A new genome-scale metabolic model of oleaginous microalgae with refined lipid metabolism clarifies Microchloropsis gaditana mutant phenotypes.

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. 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 that encompasses 2330 reactions involving 1977 metabolites and associated with 889 genes. A key advancement of iMgadit23 over other Microchloropsis models is its fully detailed lipid metabolism: pathways involved in membrane and storage glycerolipid biosynthesis and degradation that have undergone thorough manual curation and have been comprehensively described based on current knowledge of M. gaditana lipid metabolism. iMgadit23 successfully reproduced in vivo growth phenotypes under diverse environmental and genetic conditions, including heterotrophic growth on multiple carbon sources and photoautotrophic growth under varying nitrogen sources and concentrations. The model was also able to capture the role of the Bubblegum acyl-CoA synthetase in remodeling M. gaditana lipid metabolism, with predicted flux distributions consistent with the altered lipid profile observed in a mutant strain. In addition, we developed a comprehensive 2D-pathway map of model content to provide a systems-level visualization of M. gaditana metabolism. Together, iMgadit23 and its 2D map constitute valuable tools for advancing our understanding of M. gaditana metabolism and for deciphering mutant phenotypes, particularly in the context of lipid metabolism, allowing optimization of cultivation strategies, enhancement of lipid productivity, and guidance of strain engineering for biofuel production and other biotechnological processes.

• Publication year

  2025

• Venue

  Bioresource Technology

• Publication date

  2025-11-01

• Fields of study

  Biology, Medicine, Environmental Science

• Identifiers
  DOI 10.1016/j.biortech.2025.133619PMID 41218742
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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