RSM-based Model to Predict Optimum Fermentation Conditions for Soluble Expression of the Antibody Fragment Derived from 4D5MOC-B Humanized Mab in SHuffle™ T7 E. coli.

ABSTRACT: Overexpression of the EpCAM in epithelial-derived neoplasms makes this receptor a promising target in antibody-based therapy. Due to the lack of N-glycosylation, Escherichia coli (E. coli) seems to be the most appropriate choice for the expression of antibody fragments. However, developing a robust and cost-effective process that produces consistent therapeutic proteins from inclusion bodies is a major challenge. Undoubtedly, it can be circumvented by the soluble expression of these proteins. Utilization of numerous genetically modified hosts and optimization of cultivation conditions are two effective approaches widely used to overcome the insolubility problem. Due to the cytoplasmic expression of DsbC and the ability to the correct formation of disulfide bonds, the Shuffle™ T7 strain can be a suitable host for the soluble production of recombinant proteins. Here, Box-Behnken design (BBD)- Response surface methodology (RSM) modeling was employed to develop optimized culture conditions for 4D5MOC-B scFv fragment production in SHuffle™ T7 strain while solubility and production level were considered as responses. Although both responses were significantly influenced by post-induction temperature, cell density at induction time, and IPTG concentration, the temperature had the largest effect. The maximum experimental soluble protein obtained by adding 1 mM of IPTG into the M9 medium when the cell density reached 0.7 at 23 ᵒC was 693.56 µg/mL which was in good correlation with the predicted value of 720.742 µg/mL. Predictable total expression value was also experimentally verified. This strategy can be scaled-up for the production of large amounts of scFvs from SHuffle™ T7 E. coli to facilitate their potential applications as therapeutic and diagnostic agents.

SUBMITTER: Behravan A 

PROVIDER: S-EPMC8170757 | biostudies-literature |

REPOSITORIES: biostudies-literature