Increase recombinant antibody yields through optimizing vector design and production process in CHO cells.

Chinese hamster ovary (CHO) cells are the most commonly used host cells for the production of recombinant monoclonal antibodies (mAbs) due to their several advantages. Although the yields of recombinant mAbs can be greatly increased by some strategies, such as medium formulation, culture conditions, and cell engineering, most studies focused on either upstream design or downstream processes. In the present study, we first expressed recombinant adalimumab through combination of the vector design and production process optimization in CHO cells. Bicistronic vector, monocistronic vector, and dual promoter vector were constructed, and the production process was optimized using low-temperature and fed-batch culture. The results showed that the dual promoter vector exhibited the highest yield under the transient and stable transfected cells among three different vector systems in CHO cells. In addition, low-temperature and fed-batch culture could further improve the yields of adalimumab. The purified antibody displayed tumor necrosis factor-α (TNF-α) binding activity. In conclusion, combination of expression vector design and production process optimization can achieve higher expression of recombinant mAbs in CHO cells. KEY POINTS: • The dual promoter vector is more effective for expressing recombinant antibodies. • The yields of antibodies are related to the LC chain expression level. • Low-temperature and feed addition can promote antibody production.

A new probability method to understand protein-protein interface formation mechanism at amino acid level.

Protein-protein interactions and recognitions are essential for organisms to accomplish many cellular processes. The interface compositions of protein-protein complexes are the key to understand the molecular mechanisms of these processes. There are many studies on near native interface recognition for protein-protein interactions, but the formation mechanism of protein-protein interface is still ambiguous. Here, we propose a new probability method to understand protein-protein interface formation mechanism at amino acid level. The probability of two surface residues from two monomers as a true interface residue pair is estimated by their properties in the structures of protein monomers. Estimated residue pairs with interface possibilities combine together to form a protein-protein interface. The possibility values are integrated to discriminate the near native interface from non-near native ones. In this paper, five simple probability based methods are constructed for near native interface recognition based on several geometric and physicochemical descriptors. The performances are comparable to the best results reported previously, which suggests this is a promising way. The idea proposed here will have a certain significance to the future research on protein-protein interactions.

Factors and Mechanisms Affecting the Secretion of Recombinant Protein in CHO Cells.

The market demand for recombinant therapeutic proteins (RTPs) has promoted the development of various protein expression host and bioprocessing technologies. Since mammalian cells have the unique advantage of being able to direct the correct folding of proteins and provide post-translational processing such as complex glycosylation, the RTPs produced by them currently account for approximately 80% of the approved marketed RTPs. Among them, Chinese hamster ovary (CHO) cells are currently the preferred host cells for the production of RTPs. Production of RTPs in CHO cells involves the synthesis, processing, transport, and secretion of proteins. The secretion process of proteins is one of the key steps, which greatly limits the yield and quality of RTPs. Here, we review the recombinant protein secretion process of CHO cells and its influencing factors, and further discuss the optimization strategy for recombinant protein secretion and expression in CHO cells.

Stabilizing and Anti-Repressor Elements Effectively Increases Transgene Expression in Transfected CHO Cells.

Chinese hamster ovary (CHO) cells are currently the most widely used host cells for recombinant therapeutic protein (RTP) production. Currently, the RTP yields need to increase further to meet the market needs and reduce costs. In this study, three stabilizing and anti-repressor (SAR) elements from the human genome were selected, including human SAR7, SAR40, and SAR44 elements. SAR elements were cloned upstream of the promoter in the eukaryotic vector, followed by transfection into CHO cells, and were screened under G418 pressure. Flow cytometry was used to detect enhanced green fluorescent protein (eGFP) expression levels. The gene copy numbers and mRNA expression levels were determined through quantitative real-time PCR. Furthermore, the effect of the stronger SAR elements on adalimumab was investigated. The results showed that transgene expression levels in the SAR-containing vectors were higher than that of the control vector, and SAR7 and SAR40 significantly increased and maintained the long-term expression of the transgene in CHO cells. In addition, the transgene expression level increase was related with gene copy numbers and mRNA expression levels. Collectively, SAR elements can enhance the transgene expression and maintain the long-term expression of a transgene in transfected CHO cells, which may be used to increase recombinant protein production in CHO cells.

17ß-Estradiol-Induced Conformational Changes of Human Microsomal Triglyceride Transfer Protein: A Computational Molecular Modelling Study.

Human microsomal triglyceride transfer protein (hMTP) plays an essential role in the assembly of apoB-containing lipoproteins, and has become an important drug target for the treatment of several disease states, such as abetalipoproteinemia, fat malabsorption and familial hypercholesterolemia. hMTP is a heterodimer composed of a larger hMTPα subunit and a smaller hMTPß subunit (namely, protein disulfide isomerase, hPDI). hPDI can interact with 17ß-estradiol (E2), an endogenous female sex hormone. It has been reported that E2 can significantly reduce the blood levels of low-density lipoprotein, cholesterol and triglyceride, and modulate liver lipid metabolism in vivo. However, some of the estrogen's actions on lipid metabolism are not associated with estrogen receptors (ER), and the exact mechanism underlying estrogen's ER-independent lipid-modulating action is still not clear at present. In this study, the potential influence of E2 on the stability of the hMTP complex is investigated by jointly using multiple molecular dynamics analyses based on available experimental structures. The molecular dynamics analyses indicate that the hMTP complex in the presence of E2 has reduced interface contacts and surface areas. A steered molecular dynamics analysis shows that the forces required to separate the two subunits (namely, hPDI and hMTPα subunit) of the hMTP complex in the absence of E2 are significantly higher than the forces required to separate the complex in which its hPDI is already bound with E2. E2 makes the interface between hMTPα and hPDI subunits more flexible and less stable. The results of this study suggest that E2-induced conformational changes of the hMTP complex might be a novel mechanism partly accounting for the ER-independent lipid-modulating effect of E2.

Different protein-protein interface patterns predicted by different machine learning methods.

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.