Back

Immunogenicity and pre-clinical efficacy of an OMV-based SARS-CoV-2 vaccine

Grandi, A.; Tomasi, M.; Accordini, S.; Bertelli, C.; Vanzo, T.; Gagliardi, A.; Caproni, E.; Tamburini, S.; Fantappie, L.; Di Lascio, G.; Bisoffi, Z.; Piubelli, C.; Valenti, M. T.; Dalle Carbonare, L.; Zipeto, D.; Rava, M.; Fumagalli, V.; Di Lucia, P.; Marotta, D.; Sala, E.; Iannacone, M.; Cherepanov, P.; Bolognesi, M.; Pizzato, M.; Grandi, G.

2021-07-13 immunology
10.1101/2021.07.12.452027 bioRxiv
Show abstract

The vaccination campaign against SARS-CoV-2 relies on the world-wide availability of effective vaccines, with a potential need of 20 billion vaccine doses to fully vaccinate the world population. To reach this goal, the manufacturing and logistic processes should be affordable to all countries, irrespectively of economical and climatic conditions. Outer membrane vesicles (OMVs) are bacterial-derived vesicles that can be engineered to incorporate heterologous antigens. Given the inherent adjuvanticity, such modified OMVs can be used as vaccine to induce potent immune responses against the associated protein. Here we show that OMVs engineered to incorporate peptides derived from the receptor binding motif (RBM) of the spike protein from SARS-CoV-2 elicit an effective immune response in immunized mice, resulting in the production of neutralizing antibodies. The immunity induced by the vaccine is sufficient to protect K18-hACE2 transgenic mice from intranasal challenge with SARS-CoV-2, preventing both virus replication in the lungs and the pathology associated with virus infection. Furthermore, we show that OMVs can be effectively decorated with RBM peptides derived from a different genetic variant of SARS-CoV-2, inducing a similarly potent neutralization activity in vaccinated mice. Altogether, given the convenience associated with ease of engineering, production and distribution, our results demonstrate that OMV-based SARS-CoV-2 vaccines can be a crucial addition to the vaccines currently available.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.