Abstract
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.
Highlights
Microalgae have the potential to deliver biofuels without the associated competition for land resources
We develop the first compartmentalized, genome-scale model of algae metabolism based on the C. reinhardtii genome and a comprehensive evaluation of biochemical evidence found in literature complemented with missing compartmentalisation data derived from the genome scale metabolic reconstructions (GEMs) for Arabidopsis, AraGEM [18]
Genome-scale metabolic reconstruction and functional annotation The genome-scale reconstruction process was adapted from the method applied to the GEM of Mus musculus [20], Arabidopsis (AraGEM) [18], maize sorghum and sugarcane (C4GEM)[19] (Figure 1)
Summary
Microalgae have the potential to deliver biofuels without the associated competition for land resources. 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. Algae-derived hydrogen, methane, triacylglycerols, and ethanol could all serve as potential biofuels [1,2,3], but many challenges remain to be addressed [4,5]. System-level microbial metabolic engineering, genome scale metabolic reconstructions (GEMs) are used to integrate and analyse large ‘omics datasets as well as to evaluate designs in silico. A growing number of metabolic engineering studies have demonstrated the use of well-curated GEMs to generate strain designs that are neither intuitive nor obvious [7,8,9,10,11,12]
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