Transcriptomics-Driven Engineering of Yarrowia lipolytica for Enhanced Fatty Acid Biosynthesis.

Area of Science:

• Microbial biotechnology
• Systems biology
• Metabolic engineering

Background:

• Yarrowia lipolytica is a key microbial chassis for biomanufacturing.
• Limited genetic annotation and regulatory element characterization hinder efficient strain development.
• Vast transcriptomic data remains underutilized for systematic gene discovery.

Purpose of the Study:

• To develop a computational pipeline (Findgene) for efficient regulatory gene discovery in Y. lipolytica.
• To identify novel regulatory genes involved in fatty acid metabolism.
• To engineer Y. lipolytica for enhanced fatty acid production.

Main Methods:

• Integrated standardized transcriptomic meta-analysis with weighted gene coexpression network analysis (WGCNA).
• Applied the Findgene pipeline to Y. lipolytica transcriptomic data.
• Performed experimental validation of candidate genes, including gene substitution and overexpression.

Main Results:

• Identified six candidate regulatory genes related to fatty acid metabolism.
• Substitution of YALI0B12342g with the G643R mutant increased total fatty acid production by 131%.
• Overexpression of YALI0A07733g and YALI0A20207g significantly enhanced fatty acid titer.
• A combinatorial strategy achieved a 2.9-fold enhancement in total fatty acid production.

Conclusions:

• The Findgene pipeline enables efficient and economical discovery of regulatory genes.
• Engineered Y. lipolytica strains show significant potential for scale-up production of fatty acid-derived compounds.
• The Findgene framework is adaptable for engineering other nonconventional yeast species.