Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale
- Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes andMetabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.…
| Author details: | Marius ArendORCiD, David Zimmer, Rudan Xu, Frederik SommerORCiD, Timo MühlhausGND, Zoran NikoloskiORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1038/s41467-023-40498-1 |
| ISSN: | 2041-1723 |
| Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/37553325 |
| Title of parent work (English): | Nature Communications |
| Publisher: | Springer Nature |
| Place of publishing: | London |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2023/08/08 |
| Publication year: | 2023 |
| Release date: | 2024/06/24 |
| Tag: | Computational models; Enzymes; Proteomics |
| Volume: | 14 |
| Issue: | 1 |
| Number of pages: | 9 |
| Funding institution: | Research Focus Group "Evolutionary Systems Biology" of University of; Potsdam; Max Planck Society; European Union [862201]; H2020 - Industrial; Leadership [862201] Funding Source: H2020 - Industrial Leadership |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie |
| Peer review: | Referiert |
| Publishing method: | Open Access / Gold Open-Access |
| DOAJ gelistet | |
| License (German): |  CC-BY - Namensnennung 4.0 International |
