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Updated: Jul 12, 2025
In Vivo Assay for Detection of Antigen-specific T-cell Cytolytic Function Using a Vaccination Model
Published on: November 28, 2017
Preclinical efficacy of a cell division protein candidate gonococcal vaccine identified by artificial intelligence
Sunita Gulati1, Andreas Holm Mattsson2, Sophie Schussek2
1Department of Medicine, Division of Infectious Diseases and Immunology, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Importance:
Vaccines to curb the global spread of multidrug-resistant gonorrhea are urgently needed. Here, 26 vaccine candidates identified by an artificial intelligence-driven platform (Efficacy Discriminative Educated Network[EDEN]) were screened for efficacy in the mouse vaginal colonization model. Complement-dependent bactericidal activity of antisera and the EDEN protective scores both correlated positively with the reduction in overall bacterial colonization burden. NGO1549 (FtsN) and NGO0265, both involved in cell division, displayed the best activity and were selected for further development. Both antigens, when fused to create a chimeric protein, elicited bactericidal antibodies against a wide array of gonococcal isolates and significantly attenuated the duration and burden of gonococcal colonization of mouse vaginas. Protection was abrogated in mice that lacked complement C9, the last step in the formation of the membrane attack complex pore, suggesting complement-dependent bactericidal activity as a mechanistic correlate of protection of the vaccine. FtsN and NGO0265 represent promising vaccine candidates against gonorrhea.
Insights
New gonorrhea vaccines are crucial. AI identified promising candidates, FtsN and NGO0265, which significantly reduced bacterial colonization in mice by triggering a complement-dependent immune response.
Area of Science:
- Microbiology
- Immunology
- Vaccinology
Background:
- Multidrug-resistant gonorrhea poses a significant global health threat.
- Urgent need for effective vaccines to combat Neisseria gonorrhoeae spread.
Purpose of the Study:
- Screen vaccine candidates identified by artificial intelligence (AI) for efficacy.
- Evaluate the protective mechanisms of promising gonorrhea vaccine candidates.
Main Methods:
- Utilized the mouse vaginal colonization model to assess vaccine efficacy.
- Measured complement-dependent bactericidal activity and correlated with colonization reduction.
- Developed and tested a chimeric protein vaccine (FtsN-NGO0265).
Main Results:
- AI platform identified 26 vaccine candidates; FtsN and NGO0265 showed highest efficacy.
- Chimeric FtsN-NGO0265 vaccine elicited bactericidal antibodies and reduced colonization burden.
- Protection was dependent on complement C9, indicating a complement-mediated mechanism.
Conclusions:
- FtsN and NGO0265 are promising gonorrhea vaccine candidates.
- Complement-dependent bactericidal activity is a key mechanism of protection.
- AI-driven discovery accelerates vaccine development for challenging pathogens.
