Preclinical efficacy of a cell division protein candidate gonococcal vaccine identified by artificial intelligence

Abstract

ABSTRACTA safe and effective vaccine is urgently needed to combat the global threat of multidrug-resistant (MDR) Neisseria gonorrhoeae. We screened 26 gonococcal proteins discovered by an artificial intelligence-driven platform called Efficacy Discriminative Educated Network (EDEN) trained to identify novel, protective vaccine antigens against pathogenic bacteria for efficacy in the mouse vaginal colonization model of gonorrhea. Combinations of two to three antigens adjuvanted with GLA-SE (induces TH1 responses) yielded 11 groups that were used to vaccinate mice. An inverse correlation was noted between the complement-dependent bactericidal activity of antisera from each of the 11 groups and the burden of gonococcal colonization. The combination of NGO1549 (FtsN; cell divisome protein) and NGO0265 (predicted cell division protein) most substantially reduced the burden of colonization by MDR strain WHO X. The EDEN prediction score for each group of antigens correlated positively with reductions in overall bacterial burden, providing evidence for its predictive potential. FtsN and NGO0265 administered either individually, in combination, or as a chimeric protein significantly attenuated gonococcal vaginal colonization by all three test strains. IgG in antisera from mice immunized with the chimeric NGO0265-FtsN protein supported the complement-dependent killing of all 50 (100%) gonococcal isolates tested. The efficacy of the chimeric NGO0265-FtsN vaccine required the membrane attack complex (C5b-9) of complement, evidenced by loss of efficacy in complement C9−/− mice. In conclusion, a chimeric molecule comprising NGO0265 and FtsN adjuvanted with GLA-SE elicits IgG with broad anti-gonococcal bactericidal activity, attenuates gonococcal colonization in a complement-dependent manner, and represents a promising gonococcal vaccine candidate.IMPORTANCEVaccines 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.

Keywords

• Neisseria gonorrhoeae
• vaccine
• complement
• artificial intelligence