Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities

Lao-Martil, David; Verhagen, Koen J. A.; Schmitz, Joep P. J.; Teusink, Bas; Wahl, A.; Riel, Natal A. W. van

doi:10.3390/metabo12010074

Cited by 11 publications

(10 citation statements)

References 163 publications

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“…The abundance of Hsp was therefore substantially lower in the absence of oxygen, in line with the lower transcription of Hsp and lower thermo-tolerance of the anaerobic stationary-phase cultures than of the aerobic cultures observed by Bisschops et al [66]. Considering that industrial-scale processes favour anaerobic environments for practical and financial reasons, the present results provide valuable information for the construction of predictive metabolic models [5,9,[68][69][70][71][72][73].…”

Section: Discussionsupporting

confidence: 77%

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Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast

Ridder

Brandeler²,

Altiner³

et al. 2022

Preprint

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The yeast Saccharomyces cerevisiae is a widely used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimising biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates and standardised sample preparation procedures ensured reproducibility and accuracy. Additionally, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic, standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimisation of the glycolytic pathway, resulting in the quantitative assessment of over 1700 proteins across 54 proteomes. These proteins account for nearly 40% of the overall yeast proteome and approximately 99% of the total protein biomass. The anaerobic cultures showed remarkably less proteome-level changes compared to the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack time and resources to adapt to changes in the environment. This proteome dynamics study constitutes an important step towards better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.

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Section: Discussionsupporting

confidence: 77%

“…[66]. Considering that industrial-scale processes favour anaerobic environments for practical and financial reasons, the present results provide valuable information for the construction of predictive metabolic models [5, 9, 68–73].…”

Section: Discussionmentioning

confidence: 95%

Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast

Ridder

Brandeler²,

Altiner³

et al. 2022

Preprint

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show abstract

“…The before-mentioned SRE approach represents a proven methodology to provide the necessary ground to set up data-driven models [ 46 , 47 , 48 ]. For the organism under investigation, several studies quantitatively investigated the metabolome and transcriptome during a sudden shift from a glucose limitation to excess [ 26 , 47 , 49 , 50 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli

et al. 2022

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Carbon limitation is a common feeding strategy in bioprocesses to enable an efficient microbiological conversion of a substrate to a product. However, industrial settings inherently promote mixing insufficiencies, creating zones of famine conditions. Cells frequently traveling through such regions repeatedly experience substrate shortages and respond individually but often with a deteriorated production performance. A priori knowledge of the expected strain performance would enable targeted strain, process, and bioreactor engineering for minimizing performance loss. Today, computational fluid dynamics (CFD) coupled to data-driven kinetic models are a promising route for the in silico investigation of the impact of the dynamic environment in the large-scale bioreactor on microbial performance. However, profound wet-lab datasets are needed to cover relevant perturbations on realistic time scales. As a pioneering study, we quantified intracellular metabolome dynamics of Saccharomyces cerevisiae following an industrially relevant famine perturbation. Stimulus-response experiments were operated as chemostats with an intermittent feed and high-frequency sampling. Our results reveal that even mild glucose gradients in the range of 100 µmol·L−1 impose significant perturbations in adapted and non-adapted yeast cells, altering energy and redox homeostasis. Apparently, yeast sacrifices catabolic reduction charges for the sake of anabolic persistence under acute carbon starvation conditions. After repeated exposure to famine conditions, adapted cells show 2.7% increased maintenance demands.

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“…Existing yeast glycolysis models studied this pathway in isolation (Smallbone et al , 2013; van Heerden et al , 2014). To transit from pathways in isolation, to pathways embedded in a growing cell, inclusion of physiological variables informative of the bioreactor environment is crucial (Lao-Martil et al , 2022). The required data to implement these variables is now available for S. cerevisiae .…”

Section: Resultsmentioning

confidence: 99%

Glycolysis revisited: from steady state growth to glucose pulses

Lao-Martil

Schmitz

Riel

et al. 2022

Preprint

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Kinetic metabolic models of central metabolism have been proposed to understand how Saccharomyces cerevisiae navigates through nutrient perturbations. Yet, these models lacked important variables that constrain metabolism under relevant physiological conditions and thus have limited operational use such as in optimization of industrial fermentations. In this work, we developed a physiologically informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. A parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. We show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Our work suggests that by combining multiple types of data and computational methods, complex but physiologically representative and robust models can be achieved.

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Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities

Cited by 11 publications

References 163 publications

Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast

Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast

Monitoring Intracellular Metabolite Dynamics in Saccharomyces cerevisiae during Industrially Relevant Famine Stimuli

Glycolysis revisited: from steady state growth to glucose pulses

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