Transcriptomics-Driven Engineering of

While Yarrowia lipolytica 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 Y. lipolytica data sets identified six candidate regulatory genes related to fatty acid metabolism: YALI0B12342g, YALI0A07733g, YALI0C03003g, YALI0C16797g, YALI0A20207g, and YALI0D01001g. Remarkably, substitution YALI0B12342g with the G643R mutant increased total fatty acid production by 131%. Meanwhile, experimental validation revealed that plasmid-mediated overexpression of YALI0A07733g and YALI0A20207g significantly enhanced total fatty acid titer. Based on these, the combinatorial engineering strategy incorporating overexpression of YALI0A07733g/YALI0A20207g and implementation of the YALI0B12342g 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.