Delivery of spike-RBD by bacterial type three secretion system for SARS-CoV-2 vaccine development.

COVID-19 pandemic continues to spread throughout the world with an urgent demand for a safe and protective vaccine to effectuate herd protection and control the spread of SARS-CoV-2. Here, we report the development of a bacterial vector COVID-19 vaccine (aPA-RBD) that carries the gene for the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Live-attenuated strains of Pseudomonas aeruginosa (aPA) were constructed which express the recombinant RBD and effectively deliver RBD protein into various antigen presenting cells through bacterial type 3 secretion system (T3SS) in vitro. In mice, two-dose of intranasal aPA-RBD vaccinations elicited the development of RBD-specific serum IgG and IgM. Importantly, the sera from the immunized mice were able to neutralize host cell infections by SARS-CoV-2 pseudovirus as well as the authentic virus variants potently. T-cell responses of immunized mice were assessed by enzyme-linked immunospot (ELISPOT) and intracellular cytokine staining (ICS) assays. aPA-RBD vaccinations can elicit RBD-specific CD4+and CD8+T cell responses. T3SS-based RBD intracellular delivery heightens the efficiency of antigen presentation and enables the aPA-RBD vaccine to elicit CD8+T cell response. Thus, aPA vector has the potential as an inexpensive, readily manufactured, and respiratory tract vaccination route vaccine platform for other pathogens.

The Optimal Supply Decision Based on Dynamic Multiobjective Optimization and Prediction

Different material supply-related decisions intensively affect the efficiency of manufacturers. To obtain a suitable supply-related protocol, this study proposes a supply selection model which considers both manufacturers’ development orientation and material ordering. In contrast to traditional approaches that rely on expert opinions, the proposed approach in this study allows the time series analysis (ARIMA) to forecast the trend in manufacturers’ development during the execution of the plan. Based on the predicted trend, taking the minimum of total material management cost as the objective function, the control function and optimization conditions are constructed to select the appropriate protocol. The dynamic prediction protocol is obtained by considering the variation in production and material costs by an evolutionary algorithm. The model enables users to determine material supply protocol in continuous time and autonomously adapt to changes in the manufacturers’ production goals within a lower convergence time.