A Constraint-based modeling approach to reach an improved chemically defined minimal medium for recombinant antiEpEX-scFv production by Escherichia coli

Genome-scale metabolic model-based engineering of Escherichia coli enhances recombinant single-chain antibody fragment production

PURPOSE

Escherichia coli is an attractive and cost-effective cell factory for producing recombinant proteins such as single-chain variable fragments (scFvs). AntiEpEX-scFv is a small antibody fragment that has received considerable attention for its ability to target the epithelial cell adhesion molecule (EpCAM), a cancer-associated biomarker of solid tumors. Due to its metabolic burden, scFv recombinant expression causes a remarkable decrease in the maximum specific growth rate of the scFv-producing strain. In the present study, a genome-scale metabolic model (GEM)-guided engineering strategy is proposed to identify gene targets for improved antiEpEX-scFv production in E. coli.

METHODS

In this study, a genome-scale metabolic model of E. coli (iJO1366) and a metabolic modeling tool (FVSEOF) were employed to find appropriate genes to be amplified in order to improve the strain for incresed production of antiEpEX-scFv. To validate the model predictions, one target gene was overexpressed in the parent strain Escherichia coli BW25113 (DE3).

RESULTS

For improving scFv production, we applied the FVSEOF method to identify a number of potential genetic engineering targets. These targets were found to be localized in the glucose uptake system and pentose phosphate pathway. From the predicted targets, the glk gene encoding glucokinase was chosen to be overexpressed in the parent strain Escherichia coli BW25113 (DE3). By overexpressing glk, the growth capacity of the recombinant E. coli strain was recovered. Moreover, the engineered strain with glk overexpression successfully led to increased scFv production.

CONCLUSION

The genome-scale metabolic modeling can be considered for the improvement of the production of other recombinant proteins.