Elevated energy costs of biomass production in mitochondrial-respiration deficient Saccharomyces cerevisiae

The pcYeast8 model and data, required to reproduce the figures in the preprint "Elevated energy costs of biomass production in mitochondrial-respiration deficient Saccharomyces cerevisiae". Data bundle uploaded by Pranas Grigaitis, p.grigaitis [at] vu.nl. Abstract 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. Furthermore, a direct comparison of the energy costs for the biosynthesis of the new biomass across conditions is not feasible experimentally; computational models, on the contrary, allow comparing the optimal metabolic strategies and quantify the respective costs of energy and nutrients. Thus in this study, we used a resource allocation model of S. cerevisiae to compare the optimal metabolic strategies between different conditions. We found that S. cerevisiae with respiratory-impaired mitochondria required additional energetic investments for growth, while growth on amino acid-rich media was not affected. Amino acid supplementation in anaerobic conditions also was predicted to rescue the growth reduction in mitochondrial respiratory shuttle-deficient mutants of S. cerevisiae. Collectively, these results point to elevated costs of resolving the redox imbalance caused by de novo biosynthesis of amino acids in mitochondria. To sum up, our study provides an example of how resource allocation modeling can be used to address and suggest explanations to open questions in microbial physiology.PG and BT acknowledge the funding by Marie Skłodowska-Curie Actions ITN "SynCrop" (grant agreement No 764591). We thank SURFsara for the HPC resources through access to the Lisa Compute Cluster