Deep immunological imprinting due to the ancestral spike in the current bivalent COVID-19 vaccine

To combat the evolving SARS-CoV-2 Omicron variants, bivalent COVID-19 mRNA vaccines, encoding both ancestral and Omicron BA.5 spikes, have replaced monovalent vaccines in numerous countries. However, fourth doses of either vaccine result in similar neutralizing antibody titers against Omicron subvariants, raising the possibility of immunological imprinting. To address this, we investigate antibody responses in 72 participants given three doses of a monovalent mRNA vaccine, followed by a bivalent or monovalent booster, or those with breakthrough infections with BA.5 or BQ. Bivalent boosters do not show notably higher binding or virus-neutralizing titers against various SARS-CoV-2 variants compared to monovalent ones. However, breakthrough infections lead to significantly better neutralization of Omicron subvariants. Multiple analyses, including antigenic mapping, suggest that the ancestral spike in bivalent vaccines is causing deep immunological imprinting, preventing broadening of antibodies to the BA.5 component, thereby defeating its intended goal. Its removal from future vaccine compositions is therefore strongly recommended.

Rational design of a booster vaccine against COVID-19 based on antigenic distance

Current COVID-19 vaccines are highly effective against symptomatic disease, but repeated booster doses using vaccines based on the ancestral strain offer limited additional protection against SARS-CoV-2 variants of concern (VOCs). To address this, we used antigenic distance to in silico select optimized booster vaccine seed strains effective against both current and future VOCs. Our model suggests that a SARS-CoV-1-based booster vaccine has the potential to cover a broader range of VOCs. Candidate vaccines including the spike protein from ancestral SARS-CoV-2, Delta, Omicron (BA.1), SARS-CoV-1, or MERS-CoV were experimentally evaluated in mice following two doses of the BNT162b2 vaccine. The SARS-CoV-1-based booster vaccine outperformed other candidates in terms of neutralizing antibody breadth and duration, as well as protective activity against Omicron (BA.2) challenge. This study suggests a unique strategy for selecting booster vaccines based on antigenic distance, which may be useful in designing future booster vaccines as new SARS-CoV-2 variants emerge.

[Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype H3N2]

INTRODUCTION

The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data.

MATERIALS AND METHODS

In this study, a mathematical model was proposed, based on finding, using regression analysis, the dependence of HAI titers on substitutions in antigenic sites of sequences. The computer program we developed can process data (GISAID, NCBI, etc.) and create real-time databases according to the set tasks.

RESULTS

Based on our research, an additional antigenic site F was identified. The difference in 1.6 times the adjusted R2, on subsets of viruses grown in cell culture and grown in chicken embryos, demonstrates the validity of our decision to divide the original data array by passage histories. We have introduced the concept of a degree of homology between two arbitrary strains, which takes the value of a function depending on the Hamming distance, and it has been shown that the regression results significantly depend on the choice of function. The provided analysis showed that the most significant antigenic sites are A, B, and E. The obtained results on predicted HAI titers showed a good enough result, comparable to similar work by our colleagues.

CONCLUSION

The proposed method could serve as a useful tool for future forecasts, with further study to confirm its sustainability.

Prediction of Antigenic Distance in Influenza A Using Attribute Network Embedding

Owing to the rapid changes in the antigenicity of influenza viruses, it is difficult for humans to obtain lasting immunity through antiviral therapy. Hence, tracking the dynamic changes in the antigenicity of influenza viruses can provide a basis for vaccines and drug treatments to cope with the spread of influenza viruses. In this paper, we developed a novel quantitative prediction method to predict the antigenic distance between virus strains using attribute network embedding techniques. An antigenic network is built to model and combine the genetic and antigenic characteristics of the influenza A virus H3N2, using the continuous distributed representation of the virus strain protein sequence (ProtVec) as a node attribute and the antigenic distance between virus strains as an edge weight. The results show a strong positive correlation between supplementing genetic features and antigenic distance prediction accuracy. Further analysis indicates that our prediction model can comprehensively and accurately track the differences in antigenic distances between vaccines and influenza virus strains, and it outperforms existing methods in predicting antigenic distances between strains.

First Impressions Matter: Immune Imprinting and Antibody Cross-Reactivity in Influenza and SARS-CoV-2

Many rigorous studies have shown that early childhood infections leave a lasting imprint on the immune system. The understanding of this phenomenon has expanded significantly since 1960, when Dr. Thomas Francis Jr first coined the term "original antigenic sin", to account for all previous pathogen exposures, rather than only the first. Now more commonly referred to as "immune imprinting", this effect most often focuses on how memory B-cell responses are shaped by prior antigen exposure, and the resultant antibodies produced after subsequent exposure to antigenically similar pathogens. Although imprinting was originally observed within the context of influenza viral infection, it has since been applied to the pandemic coronavirus SARS-CoV-2. To fully comprehend how imprinting affects the evolution of antibody responses, it is necessary to compare responses elicited by pathogenic strains that are both antigenically similar and dissimilar to strains encountered previously. To accomplish this, we must be able to measure the antigenic distance between strains, which can be easily accomplished using data from multidimensional immunological assays. The knowledge of imprinting, combined with antigenic distance measures, may allow for improvements in vaccine design and development for both influenza and SARS-CoV-2 viruses.

SARS-CoV-2 Delta Variant Displays Moderate Resistance to Neutralizing Antibodies and Spike Protein Properties of Higher Soluble ACE2 Sensitivity, Enhanced Cleavage and Fusogenic Activity

The SARS-CoV-2 B.1.617 lineage variants, Kappa (B.1.617.1) and Delta (B.1.617.2, AY) emerged during the second wave of infections in India, but the Delta variants have become dominant worldwide and continue to evolve. Here, we compared B.1.617 variants for neutralization resistance by convalescent sera, mRNA vaccine-elicited sera, and therapeutic neutralizing antibodies using a pseudovirus neutralization assay. B.1.617.1, B.1.617.2, and AY.1 pseudoviruses showed a modest 1.5- to 4.4-fold reduction in neutralization by convalescent sera and vaccine-elicited sera. In comparison, similar modest reductions were also observed for C.37, P.1, R.1, and B.1.526 pseudoviruses, but 7- and 16-fold reductions for vaccine-elicited and convalescent sera, respectively, were seen for B.1.351 pseudoviruses. Among twenty-three therapeutic antibodies tested, four antibodies showed either complete or partial loss of neutralization against B.1.617.2 pseudoviruses and six antibodies showed either complete or partial loss of neutralization against B.1.617.1 and AY.1 pseudoviruses. Our results indicate that the current mRNA-based vaccines will likely remain effective in protecting against B.1.617 variants. Finally, the P681R substitution confers efficient cleavage of B.1.617 variants' spike proteins and the spike of Delta variants exhibited greater sensitivity to soluble ACE2 neutralization, as well as fusogenic activity, which may contribute to enhanced spread of Delta variants.

Convolutional Neural Network Based Approach to in Silico Non-Anticipating Prediction of Antigenic Distance for Influenza Virus

Evaluation of the antigenic similarity degree between the strains of the influenza virus is highly important for vaccine production. The conventional method used to measure such a degree is related to performing the immunological assays of hemagglutinin inhibition. Namely, the antigenic distance between two strains is calculated on the basis of HI assays. Usually, such distances are visualized by using some kind of antigenic cartography method. The known drawback of the HI assay is that it is rather time-consuming and expensive. In this paper, we propose a novel approach for antigenic distance approximation based on deep learning in the feature spaces induced by hemagglutinin protein sequences and Convolutional Neural Networks (CNNs). To apply a CNN to compare the protein sequences, we utilize the encoding based on the physical and chemical characteristics of amino acids. By varying (hyper)parameters of the CNN architecture design, we find the most robust network. Further, we provide insight into the relationship between approximated antigenic distance and antigenicity by evaluating the network on the HI assay database for the H1N1 subtype. The results indicate that the best-trained network gives a high-precision approximation for the ground-truth antigenic distances, and can be used as a good exploratory tool in practical tasks.

Role of vaccination-induced immunity and antigenic distance in the transmission dynamics of highly pathogenic avian influenza H5N1

Highly pathogenic avian influenza (HPAI) H5N1 epidemics in poultry cause huge economic losses as well as sporadic human morbidity and mortality. Vaccination in poultry has often been reported as being ineffective in preventing transmission and as a potential driving force in the selection of immune escape mutants. We conducted transmission experiments to evaluate the transmission dynamics of HPAI H5N1 strains in chickens vaccinated with high and low doses of immune escape mutants we have previously selected, and analysed the data using mathematical models. Remarkably, we demonstrate that the effect of antigenic distances between the vaccine and challenge strains used in this study is too small to influence the transmission dynamics of the strains used. This is because the effect of a sufficient vaccine dose on antibody levels against the challenge viruses is large enough to compensate for any decrease in antibody titres due to antigenic differences between vaccine and challenge strains. Our results show that at least under experimental conditions, vaccination will remain effective even after antigenic changes as may be caused by the initial selection in vaccinated birds.

Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses

Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA), and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA sequence data alone. The mapping can further be used to make predictions about the makeup of the future A(H3N2) seasonal influenza virus population, and we compare predictions between models with serological and sequence data. To make timely model output readily available, we developed a web browser-based application that visualizes antigenic data on a continuously updated phylogeny.

A novel sequence-based antigenic distance measure for H1N1, with application to vaccine effectiveness and the selection of vaccine strains

H1N1 influenza causes substantial seasonal illness and was the subtype of the 2009 influenza pandemic. Precise measures of antigenic distance between the vaccine and circulating virus strains help researchers design influenza vaccines with high vaccine effectiveness. We here introduce a sequence-based method to predict vaccine effectiveness in humans. Historical epidemiological data show that this sequence-based method is as predictive of vaccine effectiveness as hemagglutination inhibition assay data from ferret animal model studies. Interestingly, the expected vaccine effectiveness is greater against H1N1 than H3N2, suggesting a stronger immune response against H1N1 than H3N2. The evolution rate of hemagglutinin in H1N1 is also shown to be greater than that in H3N2, presumably due to greater immune selection pressure.

Variable efficacy of repeated annual influenza vaccination

Conclusions have differed in studies that have compared vaccine efficacy in groups receiving influenza vaccine for the first time to efficacy in groups vaccinated more than once. For example, the Hoskins study [Hoskins, T. W., Davis, J. R., Smith, A. J., Miller, C. L. & Allchin, A. (1979) Lancet i, 33-35] concluded that repeat vaccination was not protective in the long term, whereas the Keitel study [Keitel, W. A., Cate, T. R., Couch, R. B., Huggins, L. L. & Hess, K. R. (1997) Vaccine 15, 1114-1122] concluded that repeat vaccination provided continual protection. We propose an explanation, the antigenic distance hypothesis, and test it by analyzing seven influenza outbreaks that occurred during the Hoskins and Keitel studies. The hypothesis is that variation in repeat vaccine efficacy is due to differences in antigenic distances among vaccine strains and between the vaccine strains and the epidemic strain in each outbreak. To test the hypothesis, antigenic distances were calculated from historical hemagglutination inhibition assay tables, and a computer model of the immune response was used to predict the vaccine efficacy of individuals given different vaccinations. The model accurately predicted the observed vaccine efficacies in repeat vaccinees relative to the efficacy in first-time vaccinees (correlation 0.87). Thus, the antigenic distance hypothesis offers a parsimonious explanation of the differences between and within the Hoskins and Keitel studies. These results have implications for the selection of influenza vaccine strains, and also for vaccination strategies for other antigenically variable pathogens that might require repeated vaccination.