Deciphering lipid mutant phenotypes with a new genome-scale metabolic model of Microchloropsis gaditana encompassing detailed lipid metabolism

Abstract

The oleaginous microalga Microchloropsis gaditana (formerly Nannochloropsis gaditana) has gained large interest due to its potential to produce lipids for a wide range of biotechnological applications. To optimize M. gaditana growth 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 for M. 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 of M. gaditana lipid metabolism. Additionally, we developed a detailed 2D-pathway map of model content to provide a systems-level visualization of M. gaditana metabolism. We demonstrated the predictive capabilities of iMgadit23, validating its ability to qualitatively and quantitatively capture in vivo growth phenotypes under diverse environmental and genetic conditions. Model was also able to capture the role of the Bubblegum acyl-CoA synthetase in remodeling M. gaditana lipid metabolism. iMgadit23 and its 2D map constitute valuable tools to increase understanding of M. gaditana metabolism and deciphering mutant phenotypes, specifically in the context of lipid metabolism. The model holds significant promise in predicting M. gaditana metabolic capabilities, facilitating strain engineering, and optimizing cultivation processes for a broad range of industrial applications.