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.
| Original language | English |
|---|---|
| Article number | 190 |
| Journal | Nature Communications |
| Volume | 12 |
| Issue number | 1 |
| Number of pages | 12 |
| ISSN | 2041-1723 |
| DOIs | |
| Publication status | Published - Dec 2021 |
Bibliographical note
Funding Information:The authors would like to thank Tyler W. Doughty, Benjamín J. Sánchez, Avlant Nielsen, and Ibrahim Elsemman for the helpful discussions. G.L. and J.N. have received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie program, project PAcMEN (grant agreement No. 722287). J.N. also acknowledges funding from the Novo Nordisk Foundation (grant No. NNF10CC1016517), the Knut and Alice Wallenberg Foundation. J.Z. and A.Z. are supported by SciLifeLab funding. The computations were performed on resources at Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC).
Publisher Copyright:
© 2021, The Author(s).