Optimizing the strain engineering process for industrial-scale production of bio-based molecules.

Biomanufacturing could contribute as much as ${\$}$30 trillion to the global economy by 2030. However, the success of the growing bioeconomy depends on our ability to manufacture high-performing strains in a time- and cost-effective manner. The Design-Build-Test-Learn (DBTL) framework has proven to be an effective strain engineering approach. Significant improvements have been made in genome engineering, genotyping, and phenotyping throughput over the last couple of decades that have greatly accelerated the DBTL cycles. However, to achieve a radical reduction in strain development time and cost, we need to look at the strain engineering process through a lens of optimizing the whole cycle, as opposed to simply increasing throughput at each stage. We propose an approach that integrates all 4 stages of the DBTL cycle and takes advantage of the advances in computational design, high-throughput genome engineering, and phenotyping methods, as well as machine learning tools for making predictions about strain scale-up performance. In this perspective, we discuss the challenges of industrial strain engineering, outline the best approaches to overcoming these challenges, and showcase examples of successful strain engineering projects for production of heterologous proteins, amino acids, and small molecules, as well as improving tolerance, fitness, and de-risking the scale-up of industrial strains.

Engineering Bacillus megaterium Strains To Secrete Cellulases for Synergistic Cellulose Degradation in a Microbial Community.

Recent environmental concerns have intensified the need to develop systems to degrade waste biomass for use as an inexpensive carbon source for microbial chemical production. Current approaches to biomass utilization rely on pretreatment processes that include expensive enzymatic purification steps for the requisite cellulases. We aimed to engineer a synthetic microbial community to synergistically degrade cellulose by compartmentalizing the system with multiple specialized Bacillus megaterium strains. EGI1, an endoglucanase, and Cel9AT, a multimodular cellulase, were targeted for secretion from B. megaterium. A small library of signal peptides (SPs) with five amino acid linkers was selected to tag each cellulase for secretion from B. megaterium. Cellulase activity against amorphous cellulose was confirmed through a series of bioassays, and the most active SP constructs were identified as EGI1 with the LipA SP and Cel9AT with the YngK SP. The activity of the optimized cellulase secretion strains was characterized individually and in tandem to assess synergistic cellulolytic activity. The combination of EGI1 and Cel9AT yielded higher activity than either single cellulase. A coculture of EGI1 and Cel9AT secreting B. megaterium strains demonstrated synergistic behavior with higher activity than either monoculture. This cellulose degradation module can be further integrated with bioproduct synthesis modules to build complex systems for the production of high value molecules.

Improved selectivity of an engineered multi-product terpene synthase.

Mutation of the sesquiterpene synthase Cop2 was conducted with a high-throughput screen for the cyclization activity using a non-natural substrate. A mutant of Cop2 was identified that contained three amino acid substitutions. This mutant, 17H2, converted the natural substrate FPP into germacrene D-4-ol with 77% selectivity. This selectivity is in contrast to that of the parent enzyme in which germacrene D-4-ol is produced as 29% and α-cadinol is produced as 46% of the product mixture. The mutations were shown to each contribute to this selectivity, and a homology model suggested that the mutations lie near to the active site though would be unlikely to be targeted for mutation by rational methods. Kinetic comparisons show that 17H2 maintains a kcat/KM of 0.62 mM(-1) s(-1), which is nearly identical to that of the parent Cop2, which had a kcat/KM of 0.58 mM(-1) s(-1).