The Genome-Scale Metabolic Model for Chlorella vulgaris Utex 395 Reveals Quantitative Flux Distributions for Compartmental-Pathway Activity Under Polytrophic Growth

Reconstructions are biochemically, genetically and genomically structured knowledge-bases that contain information of reaction stoichiometry, reaction reversibility, and the association between genes, proteins and reactions. Currently genome-scale metabolic reconstructions are available for a number of heterotrophic organisms capable of biofuel production. Here we report on the genome-scale reconstruction for the photoautotroph algae Chlorella vulgaris UTEX 395.

The reconstruction, iCZ843, consists of six compartments: the cytoplasm, mitochondrion, chloroplast, thylakoid, glyoxysome, and the extracellular space. It contains 843 out of 7,100 annotated genes (around 12%), delineating 1,770 metabolites and 2,294 reactions. C. vulgaris can grow under different trophic conditions (i.e. photoauto-, hetero-, and mixotrophic). Each of these growth conditions is represented mathematically through different biomass objective functions (BOFs). Every equation contains the stoichiometric coefficients for the most important metabolites being part of the biomass. Major components of the biomass, such as lipids, proteins, carbohydrates and ribose in RNA were measured experimentally and values were compared with model predictions. Also the model was validated against  experimental growth results on 118 carbon and nitrogen sources.

The model accurately predicts growth rates under photoauto-, hetero-, and mixotrophic conditions. The simulated flux distributions revealed high correlation with the ¹³C flux analysis data previously reported for C. protothecoides. Predicted flux distributions under different trophic conditions show that not only central carbon metabolism but also amino acid, nucleotide, and pigment biosynthetic pathways are impacted when the microalgae is under nitrogen starvation. Furthermore, prediction of growth rates under various medium compositions using iCZ843 suggested an increased growth rate when tryptophan or methionine were added. Experimental data verified these predictions and moderately exceed the expected results.

iCZ843 represents the most comprehensive model for any eukaryotic photosynthetic organism to date based on genome size and number of genes included in the reconstruction. The highly curated model was validated against experimental data and lays the foundation for advanced strain design and medium alteration to improve yield.