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

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.