Generation of novel respiratory syncytial virus vaccine candidate antigens that can induce high levels of prefusion-specific antibodies.

Respiratory syncytial virus (RSV) infection is an acute respiratory infection caused by RSV. It occurs worldwide, and for over 50 years, several attempts have been made to research and develop vaccines to prevent RSV infection; effective preventive vaccines are eagerly awaited. The RSV fusion (F) protein, which has gained attention as a vaccine antigen, causes a dynamic structural change from the preF to postF state. Therefore, the structural changes in proteins must be regulated to produce a vaccine antigen that can efficiently induce antibodies with high virus-neutralizing activity. We successfully discovered several mutations that stabilized the antigen site Ø in the preF state, trimerized it, and improved the level of protein expression through observation and computational analysis of the RSV-F protein structure and amino acid mutation analysis of RSV strains. The four RSV-F protein mutants that resulted from the combination of these effective mutations stably conserved a wide range of preF- and trimeric preF-specific epitopes with high virus-neutralizing activity. Absorption assay using human serum revealed that mutants constructed bound to antibodies with virus-neutralizing activity that were induced by natural RSV infection, whereas they hardly bound to anti-postF antibodies without virus-neutralizing activity. Furthermore, mouse immunization demonstrated that our constructed mutants induced a high percentage of antibodies that bind to the preF-specific antigen site. These characteristics suggest that the mutants constructed can be superior vaccine antigens from the viewpoint of RSV infection prevention effect and safety.

Optimization of a pH-shift control strategy for producing monoclonal antibodies in Chinese hamster ovary cell cultures using a pH-dependent dynamic model.

To optimize monoclonal antibody (mAb) production in Chinese hamster ovary cell cultures, culture pH should be temporally controlled with high resolution. In this study, we propose a new pH-dependent dynamic model represented by simultaneous differential equations including a minimum of six system component, depending on pH value. All kinetic parameters in the dynamic model were estimated using an evolutionary numerical optimization (real-coded genetic algorithm) method based on experimental time-course data obtained at different pH values ranging from 6.6 to 7.2. We determined an optimal pH-shift schedule theoretically. We validated this optimal pH-shift schedule experimentally and mAb production increased by approximately 40% with this schedule. Throughout this study, it was suggested that the culture pH-shift optimization strategy using a pH-dependent dynamic model is suitable to optimize any pH-shift schedule for CHO cell lines used in mAb production projects.

Analysis of metabolic network based on conservation of molecular structure.

Recent work has revealed much about chemical reactions inside hundreds of organisms as well as universal characteristics of metabolic networks, which shed light on the evolution of the networks. However, characteristics of individual metabolites have been neglected. For example, some carbohydrates have structures that are decomposed into small molecules by metabolic reactions, but coenzymes such as ATP are mostly preserved. Such differences in metabolite characteristics are important for understanding the universal characteristics of metabolic networks. To quantify the structure conservation of metabolites, we defined the "structure conservation index" (SCI) for each metabolite as the fraction of metabolite atoms restored to their original positions through metabolic reactions. As expected, coenzymes and coenzyme-like metabolites that have reaction loops in the network show a higher SCI. Using the index, we found that the sum of metabolic fluxes is negatively correlated with the structure preservation of metabolite. Also, we found that each reaction path around high SCI metabolites changes independently, while changes in reaction paths involving low SCI metabolites coincide through evolution processes. These correlations may provide a clue to universal properties of metabolic networks.