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Large excess capacity of glycolytic enzymes in Saccharomyces cerevisiae under glucose-limited conditions

Authors: Grigaitis, Pranas; Teusink, Bas;

Large excess capacity of glycolytic enzymes in Saccharomyces cerevisiae under glucose-limited conditions

Abstract

Computational models and figure data for the publication "Large excess capacity of glycolytic enzymes in Saccharomyces cerevisiae under glucose-limited conditions" (to be submitted). Data put together by Pranas Grigaitis, p.grigaitis [at] vu.nl. Abstract In Nature, microbes live in very nutrient-dynamic environments. Rapid scavenging and consumption of newly introduced nutrients therefore offer a way to outcompete competitors. This may explain the observation that many microorganisms, including the budding yeast Saccharomyces cerevisiae, appear to keep “excess” glycolytic proteins at low growth rates, i.e. the maximal capacity of glycolytic enzymes (largely) exceeds the actual flux through the enzymes. However, such a strategy requires investment into preparatory protein expression that may come at the cost of current fitness. Moreover, at low nutrient levels, enzymes cannot operate at high saturation, and overcapacity is poorly defined without taking enzyme kinetics into account. Here we use computational modeling to suggest that in yeast the overcapacity of the glycolytic enzymes at low specific growth rates is a genuine excess, rather than the optimal enzyme demand dictated by enzyme kinetics. We found that the observed expression of the glycolytic enzymes did match the predicted optimal expression when S. cerevisiae exhibits mixed respiro-fermentative growth, while the expression of tricarboxylic acid cycle enzymes always follows the demand. Moreover, we compared the predicted metabolite concentrations with the experimental measurements and found the best agreement in glucose-excess conditions. We argue that the excess capacity of glycolytic proteins in glucose-scarce conditions is an adaptation of S. cerevisiae to fluctuations of nutrient availability in the environment.

The authors acknowledge the funding by Marie Skłodowska-Curie Actions ITN "SynCrop" (grant agreement No 764591) and NWO (NWO ERA-IB-2, project No 053.80.722).

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
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