Maximum Antigen Diversification in a Lyme Bacterial Population and Evolutionary Strategies to Overcome Pathogen Diversity

Abstract

Natural populations of microbes and their hosts are engaged in an arms race in which microbes diversify to escape host immunity while hosts evolve novel immunity. This co-evolutionary process, known as the "Red Queen" hypothesis, poses a fundamental challenge to the development of broadly effective vaccines and diagnostics against a diversifying pathogen. Based on surveys of natural allele frequencies and experimental immunization of mice, we show minimal antigenic cross-reactivity among natural variants of the outer surface protein C (OspC), a dominant antigen of a Lyme Disease-causing bacterium (Borrelia burgdorferi). To overcome the challenge of OspC antigenic diversity to clinical development of preventive measures, we implemented a number of evolution-based strategies to broaden OspC immunological cross-reactivity. In particular, the centroid algorithm - a genetic algorithm to minimize sequence differences with natural variants - generated synthetic OspC analogs with the greatest promise as diagnostic and vaccine candidates against diverse Lyme pathogen strains coexisting in the Northeast United States. Mechanistically, we propose a model of runaway maximum antigen di-versification (MAD) mediated by amino-acid variations distributed across hypervariable regions on the OspC molecule. Under the MAD model, evolutionary centroids display high cross-reactivity by occupying the central void in the antigenic space excavated by diversifying natural variants. In contrast to the vaccine design based on concatenated epitopes, the centroid algorithm generates analogs of native antigens and is automated. The MAD model and evolution-inspired antigen designs have broad implications for combating diversifying pathogens driven by pathogen-host coevolution. ImportanceMicrobial pathogens rely on molecular diversity of cell surface antigens to escape host immunity. Vaccines based on one antigen variant often fail to protect the host against pathogens carrying other variants. Here we show evolution-based designs of synthetic antigens that are broadly reactive to all natural variants. The evolutionary analogs of a major surface antigen of a Lyme disease bacterium (Borrelia burgdorferi) showed promise as vaccine candidates against diverse pathogen strains coexisting in the endemic areas of Lyme disease in Northeast United States. Our evolution-based computational design is automated, generates molecular analogs of natural antigens, and opens a novel path to combating fast-evolving microbial pathogens.