Yarrowia lipolytica is an important oleaginous industrial microorganism used to produce biofuels and other value-added compounds. Although several genetic engineering tools have been developed for Y. lipolytica， there is no efficient method for genomic integration of large DNA fragments. In addition， methods for constructing multigene expression libraries for biosynthetic pathway optimization are still lacking in Y. lipolytica. In this study， we demonstrate that multiple and large DNA fragments can be randomly and efficiently integrated into the genome of Y. lipolytica in a homology-independent manner. This homology-independent integration generates variation in the chromosomal locations of the inserted f... More
Yarrowia lipolytica is an important oleaginous industrial microorganism used to produce biofuels and other value-added compounds. Although several genetic engineering tools have been developed for Y. lipolytica， there is no efficient method for genomic integration of large DNA fragments. In addition， methods for constructing multigene expression libraries for biosynthetic pathway optimization are still lacking in Y. lipolytica. In this study， we demonstrate that multiple and large DNA fragments can be randomly and efficiently integrated into the genome of Y. lipolytica in a homology-independent manner. This homology-independent integration generates variation in the chromosomal locations of the inserted fragments and in gene copy numbers， resulting in the expression differences in the integrated genes or pathways. Because of these variations， gene expression libraries can be easily created through one-step integration. As a proof of concept， a LIP2 (producing lipase) expression library and a library of multiple genes in the β-carotene biosynthetic pathway were constructed， and high-production strains were obtained through library screening. Our work demonstrates the potential of homology-independent genome integration for library construction， especially for multivariate modular libraries for metabolic pathways in Y. lipolytica， and will facilitate pathway optimization in metabolic engineering applications.
