Microbes experience dynamic conditions in natural habitats as well as in engineered environments, such as large-scale bioreactors, which exhibit increased mixing times and inhomogeneities. While single perturbations have been studied for several organisms and substrates, the impact of recurring short-term perturbations remains largely unknown. In this study, we investigated the response of Saccharomyces cerevisiae to repetitive gradients of four different sugars: glucose, fructose, sucrose, and maltose. Due to different transport mechanisms and metabolic routes, nonglucose sugars lead to varied intracellular responses. To characterize the impact of the carbon sources and the dynamic substrate gradients, we applied both steady-state and dynamic cultivation conditions, comparing the physiology, intracellular metabolome, and proteome. For maltose, the repeated concentration gradients led to a significant decrease in biomass yield. Under glucose, fructose, and sucrose conditions, S. cerevisiae maintained the biomass yield observed under steady-state conditions. Although the physiology was very similar across the different sugars, the intracellular metabolome and proteome were clearly differentiated. Notably, the concentration of upper glycolytic enzymes decreased for glucose and maltose (up to −60% and −40%, respectively), while an increase was observed for sucrose and fructose when exposed to gradients. Nevertheless, for all sugar gradient conditions, a stable energy charge was maintained, ranging between 0.78 and 0.89. This response to maltose is particularly distinct compared to previous single-substrate pulse experiments or limitation to excess shifts, which led to maltose-accelerated death in earlier studies. At the same time, enzymes of lower glycolysis were elevated. Interestingly, common stress-related proteins (GO term: cellular response to oxidative stress) decreased during dynamic conditions.

Microbial metabolism is strongly dependent on the environmental conditions. While these can be well controlled under laboratory conditions, large-scale bioreactors are characterized by inhomogeneities and consequently dynamic conditions for the organisms. How Saccharomyces cerevisiae response to frequent perturbations in industrial bioreactors is still not understood mechanistically. To study the adjustments to prolonged dynamic conditions, we used published repeated substrate perturbation regime experimental data, extended it with proteomic measurements and used both for modelling approaches. Multiple types of data were combined; including quantitative metabolome, ¹³C enrichment and flux quantification data. Kinetic metabolic modelling was applied to study the relevant intracellular metabolic response dynamics. An existing model of yeast central carbon metabolism was extended, and different subsets of enzymatic kinetic constants were estimated. A novel parameter estimation pipeline based on combinatorial enzyme selection supplemented by regularization was developed to identify and predict the minimum enzyme and parameter adjustments from steady-state to dynamic substrate conditions. This approach predicted proteomic changes in hexose transport and phosphorylation reactions, which were additionally confirmed by proteome measurements. Nevertheless, the modelling also hints at a yet unknown kinetic or regulation phenomenon. Some intracellular fluxes could not be reproduced by mechanistic rate laws, including hexose transport and intracellular trehalase activity during substrate perturbation cycles.

Exposed to changes in their environment, microorganisms will adapt their phenotype, including metabolism, to ensure survival. To understand the adaptation principles, resource allocation-based approaches were successfully applied to predict an optimal proteome allocation under (quasi) steady-state conditions. Nevertheless, for a general, dynamic environment, enzyme kinetics will have to be taken into account which was not included in the linear resource allocation models. To this end, a resource-dependent kinetic model was developed and applied to the model organism Saccharomyces cerevisiae by combining published kinetic models and calibrating the model parameters to published proteomics and fluxomics datasets. Using this approach, we were able to predict specific proteomes at different dilution rates under chemostat conditions. Interestingly, the approach suggests that the occurrence of aerobic fermentation (Crabtree effect) in S. cerevisiae is not caused by space limitation in the total proteome but rather an effect of constraints on the mitochondria. When exposing the approach to repetitive, dynamic substrate conditions, the proteome space was allocated differently. Less space was predicted to be available for non-essential enzymes (reserve space). This could indicate that the perceived “overcapacity” present in experimentally measured proteomes may very likely serve a purpose in increasing the robustness of a cell to dynamic conditions, especially an increase of proteome space for the growth reaction as well as of the trehalose cycle that was shown to be essential in providing robustness upon stronger substrate perturbations. The model predictions of proteome adaptation to dynamic conditions were additionally evaluated against respective experimentally measured proteomes, which highlighted the model’s ability to accurately predict major proteome adaptation trends. This proof of principle for the approach can be extended to production organisms and applied for both understanding metabolic adaptation and improving industrial process design.

Redox metabolism plays an essential role in the central metabolic network of all living cells, connecting, but at the same time separating, catabolic and anabolic pathways. Redox metabolism is inherently linked to the excretion of overflow metabolites. Overflow metabolism allows for higher substrate uptake rates, potentially outcompeting other microorganisms for the same substrate. Within dynamically changing environments, overflow metabolism can act as storage mechanism, as is shown in many recently described processes. However, for complete understanding of these mechanisms, the intracellular state of the metabolism must be elucidated. In recent years, progress has been made in the field of metabolomics to improve the accuracy and precision of measurements of intracellular and intercompartmental metabolites. This article highlights several of these recent advances, with focus on redox cofactor measurements, both fluorescence and mass spectrometry based.