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ABSTRACT Recombinant adeno associated virus (rAAV) vectors have become popular delivery vehicles for in vivo gene therapies, but demand for rAAVs continues to outpace supply. Platform processes for rAAV production are being developed by many manufacturers, and transient chemical transfection of human embryonic kidney 293 (HEK293) cells is currently the most popular approach. However, the cutting edge nature of rAAV process development encourages manufacturers to keep cell culture media formulations, plasmid sequences, and other details proprietary, which creates hurdles for small companies and academic labs seeking to innovate in this space. To address this problem, we leveraged the resources of an academic‐industry consortium (Advanced Mammalian Biomanufacturing Innovation Center, AMBIC) to develop an rAAV production system based on transient transfection of suspension HEK293 cells adapted to an in‐house, chemically defined medium. We found that balancing iron and calcium levels in the medium were crucial for maintaining transfection efficiency and minimizing cell aggregation, respectively. A design of experiments approach was used to optimize the transient transfection process for batch rAAV production, and PEI:DNA ratio and cell density at transfection were the parameters with the strongest effects on vector genome (VG) titer. When the optimized transient process was transferred between two university sites, VG titers were within a twofold range. Analytical characterization showed that purified rAAV from the AMBIC process had comparable viral protein molecular weights versus vector derived from commercial processes, but differences in transducing unit (TU) titer were observed between vector preps. The developed media formulation, transient transfection process, and analytics for VG titer, capsid identity, and TU titer constitute a set of workflows that can be adopted by others to study fundamental problems that could improve product yield and quality in the nascent field of rAAV manufacturing. 

ABSTRACT Bacillus subtilisis an important industrial and environmental microorganism known to occupy many niches and produce many compounds of interest. Although it is one of the best-studied organisms, much of this focus including the reconstruction of genome-scale metabolic models has been placed on a few key laboratory strains. Here, we substantially expand these prior models to pan-genome-scale, representing 481 genomes ofB. subtiliswith 2,315 orthologous gene clusters, 1,874 metabolites, and 2,239 reactions. Furthermore, we incorporate data from carbon utilization experiments for eight strains to refine and validate its metabolic predictions. This comprehensive pan-genome model enables the assessment of strain-to-strain differences related to nutrient utilization, fermentation outputs, robustness, and other metabolic aspects. Using the model and phenotypic predictions, we divideB. subtilisstrains into five groups with distinct patterns of behavior that correlate across these features. The pan-genome model offers deep insights intoB. subtilis’metabolism as it varies across environments and provides an understanding as to how different strains have adapted to dynamic habitats. IMPORTANCEAs the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism.Bacillus subtilisstrain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481B. subtilisstrains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application.