Antigenic Distance Research Articles

Genetic and antigenic characterization of influenza A(H3N2) virus after 13 consecutive years of influenza surveillance in Senegal, 2010-2022.

Despite decades of influenza surveillance in many African countries, little is known about the evolutionary dynamics of seasonal influenza viruses. This study aimed to characterize the epidemiological, genetic and antigenic profiles of A/H3N2 viruses in Senegal from 2010 to 2022. A/H3N2 infection was confirmed using reverse transcription-polymerase chain reaction. Subsequently, a representative of A/H3N2 isolates was selected for genome sequencing. Predicted vaccine efficacy was measured using the Pepitope model. During the study period, 22638 samples were tested and influenza was detected in 31.8%, among which type A was confirmed in 78.1%. Of the Influenza A cases, the H3N2 subtype was detected in 29.8%, peaking at expected times during the rainy season. Genome sequencing of 123A/H3N2 isolates yielded 24 complete and 99 partial genomic sequences. Phylogenetic analysis revealed the circulation of multiple clades of A/H3N2 in Senegal, including 2a.3, 3C.2 and 3C.3a. A/H3N2 isolates were mainly susceptible to the influenza antiviral drugs oseltamivir and zanamivir, but the primary adamantine-resistance marker, S31N was encountered in all isolates. At least nine potential N-linked glycosylation sites were predicted among A/H3N2 strains, six of which (at positions 24, 38, 79, 181, 262 and 301) remains conserved among all isolates. Antigenic distances between circulating strains and vaccine viruses indicated varying vaccine efficacies, from suboptimal to moderate protection. The findings emphasize the need to enhance local genomic and antigenic surveillance and further research on influenza epidemiology and genetic evolution in sub-Saharan Africa.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997—2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

FluPMT: Prediction of Predominant Strains of Influenza A Viruses via Multi-Task Learning.

Seasonal influenza vaccines play a crucial role in saving numerous lives annually. However, the constant evolution of the influenza A virus necessitates frequent vaccine updates to ensure its ongoing effectiveness. The decision to develop a new vaccine strain is generally based on the assessment of the current predominant strains. Nevertheless, the process of vaccine production and distribution is very time-consuming, leaving a window for the emergence of new variants that could decrease vaccine effectiveness, so predictions of influenza A virus evolution can inform vaccine evaluation and selection. Hence, we present FluPMT, a novel sequence prediction model that applies an encoder-decoder architecture to predict the hemagglutinin (HA) protein sequence of the upcoming season's predominant strain by capturing the patterns of evolution of influenza A viruses. Specifically, we employ time series to model the evolution of influenza A viruses, and utilize attention mechanisms to explore dependencies among residues of sequences. Additionally, antigenic distance prediction based on graph network representation learning is incorporated into the sequence prediction as an auxiliary task through a multi-task learning framework. Experimental results on two influenza datasets highlight the exceptional predictive performance of FluPMT, offering valuable insights into virus evolutionary dynamics, as well as vaccine evaluation and production.

Glycan masking of NTD loops with a chimeric RBD of the spike protein as a vaccine design strategy against emerging SARS-CoV-2 Omicron variants.

The N-terminal domain (NTD) of the SARS-CoV-2 S protein comprises five exposed protruding loops. Deletions, insertions, and substitutions within these NTD loops play a significant role in viral evolution and contribute to immune evasion. We reported previously that introducing the glycan masking mutation R158N/Y160T in the NTD loop led to increased titers of neutralizing antibodies against the SARS-CoV-2 Wuhan-Hu-01 strain, as well as the Alpha, Beta, and Delta variants. In this study, we conducted further investigations on 10 additional glycan-masking sites in the NTD loops. Our findings indicate that the introduction of glycan masking mutations, specifically N87/G89T, H146N/N148T, N185/K187T, and V213N/D215T significantly enhanced neutralizing antibody titers against the Delta variant. The combination of dual glycan-masking mutations R158N/Y160T+V213N/D215T and R158N/Y160T+G219N results in a shift toward the Omicron BA.1. Furthermore, the introduction of the Omicron receptor binding domain(RBD) alongside these two dual glycan masking mutations of Wuhan-Hu-1 and XBB.1 NTD sequences resulted in a noticeable shift in antigenic distances, aligning with the Omicron BA.4/5, BA.2.75.2, BQ.1.1, and XBB.1 subvariants on the antigenic map. This strategic combination, which involves the dual glycan masking mutations R158N/Y160T+V213N/D215T and R158N/Y160T+G219N in the NTD loops, along with the domain swap incorporating the Omicron RBD, emerges as a promising vaccine design strategy for the continuous development of next-generation SARS-CoV-2 vaccines.

SARS-CoV-2 reinfection broadens the antibody responses and promotes the phenotypic differentiation of virus-specific memory T cells in adolescents.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron subvariants raises concerns regarding the effectiveness of immunity acquired from previous Omicron subvariants breakthrough infections (BTIs) or reinfections (RIs) against the current circulating Omicron subvariants. In this study, we prospectively investigate the dynamic changes of virus-specific antibody and T cell responses among 77 adolescents following Omicron BA.2.3 BTI with or without subsequent Omicron BA.5 RI. Notably, the neutralizing antibodies (NAbs) titers against various detected SARS-CoV-2 variants, especially the emerging Omicron CH.1.1, XBB.1.5, XBB.1.16, EG.5.1, and JN.1 subvariants, exhibited a significant decrease along the time. A lower level of IgG and NAbs titers post-BTI was found to be closely associated with subsequent RI. Elevated NAbs levels and shortened antigenic distances were observed following Omicron BA.5 RI. Robust T cell responses against both Omicron BA.2- and CH.1.1-spike peptides were observed at each point visited. The exposure to Omicron BA.5 promoted phenotypic differentiation of virus-specific memory T cells, even among the non-seroconversion adolescents. Therefore, updated vaccines are needed to provide effective protection against newly emerging SARS-CoV-2 variants among adolescents.

MetaFluAD: meta-learning for predicting antigenic distances among influenza viruses.

Influenza viruses rapidly evolve to evade previously acquired human immunity. Maintaining vaccine efficacy necessitates continuous monitoring of antigenic differences among strains. Traditional serological methods for assessing these differences are labor-intensive and time-consuming, highlighting the need for efficient computational approaches. This paper proposes MetaFluAD, a meta-learning-based method designed to predict quantitative antigenic distances among strains. This method models antigenic relationships between strains, represented by their hemagglutinin (HA) sequences, as a weighted attributed network. Employing a graph neural network (GNN)-based encoder combined with a robust meta-learning framework, MetaFluAD learns comprehensive strain representations within a unified space encompassing both antigenic and genetic features. Furthermore, the meta-learning framework enables knowledge transfer across different influenza subtypes, allowing MetaFluAD to achieve remarkable performance with limited data. MetaFluAD demonstrates excellent performance and overall robustness across various influenza subtypes, including A/H3N2, A/H1N1, A/H5N1, B/Victoria, and B/Yamagata. MetaFluAD synthesizes the strengths of GNN-based encoding and meta-learning to offer a promising approach for accurate antigenic distance prediction. Additionally, MetaFluAD can effectively identify dominant antigenic clusters within seasonal influenza viruses, aiding in the development of effective vaccines and efficient monitoring of viral evolution.

Resurgence of influenza with increased genetic diversity of circulating viruses during the 2022-2023 season.

Introduction. After two seasons of absence and low circulation, influenza activity increased significantly in the winter of 2022-2023. This study aims to characterize virological and epidemiological aspects of influenza infection in Bulgaria during the 2022-2023 season and perform a phylogenetic/molecular analysis of the hemagglutinin (HA) and neuraminidase (NA) sequences of representative influenza strains.Hypothesis/Gap Statement. Influenza A and B viruses generate new genetic groups/clades each season, replacing previously circulating variants. This results in increased antigenic distances from current vaccine strains. Strengthening existing influenza surveillance is essential to meet the challenges posed by the co-circulation of influenza and SARS-CoV-2.Methodology. We tested 2713 clinical samples from patients with acute respiratory illnesses using a multiplex real-time RT-PCR kit (FluSC2) to detect influenza A/B and Severe acute respiratory syndrome coronavirus-2(SARS-CoV-2) simultaneously. Representative Bulgarian influenza strains were sequenced at the WHO Collaborating Centres in London, UK, and Atlanta, USA.Results. Influenza virus was detected in 694 (25.6 %) patients. Of these, 364 (52.4 %), 213 (30.7 %) and 117 (16.9 %) were positive for influenza A(H1N1)pdm09, A(H3N2) and B/Victoria lineage virus, respectively. HA genes of the 47 influenza A(H1N1)pdm09 viruses fell into clades 5a.2. and 5a.2a.1 within the 6B.5A.1A.5a.2 group. Twenty-seven A(H3N2) viruses belonging to subclades 2b, 2a.1, 2a.1b and 2a.3a.1 within the 3C.2a1b.2a.2 group were analysed. All 23 sequenced B/Victoria lineage viruses were classified into the V1A.3a.2 group. We identified amino acid substitutions in HA and NA compared with the vaccine strains, including several substitutions in the HA antigenic sites.Conclusion. The study's findings showed genetic diversity among the influenza A viruses and, to a lesser extent, among B viruses, circulating in the first season after the lifting of anti-COVID-19 measures.

A speed limit on serial strain replacement from original antigenic sin

Many pathogens evolve to escape immunity, yet it remains difficult to predict whether immune pressure will lead to diversification, serial replacement of one variant by another, or more complex patterns. Pathogen strain dynamics are mediated by cross-protective immunity, whereby exposure to one strain partially protects against infection by antigenically diverged strains. There is growing evidence that this protection is influenced by early exposures, a phenomenon referred to as original antigenic sin (OAS) or imprinting. In this paper, we derive constraints on the emergence of the pattern of successive strain replacements demonstrated by influenza, SARS-CoV-2, seasonal coronaviruses, and other pathogens. We find that OAS implies that the limited diversity found with successive strain replacement can only be maintained if [Formula: see text] is less than a threshold set by the characteristic antigenic distances for cross-protection and for the creation of new immune memory. This bound implies a "speed limit" on the evolution of new strains and a minimum variance of the distribution of infecting strains in antigenic space at any time. To carry out this analysis, we develop a theoretical model of pathogen evolution in antigenic space that implements OAS by decoupling the antigenic distances required for protection from infection and strain-specific memory creation. Our results demonstrate that OAS can play an integral role in the emergence of strain structure from host immune dynamics, preventing highly transmissible pathogens from maintaining serial strain replacement without diversification.

Hemagglutinin glycosylation pattern-specific effects: implications for the fitness of H9.4.2.5-branched H9N2 avian influenza viruses

ABSTRACT Since 2007, h9.4.2.5 has emerged as the most predominant branch of H9N2 avian influenza viruses (AIVs) that affects the majority of the global poultry population. The spread of this viral branch in vaccinated chicken flocks has not been considerably curbed despite numerous efforts. The evolutionary fitness of h9.4.2.5-branched AIVs must consequently be taken into consideration. The glycosylation modifications of hemagglutinin (HA) play a pivotal role in regulating the balance between receptor affinity and immune evasion for influenza viruses. Sequence alignment showed that five major HA glycosylation patterns have evolved over time in h9.4.2.5-branched AIVs. Here, we compared the adaptive phenotypes of five virus mutants with different HA glycosylation patterns. According to the results, the mutant with 6 N-linked glycans displayed the best acid and thermal stability and a better capacity for multiplication, although having a relatively lower receptor affinity than 7 glycans. The antigenic profile between the five mutants revealed a distinct antigenic distance, indicating that variations in glycosylation level have an impact on antigenic drift. These findings suggest that changes in the number of glycans on HA can not only modulate the receptor affinity and antigenicity of H9N2 AIVs, but also affect their stability and multiplication. These adaptive phenotypes may underlie the biological basis for the dominant strain switchover of h9.4.2.5-branched AIVs. Overall, our study provides a systematic insight into how changes in HA glycosylation patterns regulate the evolutionary fitness and epidemiological dominance drift of h9.4.2.5-branched H9N2 AIVs, which will be of great benefit for the glycosylation-dependent vaccine design.

Role of prior immunity in binding to spike of “future” Omicron subvariants

BackgroundThroughout the COVID-19 pandemic, virus evolution and large-scale vaccination programs have caused multiple exposures to SARS CoV-2 spike protein, resulting in complex antibody profiles. The binding of these to spike protein of “future” variants in the context of such heterogeneous exposure has not been studied. MethodsWe tested archival sera (Delta and Omicron period) stratified by anti-spike antibody (including IgG) levels for reactivity to Omicron-subvariants(BA.1, BA.2,BA.2.12.1, BA.2.75, BA.4/5 and BF.7) spike protein. Assessed antigenic distance between groups using Antigenic Cartography and performed hierarchical clustering of antibody data in a Euclidean distance framework. ResultsAntibody (including IgG) antibody reactivity to Wild-type (CLIA) and subvariants (ELISA) spike protein were similar between periods (p > 0.05). Both 'High S′ and ‘Low S’ of Delta and Omicron periods were closely related to “future” subvariants by Antigenic Cartography. Sera from different S groups clustered together with ‘Low S’ interspersed between ‘High S’ on hierarchical clustering, suggesting common binding sites. Further, anti-spike antibodies (including IgG) to Wild-type (S1/S2 and Trimeric S) clustered with Omicron-subvariant binding antibodies. ConclusionsHybrid immunity caused by cumulative virus exposure in Delta or Omicron periods resulted in equivalent binding to “future” variants, which might be due to binding to conserved regions of spike protein of future variants. A prominent finding is that the ‘Low S’ antibody demonstrates similar binding.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection dynamics, presumably via heterosubtypic cross-immunity.

Antigenic distance between primary and secondary dengue infections correlates with disease risk.

Many pathogens continuously change their protein structure in response to immune-driven selection, resulting in weakened protection even in previously exposed individuals. In addition, for some pathogens, such as dengue virus, poorly targeted immunity is associated with increased risk of severe disease through a mechanism known as antibody-dependent enhancement. However, it remains unclear whether the antigenic distances between an individual's first infection and subsequent exposures dictate disease risk, explaining the observed large-scale differences in dengue hospitalizations across years. Here, we develop a framework that combines detailed antigenic and genetic characterization of viruses with details on hospitalized cases from 21 years of dengue surveillance in Bangkok, Thailand, to identify the role of the antigenic profile of circulating viruses in determining disease risk. We found that the risk of hospitalization depended on both the specific order of infecting serotypes and the antigenic distance between an individual's primary and secondary infections, with risk maximized at intermediate antigenic distances. These findings suggest that immune imprinting helps determine dengue disease risk and provide a pathway to monitor the changing risk profile of populations and to quantifying risk profiles of candidate vaccines.

Heterologous Sarbecovirus Receptor Binding Domains as Scaffolds for SARS-CoV-2 Receptor Binding Motif Presentation.

Structure-guided rational immunogen design can generate optimized immunogens that elicit a desired humoral response. Design strategies often center on targeting conserved sites on viral glycoproteins that will ultimately confer potent neutralization. For SARS-CoV-2 (SARS-2), the surface-exposed spike glycoprotein includes a broadly conserved portion, the receptor binding motif (RBM), that is required to engage the host cellular receptor, ACE2. Expanding humoral responses to this site may result in a more potent neutralizing antibody response against diverse sarbecoviruses. Here, we used a "resurfacing" approach and iterative design cycles to graft the SARS-2 RBM onto heterologous sarbecovirus scaffolds. The scaffolds were selected to vary the antigenic distance relative to SARS-2 to potentially focus responses to RBM. Multimerized versions of these immunogens elicited broad neutralization against sarbecoviruses in the context of preexisting SARS-2 immunity. These validated engineering approaches can help inform future immunogen design efforts for sarbecoviruses and are generally applicable to other viruses.

A sequence-based machine learning model for predicting antigenic distance for H3N2 influenza virus.

Seasonal influenza A H3N2 viruses are constantly changing, reducing the effectiveness of existing vaccines. As a result, the World Health Organization (WHO) needs to frequently update the vaccine strains to match the antigenicity of emerged H3N2 variants. Traditional assessments of antigenicity rely on serological methods, which are both labor-intensive and time-consuming. Although numerous computational models aim to simplify antigenicity determination, they either lack a robust quantitative linkage between antigenicity and viral sequences or focus restrictively on selected features. Here, we propose a novel computational method to predict antigenic distances using multiple features, including not only viral sequence attributes but also integrating four distinct categories of features that significantly affect viral antigenicity in sequences. This method exhibits low error in virus antigenicity prediction and achieves superior accuracy in discerning antigenic drift. Utilizing this method, we investigated the evolution process of the H3N2 influenza viruses and identified a total of 21 major antigenic clusters from 1968 to 2022. Interestingly, our predicted antigenic map aligns closely with the antigenic map generated with serological data. Thus, our method is a promising tool for detecting antigenic variants and guiding the selection of vaccine candidates.

Breakthrough infections by SARS-CoV-2 variants boost cross-reactive hybrid immune responses in mRNA-vaccinated Golden Syrian hamsters.

Hybrid immunity (vaccination + natural infection) to SARS-CoV-2 provides superior protection to re-infection. We performed immune profiling studies during breakthrough infections in mRNA-vaccinated hamsters to evaluate hybrid immunity induction. The mRNA vaccine, BNT162b2, was dosed to induce binding antibody titers against ancestral spike, but inefficient serum virus neutralization of ancestral SARS-CoV-2 or variants of concern (VoCs). Vaccination reduced morbidity and controlled lung virus titers for ancestral virus and Alpha but allowed breakthrough infections in Beta, Delta and Mu-challenged hamsters. Vaccination primed for T cell responses that were boosted by infection. Infection back-boosted neutralizing antibody responses against ancestral virus and VoCs. Hybrid immunity resulted in more cross-reactive sera, reflected by smaller antigenic cartography distances. Transcriptomics post-infection reflects both vaccination status and disease course and suggests a role for interstitial macrophages in vaccine-mediated protection. Therefore, protection by vaccination, even in the absence of high titers of neutralizing antibodies in the serum, correlates with recall of broadly reactive B- and T-cell responses.

Immune evasion, infectivity, and fusogenicity of SARS-CoV-2 BA.2.86 and FLip variants

Evolution of SARS-CoV-2 requires the reassessment of current vaccine measures. Here, we characterized BA.2.86 and XBB-derived variant FLip by investigating their neutralization alongside D614G, BA.1, BA.2, BA.4/5, XBB.1.5, and EG.5.1 by sera from 3-dose-vaccinated and bivalent-vaccinated healthcare workers, XBB.1.5-wave-infected first responders, and monoclonal antibody (mAb) S309. We assessed the biology of the variant spikes by measuring viral infectivity and membrane fusogenicity. BA.2.86 is less immune evasive compared to FLip and other XBB variants, consistent with antigenic distances. Importantly, distinct from XBB variants, mAb S309 was unable to neutralize BA.2.86, likely due to a D339H mutation based on modeling. BA.2.86 had relatively high fusogenicity and infectivity in CaLu-3 cells but low fusion and infectivity in 293T-ACE2 cells compared to some XBB variants, suggesting a potentially different conformational stability of BA.2.86 spike. Overall, our study underscores the importance of SARS-CoV-2 variant surveillance and the need for updated COVID-19 vaccines.

Protection of Omicron Sub-Lineage Infection Against Reinfection with Another Omicron Sub-Lineage: Systematic Review, Meta-Analysis, and Meta-Regression - Worldwide, 2022-2023.

Both the decline in immunity over time and the evolution of the virus play a role in the level of protection offered by a prior infection. Point estimates indicated variations in protection levels based on the initial infecting variant and the reinfecting variant. There was a consistent correlation between real-world protection, antigenic distance, and humoral immunity levels. Specifically, shorter antigenic distances and higher humoral immunity levels corresponded to enhanced real-world protection. Our findings suggest that virological and immunological studies could help identify and assess the epidemic risk posed by new variants before they become dominant. Prompt incorporation of the latest variants into the antigen components of the coronavirus disease 2019 (COVID-19) vaccines can significantly contribute to effective epidemic prevention and control measures.

Vaccination of poultry against highly pathogenic avian influenza - part 1. Available vaccines and vaccination strategies.

Several vaccines have been developed against highly pathogenic avian influenza (HPAI), mostly inactivated whole-virus vaccines for chickens. In the EU, one vaccine is authorised in chickens but is not fully efficacious to stop transmission, highlighting the need for vaccines tailored to diverse poultry species and production types. Off-label use of vaccines is possible, but effectiveness varies. Vaccines are usually injectable, a time-consuming process. Mass-application vaccines outside hatcheries remain rare. First vaccination varies from in-ovo to 6 weeks of age. Data about immunity onset and duration in the target species are often unavailable, despite being key for effective planning. Minimising antigenic distance between vaccines and field strains is essential, requiring rapid updates of vaccines to match circulating strains. Generating harmonised vaccine efficacy data showing vaccine ability to reduce transmission is crucial and this ability should be also assessed in field trials. Planning vaccination requires selecting the most adequate vaccine type and vaccination scheme. Emergency protective vaccination is limited to vaccines that are not restricted by species, age or pre-existing vector-immunity, while preventive vaccination should prioritise achieving the highest protection, especially for the most susceptible species in high-risk transmission areas. Model simulations in France, Italy and The Netherlands revealed that (i) duck and turkey farms are more infectious than chickens, (ii) depopulating infected farms only showed limitations in controlling disease spread, while 1-km ring-culling performed better than or similar to emergency preventive ring-vaccination scenarios, although with the highest number of depopulated farms, (iii) preventive vaccination of the most susceptible species in high-risk transmission areas was the best option to minimise the outbreaks' number and duration, (iv) during outbreaks in such areas, emergency protective vaccination in a 3-km radius was more effective than 1- and 10-km radius. Vaccine efficacy should be monitored and complement other surveillance and preventive efforts.

Vaccine-induced neutralizing antibody responses to seasonal influenza virus H1N1 strains are not enhanced during subsequent pandemic H1N1 infection.

The first exposure to influenza is presumed to shape the B-cell antibody repertoire, leading to preferential enhancement of the initially formed responses during subsequent exposure to viral variants. Here, we investigated whether this principle remains applicable when there are large genetic and antigenic differences between primary and secondary influenza virus antigens. Because humans usually have a complex history of influenza virus exposure, we conducted this investigation in influenza-naive cynomolgus macaques. Two groups of six macaques were immunized four times with influenza virus-like particles (VLPs) displaying either one (monovalent) or five (pentavalent) different hemagglutinin (HA) antigens derived from seasonal H1N1 (H1N1) strains. Four weeks after the final immunization, animals were challenged with pandemic H1N1 (H1N1pdm09). Although immunization resulted in robust virus-neutralizing responses to all VLP-based vaccine strains, there were no cross-neutralization responses to H1N1pdm09, and all animals became infected. No reductions in viral load in the nose or throat were detected in either vaccine group. After infection, strong virus-neutralizing responses to H1N1pdm09 were induced. However, there were no increases in virus-neutralizing titers against four of the five H1N1 vaccine strains; and only a mild increase was observed in virus-neutralizing titer against the influenza A/Texas/36/91 vaccine strain. After H1N1pdm09 infection, both vaccine groups showed higher virus-neutralizing titers against two H1N1 strains of intermediate antigenic distance between the H1N1 vaccine strains and H1N1pdm09, compared with the naive control group. Furthermore, both vaccine groups had higher HA-stem antibodies early after infection than the control group. In conclusion, immunization with VLPs displaying HA from antigenically distinct H1N1 variants increased the breadth of the immune response during subsequent H1N1pdm09 challenge, although this phenomenon was limited to intermediate antigenic variants.