Genome-scale metabolic model accurately predicts fermentation of glucose by Chromochloris zofingiensis

Abstract

Algae have the potential to be sources of renewable fuels and chemicals. One particular strain, Chromochloris zofingiensis, is of interest due to the co-production of triacylglycerols (TAGs) and astaxanthin, a valuable nutraceutical. To aid in future engineering efforts, we have developed the first genome-scale metabolic model on C. zofingiensis, iCzof1915. This model includes 1915 genes, 3449 metabolic reactions, and 2682 metabolites across 9 cellular compartments. We performed detailed biomass composition analysis for three growth conditions: autotrophic, mixotrophic and heterotrophic and used this data to develop biomass formation equations for each growth condition. The completed model was then used to predict flux distributions for each growth condition; interestingly, for heterotrophic growth, the model predicts the excretion of fermentation products due to overflow metabolism. We confirmed this experimentally via metabolomics of spent medium and fermentation product assays. An in silico gene essentiality analysis was also performed on this model to evaluate metabolism robustness in each growth condition. In this work, we present the first genome-scale metabolic model of C. zofingiensis and demonstrate its use predicting metabolic activity in different growth conditions, setting up a foundation for future metabolic engineering studies in this organism.