Improving Lipid Accumulation In Yarrowia Lipolytica Based On Genome-scale Metabolic Model

Recently,in light of the scarcity of fossil energy and effect of green gas,it is urgent to exploit bio-diesel as alternate energy source.Therein,microbial lipid production,as a promising industrialization approach,has many advantages,such as high efficiency,environment-friendly and hardly affected by climate etc.In addition,microbial lipids have been applied in healthcare products and cosmetics industry.As a non-conventional yeast,Yarrowia lipolytica has many advantages as it possesses the outstanding capacity of lipid production,the clear genomic information,the ability to utilize broad range of substrates and availability of gene editing tools.The triacylglycerol(TAG)biosynthesis pathway in Y.lipolytica is the Kennedy pathway,which requests plenty of cofactor NADPH.In this study,based on the simulation and optimization of the genome-scale metabolic network model(GEM),the biosynthesis of TAG was improved in Y.lipolytica.First,the existing genome-scale metabolic model iYL₂.0 was modified.Subsequently,algorithm "OptSwap" was employed to identify targets for improving lipid accumulation in Y.lipolytica,in which gdhA and gdhB were chosen for experimental verification.We proposed to replace the endogenous genes with exogenous genes through the gene editing method CRISPR.The results showed that by exchanging the redox dependence of gdhA gene from NAP+ to NADP+,lipid production and content reached to 0.968 g/L and 0.134 g/g,and by inserting exogenous gdhB gene(NADH-dependent)to the original strain,lipid production and content reached to 0.886 g/L and 0.119 g/g,resulting in 36?50%increases in lipid contentcompared to the control group.We propose that engineering redox cofactor in GDH pathway is efficient for improving the lipid production in Y.lipolytica,and in silico simulation with GEM is feasible to guide the metabolic engineering of this strain.