RT Journal Article SR Electronic T1 Genome-Scale Metabolic Model Accurately Predicts Fermentation of Glucose by Chromochloris zofingiensis JF bioRxiv FD Cold Spring Harbor Laboratory SP 2021.06.22.449518 DO 10.1101/2021.06.22.449518 A1 Meagher, Michelle A1 Metcalf, Alex A1 Ramsey, S. Alex A1 Prentice, Walter A1 Boyle, Nanette R. YR 2021 UL http://biorxiv.org/content/early/2021/11/20/2021.06.22.449518.abstract AB 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, iChr1915. This model includes 1915 genes, 3413 metabolic reactions and 2652 metabolites. 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 performed on this model, as well as a flux variability analysis to test the production capabilities of this organism. 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.Competing Interest StatementThe authors have declared no competing interest.