A Refined Genome-Scale Reconstruction of Chlamydomonas Metabolism

Microalgae are of prime biotechnological interest due to their potential capability of providing reasonable quantities of sustainable edible oils or biofuel. Large scale mechanistic models of metabolic pathways are a critical prerequisite to assess the potential capabilities of these organisms. Here, we present our efforts to revise and improve a genome scale metabolic model reconstruction of the microalgae Chlamydomonas reinhardtii. Our model, termed iCre1355 and updated to current literature and database knowledge, represents a major advancement over previous models and encompasses 1355 genes, 2394 reactions and 1133 metabolites encoded across the nuclear, chloroplast and mitochondrial genomes.
We verified the performance of iCre1355 by comparing its predictive accuracy across 306 phenotypes (81 mutants), lipid yield analysis and chemostat-grown cells. We show that particularly carbon and phosphate uptake rates are good predictors of growth rate. By incorporating high-resolution time series transcriptomics data into iCre1355, we uncover sequential metabolic pathway responses to nitrogen starvation and light intensity changes. Moreover, with this approach we can recapitulate cease of growth and lipid accumulation. Taken together, iCre1355 represents an experimentally-validated genome-scale reconstruction of C. reinhardtii metabolism, which potentially will serve as a useful resource for studying the metabolic processes of this and related microalgae.