Abstract
AbstractMetabolic 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.
Funder
EC | Horizon 2020 Framework Programme
Universität Potsdam
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Reference45 articles.
1. Rasala, B. A. & Mayfield, S. P. Photosynthetic biomanufacturing in green algae; production of recombinant proteins for industrial, nutritional, and medical uses. Photosynth. Res. 123, 227–239 (2015).
2. Chisti, Y. Biodiesel from microalgae. Biotechnol. Adv. 25, 294–306 (2007).
3. Gonzalez-Fernandez, C. & Muñoz, R. (eds.). Microalgae-Based Biofuels and Bioproducts. From Feedstock Cultivation to End-products (Woodhead Publishing an imprint of Elsevier, Duxford, Cambridge, MA, Kidlington, 2017).
4. Fabris, M. et al. Emerging technologies in algal biotechnology: toward the establishment of a sustainable, algae-based bioeconomy. Front. Plant Sci. 11, 279 (2020).
5. Chang, R. L. et al. Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism. Mol. Syst. Biol. 7, 518 (2011).
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