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

Abstract

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 cancerassociated biomarker of solid tumors. Due to its metabolic burden, scFv recombinant expression causes a remarkable decrease in the maximum speci c 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. For improving scFv production, ux balance analysis (FBA) and FVSEOF methods identi ed a number of potential genetic engineering targets localized in the glucose uptake system and pentose phosphate pathway. From the predicted targets, 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 lead to increased scFv production. This approach can be considered for the improvement of other recombinant proteins production.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

“…Microbial GEMs have played a pivotal role in improving the efficiency, speed, and scale of production of these molecules, as the use of microbial GEMs enables systematic prediction of all cellular metabolic processes and provides detailed insights into the relationship between genetic manipulations and the efficiency and quantity of the intended products. [32][33][34][35] These advances have yielded highly favourable outcomes and there are many success stories in metabolic engineering with GEMs that will benefit translational medicine. 36 Microbial GEMs have long been used in metabolic engineering to identify gene targets for effective strain improvement and the design of recombinant microbial cell factories for the enhanced production of biomolecules that are in high demand for medical purposes.…”

“…Microbial GEMs have played a pivotal role in improving the efficiency, speed, and scale of production of these molecules, as the use of microbial GEMs enables systematic prediction of all cellular metabolic processes and provides detailed insights into the relationship between genetic manipulations and the efficiency and quantity of the intended products. 32–35 These advances have yielded highly favourable outcomes and there are many success stories in metabolic engineering with GEMs that will benefit translational medicine. 36…”