RESUMEN
Combinatorial library-based metabolic engineering approaches allow lower cost and faster strain development. We developed a genetic toolbox EXPRESSYALI for combinatorial engineering of the oleaginous yeast Yarrowia lipolytica. The toolbox enables consecutive rounds of engineering, where up to three combinatorially assembled gene expression cassettes can be integrated into each yeast clone per round. The cassettes are integrated into distinct intergenic sites or an open reading frame of a target gene if a simultaneous gene knockout is desired. We demonstrate the application of the toolbox by optimizing the Y. lipolytica to produce the red beet color betanin via six consecutive rounds of genome editing and screening. The library size varied between 24-360. Library screening was facilitated by automated color-based colony picking. In the first round, betanin pathway genes were integrated, resulting in betanin titer of around 20 mg/L. Through the following five consecutive rounds, additional biosynthetic genes were integrated, and the precursor supply was optimized, resulting in a titer of 70 mg/L. Three beta-glucosidases were deleted to prevent betanin deglycosylation, which led to a betanin titer of 130 mg/L in a small scale and a titer of 1.4 g/L in fed-batch bioreactors. The EXPRESSYALI toolbox can facilitate metabolic engineering efforts in Y. lipolytica (available via AddGene Cat. Nr. 212682-212704, Addgene kit ID # 1000000245).
RESUMEN
We report that two-stage dynamic control improves bioprocess robustness as a result of the dynamic deregulation of central metabolism. Dynamic control is implemented during stationary phase using combinations of CRISPR interference and controlled proteolysis to reduce levels of central metabolic enzymes. Reducing the levels of key enzymes alters metabolite pools resulting in deregulation of the metabolic network. Deregulated networks are less sensitive to environmental conditions improving process robustness. Process robustness in turn leads to predictable scalability, minimizing the need for traditional process optimization. We validate process robustness and scalability of strains and bioprocesses synthesizing the important industrial chemicals alanine, citramalate and xylitol. Predictive high throughput approaches that translate to larger scales are critical for metabolic engineering programs to truly take advantage of the rapidly increasing throughput and decreasing costs of synthetic biology.