Transcriptomics-Driven Engineering of <i>Yarrowia lipolytica</i> for Enhanced Fatty Acid Biosynthesis

While <i>Yarrowia lipolytica</i> has gained prominence as a microbial chassis for biomanufacturing, its broader application faces two critical limitations: incomplete genetic annotation and insufficient characterization of regulatory elements, rendering the construction of high-efficiency microbial cell factories a time-consuming and empirically driven process. Notably, vast transcriptomic data sets in public database remain underutilized for systematic gene discovery. To address these limitations, we developed Findgene─a computational pipeline integrating standardized transcriptomic meta-analysis with weighted gene coexpression network analysis (WGCNA). Application of this tool to consolidated <i>Y. lipolytica</i> data sets identified six candidate regulatory genes related to fatty acid metabolism: <i>YALI0B12342g</i>, <i>YALI0A07733g</i>, <i>YALI0C03003g</i>, <i>YALI0C16797g</i>, <i>YALI0A20207g</i>, and <i>YALI0D01001g</i>. Remarkably, substitution <i>YALI0B12342g</i> with the G643R mutant increased total fatty acid production by 131%. Meanwhile, experimental validation revealed that plasmid-mediated overexpression of <i>YALI0A07733g</i> and <i>YALI0A20207g</i> significantly enhanced total fatty acid titer. Based on these, the combinatorial engineering strategy incorporating overexpression of <i>YALI0A07733g</i>/<i>YALI0A20207g</i> and implementation of the <i>YALI0B12342g</i> G643R variant achieved a 2.9-fold enhancement in total fatty acid production compared to wild type Po1f strains. This optimized chassis demonstrates substantial potential for scale-up production of fatty acid-derived compounds. Furthermore, the FindGene framework establishes a generalized methodology for regulatory gene efficient and economical discovery that could be adapted to engineer other nonconventional yeast species.