RT Journal Article
SR Electronic
T1 Bayesian genome scale modelling identifies thermal determinants of yeast metabolism
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.04.01.019620
DO 10.1101/2020.04.01.019620
A1 Gang Li
A1 Yating Hu
A1 Hao Wang
A1 Aleksej Zelezniak
A1 Boyang Ji
A1 Jan Zrimec
A1 Jens Nielsen
YR 2020
UL http://biorxiv.org/content/early/2020/04/02/2020.04.01.019620.abstract
AB 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 uncovered enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) was predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtained a thermotolerant strain that outgrew 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.