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.

Systematically gap-filling the genome-scale metabolic model of CHO cells.

OBJECTIVE: Chinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells. RESULTS: We expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased. CONCLUSIONS: The present CHO model is an important step towards more complete metabolic models of CHO cells.

A reconciliation of genome-scale metabolic network model of Zymomonas mobilis ZM4.

Zymomonas mobilis ZM4 has recently been used for a variety of biotechnological purposes. To rationally enhance its metabolic performance, a reliable genome-scale metabolic network model (GEM) of this organism is required. To this end, we reconstructed a genome-scale metabolic model (iHN446) for Z. mobilis, which involves 446 genes, 859 reactions, and 894 metabolites. We started by first reconciling the existing GEMs previously constructed for Z. mobilis to obtain a draft network. Next, recent gene annotations, up-to-date literature, physiological data and biochemical databases were used to upgrade the network. Afterward, the draft network went through a curative and iterative process of gap-filling by computational tools and manual refinement. The final model was evaluated using experimental data and literature information. We next applied this model as a platform for analyzing the links between transcriptome-flux and transcriptome-metabolome. We found that experimental observations were in agreement with the predicted results from our final GEM. Taken together, this comprehensive model (iHN446) can be utilized for studying metabolism in Z. mobilis and finding rational targets for metabolic engineering applications.

A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production.

Chinese hamster ovary (CHO) cells are the main workhorse in the biopharmaceutical industry for the production of recombinant proteins, such as monoclonal antibodies. To date, a variety of metabolic engineering approaches have been used to improve the productivity of CHO cells. While genetic manipulations are potentially laborious in mammalian cells, rational design of CHO cell culture medium or efficient fed-batch strategies are more popular approaches for bioprocess optimization. In this study, a genome-scale metabolic network model of CHO cells was used to design feeding strategies for CHO cells to improve monoclonal antibody (mAb) production. A number of metabolites, including threonine and arachidonate, were suggested by the model to be added into cell culture medium. The designed composition has been experimentally validated, and then optimized, using design of experiment methods. About a two-fold increase in the total mAb expression has been observed using this strategy. Our approach can be used in similar bioprocess optimization problems, to suggest new ways of increasing production in different cell factories.

Applications of a metabolic network model of mesenchymal stem cells for controlling cell proliferation and differentiation.

Mesenchymal stem cells (MSCs) can be isolated from several tissues of adults. In addition, MSCs have the potential of differentiation into several cell types. Therefore, MSCs are very useful in stem cell therapy and regenerative medicine. MSCs have also been used as gene or protein carriers. As a result, maintaining MSCs in a desirable metabolic state has been the subject of several studies. Here, we used a genome scale metabolic network model of bone marrow derived MSCs for exploring the metabolism of these cells. We analyzed metabolic fluxes of the model in order to find ways of increasing stem cell proliferation and differentiation. Consequently, the experimental results were in consistency with computational results. Therefore, analyzing metabolic models was proven to be a promising field in biomedical researches of stem cells.

Biomedical applications of cell- and tissue-specific metabolic network models.

The essential goal of biomedical research is to understand the underlying mechanism of disease development. Unfortunately, achieving this goal requires expensive and time-consuming efforts in medical biotechnology. This review focuses on how context-specific genome-scale metabolic network models may facilitate reaching this goal. Such models provide an in silico framework for computational simulation of cellular metabolism, predicting the outcome of experiments. Therefore, by using these models at the initial stages of experimental design, time and cost in biomedical researches may be reduced. Furthermore, with the availability of such models, not only important pathways involved in cell dysfunction may be better understood, but also drug targets predicted based on these models can be seen as novel targets for in vivo validation. The main point of this review is that metabolic modeling can predict drug targets and biomarkers without the need for kinetics data. We provide a comprehensive review of human metabolic models and their applications, in addition to the methods used for analyzing models. We discuss how these models have been used in analyzing metabolic capabilities of different cells and tissues, in identifying disease-related metabolic pathways and biomarkers, and in understanding the human-microbe interaction.

Tissue engineering; strategies, tissues, and biomaterials.

Current tissue regenerative strategies rely mainly on tissue repair by transplantation of the synthetic/natural implants. However, limitations of the existing strategies have increased the demand for tissue engineering approaches. Appropriate cell source, effective cell modification, and proper supportive matrices are three bases of tissue engineering. Selection of appropriate methods for cell stimulation, scaffold synthesis, and tissue transplantation play a definitive role in successful tissue engineering. Although the variety of the players are available, but proper combination and functional synergism determine the practical efficacy. Hence, in this review, a comprehensive view of tissue engineering and its different aspects are investigated.