In silico prediction of influenza vaccine effectiveness by sequence analysis

Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations

Influenza virus continuously evolves to escape human adaptive immunity and generates seasonal epidemics. Therefore, influenza vaccine strains need to be updated annually for the upcoming flu season to ensure vaccine effectiveness. We develop a computational approach, beth-1, to forecast virus evolution and select representative virus for influenza vaccine. The method involves modelling site-wise mutation fitness. Informed by virus genome and population sero-positivity, we calibrate transition time of mutations and project the fitness landscape to future time, based on which beth-1 selects the optimal vaccine strain. In season-to-season prediction in historical data for the influenza A pH1N1 and H3N2 viruses, beth-1 demonstrates superior genetic matching compared to existing approaches. In prospective validations, the model shows superior or non-inferior genetic matching and neutralization against circulating virus in mice immunization experiments compared to the current vaccine. The method offers a promising and ready-to-use tool to facilitate vaccine strain selection for the influenza virus through capturing heterogeneous evolutionary dynamics over genome space-time and linking molecular variants to population immune response.

Respiratory Vaccination with Hemagglutinin Nanoliposomes Protects Mice from Homologous and Heterologous Strains of Influenza Virus

Intranasal vaccination offers the potential advantage of needle-free prevention of respiratory pathogens such as influenza viruses with induction of mucosal immune responses. Optimal design of adjuvants and antigen delivery vehicles for intranasal delivery has not yet been well established. Here, we report that an adjuvant-containing nanoliposome antigen display system that converts soluble influenza hemagglutinin antigens into nanoparticles is effective for intranasal immunization. Intranasal delivery of nanoliposomes in mice delivers the particles to resident immune cells in the respiratory tract, inducing a mucosal response in the respiratory system as evidenced by nasal and lung localized IgA antibody production, while also producing systemic IgG antibodies. Intranasal vaccination with nanoliposome particles decorated with nanogram doses of hemagglutinin protected mice from homologous and heterologous H3N2 and H1N1 influenza virus challenge. IMPORTANCE A self-assembling influenza virus vaccine platform that seamlessly converts soluble antigens into nanoparticles is demonstrated with various H1N1 and H3N2 influenza antigens to protect mice against influenza virus challenge following intranasal vaccination. Mucosal immune responses following liposome delivery to lung antigen-presenting cells are demonstrated.

Rapid evaluation of COVID-19 vaccine effectiveness against symptomatic infection with SARS-CoV-2 variants by analysis of genetic distance

Timely evaluation of the protective effects of Coronavirus Disease 2019 (COVID-19) vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is urgently needed to inform pandemic control planning. Based on 78 vaccine efficacy or effectiveness (VE) data from 49 studies and 1,984,241 SARS-CoV-2 sequences collected from 31 regions, we analyzed the relationship between genetic distance (GD) of circulating viruses against the vaccine strain and VE against symptomatic infection. We found that the GD of the receptor-binding domain of the SARS-CoV-2 spike protein is highly predictive of vaccine protection and accounted for 86.3% (P = 0.038) of the VE change in a vaccine platform-based mixed-effects model and 87.9% (P = 0.006) in a manufacturer-based model. We applied the VE-GD model to predict protection mediated by existing vaccines against new genetic variants and validated the results by published real-world and clinical trial data, finding high concordance of predicted VE with observed VE. We estimated the VE against the Delta variant to be 82.8% (95% prediction interval: 68.7-96.0) using the mRNA vaccine platform, closely matching the reported VE of 83.0% from an observational study. Among the four sublineages of Omicron, the predicted VE varied between 11.9% and 33.3%, with the highest VE predicted against BA.1 and the lowest against BA.2, using the mRNA vaccine platform. The VE-GD framework enables predictions of vaccine protection in real time and offers a rapid evaluation method against novel variants that may inform vaccine deployment and public health responses.

Quantifying the effect of government interventions and virus mutations on transmission advantage during COVID-19 pandemic

Background

The coronavirus disease 2019 (COVID-19) pandemic has become a major public health threat. This study aims to evaluate the effect of virus mutation activities and policy interventions on COVID-19 transmissibility in Hong Kong.

Methods

In this study, we integrated the genetic activities of multiple proteins, and quantified the effect of government interventions and mutation activities against the time-varying effective reproduction number R_t.

Findings

We found a significantly positive relationship between R_t and mutation activities and a significantly negative relationship between R_t and government interventions. The results showed that the mutations that contributed most to the increase of R_t were from the spike, nucleocapsid and ORF1b genes. Policy of prohibition on group gathering was estimated to have the largest impact on mitigating virus transmissibility. The model explained 63.2% of the R_t variability with the R².

Conclusion

Our study provided a convenient framework to estimate the effect of genetic contribution and government interventions on pathogen transmissibility. We showed that the S, N and ORF1b protein had significant contribution to the increase of transmissibility of SARS-CoV-2 in Hong Kong, while restrictions of public gathering and suspension of face-to-face class are the most effective government interventions strategies.

Differential Influence of Age on the Relationship between Genetic Mismatch and A(H1N1)pdm09 Vaccine Effectiveness

Assessment of influenza vaccine effectiveness (VE) and identification of relevant influencing factors are the current priorities for optimizing vaccines to reduce the impacts of influenza. To date, how the difference between epidemic strains and vaccine strains at genetic scale affects age-specific vaccine performance remains ambiguous. This study investigated the association between genetic mismatch on hemagglutinin and neuraminidase genes and A(H1N1)pdm09 VE in different age groups with a novel computational approach. We found significant linear relationships between VE and genetic mismatch in children, young adults, and middle-aged adults. In the children’s group, each 3-key amino acid mutation was associated with an average of 10% decrease in vaccine effectiveness in a given epidemic season, and genetic mismatch exerted no influence on VE for the elderly group. We demonstrated that present vaccines were most effective for children, while protection for the elderly was reduced and indifferent to vaccine component updates. Modeling such relationships is practical to inform timely evaluation of VE in different groups of populations during mass vaccination and may inform age-specific vaccination regimens.