Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

Gang Li, Yating Hu, Zrimec, Hao Luo, Hao Wang, Aleksej Zelezniak, Boyang Ji and Jens Nielsen ()
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Gang Li: Department of Biology and Biological Engineering, Chalmers University of Technology
Yating Hu: Department of Biology and Biological Engineering, Chalmers University of Technology
Zrimec: Department of Biology and Biological Engineering, Chalmers University of Technology
Hao Luo: Department of Biology and Biological Engineering, Chalmers University of Technology
Hao Wang: Department of Biology and Biological Engineering, Chalmers University of Technology
Aleksej Zelezniak: Department of Biology and Biological Engineering, Chalmers University of Technology
Boyang Ji: Department of Biology and Biological Engineering, Chalmers University of Technology
Jens Nielsen: Department of Biology and Biological Engineering, Chalmers University of Technology

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovers enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) is predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtain a thermotolerant strain that outgrows the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.

Date: 2021
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DOI: 10.1038/s41467-020-20338-2

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