Nanoparticle geometry, immune memory, and antigen presentation determine the cross-reactive antibody response against sarbecoviruses

Eric Wang, Alexander A. Cohen, Luis F. Caldera Guzman,Pamela J. Bjorkman,Arup K. Chakraborty

Journal of Immunology(2023)

引用 0|浏览1
Abstract In response to the threat of emerging SARS-CoV-2 variants and SARS-like betacoronaviruses (sarbecoviruses), mosaic nanoparticles presenting receptor-binding domains (RBDs) of multiple sarbecoviruses have been developed to elicit cross-reactive antibodies that target conserved regions of the RBD. To better understand vaccination with these antigens, we developed a computational model of the humoral response to nanoparticle and spike antigens using a combination of molecular dynamics, Markov processes, and population dynamics simulations. Avidity is currently only accounted for by introducing a second arm on rate, but additional effects of avidity will be considered in the future. Our model agrees with titers measured from nanoparticle and spike vaccinations in animals as well as clinical data from mRNA vaccines. We then used our model to predict the effect of previous vaccinations with wildtype spike on nanoparticle vaccinations. Compared to naïve individuals, previously vaccinated individuals produce a higher cross-reactive titer on the first nanoparticle vaccination and a lower cross-reactive titer on the second. This difference is due to expansion of cross-reactive B cells and competition with strain-specific B cells from previous vaccinations. Furthermore, we find that a cocktail of homotypic nanoparticles produces the same cross-reactive titers as the mosaic nanoparticle, which is because cross-reactive B cells can bind a greater fraction of presented antigen and can survive successive rounds of selection when the nanoparticle density is low. Supported by grants from the National Science Foundation (1745302), NIH (1-R61- AI161805-01), and the Ragon Institute of MGH, MIT, and Harvard.
antibody,immune memory,nanoparticle geometry,antigen presentation,cross-reactive
AI 理解论文
Chat Paper