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)]

Towards a kinetic model of the phospholipids biosynthesis of the yeast Saccharomyces cerevisiae

Meriç Ataman, Vassily Hatzimanikatis, Iro Oikonomidi, Georgios Savoglidis

Lipids, as vital eukaryotic cellular components, play a major role in cell, tissue and organ physiology. Alterations of lipid enzymes and metabolic pathways are related to many human diseases, such as cancer, diabetes and neurodegenerative diseases. In this study, we focus on phospholipids, the major components of all cell membranes, which not only act as structural membrane constituents but also play a vital role in cellular energy storage and in signaling pathways. The yeast, Saccharomyces cerevisiae, has been chosen as a cell model, due to its strong homology of proteins and pathways with higher eukaryotes. The benefits of studying the glycerophospholipid metabolism of S. cerevisiae will provide critical insight into phospholipid homeostasis of this model organism in various cellular states. The reconstructed model of phospholipid metabolism consists of 118 reactions and 80 metabolites. Beyond combining knowledge of all published yeast genome-scale models, it incorporates missing reactions reported in the literature along with the current state of the art concerning the participating enzymes. Pathways regarding compounds from the glycolysis pathway and synthesis of acyl-CoAs, required for phospholipid biosynthesis, have also been included. A steady state flux analysis was performed with the growth capacity of the cell being able to vary between maintenance and a maximum experimentally observed growth rate, in aerobic carbon-limiting conditions, with mass balance constraints calculated based on experimental data reported in the most recent yeast genome scale model. A thermodynamic curation of all the reactions of the pathway followed, which constrains the allowed space of the concentration ranges of metabolites of the pathway and also provides information about the directionality of all the reactions. Finally, we applied the ORACLE methodology in order to identify enzymes that determine the lipid composition in the various steady states of the cell. Generating a comprehensive kinetic model will be a useful tool for predictive analysis, drug designing or further biomedical research.

2014

A unified stoichiometric model for oxidative and oxidoreductive growth of yeasts

Urs von Stockar

Yeasts degrade glucose through different metabolic pathways, where the choice of the pathway is dependent on the nature of the limitation in the various substrates. When oxygen is limiting in addn. to glucose, yeasts often grow according to a mixt. of oxidative and reductive metab. Oxygen may be limiting either by supply or by inherent biol. restrictions such as the respiratory bottleneck in Saccharomyces cerevisiae or by both. A unified model incorporating both supply and biol. limitations is proposed for the quant. prediction of growth rates, consumption and prodn. rates, as well as key metabolite concns. during mixed oxidoreductive metab. occurring as a result of such oxygen limitations. This simple unstructured model can be applied to different yeast strains while at the same time requiring a min. no. of measured parameters. Estimators are utilized in order to predict the presence of supply-side or biol. limitations. The values of these estimators also characterize the relative importance of oxidative to total metab. Results from the aerobic and oxygen-limited chemostat cultures were used to corroborate the model predictions. During these expts., the heat released by the yeast cultures was also monitored online. The model correctly predicted the over-all stoichiometry, steady-state concns., and rates including heat dissipation rates measured in the various situations of oxygen limitations. Direct continuous measurements such as heat can be used in conjunction with the unified model for online process control. [on SciFinder (R)]

1992

Analysis and Design of Metabolic Engineering and Synthetic Biology Strategies using Large Scale Dynamic Models of Metabolism

Georgios Fengos, Vassily Hatzimanikatis, Ljubisa Miskovic

Systematic analysis for the redirection of carbon flux in metabolite-producing microorganisms requires the comprehensive understanding of their complex metabolic processes. The use of large-scale dynamic models of metabolism plays key role in the understanding of these processes and the study of possible metabolic engineering interventions. However, the generation of such models is hampered by the intrinsic nonlinearities of enzymatic reactions, and the uncertainties at different levels. In particular there is limited knowledge about the exact kinetic mechanisms, and many of the parameters involved in these mechanisms remain largely unknown. In this study we propose a systematic methodology to generate large populations of dynamic non-linear models of metabolism using the ORACLE[1] (Optimization and Risk Analysis of Complex Living Entities) framework. Instead of seeking for an optimal value of the estimated kinetic model parameter values, we integrate thermodynamics, available omics, and kinetic data to construct populations of models that are locally stable, and consistent with the observed physiology. To demonstrate the utility of this methodology we constructed a population of large-scale dynamical models of optimally grown E. coli that involves 283 metabolites and 409 reactions. We used these models to (i) study the response of E. coli metabolism upon large-scale perturbations, such as gene knockouts; (ii) identify and analyze multiple steady states; and (iii) characterize basins of attraction around the identified steady states. The aforementioned analyses provide valuable insight for the design of metabolic engineering strategies towards amplification of desired product-forming pathways. 1. Miskovic, L. & Hatzimanikatis, V. Production of biofuels and biochemicals: in need of an ORACLE. Trends in biotechnology 28, 391–7 (2010).