Publications by authors named "Teusink B"

Article Synopsis
  • Metabolic control analysis (MCA) has evolved to study how cells manage metabolism by adjusting enzyme activity, now applied in a whole-cell context to understand growth-rate optimization through protein concentration.
  • The research shows that elementary flux modes (EFMs) are the most efficient metabolic networks, determined by protein-concentration constraints needed for maximizing growth.
  • Using data from S. cerevisiae and E. coli, the study illustrates how specific metabolic patterns emerge under different growth conditions, aiming to renew interest in MCA for uncovering universal biochemical principles across species.
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Article Synopsis
  • The regulation of metabolic protein expression is influenced by genetic circuits that respond to metabolic needs and environmental changes to optimize metabolic fitness.
  • Understanding these regulatory mechanisms can inform synthetic biology by helping to design effective synthetic circuits for biotechnological applications.
  • The review discusses an approach to design genetic circuits that optimize metabolic networks, focusing on E. coli's regulation of ribosome expression and amino acid biosynthesis, which appear to have evolved for maximum efficiency in protein use.
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S. cerevisiae (or budding yeast) is an important micro-organism for sucrose-based fermentation in biotechnology. Yet, it is largely unknown how budding yeast adapts to sucrose transitions.

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Lactate dehydrogenase (LDH) from Schistosoma mansoni has peculiar properties for a eukaryotic LDH. Schistosomal LDH (SmLDH) isolated from schistosomes, and the recombinantly expressed protein, are strongly inhibited by ATP, which is neutralized by fructose-1,6-bisphosphate (FBP). In the conserved FBP/anion binding site we identified two residues in SmLDH (Val187 and Tyr190) that differ from the conserved residues in LDHs of other eukaryotes, but are identical to conserved residues in FBP-sensitive prokaryotic LDHs.

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adjusts its metabolism based on nutrient availability, typically transitioning from glucose fermentation to ethanol respiration as glucose becomes limiting. However, our understanding of the regulation of metabolism is largely based on population averages, whereas nutrient transitions may cause heterogeneous responses. Here we introduce iCRAFT, a method that couples the ATP Förster resonance energy transfer (FRET)-based biosensor yAT1.

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The availability of nutrients to microorganisms varies considerably between different environments, and changes can occur rapidly. As a general rule, a fast growth rate-typically growth on glucose-is associated with the repression of other carbohydrate utilization genes, but it is not clear to what extent catabolite repression is exerted by other sugars. We investigated the hierarchy of sugar utilization after substrate transitions in .

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The fermentation process of milk to yoghurt using Lactobacillus delbrueckii subsp. bulgaricus in co-culture with Streptococcus thermophilus is hallmarked by the breakdown of lactose to organic acids such as lactate. This leads to a substantial decrease in pH - both in the medium, as well as cytosolic.

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Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness.

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Budding yeast uses the TORC1-Sch9p and cAMP-PKA signalling pathways to regulate adaptations to changing nutrient environments. Dynamic and single-cell measurements of the activity of these cascades will improve our understanding of the cellular adaptation of yeast. Here, we employed the AKAR3-EV biosensor developed for mammalian cells to measure the cellular phosphorylation status determined by Sch9p and PKA activity in budding yeast.

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Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry.

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Microbial growth requires energy for maintaining the existing cells and producing components for the new ones. Microbes therefore invest a considerable amount of their resources into proteins needed for energy harvesting. Growth in different environments is associated with different energy demands for growth of yeast Saccharomyces cerevisiae, although the cross-condition differences remain poorly characterized.

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Climate change increases sugar content in grapes, resulting in unwanted increase in ethanol content of wine. Lachancea thermotolerans ferments glucose and fructose into both ethanol and lactate, decreasing final ethanol content and positively affecting wine acidity. Reported Lachancea thermotolerans strains show big variation in lactate production during fermentation.

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Microorganisms, including the budding yeast Saccharomyces cerevisiae, express glycolytic proteins to a maximal capacity that (largely) exceeds the actual flux through the enzymes, especially at low growth rates. An open question is if this apparent expression level is really an overcapacity, or maintains the (optimal) enzyme capacity needed to carry flux at (very) low substrate availability. Here, we use computational modelling to suggest that yeast maintains a genuine excess of glycolytic enzymes at low specific growth rates.

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The fission yeast, Schizosaccharomyces pombe, is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications.

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Wine fermentation is an ancient biotechnological process mediated by different microorganisms such as yeast and bacteria. Understanding of the metabolic and physiological phenomena taking place during this process can be now attained at a genome scale with the help of metabolic models. In this chapter, we present a detailed protocol for modeling wine fermentation using genome-scale metabolic models.

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Under carbon source transitions, the intracellular pH of Saccharomyces cerevisiae is subject to change. Dynamics in pH modulate the activity of the glycolytic enzymes, resulting in a change in glycolytic flux and ultimately cell growth. To understand how pH affects the global behavior of glycolysis and ethanol fermentation, we measured the activity of the glycolytic and fermentative enzymes in S.

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Article Synopsis
  • Unicellular organisms adjust their metabolism to manage growth and maintenance when environmental conditions change, involving a re-allocation of resources under specific cellular limitations.
  • A detailed metabolic model of yeast was developed, incorporating reactions related to protein synthesis and degradation, which helps predict metabolic activity and protein expression based on growth optimization.
  • The model reveals that under limited glucose, mitochondrial restrictions affect growth (e.g., the Crabtree effect), while excess sugars lead to constraints on cytosolic volume, influencing metabolic strategies and protein profiles.
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Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism . These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented.

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Overflow metabolism is ubiquitous in nature, and it is often considered inefficient because it leads to a relatively low biomass yield per consumed carbon. This metabolic strategy has been described as advantageous because it supports high growth rates during nutrient competition. Here, we experimentally evolved bacteria without nutrient competition by repeatedly growing and mixing millions of parallel batch cultures of Escherichia coli.

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Yeasts constitute over 1,500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications, and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts' metabolic specificity in batch cultures.

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As natural selection acts on individual organisms the evolution of costly cooperation between microorganisms is an intriguing phenomenon. Introduction of spatial structure to privatize exchanged molecules can explain the evolution of cooperation. However, in many natural systems cells can also grow to low cell concentrations in the absence of these exchanged molecules, thus showing "cooperation-independent background growth".

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The turbidity of milk prohibits the use of optical density measurements for strain characterizations. This often limits research to laboratory media. Here, we cleared milk through centrifugation to remove insoluble milk solids.

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Genome-scale stoichiometric modeling methods, in particular Flux Balance Analysis (FBA) and variations thereof, are widely used to investigate cell metabolism and to optimize biotechnological processes. Given (1) a metabolic network, which can be reconstructed from an organism's genome sequence, and (2) constraints on reaction rates, which may be based on measured nutrient uptake rates, FBA predicts which reactions maximize an objective flux, usually the production of cell components. Although FBA solutions may accurately predict the metabolic behavior of a cell, the actual flux predictions are often hard to interpret.

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The cAMP-PKA signaling cascade in budding yeast regulates adaptation to changing environments. We developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We used this sensor with flow cytometry for high-throughput single cell-level quantification during dynamic changes in response to sudden nutrient transitions.

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