RESUMEN
The Omicron variant of SARS-CoV-2 evades neutralization by most serum antibodies elicited by two doses of mRNA vaccines, but a third dose of the same vaccine increases anti-Omicron neutralizing antibodies. By combining computational modeling with data from vaccinated humans we reveal mechanisms underlying this observation. After the first dose, limited antigen availability in germinal centers results in a response dominated by B cells with high germline affinities for immunodominant epitopes that are significantly mutated in an Omicron-like variant. After the second dose, expansion of these memory cells and differentiation into plasma cells shape antibody responses that are thus ineffective for such variants. However, in secondary germinal centers, pre-existing higher affinity antibodies mediate enhanced antigen presentation and they can also partially mask dominant epitopes. These effects generate memory B cells that target subdominant epitopes that are less mutated in Omicron. The third dose expands these cells and boosts anti-variant neutralizing antibodies.
RESUMEN
The rise of SARS-CoV-2 variants and the history of outbreaks caused by zoonotic coronaviruses point to the need for next-generation vaccines that confer protection against variant strains. Here, we combined analyses of diverse sequences and structures of coronavirus spikes with data from deep mutational scanning to design SARS-CoV-2 variant antigens containing the most significant mutations that may emerge. We trained a neural network to predict RBD expression and ACE2 binding from sequence, which allowed us to determine that these antigens are stable and bind to ACE2. Thus, they represent viable variants. We then used a computational model of affinity maturation (AM) to study the antibody response to immunization with different combinations of the designed antigens. The results suggest that immunization with a cocktail of the antigens is likely to promote evolution of higher titers of antibodies that target SARS-CoV-2 variants than immunization or infection with the wildtype virus alone. Finally, our analysis of 12 coronaviruses from different genera identified the S2 cleavage site and fusion peptide as potential pan-coronavirus vaccine targets. Author SummarySARS-CoV-2 variants have already emerged and future variants may pose greater threats to the efficacy of current vaccines. Rather than using a reactive approach to vaccine development that would lag behind the evolution of the virus, such as updating the sequence in the vaccine with a current variant, we sought to use a proactive approach that predicts some of the mutations that could arise that could evade current immune responses. Then, by including these mutations in a new vaccine antigen, we might be able to protect against those potential variants before they appear. Toward this end, we used various computational methods including sequence analysis and machine learning to design such antigens. We then used simulations of antibody development, and the results suggest that immunization with our designed antigens is likely to result in an antibody response that is better able to target SARS-CoV-2 variants than current vaccines. We also leveraged our sequence analysis to suggest that a particular site on the spike protein could serve as a useful target for a pan-coronavirus vaccine.
RESUMEN
Different virus families, like influenza or corona viruses, exhibit characteristic traits such as typical modes of transmission and replication as well as specific animal reservoirs in which each family of viruses circulate. These traits of genetically related groups of viruses influence how easily an animal virus can adapt to infect humans, how well novel human variants can spread in the population, and the risk of causing a global pandemic. Relating the traits of virus families to their risk of causing future pandemics, and identification of the key time scales within which public health interventions can control the spread of a new virus that could cause a pandemic, are obviously significant. We address these issues using a minimal model whose parameters are related to characteristic traits of different virus families. A key trait of viruses that "spillover" from animal reservoirs to infect humans is their ability to propagate infection through the human population (fitness). We find that the risk of pandemics emerging from virus families characterized by a wide distribution of the fitness of spillover strains is much higher than if such strains were characterized by narrow fitness distributions around the same mean. The dependences of the risk of a pandemic on various model parameters exhibit inflection points. We find that these inflection points define informative thresholds. For example, the inflection point in variation of pandemic risk with time after the spillover represents a threshold time beyond which global interventions would likely be too late to prevent a pandemic.
RESUMEN
We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic T Lymphocyte (CTL) epitopes derived from diverse pathogens, given a Human Leukocyte Antigen (HLA) genotype. The model was trained and tested on experimental data on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. The method is more accurate than publicly available models. Our model predicts that only a fraction of SARS-CoV-2 epitopes that have been predicted to bind to HLA molecules is immunogenic. The immunogenic CTL epitopes across all SARS-CoV-2 proteins are predicted to provide broad population coverage, but the immunogenic epitopes in the SARS-CoV-2 spike protein alone are unlikely to do so. Our model predicts that several immunogenic SARS-CoV-2 CTL epitopes are identical to those contained in low-pathogenicity coronaviruses circulating in the population. Thus, we suggest that some level of CTL immunity against COVID-19 may be present in some individuals prior to SARS-CoV-2 infection.