High incidence, severity and increasing antibiotic resistance characterize Pseudomonas aeruginosa infections, highlighting the need for new therapeutic options. Vaccination strategies to prevent or limit P. aeruginosa infections represent a rational approach to positively impact the clinical outcome of risk patients; nevertheless this bacterium remains a challenging vaccine target. To identify novel vaccine candidates, we started from the genome sequence analysis of the P. aeruginosa reference strain PAO1 exploring the reverse vaccinology approach integrated with additional bioinformatic tools. The bioinformatic approaches resulted in the selection of 52 potential antigens. These vaccine candidates were conserved in P. aeruginosa genomes from different origin and among strains isolated longitudinally from cystic fibrosis patients. To assess the immune-protection of single or antigens combination against P. aeruginosa infection, a vaccination protocol was established in murine model of acute respiratory infection. Combinations of selected candidates, rather than single antigens, effectively controlled P. aeruginosa infection in the in vivo model of murine pneumonia. Five combinations were capable of significantly increase survival rate among challenged mice and all included PA5340, a hypothetical protein exclusively present in P. aeruginosa. PA5340 combined with PA3526-MotY gave the maximum protection. Both proteins were surface exposed by immunofluorescence and triggered a specific immune response. Combination of these two protein antigens could represent a potential vaccine to prevent P. aeruginosa infection.

Genome-based approach delivers vaccine candidates against Pseudomonas aeruginosa / Bianconi, I.; Alcala-Franco, B.; Scarselli, M.; Dalsass, M.; Buccato, S.; Colaprico, A.; Marchi, S.; Masignani, V.; Bragonzi, A.. - In: FRONTIERS IN IMMUNOLOGY. - ISSN 1664-3224. - ELETTRONICO. - 9:(2019), pp. 30211-30219. [10.3389/fimmu.2018.03021]

Genome-based approach delivers vaccine candidates against Pseudomonas aeruginosa

Bianconi I.;
2019-01-01

Abstract

High incidence, severity and increasing antibiotic resistance characterize Pseudomonas aeruginosa infections, highlighting the need for new therapeutic options. Vaccination strategies to prevent or limit P. aeruginosa infections represent a rational approach to positively impact the clinical outcome of risk patients; nevertheless this bacterium remains a challenging vaccine target. To identify novel vaccine candidates, we started from the genome sequence analysis of the P. aeruginosa reference strain PAO1 exploring the reverse vaccinology approach integrated with additional bioinformatic tools. The bioinformatic approaches resulted in the selection of 52 potential antigens. These vaccine candidates were conserved in P. aeruginosa genomes from different origin and among strains isolated longitudinally from cystic fibrosis patients. To assess the immune-protection of single or antigens combination against P. aeruginosa infection, a vaccination protocol was established in murine model of acute respiratory infection. Combinations of selected candidates, rather than single antigens, effectively controlled P. aeruginosa infection in the in vivo model of murine pneumonia. Five combinations were capable of significantly increase survival rate among challenged mice and all included PA5340, a hypothetical protein exclusively present in P. aeruginosa. PA5340 combined with PA3526-MotY gave the maximum protection. Both proteins were surface exposed by immunofluorescence and triggered a specific immune response. Combination of these two protein antigens could represent a potential vaccine to prevent P. aeruginosa infection.
2019
Bianconi, I.; Alcala-Franco, B.; Scarselli, M.; Dalsass, M.; Buccato, S.; Colaprico, A.; Marchi, S.; Masignani, V.; Bragonzi, A.
Genome-based approach delivers vaccine candidates against Pseudomonas aeruginosa / Bianconi, I.; Alcala-Franco, B.; Scarselli, M.; Dalsass, M.; Buccato, S.; Colaprico, A.; Marchi, S.; Masignani, V.; Bragonzi, A.. - In: FRONTIERS IN IMMUNOLOGY. - ISSN 1664-3224. - ELETTRONICO. - 9:(2019), pp. 30211-30219. [10.3389/fimmu.2018.03021]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/280565
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