterization and physiol. studies. [on SciFinder (R)]