Author
Listed:
- 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
National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology
Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg)
- Aleksej Zelezniak
(Department of Biology and Biological Engineering, Chalmers University of Technology
Science for Life Laboratory)
- Boyang Ji
(Department of Biology and Biological Engineering, Chalmers University of Technology
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark)
- Jens Nielsen
(Department of Biology and Biological Engineering, Chalmers University of Technology
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark
BioInnovation Institute)
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.
Suggested Citation
Gang Li & Yating Hu & Zrimec & Hao Luo & Hao Wang & Aleksej Zelezniak & Boyang Ji & Jens Nielsen, 2021.
"Bayesian genome scale modelling identifies thermal determinants of yeast metabolism,"
Nature Communications, Nature, vol. 12(1), pages 1-12, December.
Handle:
RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20338-2
DOI: 10.1038/s41467-020-20338-2
Download full text from publisher
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Charlotte J. Alster & Allycia Laar & Jordan P. Goodrich & Vickery L. Arcus & Julie R. Deslippe & Alexis J. Marshall & Louis A. Schipper, 2023.
"Quantifying thermal adaptation of soil microbial respiration,"
Nature Communications, Nature, vol. 14(1), pages 1-12, December.
- Iván Domenzain & Benjamín Sánchez & Mihail Anton & Eduard J. Kerkhoven & Aarón Millán-Oropeza & Céline Henry & Verena Siewers & John P. Morrissey & Nikolaus Sonnenschein & Jens Nielsen, 2022.
"Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0,"
Nature Communications, Nature, vol. 13(1), pages 1-13, December.
- Jia Zheng & Ning Guo & Yuxiang Huang & Xiang Guo & Andreas Wagner, 2024.
"High temperature delays and low temperature accelerates evolution of a new protein phenotype,"
Nature Communications, Nature, vol. 15(1), pages 1-14, December.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20338-2. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.