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Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale.

ABSTRACT: 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 and improve biotechnological applications of Chlamydomonas.

SUBMITTER: Arend M

PROVIDER: S-EPMC10409818 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale.

Arend Marius M Zimmer David D Xu Rudan R Sommer Frederik F Mühlhaus Timo T Nikoloski Zoran Z

Nature communications 20230808 1

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 abu ...[more]

PMID: 37553325

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Project description:Organelles are intracellular compartments which are themselves compartmentalized. Biogenic and metabolic processes are localized to specialized domains or microcompartments to enhance their efficiency and suppress deleterious side reactions. An example of intra-organellar compartmentalization is the pyrenoid in the chloroplasts of algae and hornworts. This microcompartment enhances the photosynthetic CO2-fixing activity of the Calvin-Benson cycle enzyme Rubisco, suppresses an energetically wasteful oxygenase activity of Rubisco, and mitigates limiting CO2 availability in aquatic environments. Hence, the pyrenoid is functionally analogous to the carboxysomes in cyanobacteria. However, a comprehensive analysis of pyrenoid functions based on its protein composition is lacking. Here we report a proteomic characterization of the pyrenoid in the green alga Chlamydomonas reinhardtii. Pyrenoid-enriched fractions were analyzed by quantitative mass spectrometry. Contaminant proteins were identified by parallel analyses of pyrenoid-deficient mutants. This pyrenoid proteome contains 190 proteins, many of which function in processes that are known or proposed to occur in pyrenoids: e.g. the carbon concentrating mechanism, starch metabolism or RNA metabolism and translation. Using radioisotope pulse labeling experiments, we show that pyrenoid-associated ribosomes could be engaged in the localized synthesis of the large subunit of Rubisco. New pyrenoid functions are supported by proteins in tetrapyrrole and chlorophyll synthesis, carotenoid metabolism or amino acid metabolism. Hence, our results support the long-standing hypothesis that the pyrenoid is a hub for metabolism. The 81 proteins of unknown function reveal candidates for new participants in these processes. Our results provide biochemical evidence of pyrenoid functions and a resource for future research on pyrenoids and their use to enhance agricultural plant productivity. Data are available via ProteomeXchange with identifier PXD004509.

Project description:BackgroundMicroalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for in silico analysis and design. While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data.ResultsWe have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and identified new targets to further improve H2 yield.ConclusionsAlgaGEM is a viable and comprehensive framework for in silico functional analysis and can be used to derive new, non-trivial hypotheses for exploring this metabolically versatile organism. Flux balance analysis can be used to identify bottlenecks and new targets to metabolically engineer microalgae for production of biofuels.

Dataset Information

Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale.

Publications

Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale.

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