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In the centenary year of the devastating 1918‐19 pandemic, it seems opportune to reflect on the success of the WHO Global Influenza Surveillance and Response System (GISRS) initiated 70 years ago to provide early warning of changes in influenza viruses circulating in the global population to help mitigate the consequences of such a pandemic and maintain the efficacy of seasonal influenza vaccines. Three pandemics later and in the face of pandemic threats from highly pathogenic zoonotic infections by different influenza A subtypes, it continues to represent a model platform for global collaboration and timely sharing of viruses, reagents and information to forestall and respond to public health emergencies.

Surface antigens of pathogens are commonly targeted by vaccine-elicited antibodies but antigenic variability, notably in RNA viruses such as influenza, HIV and SARS-CoV-2, pose challenges for control by vaccination. For example, influenza A(H3N2) entered the human population in 1968 causing a pandemic and has since been monitored, along with other seasonal influenza viruses, for the emergence of antigenic drift variants through intensive global surveillance and laboratory characterisation. Statistical models of the relationship between genetic differences among viruses and their antigenic similarity provide useful information to inform vaccine development, though accurate identification of causative mutations is complicated by highly correlated genetic signals that arise due to the evolutionary process. Here, using a sparse hierarchical Bayesian analogue of an experimentally validated model for integrating genetic and antigenic data, we identify the genetic changes in influenza A(H3N2) virus that underpin antigenic drift. We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. The accuracy of variable selection judged by proximity to experimentally determined antigenic sites was improved simultaneously. Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharacterised virus from genetic sequence. Combined, these analyses have the potential to inform choices of reference viruses, the targeting of laboratory assays, and predictions of the evolutionary success of different genotypes, and can therefore be used to inform vaccine selection processes.

An examination of the receptor-binding properties of the H7N9 virus, which has recently emerged in China, shows that the virus has acquired the ability to bind the human α-2,6-linked sialic acid receptor while retaining binding to the avian α-2,3-linked receptor, and therefore does not have the preference for human versus avian receptors characteristic of pandemic viruses. H7N9 avian flu virus isolates examined The H7N9 avian flu virus emerged in the human population on mainland China in February 2013, and by the first week of July WHO had recorded 133 cases including 43 deaths. Most cases so far have been linked to live bird markets. In this issue of Nature two groups report on the receptor-binding properties of H7N9. Both find that the virus has acquired the ability to bind the human α-2,3-linked sialic acid receptor yet has a retained preference for binding to the avian 2,3-linked receptor, a factor that may restrict its further evolution towards efficient transmission between humans. Steven Gamblin and colleagues also solve the crystal structure of the H7 haemagglutinin in complex with the receptor analogues, revealing details of how the human-receptor-binding properties may have arisen. Yuelong Shu and colleagues examine the pattern of virus infection in lung tissue. In human tracheal and lung explants, the virus infects epithelial cells in the lower respiratory tract and type II pneumocytes in the alveoli, and is better able to replicate in the lower respiratory tract compared with the trachea, a possible factor in the inefficient human-to-human transmission seen to date. They also report hypercytokinaemia in some patients — a cytokine storm that can contribute to disease severity — comparable to that seen in some H5N1 infections. Of the 132 people known to have been infected with H7N9 influenza viruses in China, 37 died, and many were severely ill 1 . Infection seems to have involved contact with infected poultry 2 , 3 . We have examined the receptor-binding properties of this H7N9 virus and compared them with those of an avian H7N3 virus. We find that the human H7 virus has significantly higher affinity for α-2,6-linked sialic acid analogues (‘human receptor’) than avian H7 while retaining the strong binding to α-2,3-linked sialic acid analogues (‘avian receptor’) characteristic of avian viruses. The human H7 virus does not, therefore, have the preference for human versus avian receptors characteristic of pandemic viruses. X-ray crystallography of the receptor-binding protein, haemagglutinin (HA), in complex with receptor analogues indicates that both human and avian receptors adopt different conformations when bound to human H7 HA than they do when bound to avian H7 HA. Human receptor bound to human H7 HA exits the binding site in a different direction to that seen in complexes formed by HAs from pandemic viruses 4 , 5 and from an aerosol-transmissible H5 mutant 6 . The human-receptor-binding properties of human H7 probably arise from the introduction of two bulky hydrophobic residues by the substitutions Gln226Leu and Gly186Val. The former is shared with the 1957 H2 and 1968 H3 pandemic viruses and with the aerosol-transmissible H5 mutant. We conclude that the human H7 virus has acquired some of the receptor-binding characteristics that are typical of pandemic viruses, but its retained preference for avian receptor may restrict its further evolution towards a virus that could transmit efficiently between humans, perhaps by binding to avian-receptor-rich mucins in the human respiratory tract 7 rather than to cellular receptors.

Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution. Every year, seasonal influenza, commonly called flu, infects up to one in five people around the world, and causes up to half a million deaths. Even though the human immune system can detect and destroy the virus that causes influenza, people can catch flu many times throughout their lifetimes because the virus keeps evolving in an effort to avoid the immune system. This antigenic drift—so-called because the antigens displayed by the virus keep changing—also explains why influenza vaccines become less effective over time and need to be reformulated every year. It is possible to determine which antigens are displayed by a new strain of the virus by observing how blood samples that respond to known strains respond to the new strain. This information about the “antigenic phenotype” of the virus can be plotted on an antigenic map in which strains with similar antigens cluster together. Gene sequencing has shown that there are four subtypes of the flu virus that commonly infect people; but the relationship between changes in antigenic phenotype and changes in gene sequences of the influenza virus is poorly understood. Bedford et al. have now developed an approach to combine antigenic maps with genetic information about the four subtypes of the human flu virus. This revealed that the antigenic phenotype of H3N2—a subtype that is becoming increasingly common—evolved faster than the other three subtypes. Further, a correlation was observed between antigenic drift and the number of new influenza cases per year for each flu strain. This suggests that knowing which antigenic phenotypes are present at the start of flu season could help predict which strains of the virus will predominate later on. The work of Bedford et al. provides a useful framework to study influenza, and could help to pinpoint which changes in viral genes cause the changes in antigens. This information could potentially speed up the development of new flu vaccines for each flu season.

The hemagglutinin (HA) of influenza A(H3N2) virus responsible for the 1968 influenza pandemic derived from an avian virus. On introduction into humans, its receptor binding properties had changed from a preference for avian receptors (α2,3-linked sialic acid) to a preference for human receptors (α2,6-linked sialic acid). By 2001, the avidity of human H3 viruses for avian receptors had declined, and since then the affinity for human receptors has also decreased significantly. These changes in receptor binding, which correlate with increased difficulties in virus propagation in vitro and in antigenic analysis, have been assessed by virus hemagglutination of erythrocytes from different species and quantified by measuring virus binding to receptor analogs using surface biolayer interferometry. Crystal structures of HA–receptor analog complexes formed with HAs from viruses isolated in 2004 and 2005 reveal significant differences in the conformation of the 220-loop of HA1, relative to the 1968 structure, resulting in altered interactions between the HA and the receptor analog that explain the changes in receptor affinity. Site-specific mutagenesis shows the HA1 Asp-225→Asn substitution to be the key determinant of the decreased receptor binding in viruses circulating since 2005. Our results indicate that the evolution of human influenza A(H3N2) viruses since 1968 has produced a virus with a low propensity to bind human receptor analogs, and this loss of avidity correlates with the marked reduction in A(H3N2) virus disease impact in the last 10 y.

Background Monitoring antigenic drift in human influenza A viruses is essential for vaccine strain selection and ensuring protection against circulating strains. Antigenic drift is traditionally assessed using ferret antisera, which provide monospecific responses and human vaccinee sera, which reflect exposure to multiple antigens. In this study, we evaluated the pig as an alternative source of antisera to study antigenic drift compared with immune responses in ferrets and humans. We included seasonal influenza A(H1N1pdm09) human viruses that had shown different antigenic characteristics when using ferret or human antisera. Methods Pairs of pigs were inoculated with six human A(H1N1)pdm09 viruses circulating between 2019 and 2023, a period of marked antigenic drift. Pig and ferret antisera raised against these six reference viruses were analysed by haemagglutination inhibition (HI) and virus neutralisation (VN), and homologous and heterologous titre differences were used to assess antigenic reactivity profiles among the viruses between species. Results Pigs were successfully infected with all strains, shedding virus and producing antibody responses, confirming their susceptibility to human influenza A viruses. Antigenic reactivity of pig antisera was qualitatively comparable to ferret antisera in both HI and VN assays, although maximum homologous antibody titres were significantly higher in ferrets (on average 16‐fold for HI and from around 12‐ to 210‐fold for VN). The antisera raised against viruses in circulation in 2019 and before, exemplified by A/Guangdong‐Maonan/SWL1536/2019, clade 5a.1, were clearly differentiated by both ferret and pig antisera from those in clade 5a.2 and its derivatives that became predominant. Conclusions Ferrets and pigs showed comparable responses and both distinguished clade 5a.1 from clade 5a.2. However, neither model recognised antigenically drifted variants from 2019 to 2022, including subclades 5a.2‐C, 5a.2a‐C.1/C.1.9 and 5a.2a.1‐C.1.1/D, which were distinguishable using human postvaccination antisera.

Building on previous work that identified a mutant avian H5 virus that is transmissible between ferrets, the authors present an algorithm to predict virus avidity from the affinity of single haemagglutinin (HA)–receptor interactions; these studies predict that the mutant has a 200-fold preference for the human over the avian receptor, and crystal structures of the mutant HA in complex with human and avian receptors shed light on the molecular basis for these altered binding properties. Receptor binding of transmissible flu virus The recent identification of an avian H5 haemagglutinin (HA) that can mediate aerosol transmission in ferrets when incorporated into a human influenza virus backbone has provided a model in which the nature of transmission of this type of virus can be closely examined. This new study goes further in demonstrating that this same transmissible-mutant virus has acquired a small increase in affinity for the human receptor, but a marked decrease in affinity for the avian receptor, leading to a 200-fold preference for binding human over avian receptors. The authors provide a crystal structure of this mutant HA in complex with human and avian receptor analogues, revealing something of the molecular basis for the altered binding properties. Cell-surface-receptor binding by influenza viruses is a key determinant of their transmissibility, both from avian and animal species to humans as well as from human to human. Highly pathogenic avian H5N1 viruses that are a threat to public health have been observed to acquire affinity for human receptors, and transmissible-mutant-selection experiments have identified a virus that is transmissible in ferrets 1 , 2 , 3 , the generally accepted experimental model for influenza in humans. Here, our quantitative biophysical measurements of the receptor-binding properties of haemagglutinin (HA) from the transmissible mutant indicate a small increase in affinity for human receptor and a marked decrease in affinity for avian receptor. From analysis of virus and HA binding data we have derived an algorithm that predicts virus avidity from the affinity of individual HA–receptor interactions. It reveals that the transmissible-mutant virus has a 200-fold preference for binding human over avian receptors. The crystal structure of the transmissible-mutant HA in complex with receptor analogues shows that it has acquired the ability to bind human receptor in the same folded-back conformation as seen for HA from the 1918, 1957 (ref. 4 ), 1968 (ref. 5 ) and 2009 (ref. 6 ) pandemic viruses. This binding mode is substantially different from that by which non-transmissible wild-type H5 virus HA binds human receptor. The structure of the complex also explains how the change in preference from avian to human receptors arises from the Gln226Leu substitution, which facilitates binding to human receptor but restricts binding to avian receptor. Both features probably contribute to the acquisition of transmissibility by this mutant virus.

Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence-only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth. Vaccination is the best protection against seasonal flu. It teaches the immune system what the flu virus looks like, preparing it to fight off an infection. But the flu virus changes its molecular appearance every year, escaping the immune defences learnt the year before. So, every year, the vaccine needs updating. Since it takes almost a year to design and make a new flu vaccine, researchers need to be able to predict what flu viruses will look like in the future. Currently, this prediction relies on experiments that assess the molecular appearance of flu viruses, a complex and slow approach. One alternative is to examine the virus's genetic code. Mathematical models try to predict which genetic changes might alter the appearance of a flu virus, saving the cost of performing specialised experiments. Recent research has shown that these models can make good predictions, but including experimental measures of the virus’ appearance could improve them even further. This could help the model to work out which genetic changes are likely to be beneficial to the virus, and which are not. To find out whether experimental data improves model predictions, Huddleston et al. designed a new forecasting tool which used 25 years of historical data from past flu seasons. Each forecast predicted what the virus population might look like the next year using the previous year's genetic code, experimental data, or both. Huddleston et al. then compared the predictions with the historical data to find the most useful data types. This showed that the best predictions combined changes from the virus's genetic code with experimental measures of its appearance. This new forecasting tool is open source, allowing teams across the world to start using it to improve their predictions straight away. Seasonal flu infects between 5 and 15% of the world's population every year, causing between quarter of a million and half a million deaths. Better predictions could lead to better flu vaccines and fewer illnesses and deaths.

Background Surveillance of influenza infections and virus characterisation are essential to guide prevention strategies. In the Central African Republic (CAR), data on influenza viruses are patchy and poorly documented. Objective To study the clinical, seasonal, genetic and phenotypic characteristics of influenza viruses circulating in the CAR population. Materials and Methods From January 2015 to December 2018, the presence of influenza A and B viruses in patients presenting with influenza‐like illness (ILI) symptoms or severe acute respiratory infections (SARI) was investigated by RT‐qPCR. Influenza genetic diversity was evaluated by phylogenetic analyses, and antigenic properties were investigated by haemagglutination inhibition assays, whereas reduced susceptibility to neuraminidase inhibitors was assessed through the presence of known genetic markers and neuraminidase assay. The relationship between patients' clinical characteristics and infection status was investigated using statistical analyses. Results Over the surveillance period influenza viruses were detected in 9.7% of samples (n = 6134), with the highest intensity of circulation occurring in 2016 (15.8%), attributed mainly to A(H3N2). Periods of increased influenza transmission (June to October) generally coincided with rainy seasons; however, variations in terms of monthly distribution of cases between years were evident. Hospitalisation rates (SARI) were most frequent in infants (0–11 months, 37.9%) and young children (1–4 years, 24.8%), whereas influenza prevalences were highest in the 15–49 (12.0%) and ≥ 50 (15.2%) years old categories. A new A(H1N1)pdm09 6B.1 hemagglutinin subgroup characterised by amino acid substitutions S84N, S162N and I216T was detected in 2015, with associated antigenic drift, and subsequently, two of these viruses showed highly reduced inhibition by oseltamivir. Conclusion This study showcases the value of local influenza sentinel networks to specifically inform vaccination strategies and highlights the need for improved strain characterisation in tropical regions.

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. Seasonal influenza (flu) viruses cause outbreaks every winter. People infected with influenza typically develop mild respiratory symptoms. But flu infections can cause serious illness in young children, older adults and people with chronic medical conditions. Infected or vaccinated individuals develop some immunity, but the viruses evolve quickly to evade these defenses in a process called antigenic drift. As the viruses change, they can re-infect previously immune people. Scientists update the flu vaccine yearly to keep up with this antigenic drift. The immune system fights flu infections by recognizing two proteins, known as antigens, on the virus’s surface, called hemagglutinin (HA) and neuraminidase (NA). However, mutations in the genes encoding these proteins can make them unrecognizable, letting the virus slip past the immune system. Scientists would like to know how these changes affect the size, severity and timing of annual influenza outbreaks. Perofsky et al. show that tracking genetic changes in HA and NA may help improve flu season predictions. The experiments compared the severity of 22 flu seasons caused by the A(H3N2) subtype in the United States with how much HA and NA had evolved since the previous year. The A(H3N2) subtype experiences the fastest rates of antigenic drift and causes more cases and deaths than other seasonal flu viruses. Genetic changes in HA and NA were a better predictor of A(H3N2) outbreak severity than the blood tests for protective antibodies that epidemiologists traditionally use to track flu evolution. However, the prevalence of another subtype of influenza A circulating in the population, called A(H1N1), was an even better predictor of how severe A(H3N2) outbreaks would be. Perofsky et al. are the first to show that genetic changes in NA contribute to the severity of flu seasons. Previous studies suggested a link between genetic changes in HA and flu season severity, and flu vaccines include the HA protein to help the body recognize new influenza strains. The results suggest that adding the NA protein to flu vaccines may improve their effectiveness. In the future, flu forecasters may want to analyze genetic changes in both NA and HA to make their outbreak predictions. Tracking how much of the A(H1N1) subtype is circulating may also be useful for predicting the severity of A(H3N2) outbreaks.