Deciphering lipid mutant phenotypes with a new genome-scale metabolic model ofMicrochloropsis gaditanaencompassing detailed lipid metabolism

Abstract

AbstractThe oleaginous microalgaMicrochloropsis gaditana(formerlyNannochloropsis gaditana) has gained large interest due to its potential to produce lipids for a wide range of biotechnological applications. To optimizeM. gaditanagrowth conditions and develop new strains to enhance lipid synthesis and accumulation, a broad understanding of the organism metabolism is essential. Computational models such as genome-scale metabolic models constitute powerful tools for unravelling microorganism metabolism. In this work we present iMgadit23, a new genome-scale metabolic model forM. gaditana. Model covers 2330 reactions involving 1977 metabolites and associated with 889 genes. Pathways involved in membrane and storage glycerolipid biosynthesis and degradation have undergone thorough manual curation and have been comprehensively described based on current knowledge ofM. gaditanalipid metabolism. Additionally, we developed a detailed 2D-pathway map of model content to provide a systems-level visualization ofM. gaditanametabolism. We demonstrated the predictive capabilities of iMgadit23, validating its ability to qualitatively and quantitatively capturein vivogrowth phenotypes under diverse environmental and genetic conditions. Model was also able to capture the role of the Bubblegum acyl-CoA synthetase in remodelingM. gaditanalipid metabolism. iMgadit23 and its 2D maps constitute valuable tools to increase understanding ofM. gaditanametabolism and deciphering mutant phenotypes, specifically in the context of lipid metabolism. The model holds significant promise in predictingM. gaditanametabolic capabilities under varying genetic and environmental conditions, facilitating strain engineering, and optimizing cultivation processes for a broad range of industrial applications.