A shotgun approach for highly multiplexed mammalian metabolic engineering

Abstract

Mammalian metabolic engineering is critical to advancing basic biology, bioproduction, and cell therapy. However, as pathway complexity increases, so does the size of both the combinatorial design space and the DNA constructs required, rendering unbiased screens intractable. To address this, we developed Shotgun Genetic Engineering (SGE), a scalable approach that exploits the ease of delivering many barcoded small constructs--rather than a single large one--into mammalian cells. This allows each cell to serve as an independent experiment, carrying a unique synthetic metabolic pathway that explores combinations of gene content, stoichiometry, and organellar localization. Functional pathways are identified by sequencing barcodes from cells exhibiting the desired phenotype. Using SGE, we screened millions of pathway combinations to engineer essential amino acid biosynthesis in two mammalian cell lines (CHO and Jurkat), achieving near-wild-type growth in valine-free medium and, for the first time, enabling isoleucine prototrophy in CHO cells. Successful solutions favored mitochondrial localization and required integration of 23-52 kb of synthetic DNA--lengths that are impractical to screen by conventional methods. The resulting datasets are compatible with machine learning frameworks, positioning SGE as a powerful platform for decoding and engineering complex biosynthetic traits in mammalian systems.